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Sample records for insured claims database

  1. Analysis of the evidence-practice gap to facilitate proper medical care for the elderly: investigation, using databases, of utilization measures for National Database of Health Insurance Claims and Specific Health Checkups of Japan (NDB).

    Science.gov (United States)

    Nakayama, Takeo; Imanaka, Yuichi; Okuno, Yasushi; Kato, Genta; Kuroda, Tomohiro; Goto, Rei; Tanaka, Shiro; Tamura, Hiroshi; Fukuhara, Shunichi; Fukuma, Shingo; Muto, Manabu; Yanagita, Motoko; Yamamoto, Yosuke

    2017-06-06

    As Japan becomes a super-aging society, presentation of the best ways to provide medical care for the elderly, and the direction of that care, are important national issues. Elderly people have multi-morbidity with numerous medical conditions and use many medical resources for complex treatment patterns. This increases the likelihood of inappropriate medical practices and an evidence-practice gap. The present study aimed to: derive findings that are applicable to policy from an elucidation of the actual state of medical care for the elderly; establish a foundation for the utilization of National Database of Health Insurance Claims and Specific Health Checkups of Japan (NDB), and present measures for the utilization of existing databases in parallel with NDB validation.Cross-sectional and retrospective cohort studies were conducted using the NDB built by the Ministry of Health, Labor and Welfare of Japan, private health insurance claims databases, and the Kyoto University Hospital database (including related hospitals). Medical practices (drug prescription, interventional procedures, testing) related to four issues-potential inappropriate medication, cancer therapy, chronic kidney disease treatment, and end-of-life care-will be described. The relationships between these issues and clinical outcomes (death, initiation of dialysis and other adverse events) will be evaluated, if possible.

  2. Increased Risk of Hospitalization for Heart Failure with Newly Prescribed Dipeptidyl Peptidase-4 Inhibitors and Pioglitazone Using the Korean Health Insurance Claims Database

    Directory of Open Access Journals (Sweden)

    Sunghwan Suh

    2015-06-01

    Full Text Available BackgroundWe assessed the association of dipeptidyl peptidase 4 inhibitors (DPP4i with hospitalization for heart failure (HF using the Korean Health Insurance claims database.MethodsWe collected data on newly prescribed sitagliptin, vildagliptin, and pioglitazone between January 1, 2009 and December 31, 2012 (mean follow-up of 336.8 days to 935,519 patients with diabetes (518,614 males and 416,905 females aged 40 to 79 years (mean age of 59.4 years.ResultsDuring the study, 998 patients were hospitalized for primary HF (115.7 per 100,000 patient-years. The incidence rate of hospitalization for HF was 117.7 per 100,000 per patient-years among patients on pioglitazone, 105.7 for sitagliptin, and 135.8 for vildagliptin. The hospitalization rate for HF was greatest in the first 30 days after starting the medication, which corresponded to a significantly higher incidence at days 0 to 30 compared with days 31 to 360 for all three drugs. The hazard ratios were 1.85 (pioglitazone, 2.00 (sitagliptin, and 1.79 (vildagliptin. The incidence of hospitalization for HF did not differ between the drugs for any time period.ConclusionThis study showed an increase in hospitalization for HF in the initial 30 days of the DPP4i and pioglitazone compared with the subsequent follow-up period. However, the differences between the drugs were not significant.

  3. A logistic regression model for Ghana National Health Insurance claims

    Directory of Open Access Journals (Sweden)

    Samuel Antwi

    2013-07-01

    Full Text Available In August 2003, the Ghanaian Government made history by implementing the first National Health Insurance System (NHIS in Sub-Saharan Africa. Within three years, over half of the country’s population had voluntarily enrolled into the National Health Insurance Scheme. This study had three objectives: 1 To estimate the risk factors that influences the Ghana national health insurance claims. 2 To estimate the magnitude of each of the risk factors in relation to the Ghana national health insurance claims. In this work, data was collected from the policyholders of the Ghana National Health Insurance Scheme with the help of the National Health Insurance database and the patients’ attendance register of the Koforidua Regional Hospital, from 1st January to 31st December 2011. Quantitative analysis was done using the generalized linear regression (GLR models. The results indicate that risk factors such as sex, age, marital status, distance and length of stay at the hospital were important predictors of health insurance claims. However, it was found that the risk factors; health status, billed charges and income level are not good predictors of national health insurance claim. The outcome of the study shows that sex, age, marital status, distance and length of stay at the hospital are statistically significant in the determination of the Ghana National health insurance premiums since they considerably influence claims. We recommended, among other things that, the National Health Insurance Authority should facilitate the institutionalization of the collection of appropriate data on a continuous basis to help in the determination of future premiums.

  4. Impact of statins on risk of new onset diabetes mellitus: a population-based cohort study using the Korean National Health Insurance claims database

    Directory of Open Access Journals (Sweden)

    Lee J

    2016-10-01

    Full Text Available Jimin Lee,1 Yoojin Noh,1 Sooyoung Shin,1 Hong-Seok Lim,2 Rae Woong Park,3 Soo Kyung Bae,4 Euichaul Oh,4 Grace Juyun Kim,5 Ju Han Kim,5 Sukhyang Lee1 1Division of Clinical Pharmacy, College of Pharmacy, Ajou University, Suwon, South Korea; 2Department of Cardiology, School of Medicine, Ajou University, Suwon, South Korea; 3Department of Biomedical Informatics, School of Medicine, Ajou University, Suwon, South Korea; 4Division of Pharmaceutical Sciences, College of Pharmacy, The Catholic University of Korea, Bucheon, South Korea; 5Division of Biomedical Informatics, College of Medicine, Seoul National University, Seoul, South Korea Abstract: Statin therapy is beneficial in reducing cardiovascular events and mortalities in patients with atherosclerotic cardiovascular diseases. Yet, there have been concerns of increased risk of diabetes with statin use. This study was aimed to evaluate the association between statins and new onset diabetes mellitus (NODM in patients with ischemic heart disease (IHD utilizing the Korean Health Insurance Review and Assessment Service claims database. Among adult patients with preexisting IHD, new statin users and matched nonstatin users were identified on a 1:1 ratio using proportionate stratified random sampling by sex and age. They were subsequently propensity score matched further with age and comorbidities to reduce the selection bias. Overall incidence rates, cumulative rates and hazard ratios (HRs between statin use and occurrence of NODM were estimated. The subgroup analyses were performed according to sex, age groups, and the individual agents and intensities of statins. A total of 156,360 patients (94,370 in the statin users and 61,990 in the nonstatin users were included in the analysis. The incidence rates of NODM were 7.8% and 4.8% in the statin users and nonstatin users, respectively. The risk of NODM was higher among statin users (crude HR 2.01, 95% confidence interval [CI] 1.93–2.10; adjusted HR 1

  5. The claims handling process of liability insurance in South Africa

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    Jacoline van Jaarsveld

    2015-04-01

    Full Text Available Liabilities play a very important financial role in business operations, professional service providers as well as in the personal lives of people. It is possible that a single claim may even lead to the bankruptcy of the defendant. The claims handling process of liability insurance by short-term insurers is therefore very important to these parties as it should be clear that liability claims may have enormous and far-reaching financial implications for them. The objective of this research paper embodies the improvement of financial decision-making by short-term insurers with regard to the claims handling process of liability insurance. Secondary data was initially studied which provided the basis to compile a questionnaire for the empirical survey. The leaders of liability insurance in the South African short-term insurance market that represented 69.5% of the annual gross written premiums received for liability insurance in South Africa were the respondents of the empirical study. The perceptions of these short-term insurers provided the primary data for the vital conclusions of this research. This paper pays special attention to the importance of the claims handling factors of liability insurance, how often the stipulations of liability insurance policies are adjusted by the short-term insurers to take the claims handling factors into consideration, as well as the problem areas which short-term insurers may experience during the claims handling process. Feasible solutions to address the problem areas are also discussed.

  6. Clinical outcomes in low risk coronary artery disease patients treated with different limus-based drug-eluting stents--a nationwide retrospective cohort study using insurance claims database.

    Directory of Open Access Journals (Sweden)

    Chao-Lun Lai

    Full Text Available The clinical outcomes of different limus-based drug-eluting stents (DES in a real-world setting have not been well defined. The aim of this study was to investigate the clinical outcomes of three different limus-based DES, namely sirolimus-eluting stent (SES, Endeavor zotarolimus-eluting stent (E-ZES and everolimus-eluting stent (EES, using a national insurance claims database. We identified all patients who received implantation of single SES, E-ZES or EES between January 1, 2007 and December 31, 2009 from the National Health Insurance claims database, Taiwan. Follow-up was through December 31, 2011 for all selected clinical outcomes. The primary end-point was all-cause mortality. Secondary end-points included acute coronary events, heart failure needing hospitalization, and cerebrovascular disease. Cox regression model adjusting for baseline characteristics was used to compare the relative risks of different outcomes among the three different limus-based DES. Totally, 6584 patients were evaluated (n=2142 for SES, n=3445 for E-ZES, and n=997 for EES. After adjusting for baseline characteristics, we found no statistically significant difference in the risk of all-cause mortality in three DES groups (adjusted hazard ratio [HR]: 1.14, 95% confidence interval [CI]: 0.94-1.38, p=0.20 in E-ZES group compared with SES group; adjusted HR: 0.77, 95% CI: 0.54-1.10, p=0.15 in EES group compared with SES group. Similarly, we found no difference in the three stent groups in risks of acute coronary events, heart failure needing hospitalization, and cerebrovascular disease. In conclusion, we observed no difference in all-cause mortality, acute coronary events, heart failure needing hospitalization, and cerebrovascular disease in patients treated with SES, E-ZES, and EES in a real-world population-based setting in Taiwan.

  7. Database setup insuring radiopharmaceuticals traceability

    International Nuclear Information System (INIS)

    Robert, N.; Salmon, F.; Clermont-Gallerande, H. de; Celerier, C.

    2002-01-01

    Having to organize radiopharmacy and to insure proper traceability of radiopharmaceutical medicines brings numerous problems, especially for the departments which are not assisted with global management network systems. Our work has been to find a solution enabling to use high street software to cover those needs. We have set up a PC database run by the Microsoft software ACCESS 97. Its use consists in: saving data related to generators, isotopes and kits reception and deletion, as well as the results of quality control; transferring data collected from the software that is connected to the activimeter (elutions and preparations registers, prescription book). By relating all the saved data, ACCESS enables to mix all information in order to proceed requests. At this stage, it is possible to edit all regular registers (prescription book, generator and radionuclides follow-up, blood derived medicines traceability) and to quickly retrieve patients who have received a particular radiopharmaceutical, or the radiopharmaceutical that has been given to a particular patient. This user-friendly database provides a considerable support to nuclear medicine department that don't possess any network management for their radiopharmaceutical activity. (author)

  8. Do Insurers Have to Pay for Bad Behaviour in Settling Claims? Legal Aspects of Insurers' Wrongful Claims Handling

    NARCIS (Netherlands)

    W.H. van Boom (Willem)

    2011-01-01

    textabstractAbstract: This article presents a comparative legal analysis of wrongful claims handling by insurance companies in indemnity and liability insurance. From the outset, it is clear that it may be difficult to draw the line between legitimate claims denial and refusal to pay, on the one

  9. Do Insurers Have to Pay for Bad Behaviour in Settling Claims? Legal Aspects of Insurers' Wrongful Claims Handling

    OpenAIRE

    Boom, Willem

    2011-01-01

    textabstractAbstract: This article presents a comparative legal analysis of wrongful claims handling by insurance companies in indemnity and liability insurance. From the outset, it is clear that it may be difficult to draw the line between legitimate claims denial and refusal to pay, on the one hand, and malicious protraction, procrastination and rejection of valid claims, on the other hand. Therefore, it is interesting to find that European legal systems diverge considerably in their stance...

  10. The claims handling process of engineering insurance in South Africa

    Directory of Open Access Journals (Sweden)

    I.C. de Beer

    2015-05-01

    Full Text Available Due to technological developments, the complicated world of engineering and its associated products are continuously becoming more specialized. Short-term insurers provide engineering insurance to enable the owners and operators of engineering assets to combat the negative impact of the associated risks. It is, however, a huge challenge to the insurers of engineering insurance to manage the particular risks against the background of technological enhancement. The skills gap in the short-term insurance market and the engineering environment may be the main factor which inhibits the growth of the engineering insurance market. The objective of this research embodies the improvement of financial decision-making concerning the claims handling process of engineering insurance. Secondary as well as primary data were necessary to achieve the stated objective. The secondary data provided the background of the research and enabled the researchers to compile a questionnaire for the empirical survey. The questionnaire and a cover letter were sent to the top 10 short-term insurers in South Africa that are providing engineering insurance. Their perceptions should provide guidelines to other short-term insurers who are engaged in engineering insurance, as they are regarded as the market leaders of engineering insurance in South Africa. The empirical results of this research focus on the importance of various claims handling factors when assessing the claims handling process of engineering insurance, the problem areas in the claims handling process concerned, as well as how often the stipulations of engineering insurance policies are adjusted to take the claims handling factors into account.

  11. 24 CFR 207.258 - Insurance claim requirements.

    Science.gov (United States)

    2010-04-01

    ... in 24 CFR part 200, subpart B, of its intention to file an insurance claim and of its election either..., ledger cards, documents, books, papers, and accounts relating to the mortgage transaction. (iv) All...

  12. Simulation Of Premi Calculation Claims Insurance Base On Web; Case Study PT. Sinarmas Insurance Padang

    OpenAIRE

    Rohendi, Keukeu; Putra, Ilham Eka

    2016-01-01

    Sinarmas currently has several insurance services featured. To perform its function as a good insurance company is need for reform in terms of services in the process of calculating insurance premiums of insurance carried by marketing to use a calculator which interferes with the activities of marketing activities, slow printing insurance policies, automobile claims process that requires the customer to come to the office ASM, slow printing of Work Order (SPK) and the difficulty recap custome...

  13. DIGITAL FLOOD INSURANCE RATE MAP DATABASE,

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk Information And supporting data used to develop the risk data. The primary risk;...

  14. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, , USA

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk Information And supporting data used to develop the risk data. The primary risk;...

  15. Strategy for a transparent, accessible, and sustainable national claims database.

    Science.gov (United States)

    Gelburd, Robin

    2015-03-01

    The article outlines the strategy employed by FAIR Health, Inc, an independent nonprofit, to maintain a national database of over 18 billion private health insurance claims to support consumer education, payer and provider operations, policy makers, and researchers with standard and customized data sets on an economically self-sufficient basis. It explains how FAIR Health conducts all operations in-house, including data collection, security, validation, information organization, product creation, and transmission, with a commitment to objectivity and reliability in data and data products. It also describes the data elements available to researchers and the diverse studies that FAIR Health data facilitate.

  16. Joint Asymptotic Distributions of Smallest and Largest Insurance Claims

    Directory of Open Access Journals (Sweden)

    Hansjörg Albrecher

    2014-07-01

    Full Text Available Assume that claims in a portfolio of insurance contracts are described by independent and identically distributed random variables with regularly varying tails and occur according to a near mixed Poisson process. We provide a collection of results pertaining to the joint asymptotic Laplace transforms of the normalised sums of the smallest and largest claims, when the length of the considered time interval tends to infinity. The results crucially depend on the value of the tail index of the claim distribution, as well as on the number of largest claims under consideration.

  17. The first report of Japanese antimicrobial use measured by national database based on health insurance claims data (2011-2013): comparison with sales data, and trend analysis stratified by antimicrobial category and age group.

    Science.gov (United States)

    Yamasaki, Daisuke; Tanabe, Masaki; Muraki, Yuichi; Kato, Genta; Ohmagari, Norio; Yagi, Tetsuya

    2018-04-01

    Our objective was to evaluate the utility of the national database (NDB) based on health insurance claims data for antimicrobial use (AMU) surveillance in medical institutions in Japan. The population-weighted total AMU expressed as defined daily doses (DDDs) per 1000 inhabitants per day (DID) was measured by the NDB. The data were compared with our previous study measured by the sales data. Trend analysis of DID from 2011 to 2013 and subgroup analysis stratified by antimicrobial category and age group were performed. There was a significant linear correlation between the AMUs measured by the sales data and the NDB. Total oral and parenteral AMUs (expressed in DID) were 1.04-fold from 12.654 in 2011 to 13.202 in 2013 and 1.13-fold from 0.734 to 0.829, respectively. Percentage of oral form among total AMU was high with more than 94% during the study period. AMU in the children group (0-14 years) decreased from 2011 to 2013 regardless of dosage form, although the working age group (15-64 years) and elderly group (65 and above years) increased. Oral AMU in the working age group was approximately two-thirds of those in the other age groups. In contrast, parenteral AMU in the elderly group was extremely high compared to the other age groups. The trend of AMU stratified by antimicrobial category and age group were successfully measured using the NDB, which can be a tool to monitor outcome indices for the national action plan on antimicrobial resistance.

  18. Risk of new acute myocardial infarction hospitalization associated with use of oral and parenteral non-steroidal anti-inflammation drugs (NSAIDs: a case-crossover study of Taiwan's National Health Insurance claims database and review of current evidence

    Directory of Open Access Journals (Sweden)

    Shau Wen-Yi

    2012-02-01

    Full Text Available Abstract Background Previous studies have documented the increased cardiovascular risk associated with the use of some nonsteroidal anti-inflammatory drugs (NSAIDs. Despite this, many old NSAIDs are still prescribed worldwide. Most of the studies to date have been focused on specific oral drugs or limited by the number of cases examined. We studied the risk of new acute myocardial infarction (AMI hospitalization with current use of a variety of oral and parenteral NSAIDs in a nationwide population, and compared our results with existing evidence. Methods We conducted a case-crossover study using the Taiwan's National Health Insurance claim database, identifying patients with new AMI hospitalized in 2006. The 1-30 days and 91-120 days prior to the admission were defined as case and matched control period for each patient, respectively. Uses of NSAIDs during the respective periods were compared using conditional logistic regression and adjusted for use of co-medications. Results 8354 new AMI hospitalization patients fulfilled the study criteria. 14 oral and 3 parenteral NSAIDs were selected based on drug utilization profile among 13.7 million NSAID users. The adjusted odds ratio, aOR (95% confidence interval, for risk of AMI and use of oral and parenteral non-selective NSAIDs were 1.42 (1.29, 1.56 and 3.35 (2.50, 4.47, respectively, and significantly greater for parenteral than oral drugs (p for interaction Conclusions The collective evidence revealed the tendency of increased AMI risk with current use of some NSAIDs. A higher AMI risk associated with use of parenteral NSAIDs was observed in the present study. Ketorolac had the highest associated risk in both oral and parenteral NSAIDs studied. Though further investigation to confirm the association is warranted, prescribing physicians and the general public should be cautious about the potential risk of AMI when using NSAIDs.

  19. Claims-based definition of death in Japanese claims database: validity and implications.

    Science.gov (United States)

    Ooba, Nobuhiro; Setoguchi, Soko; Ando, Takashi; Sato, Tsugumichi; Yamaguchi, Takuhiro; Mochizuki, Mayumi; Kubota, Kiyoshi

    2013-01-01

    For the pending National Claims Database in Japan, researchers will not have access to death information in the enrollment files. We developed and evaluated a claims-based definition of death. We used healthcare claims and enrollment data between January 2005 and August 2009 for 195,193 beneficiaries aged 20 to 74 in 3 private health insurance unions. We developed claims-based definitions of death using discharge or disease status and Charlson comorbidity index (CCI). We calculated sensitivity, specificity and positive predictive values (PPVs) using the enrollment data as a gold standard in the overall population and subgroups divided by demographic and other factors. We also assessed bias and precision in two example studies where an outcome was death. The definition based on the combination of discharge/disease status and CCI provided moderate sensitivity (around 60%) and high specificity (99.99%) and high PPVs (94.8%). In most subgroups, sensitivity of the preferred definition was also around 60% but varied from 28 to 91%. In an example study comparing death rates between two anticancer drug classes, the claims-based definition provided valid and precise hazard ratios (HRs). In another example study comparing two classes of anti-depressants, the HR with the claims-based definition was biased and had lower precision than that with the gold standard definition. The claims-based definitions of death developed in this study had high specificity and PPVs while sensitivity was around 60%. The definitions will be useful in future studies when used with attention to the possible fluctuation of sensitivity in some subpopulations.

  20. Claims-Based Definition of Death in Japanese Claims Database: Validity and Implications

    Science.gov (United States)

    Ooba, Nobuhiro; Setoguchi, Soko; Ando, Takashi; Sato, Tsugumichi; Yamaguchi, Takuhiro; Mochizuki, Mayumi; Kubota, Kiyoshi

    2013-01-01

    Background For the pending National Claims Database in Japan, researchers will not have access to death information in the enrollment files. We developed and evaluated a claims-based definition of death. Methodology/Principal Findings We used healthcare claims and enrollment data between January 2005 and August 2009 for 195,193 beneficiaries aged 20 to 74 in 3 private health insurance unions. We developed claims-based definitions of death using discharge or disease status and Charlson comorbidity index (CCI). We calculated sensitivity, specificity and positive predictive values (PPVs) using the enrollment data as a gold standard in the overall population and subgroups divided by demographic and other factors. We also assessed bias and precision in two example studies where an outcome was death. The definition based on the combination of discharge/disease status and CCI provided moderate sensitivity (around 60%) and high specificity (99.99%) and high PPVs (94.8%). In most subgroups, sensitivity of the preferred definition was also around 60% but varied from 28 to 91%. In an example study comparing death rates between two anticancer drug classes, the claims-based definition provided valid and precise hazard ratios (HRs). In another example study comparing two classes of anti-depressants, the HR with the claims-based definition was biased and had lower precision than that with the gold standard definition. Conclusions/Significance The claims-based definitions of death developed in this study had high specificity and PPVs while sensitivity was around 60%. The definitions will be useful in future studies when used with attention to the possible fluctuation of sensitivity in some subpopulations. PMID:23741526

  1. Medical insurance claims associated with international business travel.

    Science.gov (United States)

    Liese, B; Mundt, K A; Dell, L D; Nagy, L; Demure, B

    1997-07-01

    Preliminary investigations of whether 10,884 staff and consultants of the World Bank experience disease due to work related travel. Medical insurance claims filed by 4738 travellers during 1993 were compared with claims of non-travellers. Specific diagnoses obtained from claims were analysed overall (one or more v no missions) and by frequency of international mission (1, 2-3, or > or = 4). Standardised rate of claims ratios (SSRs) for each diagnostic category were obtained by dividing the age adjusted rate of claims for travellers by the age adjusted rate of claims for non-travellers, and were calculated for men and women travellers separately. Overall, rates of insurance claims were 80% higher for men and 18% higher for women travellers than their non-travelling counterparts. Several associations with frequency of travel were found. SRRs for infectious disease were 1.28, 1.54, and 1.97 among men who had completed one, two or three, and four or more missions, and 1.16, 1.28, and 1.61, respectively, among women. The greatest excess related to travel was found for psychological disorders. For men SRRs were 2.11, 3.13, and 3.06 and for women, SRRs were 1.47, 1.96, and 2.59. International business travel may pose health risks beyond exposure to infectious diseases. Because travellers file medical claims at a greater rate than non-travellers, and for many categories of disease, the rate of claims increases with frequency of travel. The reasons for higher rates of claims among travellers are not well understood. Additional research on psychosocial factors, health practices, time zones crossed, and temporal relation between travel and onset of disease is planned.

  2. Primary care closed claims experience of Massachusetts malpractice insurers.

    Science.gov (United States)

    Schiff, Gordon D; Puopolo, Ann Louise; Huben-Kearney, Anne; Yu, Winnie; Keohane, Carol; McDonough, Peggy; Ellis, Bonnie R; Bates, David W; Biondolillo, Madeleine

    Despite prior focus on high-impact inpatient cases, there are increasing data and awareness that malpractice in the outpatient setting, particularly in primary care, is a leading contributor to malpractice risk and claims. To study patterns of primary care malpractice types, causes, and outcomes as part of a Massachusetts ambulatory malpractice risk and safety improvement project. Retrospective review of pooled closed claims data of 2 malpractice carriers covering most Massachusetts physicians during a 5-year period (January 1, 2005, through December 31, 2009). Data were harmonized between the 2 insurers using a standardized taxonomy. Primary care practices in Massachusetts. All malpractice claims that involved primary care practices insured by the 2 largest insurers in the state were screened. A total of 551 claims from primary care practices were identified for the analysis. Numbers and types of claims, including whether claims involved primary care physicians or practices; classification of alleged malpractice (eg, misdiagnosis or medication error); patient diagnosis; breakdown in care process; and claim outcome (dismissed, settled, verdict for plaintiff, or verdict for defendant). During a 5-year period there were 7224 malpractice claims of which 551 (7.7%) were from primary care practices. Allegations were related to diagnosis in 397 (72.1%), medications in 68 (12.3%), other medical treatment in 41 (7.4%), communication in 15 (2.7%), patient rights in 11 (2.0%), and patient safety or security in 8 (1.5%). Leading diagnoses were cancer (n = 190), heart diseases (n = 43), blood vessel diseases (n = 27), infections (n = 22), and stroke (n = 16). Primary care cases were significantly more likely to be settled (35.2% vs 20.5%) or result in a verdict for the plaintiff (1.6% vs 0.9%) compared with non-general medical malpractice claims (P < .001). In Massachusetts, most primary care claims filed are related to alleged misdiagnosis. Compared with malpractice

  3. Organized investigation expedites insurance claims following a blowout

    International Nuclear Information System (INIS)

    Armstreet, R.

    1996-01-01

    Various types of insurance policies cover blowouts to different degrees, and a proper understanding of the incident and the coverage can expedite the adjustment process. Every well control incident, and the claim arising therefrom, has a unique set of circumstances which must be analyzed thoroughly. A blowout incident, no matter what size or how severe, can have an emotional impact on all who become involved. Bodily injuries or death of friends and coworkers can result in additional stress following a blowout. Thus, it is important that all parties involved remain mindful of sensitive matters when investigating a blowout. This paper reviews the definition of a blowout based on insurance procedures and claims. It reviews blowout expenses and contractor cost and accepted well control policies. Finally, it reviews the investigation procedures normally followed by an agent and the types of information requested from the operator

  4. Dystocia in the cat evaluated using an insurance database.

    Science.gov (United States)

    Holst, Bodil Ström; Axnér, Eva; Öhlund, Malin; Möller, Lotta; Egenvall, Agneta

    2017-01-01

    Objectives The aim of this study was to describe the incidence of feline dystocia with respect to breed. Methods The data used were reimbursed claims for veterinary care insurance and/or life insurance claims in cats registered in a Swedish insurance database from 1999-2006. Results The incidence rates for dystocia were about 22 cats per 10,000 cat-years at risk, 67 per 10,000 for purebred cats and seven per 10,000 for domestic shorthair cats. The median age was 2.5 years. A significant effect of breed was seen. An incidence rate ratio (IRR) that was significantly higher compared with other purebred cats was seen in the British Shorthair (IRR 2.5), the Oriental group (IRR 2.2), Birman (IRR 1.7), Ragdoll (IRR 1.5) and the Abyssinian group (IRR 1.5). A significantly lower IRR was seen in the Norwegian Forest Cat (IRR 0.38), the Maine Coon (IRR 0.48), the Persian/Exotic group (IRR 0.49) and the Cornish Rex (IRR 0.50). No common factor among the high-risk breeds explained their high risk for dystocia. There was no effect of location; that is, the incidence rate did not differ depending on whether the cat lived in an urban or rural area. Caesarean section was performed in 56% of the cats with dystocia, and the case fatality was 2%. Conclusions and relevance The incidence rate for dystocia was of a similar magnitude in purebred cats as in dogs. The IRR varied significantly among breeds, and the main cause for dystocia should be identified separately for each breed. A selection for easy parturitions in breeding programmes is suggested.

  5. Gender Disparities in Ghana National Health Insurance Claims: An Econometric Analysis

    Directory of Open Access Journals (Sweden)

    Samuel Antwi

    2014-01-01

    Full Text Available The objective of this study was to find out the gender disparities in Ghana national health insurance claims. In this work, data was collected from the policyholders of the Ghana National Health Insurance Scheme with the help of the National Health Insurance database and the patients’ attendance register of the Koforidua Regional Hospital, from 1st January to 31st December 2011. The generalized linear regression (GLR models and the SPSS version 17.0 were used for the analysis. Among men, the younger people prefer attending hospital for treatment as compared to their adult counterparts. In contrast to women, younger women favor attending hospital for treatment as compared to their adult counterparts. Among men, various levels of income impact greatly on their propensity to make an insurance claim, whereas among women only the highest income level did as compared to lowest income level.Men, who completed senior high school education, were less likely to make an insurance claim as compared to their counterparts with basic or no education. However it was women who had basic education that preferred using the hospital as compared to their more educated counterparts. It is suggested that the government should consider building more health centers, clinics and cheap-compounds in at least every community, to help reduce the travel time in accessing health care.  The ministry of health and the Ghana health service should engage older citizens by encouraging them to use hospitals when they are sick instead of other alternative care providers.

  6. The use of breast conserving surgery: linking insurance claims with tumor registry data

    International Nuclear Information System (INIS)

    Maskarinec, Gertraud; Dhakal, Sanjaya; Yamashiro, Gladys; Issell, Brian F

    2002-01-01

    The purpose of this study was to use insurance claims and tumor registry data to examine determinants of breast conserving surgery (BCS) in women with early stage breast cancer. Breast cancer cases registered in the Hawaii Tumor Registry (HTR) from 1995 to 1998 were linked with insurance claims from a local health plan. We identified 722 breast cancer cases with stage I and II disease. Surgical treatment patterns and comorbidities were identified using diagnostic and procedural codes in the claims data. The HTR database provided information on demographics and disease characteristics. We used logistic regression to assess determinants of BCS vs. mastectomy. The linked data set represented 32.8% of all early stage breast cancer cases recorded in the HTR during the study period. Due to the nature of the health plan, 79% of the cases were younger than 65 years. Women with early stage breast cancer living on Oahu were 70% more likely to receive BCS than women living on the outer islands. In the univariate analysis, older age at diagnosis, lower tumor stage, smaller tumor size, and well-differentiated tumor grade were related to receiving BCS. Ethnicity, comorbidity count, menopausal and marital status were not associated with treatment type. In addition to developing solutions that facilitate access to radiation facilities for breast cancer patients residing in remote locations, future qualitative research may help to elucidate how women and oncologists choose between BCS and mastectomy

  7. DRAFT DIGITAL FLOOD INSURANCE RATE MAP DATABASE, CHEROKEE COUNTY, SC

    Data.gov (United States)

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  8. Digital Flood Insurance Rate Map Database, Buchanan County, Iowa, USA

    Data.gov (United States)

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  9. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, FLEMING COUNTY, KY

    Data.gov (United States)

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  10. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, Charles COUNTY, MD, USA

    Data.gov (United States)

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  11. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, BUTLER COUNTY, NE

    Data.gov (United States)

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  12. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, SACRAMENTO COUNTY, CALIFORNIA, USA

    Data.gov (United States)

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  13. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, CITY OF SACRAMENTO, CALIFORNIA

    Data.gov (United States)

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  14. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, HONOLULU COUNTY, HI, USA

    Data.gov (United States)

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  15. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, KAUAI COUNTY, HAWAII, USA

    Data.gov (United States)

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  16. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, SARPY COUNTY, USA

    Data.gov (United States)

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  17. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, LUCAS COUNTY, OHIO

    Data.gov (United States)

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  18. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, NASSAU COUNTY, NEW YORK

    Data.gov (United States)

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  19. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, HARDIN COUNTY, TX

    Data.gov (United States)

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  20. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, SULLIVAN COUNTY, NEW YORK

    Data.gov (United States)

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  1. Digital Flood Insurance Rate Map Database, PRINCE GEORGE, VA, USA

    Data.gov (United States)

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  2. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, SHELBY COUNTY, OHIO, USA

    Data.gov (United States)

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  3. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, WAGONER COUNTY, OKLAHOMA, USA

    Data.gov (United States)

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  4. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, SUFFOLK COUNTY, MASSACHUSETTS

    Data.gov (United States)

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  5. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, FAIRFIELD COUNTY, CONNECTICUT

    Data.gov (United States)

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  6. DIGITAL FLOOD INSURANCE RATE MAP DATABASE ROCKLAND COUNTY, NY, USA

    Data.gov (United States)

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  7. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, OTTAWA COUNTY, OHIO, USA

    Data.gov (United States)

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  8. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, SAUNDERS COUNTY, NEBRASKA

    Data.gov (United States)

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  9. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, RED WILLOW COUNTY, NEBRASKA

    Data.gov (United States)

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  10. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, CHEROKEE COUNTY, KANSAS

    Data.gov (United States)

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  11. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, POLK COUNTY, NEBRASKA

    Data.gov (United States)

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  12. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, HARLAN COUNTY, NEBRASKA

    Data.gov (United States)

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  13. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, RENO COUNTY, KANSAS

    Data.gov (United States)

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  14. DIGITAL FLOOD INSURANCE RATE MAP DATABASE FOR HOWARD COUNTY, NEBRASKA

    Data.gov (United States)

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  15. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, FURNAS COUNTY, NEBRASKA

    Data.gov (United States)

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  16. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, LANCASTER COUNTY, NE, USA

    Data.gov (United States)

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  17. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, ALLAMAKEE COUNTY, IOWA

    Data.gov (United States)

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  18. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, CASS COUNTY, NEBRASKA

    Data.gov (United States)

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  19. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, MCPHERSON COUNTY, KANSAS

    Data.gov (United States)

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  20. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, DAWES COUNTY, NEBRASKA

    Data.gov (United States)

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  1. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, VALLEY COUNTY, NEBRASKA

    Data.gov (United States)

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  2. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, ELLSWORTH COUNTY, KANSAS

    Data.gov (United States)

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  3. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, SHERMAN COUNTY, NEBRASKA

    Data.gov (United States)

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  4. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, HARVEY COUNTY, KANSAS

    Data.gov (United States)

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  5. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, PLATTE COUNTY, NEBRASKA

    Data.gov (United States)

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  6. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, COLFAX COUNTY, NEBRASKA

    Data.gov (United States)

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  7. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, THURSTON COUNTY, NEBRASKA

    Data.gov (United States)

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  8. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, TAMA COUNTY, IOWA

    Data.gov (United States)

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  9. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, WEBSTER COUNTY, NEBRASKA

    Data.gov (United States)

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  10. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, Eddy County, NM

    Data.gov (United States)

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  11. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, RAY COUNTY, MISSOURI, USA

    Data.gov (United States)

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  12. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, JEFFERSON COUNTY, IDAHO, USA

    Data.gov (United States)

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  13. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, LAWRENCE COUNTY, OHIO, USA

    Data.gov (United States)

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  14. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, GRANT COUNTY, WISCONSIN, USA

    Data.gov (United States)

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  15. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, SEBASTIAN COUNTY, AR

    Data.gov (United States)

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  16. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, SEBASTIAN COUNTY, ARKANSAS

    Data.gov (United States)

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  17. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, FERGUS COUNTY, MONTANA, USA

    Data.gov (United States)

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  18. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, BONNER COUNTY, IDAHO

    Data.gov (United States)

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  19. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, SAN JOAQUIN COUNTY, CALIFORNIA

    Data.gov (United States)

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  20. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, LORAIN COUNTY, OHIO USA

    Data.gov (United States)

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  1. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, ROBERTSON COUNTY, KY

    Data.gov (United States)

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  2. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, COSHOCTON COUNTY, OHIO, USA

    Data.gov (United States)

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  3. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, NEWTON COUNTY, MISSOURI, USA

    Data.gov (United States)

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  4. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, CHEROKEE COUNTY, TX

    Data.gov (United States)

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  5. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, HAMILTON COUNTY, OHIO, USA

    Data.gov (United States)

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  6. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, GRANT COUNTY, KY

    Data.gov (United States)

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  7. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, ESSEX COUNTY, MA

    Data.gov (United States)

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  8. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, CARROLL COUNTY, GEORGIA, USA

    Data.gov (United States)

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  9. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, DOUGLAS COUNTY, ILLINOIS USA

    Data.gov (United States)

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  10. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, Douglas County, Oregon, USA

    Data.gov (United States)

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  11. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, LINCOLN COUNTY, ARKANSAS, USA

    Data.gov (United States)

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  12. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, POWELL COUNTY, KY

    Data.gov (United States)

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  13. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, COLFAX COUNTY, New Mexico

    Data.gov (United States)

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  14. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, Winston COUNTY, AL

    Data.gov (United States)

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  15. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, CONVERSE COUNTY, WYOMING, USA.

    Data.gov (United States)

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  16. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, Dougherty County, GA

    Data.gov (United States)

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  17. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, POLK COUNTY, TX

    Data.gov (United States)

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  18. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, HUNTERDON CO., NJ

    Data.gov (United States)

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  19. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, SEDGWICK COUNTY, KANSAS, USA

    Data.gov (United States)

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  20. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, FINNEY COUNTY, USA

    Data.gov (United States)

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  1. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, CULLMAN COUNTY, AL

    Data.gov (United States)

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  2. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, JOHNSON COUNTY, GEORGIA, USA

    Data.gov (United States)

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  3. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, Northumberland County, VA, USA

    Data.gov (United States)

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  4. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, CADDO PARISH, LOUISIANA, USA

    Data.gov (United States)

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  5. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, ETOWAH COUNTY, AL

    Data.gov (United States)

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  6. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, FORT BEND COUNTY, TEXAS

    Data.gov (United States)

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  7. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, LACLEDE COUNTY, MISSOURI, USA

    Data.gov (United States)

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  8. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, CLINTON COUNTY, MISSOURI, USA

    Data.gov (United States)

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  9. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, MCDONALD COUNTY, USA

    Data.gov (United States)

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  10. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, MERCER COUNTY, KY

    Data.gov (United States)

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  11. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, AUGUSTA COUNTY, VA, USA

    Data.gov (United States)

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  12. Digital Flood Insurance Rate Map Database, Crawford County, PA

    Data.gov (United States)

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  13. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, WARREN COUNTY, OH, USA

    Data.gov (United States)

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  14. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, BATH COUNTY, KY

    Data.gov (United States)

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  15. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, Spartanburg County, South Carolina

    Data.gov (United States)

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  16. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, Douglas COUNTY, Nevada

    Data.gov (United States)

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  17. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, Rio Grande County, Colorado

    Data.gov (United States)

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  18. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, Elbert County, Colorado

    Data.gov (United States)

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  19. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, JASPER COUNTY, TX

    Data.gov (United States)

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  20. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, Roosevelt COUNTY, New Mexico

    Data.gov (United States)

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  1. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, Linn County, Oregon, USA

    Data.gov (United States)

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  2. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, TEHAMA COUNTY, CALIFORNIA, USA

    Data.gov (United States)

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  3. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, Wythe County, VA, USA

    Data.gov (United States)

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  4. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, SULLIVAN COUNTY, PA, USA

    Data.gov (United States)

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  5. Digital Flood Insurance Database Submission for Saline County, AR ,USA

    Data.gov (United States)

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  6. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, Cleburne COUNTY, AL

    Data.gov (United States)

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  7. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, KITSAP COUNTY, WASHINGTON, USA

    Data.gov (United States)

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  8. DIGITAL FLOOD INSURANCE RATE MAP DATABASE,CAMDEN COUNTY, GEORGIA

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  9. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, MENDOCINO COUNTY, CALIFORNIA

    Data.gov (United States)

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  10. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, APPLING COUNTY, GEORGIA, USA

    Data.gov (United States)

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  11. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, Wilcox COUNTY, AL

    Data.gov (United States)

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  12. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, WASHINGTON COUNTY, USA

    Data.gov (United States)

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  13. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, BALLARD COUNTY, KY

    Data.gov (United States)

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  14. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, HARRISON COUNTY, KY

    Data.gov (United States)

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  15. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, PASCO COUNTY, FLORIDA, USA

    Data.gov (United States)

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  16. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, GILMER COUNTY, GEORGIA, USA

    Data.gov (United States)

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  17. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, MIAMI - DADE COUNTY, FLORIDA

    Data.gov (United States)

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  18. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, WALKER COUNTY, TX

    Data.gov (United States)

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  19. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, WALKER COUNTY, AL

    Data.gov (United States)

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  20. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, WALKER COUNTY, GEORGIA, USA

    Data.gov (United States)

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  1. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, BENTON COUNTY, MINNESOTA, USA

    Data.gov (United States)

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  2. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, DAVIESS COUNTY, KY

    Data.gov (United States)

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  3. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, MONROE COUNTY, GEORGIA, USA

    Data.gov (United States)

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  4. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, HENRY COUNTY, GEORGIA, USA

    Data.gov (United States)

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  5. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, MARLBORO COUNTY, SC

    Data.gov (United States)

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  6. FINAL DIGITAL FLOOD INSURANCE RATE MAP DATABASE, GREENWOOD COUNTY, SC

    Data.gov (United States)

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  7. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, RICE COUNTY, MINNESOTA, USA

    Data.gov (United States)

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  8. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, CHRISTIAN COUNTY, KY

    Data.gov (United States)

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  9. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, CHRISTIAN COUNTY, ILLINOIS USA

    Data.gov (United States)

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  10. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, SHELBY COUNTY, IA, USA

    Data.gov (United States)

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  11. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, POTTAWATTAMIE COUNTY, IOWA, USA

    Data.gov (United States)

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  12. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, MITCHELL COUNTY, IOWA, USA

    Data.gov (United States)

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  13. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, WARREN COUNTY, USA

    Data.gov (United States)

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  14. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, CLAYTON COUNTY, IOWA, USA

    Data.gov (United States)

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  15. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, HOWARD COUNTY, IOWA, USA

    Data.gov (United States)

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  16. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, SEWARD COUNTY, USA

    Data.gov (United States)

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  17. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, SCOTT COUNTY, KY

    Data.gov (United States)

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  18. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, NEWTON COUNTY, GEORGIA

    Data.gov (United States)

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  19. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, NEWTON COUNTY, GEORGIA, USA

    Data.gov (United States)

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  20. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, MIDDLESEX, VA, USA

    Data.gov (United States)

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  1. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, KARNES COUNTY, TEXAS, USA

    Data.gov (United States)

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  2. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, VOLUSIA COUNTY, FL, USA

    Data.gov (United States)

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  3. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, Marshall COUNTY, AL

    Data.gov (United States)

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  4. DRAFT DIGITAL FLOOD INSURANCE RATE MAP DATABASE, HEMPSTEAD COUNTY, AR

    Data.gov (United States)

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  5. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, TOM GREEN COUNTY, TEXAS

    Data.gov (United States)

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  6. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, Berks County, Pennsylvania, USA

    Data.gov (United States)

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  7. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, STONE COUNTY, MISSOURI, USA

    Data.gov (United States)

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  8. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, VAL VERDE COUNTY, TEXAS

    Data.gov (United States)

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  9. Digital Flood Insurance Rate Map Database, Richmond County, Virginia, USA

    Data.gov (United States)

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  10. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, WASATCH COUNTY, UTAH, USA

    Data.gov (United States)

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  11. Digital Flood Insurance Rate Map Database, Westmoreland County, Virginia, USA

    Data.gov (United States)

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  12. Flood Insurance Rate Map Database, Kent County, Delaware, USA

    Data.gov (United States)

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  13. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, SHELBY COUNTY, KENTUCKY, USA

    Data.gov (United States)

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  14. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, MCCRACKEN COUNTY, KY

    Data.gov (United States)

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  15. DIGITAL FLOOD INSURANCE RATE MAP DATABASE,GRAVES COUNTY, KY

    Data.gov (United States)

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  16. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, LYON COUNTY, KY

    Data.gov (United States)

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  17. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, WOLFE COUNTY, KY

    Data.gov (United States)

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  18. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, WEBSTER COUNTY, KY

    Data.gov (United States)

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  19. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, HAWAII COUNTY, HAWAII, USA

    Data.gov (United States)

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  20. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, MCINTOSH COUNTY, GEORGIA, USA

    Data.gov (United States)

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  1. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, HART COUNTY, KY

    Data.gov (United States)

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  2. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, OSCEOLA COUNTY, FL

    Data.gov (United States)

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  3. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, ELLIOTT COUNTY, KY

    Data.gov (United States)

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  4. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, GAGE COUNTY, NEBRASKA

    Data.gov (United States)

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  5. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, CLARK COUNTY, USA

    Data.gov (United States)

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  6. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, ALLEN COUNTY, INDIANA, USA

    Data.gov (United States)

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  7. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, ANGELINA COUNTY, TX

    Data.gov (United States)

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  8. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, DOUGLAS COUNTY, NEBRASKA, USA

    Data.gov (United States)

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  9. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, NEWPORT COUNTY, RHODE ISLAND

    Data.gov (United States)

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  10. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, ATASCOSA COUNTY, TEXAS

    Data.gov (United States)

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  11. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, DOUGLAS COUNTY, USA

    Data.gov (United States)

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  12. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, POLK COUNTY, USA

    Data.gov (United States)

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  13. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, MARION COUNTY, KY

    Data.gov (United States)

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  14. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, LEBANON COUNTY, PENNSYLVANIA

    Data.gov (United States)

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  15. Digital Flood Insurance Database Submission for Boone County, AR ,USA

    Data.gov (United States)

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  16. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, CRAWFORD COUNTY, AR ,USA

    Data.gov (United States)

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  17. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, SOLANO COUNTY, CALIFORNIA, USA

    Data.gov (United States)

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  18. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, Mitchell County, GA

    Data.gov (United States)

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  19. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, TAYLOR COUNTY, FL, USA

    Data.gov (United States)

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  20. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, GRIMES COUNTY, TX

    Data.gov (United States)

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  1. Digital Flood Insurance Rate Map Database, Allegheny County, Pennsylvania, USA

    Data.gov (United States)

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  2. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, ST. LOUIS, USA

    Data.gov (United States)

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  3. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, DELAWARE COUNTY, OK, USA

    Data.gov (United States)

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  4. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, UNION COUNTY, FLORIDA, USA

    Data.gov (United States)

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  5. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, LEE COUNTY, FLORIDA

    Data.gov (United States)

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  6. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, HAMILTON COUNTY, FLORIDA, USA

    Data.gov (United States)

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  7. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, WAYNE COUNTY, USA

    Data.gov (United States)

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  8. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, Jefferson COUNTY, AL

    Data.gov (United States)

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  9. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, WALTON COUNTY, FL, USA

    Data.gov (United States)

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  10. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, Kenton COUNTY, Kentucky

    Data.gov (United States)

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  11. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, RUSSELL COUNTY, KY

    Data.gov (United States)

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  12. DRAFT DIGITAL FLOOD INSURANCE RATE MAP DATABASE, LANCASTER COUNTY, SC

    Data.gov (United States)

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  13. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, CHAMBERS COUNTY, TEXAS

    Data.gov (United States)

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  14. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, CHATHAM COUNTY, GEORGIA, USA

    Data.gov (United States)

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  15. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, HILLSBOROUGH COUNTY, FLORIDA

    Data.gov (United States)

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  16. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, WALLER COUNTY, TX

    Data.gov (United States)

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  17. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, CARBON COUNTY, UTAH

    Data.gov (United States)

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  18. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, Liberty County, TX

    Data.gov (United States)

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  19. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, SAN JACINTO COUNTY, TX

    Data.gov (United States)

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  20. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, New London County, CT

    Data.gov (United States)

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  1. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, Westmoreland County, PA, USA

    Data.gov (United States)

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  2. Digital Flood Insurance Rate Map Database, Sussex County, Delaware, USA

    Data.gov (United States)

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  3. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, TYLER COUNTY, TX

    Data.gov (United States)

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  4. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, Stafford County , VIRGINIA

    Data.gov (United States)

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  5. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, SUMNER COUNTY, USA

    Data.gov (United States)

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  6. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, GREENVILLE COUNTY, SOUTH CAROLINA

    Data.gov (United States)

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  7. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, DICKENSON COUNTY, VA, USA

    Data.gov (United States)

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  8. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, HOUSTON COUNTY, TX

    Data.gov (United States)

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  9. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, DESOTO COUNTY, FL, USA

    Data.gov (United States)

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  10. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, FANNIN COUNTY, GEORGIA, USA

    Data.gov (United States)

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  11. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, ST. FRANCOIS COUNTY, USA

    Data.gov (United States)

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  12. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, LAFAYETTE COUNTY, MISSOURI, USA

    Data.gov (United States)

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  13. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, RANDALL COUNTY, TX, USA

    Data.gov (United States)

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  14. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, PEMBINA COUNTY, USA

    Data.gov (United States)

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  15. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, MCLEAN COUNTY, KY

    Data.gov (United States)

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  16. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, RUSK COUNTY, TX

    Data.gov (United States)

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  17. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, Nelson County, VA, USA

    Data.gov (United States)

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  18. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, CHEROKEE COUNTY, GEORGIA, USA

    Data.gov (United States)

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  19. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, NELSON COUNTY, KY

    Data.gov (United States)

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  20. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, MIDDLESEX COUNTY, MASSACHUSETTS

    Data.gov (United States)

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  1. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, ESCAMBIA COUNTY, AL

    Data.gov (United States)

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  2. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, Baldwin COUNTY, AL

    Data.gov (United States)

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  3. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, BARTOW COUNTY, GEORGIA, USA

    Data.gov (United States)

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  4. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, ALPENA COUNTY, MI

    Data.gov (United States)

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  5. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, CALDWELL PARISH, LOUISIANA, USA

    Data.gov (United States)

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  6. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, SHIAWASSEE COUNTY, MICHIGAN, USA

    Data.gov (United States)

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  7. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, EL DORADO COUNTY, CALIFORNIA

    Data.gov (United States)

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  8. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, GADSDEN COUNTY, FL, USA

    Data.gov (United States)

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  9. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, MARION COUNTY, FLORIDA

    Data.gov (United States)

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  10. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, SHELBY COUNTY, AL

    Data.gov (United States)

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  11. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, Harris COUNTY, TX

    Data.gov (United States)

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  12. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, PIKE COUNTY, AL

    Data.gov (United States)

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  13. Digital Flood Insurance Rate Map Database, Bradford County, Pennsylvania, USA

    Data.gov (United States)

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  14. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, JOHNSON COUNTY, KY

    Data.gov (United States)

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  15. Digital Flood Insurance Rate Map Database, Charles County, Maryland, USA

    Data.gov (United States)

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  16. Digital Flood Insurance Rate Map Database, Essex County, Virginia, USA

    Data.gov (United States)

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  17. Digital Flood Insurance Rate Map Database, Calvert County, Maryland, USA

    Data.gov (United States)

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  18. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, COBB COUNTY, GA, USA

    Data.gov (United States)

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  19. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, MATHEWS COUNTY, VIRGINIA

    Data.gov (United States)

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  20. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, HOLMES COUNTY, OHIO

    Data.gov (United States)

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  1. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, HOLMES COUNTY, USA

    Data.gov (United States)

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  2. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, KOOTENAI COUNTY, USA

    Data.gov (United States)

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  3. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, Accomack County, VIRGINIA

    Data.gov (United States)

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  4. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, CLARKE COUNTY, GEORGIA, USA

    Data.gov (United States)

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  5. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, LYCOMING COUNTY, PENNSYLVANIA

    Data.gov (United States)

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  6. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, WHATCOM COUNTY, WASHINGTON

    Data.gov (United States)

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  7. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, HARRISON COUNTY, TEXAS

    Data.gov (United States)

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  8. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, DELTA COUNTY, COLORADO, USA

    Data.gov (United States)

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  9. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, GULF COUNTY, FLORIDA, USA

    Data.gov (United States)

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  10. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, Delaware County, Pennsylvania, USA

    Data.gov (United States)

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  11. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, HALL COUNTY, NE, USA

    Data.gov (United States)

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  12. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, NORFOLK COUNTY, MASSACHUSETTS

    Data.gov (United States)

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  13. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, COWLEY COUNTY, USA

    Data.gov (United States)

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  14. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, TALBOT, MARYLAND, USA

    Data.gov (United States)

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  15. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, NANTUCKET COUNTY, MASSACHUSETTS

    Data.gov (United States)

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  16. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, PATRICK COUNTY, VA, USA

    Data.gov (United States)

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  17. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, RANDOLPH COUNTY, WV, USA

    Data.gov (United States)

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  18. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, GRAYSON COUNTY, VA, USA

    Data.gov (United States)

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  19. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, CHESTERFIELD, VA, USA

    Data.gov (United States)

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  20. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, SURRY COUNTY, VA, USA

    Data.gov (United States)

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  1. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, WORCESTER COUNTY, MASSACHUSETTS

    Data.gov (United States)

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  2. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, Buckingham County, VA, USA

    Data.gov (United States)

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  3. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, GARRETT COUNTY, Maryland, USA

    Data.gov (United States)

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  4. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, OTTAWA COUNTY, USA

    Data.gov (United States)

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  5. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, RALEIGH COUNTY, WV, USA

    Data.gov (United States)

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  6. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, Essex County, VA, USA

    Data.gov (United States)

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  7. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, GRAND COUNTY, USA

    Data.gov (United States)

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  8. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, Caroline COUNTY, Maryland, USA

    Data.gov (United States)

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  9. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, TUCKER COUNTY, WV, USA

    Data.gov (United States)

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  10. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, Sussex County, VA, USA

    Data.gov (United States)

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  11. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, WESTMORELAND COUNTY, VA, USA

    Data.gov (United States)

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  12. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, FLUVANNA COUNTY, VA, USA

    Data.gov (United States)

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  13. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, Richmond County, VA, USA

    Data.gov (United States)

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  14. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, Pulaski County, VA, USA

    Data.gov (United States)

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  15. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, Scott County, VA, USA

    Data.gov (United States)

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  16. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, DILLON COUNTY, USA

    Data.gov (United States)

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  17. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, Upshur County, WV, USA

    Data.gov (United States)

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  18. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, ALLEN COUNTY, USA

    Data.gov (United States)

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  19. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, LINN COUNTY, IA, USA

    Data.gov (United States)

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  20. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, CAROLINE COUNTY, VA, USA

    Data.gov (United States)

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  1. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, BANDERA COUNTY, TEXAS

    Data.gov (United States)

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  2. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, Washington COUNTY, NE

    Data.gov (United States)

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  3. Digital Flood Insurance Rate Map Database, Mercer County, PA

    Data.gov (United States)

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  4. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, ST JOSEPH COUNTY, MI

    Data.gov (United States)

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  5. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, SHERBURNE COUNTY, MINNESOTA, USA

    Data.gov (United States)

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  6. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, DUKES COUNTY, MA

    Data.gov (United States)

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  7. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, Terrell County, GA

    Data.gov (United States)

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  8. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, GLOUCESTER, VA, USA

    Data.gov (United States)

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  9. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, WORCESTER COUNTY, MA

    Data.gov (United States)

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  10. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, SONOMA COUNTY, CALIFORNIA

    Data.gov (United States)

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  11. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, MONMOUTH COUNTY, NEW JERSEY

    Data.gov (United States)

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  12. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, YORK COUNTY, PA, USA

    Data.gov (United States)

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  13. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, BENTON COUNTY, ARKANSAS

    Data.gov (United States)

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  14. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, GORDON COUNTY, GEORGIA

    Data.gov (United States)

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  15. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, SUSSEX COUNTY, NEW JERSEY

    Data.gov (United States)

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  16. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, MARIN COUNTY, CALIFORNIA

    Data.gov (United States)

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  17. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, CLEARFIELD COUNTY, PA, USA

    Data.gov (United States)

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  18. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, COOPER COUNTY, MISSOURI, USA

    Data.gov (United States)

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  19. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, CAMERON COUNTY, PA, USA

    Data.gov (United States)

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  20. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, BOYLE COUNTY, KY

    Data.gov (United States)

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  1. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, WARREN COUNTY, NEW JERSEY

    Data.gov (United States)

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  2. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, FAYETTE COUNTY, GEORGIA, USA

    Data.gov (United States)

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  3. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, SUFFOLK COUNTY, NEW YORK

    Data.gov (United States)

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  4. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, Bell COUNTY, Kentucky

    Data.gov (United States)

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  5. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, BATH COUNTY, VIRGINIA

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  6. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, INDIAN RIVER COUNTY, FL

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  7. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, SIMPSON COUNTY, KY

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  8. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, Cherokee COUNTY, AL

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  9. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, JACKSON COUNTY, AL

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  10. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, Lancaster County, VA, USA

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  11. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, BEDFORD COUNTY, VA, USA

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  12. DRAFT DIGITAL FLOOD INSURANCE RATE MAP DATABASE, HONOLULU COUNTY, HI

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  13. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, MAYES COUNTY, OK

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  14. DRAFT DIGITAL FLOOD INSURANCE RATE MAP DATABASE, NEWBERRY COUNTY, SC

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  15. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, GREENE COUNTY, GEORGIA, USA

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  16. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, FRANKLIN COUNTY, VA, USA

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  17. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, HIGHLAND COUNTY, VIRGINIA

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  18. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, SUMTER COUNTY, AL

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  19. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, GRADY COUNTY, OKLAHOMA

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  20. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, SHASTA COUNTY, CALIFORNIA

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  1. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, HOWELL COUNTY, MISSOURI, USA

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  2. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, SISKIYOU COUNTY, CALIFORNIA, USA

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  3. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, YOLO COUNTY, CALIFORNIA

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  4. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, PLUMAS COUNTY, CALIFORNIA, USA

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  5. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, ORANGE COUNTY, CALIFORNIA, USA

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  6. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, RIVERSIDE COUNTY, CALIFORNIA, USA

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  7. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, Tuolumne County, USA

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  8. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, PIMA COUNTY, ARIZONA, USA

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  9. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, COCHISE COUNTY, ARIZONA, USA

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  10. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, MADERA COUNTY, CALIFORNIA

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  11. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, TEHAMA COUNTY, CALIFORNIA

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  12. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, YUMA COUNTY, ARIZONA, USA

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  13. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, SACRAMENTO COUNTY, CALIFORNIA

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  14. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, BUTTE COUNTY, CALIFORNIA, USA

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  15. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, CLATSOP COUNTY, OR, USA

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  16. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, HENRY COUNTY, VA, USA

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  17. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, RALLS COUNTY, USA

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  18. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, MARION COUNTY, USA

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  19. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, HOUSTON COUNTY, Georgia

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  20. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, MIDLAND COUNTY, MICHIGAN, USA

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk Information And supporting data used to develop the risk data. The primary risk;...

  1. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, HOPKINS COUNTY, KY

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  2. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, EATON COUNTY, MICHIGAN, USA

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk Information And supporting data used to develop the risk data. The primary risk;...

  3. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, Oswego COUNTY, New York

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  4. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, Butts County, GA

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  5. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, BULLOCH COUNTY, GEORGIA, USA

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk Information And supporting data used to develop the risk data. The primary risk;...

  6. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, JONES COUNTY, GEORGIA, USA

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk Information And supporting data used to develop the risk data. The primary risk;...

  7. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, LUNA COUNTY, New Mexico

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  8. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, ROSS COUNTY, USA

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  9. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, CALHOUN COUNTY, FL, USA

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  10. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, BALTIMORE CITY, MARYLAND

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  11. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, Bucks COUNTY, PA, USA

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  12. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, Chambers COUNTY, AL

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  13. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, WALTON COUNTY, GEORGIA, USA

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk Information And supporting data used to develop the risk data. The primary risk;...

  14. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, HURON COUNTY, MICHIGAN USA

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  15. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, LEVY COUNTY, FL, USA

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  16. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, ROGERS COUNTY, OKLAHOMA

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  17. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, MEDINA COUNTY, TX

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  18. Digital Flood Insurance Rate Map Database, Middlesex County, Virginia, USA

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  19. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, LAKE COUNTY, ILLINOIS USA

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  20. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, STARK COUNTY, OHIO

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  1. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, DAUPHIN COUNTY, PENNSYLVANIA, USA

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk Information And supporting data used to develop the risk data. The primary risk;...

  2. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, ALBEMARLE COUNTY, VIRGINIA

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  3. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, City of Poquoson, Virginia

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  4. National Flood Insurance Program (NFIP) Residential Historical Claims

    Data.gov (United States)

    Department of Homeland Security — The National Flood Insurance Program (NFIP) aims to reduce the impact of flooding—a burden not covered by homeowner’s insurance—by providing insurance to homeowners,...

  5. Reducing medical claims cost to Ghana?s National Health Insurance scheme: a cross-sectional comparative assessment of the paper- and electronic-based claims reviews

    OpenAIRE

    Nsiah-Boateng, Eric; Asenso-Boadi, Francis; Dsane-Selby, Lydia; Andoh-Adjei, Francis-Xavier; Otoo, Nathaniel; Akweongo, Patricia; Aikins, Moses

    2017-01-01

    Background A robust medical claims review system is crucial for addressing fraud and abuse and ensuring financial viability of health insurance organisations. This paper assesses claims adjustment rate of the paper- and electronic-based claims reviews of the National Health Insurance Scheme (NHIS) in Ghana. Methods The study was a cross-sectional comparative assessment of paper- and electronic-based claims reviews of the NHIS. Medical claims of subscribers for the year, 2014 were requested fr...

  6. Data analytics for insurance loss modelling, telematics pricing and claims reserving.:Data analytics for insurance loss modelling, telematics pricing and claims reserving.

    OpenAIRE

    Verbelen, Roel

    2017-01-01

    Today's society generates data more rapidly than ever before, creating many opportunities as well as challenges for statisticians. Many industries become increasingly dependent on high-quality data, and the demand for sound statistical analysis of these data is rising accordingly. In the insurance sector, data have always played a major role. When selling a contract to a client, the insurance company is liable for the claims arising from this contract and will hold capital aside to meet th...

  7. Model estimation of claim risk and premium for motor vehicle insurance by using Bayesian method

    Science.gov (United States)

    Sukono; Riaman; Lesmana, E.; Wulandari, R.; Napitupulu, H.; Supian, S.

    2018-01-01

    Risk models need to be estimated by the insurance company in order to predict the magnitude of the claim and determine the premiums charged to the insured. This is intended to prevent losses in the future. In this paper, we discuss the estimation of risk model claims and motor vehicle insurance premiums using Bayesian methods approach. It is assumed that the frequency of claims follow a Poisson distribution, while a number of claims assumed to follow a Gamma distribution. The estimation of parameters of the distribution of the frequency and amount of claims are made by using Bayesian methods. Furthermore, the estimator distribution of frequency and amount of claims are used to estimate the aggregate risk models as well as the value of the mean and variance. The mean and variance estimator that aggregate risk, was used to predict the premium eligible to be charged to the insured. Based on the analysis results, it is shown that the frequency of claims follow a Poisson distribution with parameter values λ is 5.827. While a number of claims follow the Gamma distribution with parameter values p is 7.922 and θ is 1.414. Therefore, the obtained values of the mean and variance of the aggregate claims respectively are IDR 32,667,489.88 and IDR 38,453,900,000,000.00. In this paper the prediction of the pure premium eligible charged to the insured is obtained, which amounting to IDR 2,722,290.82. The prediction of the claims and premiums aggregate can be used as a reference for the insurance company’s decision-making in management of reserves and premiums of motor vehicle insurance.

  8. Index for Predicting Insurance Claims from Wind Storms with an Application in France.

    Science.gov (United States)

    Mornet, Alexandre; Opitz, Thomas; Luzi, Michel; Loisel, Stéphane

    2015-11-01

    For insurance companies, wind storms represent a main source of volatility, leading to potentially huge aggregated claim amounts. In this article, we compare different constructions of a storm index allowing us to assess the economic impact of storms on an insurance portfolio by exploiting information from historical wind speed data. Contrary to historical insurance portfolio data, meteorological variables show fewer nonstationarities between years and are easily available with long observation records; hence, they represent a valuable source of additional information for insurers if the relation between observations of claims and wind speeds can be revealed. Since standard correlation measures between raw wind speeds and insurance claims are weak, a storm index focusing on high wind speeds can afford better information. A storm index approach has been applied to yearly aggregated claim amounts in Germany with promising results. Using historical meteorological and insurance data, we assess the consistency of the proposed index constructions with respect to various parameters and weights. Moreover, we are able to place the major insurance events since 1998 on a broader horizon beyond 40 years. Our approach provides a meteorological justification for calculating the return periods of extreme-storm-related insurance events whose magnitude has rarely been reached. © 2015 Society for Risk Analysis.

  9. Double generalized linear compound poisson models to insurance claims data

    DEFF Research Database (Denmark)

    Andersen, Daniel Arnfeldt; Bonat, Wagner Hugo

    2017-01-01

    This paper describes the specification, estimation and comparison of double generalized linear compound Poisson models based on the likelihood paradigm. The models are motivated by insurance applications, where the distribution of the response variable is composed by a degenerate distribution...... implementation and illustrate the application of double generalized linear compound Poisson models using a data set about car insurances....

  10. Medical research using governments' health claims databases: with or without patients' consent?

    Science.gov (United States)

    Tsai, Feng-Jen; Junod, Valérie

    2018-03-01

    Taking advantage of its single-payer, universal insurance system, Taiwan has leveraged its exhaustive database of health claims data for research purposes. Researchers can apply to receive access to pseudonymized (coded) medical data about insured patients, notably their diagnoses, health status and treatments. In view of the strict safeguards implemented, the Taiwanese government considers that this research use does not require patients' consent (either in the form of an opt-in or in the form of an opt-out). A group of non-governmental organizations has challenged this view in the Taiwanese Courts, but to no avail. The present article reviews the arguments both against and in favor of patients' consent for re-use of their data in research. It concludes that offering patients an opt-out would be appropriate as it would best balance the important interests at issue.

  11. 76 FR 44491 - Group Health Plans and Health Insurance Issuers: Rules Relating to Internal Claims and Appeals...

    Science.gov (United States)

    2011-07-26

    ... 37208) entitled, ``Group Health Plans and Health Insurance Issuers: Rules Relating to Internal Claims..., ``Group Health Plans and Health Insurance Issuers: Rules Relating to Internal Claims and Appeals and... external review processes for group health plans and health insurance issuers offering coverage in the...

  12. Pricing the property claim service (PCS) catastrophe insurance options using gamma distribution

    Science.gov (United States)

    Noviyanti, Lienda; Soleh, Achmad Zanbar; Setyanto, Gatot R.

    2017-03-01

    The catastrophic events like earthquakes, hurricanes or flooding are characteristics for some areas, a properly calculated annual premium would be closely as high as the loss insured. From an actuarial perspective, such events constitute the risk that are not insurable. On the other hand people living in such areas need protection. In order to securitize the catastrophe risk, futures or options based on a loss index could be considered. Chicago Board of Trade launched a new class of catastrophe insurance options based on new indices provided by Property Claim Services (PCS). The PCS-option is based on the Property Claim Service Index (PCS-Index). The index are used to determine and payout in writing index-based insurance derivatives. The objective of this paper is to price PCS Catastrophe Insurance Option based on PCS Catastrophe index. Gamma Distribution is used to estimate PCS Catastrophe index distribution.

  13. Analysis of the Romanian Insurance Market Based on Ensuring and Exercising Consumers` Right to Claim

    Directory of Open Access Journals (Sweden)

    Dan Armeanu

    2014-05-01

    Full Text Available In the financial market of insurance, consumer protection represents an important component contributing to the stability, discipline and efficiency of the market. In this respect, the activity of educating and informing insurance consumers on ensuring and exercising their right to claim plays a leading role in the mechanism of consumer protection. This study aims to improve the decision-making capacity of the financial services consumers from the Romanian insurance market through better information on ensuring and exercising their right to claim under the legislation. Thus, by applying three data analysis techniques – principal components analysis, cluster analysis and discriminant analysis – to the data regarding the petitions that were registered by the 41 insurance companies which operated in the Romanian market in 2012, a classification that assesses the insurance market transparency is achieved, resulting in a better information for consumers and, hence, the improvement of their protection through reducing the level of transactions that are harmful to consumers

  14. Prevalence rates for depression by industry: a claims database analysis.

    Science.gov (United States)

    Wulsin, Lawson; Alterman, Toni; Timothy Bushnell, P; Li, Jia; Shen, Rui

    2014-11-01

    To estimate and interpret differences in depression prevalence rates among industries, using a large, group medical claims database. Depression cases were identified by ICD-9 diagnosis code in a population of 214,413 individuals employed during 2002-2005 by employers based in western Pennsylvania. Data were provided by Highmark, Inc. (Pittsburgh and Camp Hill, PA). Rates were adjusted for age, gender, and employee share of health care costs. National industry measures of psychological distress, work stress, and physical activity at work were also compiled from other data sources. Rates for clinical depression in 55 industries ranged from 6.9 to 16.2 %, (population rate = 10.45 %). Industries with the highest rates tended to be those which, on the national level, require frequent or difficult interactions with the public or clients, and have high levels of stress and low levels of physical activity. Additional research is needed to help identify industries with relatively high rates of depression in other regions and on the national level, and to determine whether these differences are due in part to specific work stress exposures and physical inactivity at work. Claims database analyses may provide a cost-effective way to identify priorities for depression treatment and prevention in the workplace.

  15. Prevalence rates for depression by industry: a claims database analysis

    Science.gov (United States)

    Alterman, Toni; Bushnell, P. Timothy; Li, Jia; Shen, Rui

    2015-01-01

    Purpose To estimate and interpret differences in depression prevalence rates among industries, using a large, group medical claims database. Methods Depression cases were identified by ICD-9 diagnosis code in a population of 214,413 individuals employed during 2002–2005 by employers based in western Pennsylvania. Data were provided by Highmark, Inc. (Pittsburgh and Camp Hill, PA). Rates were adjusted for age, gender, and employee share of health care costs. National industry measures of psychological distress, work stress, and physical activity at work were also compiled from other data sources. Results Rates for clinical depression in 55 industries ranged from 6.9 to 16.2 %, (population rate = 10.45 %). Industries with the highest rates tended to be those which, on the national level, require frequent or difficult interactions with the public or clients, and have high levels of stress and low levels of physical activity. Conclusions Additional research is needed to help identify industries with relatively high rates of depression in other regions and on the national level, and to determine whether these differences are due in part to specific work stress exposures and physical inactivity at work. Clinical significance Claims database analyses may provide a cost-effective way to identify priorities for depression treatment and prevention in the workplace. PMID:24907896

  16. Claims expenses and limits of liability in third party liability insurances

    International Nuclear Information System (INIS)

    Rehmann, J.

    1992-01-01

    After the Chernobyl accident, more than 300,000 individual claims totalling DM 440 million were settled in Germany, even though the level of radiation was relatively low. This has alerted insurers to the potential level of expenses connected with the handling and settlement of claims following a major nuclear accident which, it is estimated, could amount to DM 50 million per 100,000 claims. The Paris Convention (PC) states the principle of congruence between liability and coverage for nuclear installations. The minimum amounts of liability and coverage must be exclusively reserved for the compensation of accident victims. This paper will show that in PC countries, the majority of claims expenses - both internal and external -are borne by the insurers in addition to the sums insured for the compensation of third parties, with limited extensions of coverage in some cases. The situation is different in non-PC countries, and particularly in the United States of America, where expenses are included in the total sum insured together with compensation payments to third parties. This situation would not pose a problem if the minimum amounts of liability and coverage as stated in the PC were still applicable. In practice, most countries have since increased these amounts substantially, thus reducing the insurers' ability to make the maximum possible capacity available for indemnities to victims. Thus, before further increasing the statutory limits of liability, governments should, when conducting the Nuclear Energy Agency revision of the PC, consider allowing insurers to include claims handling expenses in their total sums insured; with a finite amount of risk, insurers would then be able to commit their full capacity instead of withholding a safety buffer for an open-ended commitment. (author)

  17. 76 FR 37037 - Requirements for Group Health Plans and Health Insurance Issuers Relating to Internal Claims and...

    Science.gov (United States)

    2011-06-24

    ... Requirements for Group Health Plans and Health Insurance Issuers Relating to Internal Claims and Appeals and... interim final regulations published July 23, 2010 with respect to group health plans and health insurance..., group health plans, and health insurance issuers providing group health insurance coverage. The text of...

  18. The Impact of Changes to the Unemployment Rate on Australian Disability Income Insurance Claim Incidence

    Directory of Open Access Journals (Sweden)

    Gaurav Khemka

    2017-03-01

    Full Text Available We explore the extent to which claim incidence in Disability Income Insurance (DII is affected by changes in the unemployment rate in Australia. Using data from 1986 to 2001, we fit a hurdle model to explore the presence and magnitude of the effect of changes in unemployment rate on the incidence of DII claims, controlling for policy holder characteristics and seasonality. We find a clear positive association between unemployment and claim incidence, and we explore this further by gender, age, deferment period, and occupation. A multinomial logistic regression model is fitted to cause of claim data in order to explore the relationship further, and it is shown that the proportion of claims due to accident increases markedly with rising unemployment. The results suggest that during periods of rising unemployment, insurers may face increased claims from policy holders with shorter deferment periods for white-collar workers and for medium and heavy manual workers. Our findings indicate that moral hazard may have a material impact on DII claim incidence and insurer business in periods of declining economic conditions.

  19. Recursive estimation of the claim rates and sizes in an insurance model

    Directory of Open Access Journals (Sweden)

    Lakhdar Aggoun

    2004-01-01

    Full Text Available It is a common fact that for most classes of general insurance, many possible sources of heterogeneity of risk exist. Premium rates based on information from a heterogeneous portfolio might be quite inadequate. One way of reducing this danger is by grouping policies according to the different levels of the various risk factors involved. Using measure change techniques, we derive recursive filters and predictors for the claim rates and claim sizes for the different groups.

  20. 24 CFR 266.626 - Notice of default and filing an insurance claim.

    Science.gov (United States)

    2010-04-01

    ... 24 Housing and Urban Development 2 2010-04-01 2010-04-01 false Notice of default and filing an... AND OTHER AUTHORITIES HOUSING FINANCE AGENCY RISK-SHARING PROGRAM FOR INSURED AFFORDABLE MULTIFAMILY PROJECT LOANS Contract Rights and Obligations Claim Procedures § 266.626 Notice of default and filing an...

  1. 75 FR 43109 - Requirements for Group Health Plans and Health Insurance Issuers Relating to Internal Claims and...

    Science.gov (United States)

    2010-07-23

    ... Requirements for Group Health Plans and Health Insurance Issuers Relating to Internal Claims and Appeals and... the Office of Consumer Information and Insurance Oversight of the U.S. Department of Health and Human... health insurance coverage offered in connection with a group health plan under the Employee Retirement...

  2. Health insurance and the demand for medical care: Instrumental variable estimates using health insurer claims data.

    Science.gov (United States)

    Dunn, Abe

    2016-07-01

    This paper takes a different approach to estimating demand for medical care that uses the negotiated prices between insurers and providers as an instrument. The instrument is viewed as a textbook "cost shifting" instrument that impacts plan offerings, but is unobserved by consumers. The paper finds a price elasticity of demand of around -0.20, matching the elasticity found in the RAND Health Insurance Experiment. The paper also studies within-market variation in demand for prescription drugs and other medical care services and obtains comparable price elasticity estimates. Published by Elsevier B.V.

  3. Registry and health insurance claims data in vascular research and quality improvement.

    Science.gov (United States)

    Behrendt, Christian-Alexander; Heidemann, Franziska; Rieß, Henrik Christian; Stoberock, Konstanze; Debus, Sebastian Eike

    2017-01-01

    The expansion of procedures in multidisciplinary vascular medicine has sparked a controversy regarding measures of quality improvement. In addition to primary registries, the use of health insurance claims data is becoming of increasing importance. However, due to the fact that health insurance claims data are not collected for scientific evaluation but rather for reimbursement purposes, meticulous validation is necessary before and during usage in research and quality improvement matters. This review highlights the advantages and disadvantages of such data sources. A recent comprehensive expert opinion panel examined the use of health insurance claims data and other administrative data sources in medicine. Results from several studies concerning the validity of administrative data varied significantly. Validity of these data sources depends on the clinical relevance of the diagnoses considered. The rate of implausible information was 0.04 %, while the validity of the considered diagnoses varied between 80 and 97 % across multiple validation studies. A matching study between health insurance claims data of the third-largest German health insurance provider, DAK-Gesundheit, and a prospective primary registry of the German Society for Vascular Surgery demonstrated a good level of validity regarding the mortality of endovascular and open surgical treatment of abdominal aortic aneurysm in German hospitals. In addition, a large-scale international comparison of administrative data for the same disorder presented important results in treatment reality, which differed from those from earlier randomized controlled trials. The importance of administrative data for research and quality improvement will continue to increase in the future. When discussing the internal and external validity of this data source, one has to distinguish not only between its intended usage (research vs. quality improvement), but also between the included diseases and/or treatment procedures

  4. Premium analysis for copula model: A case study for Malaysian motor insurance claims

    Science.gov (United States)

    Resti, Yulia; Ismail, Noriszura; Jaaman, Saiful Hafizah

    2014-06-01

    This study performs premium analysis for copula models with regression marginals. For illustration purpose, the copula models are fitted to the Malaysian motor insurance claims data. In this study, we consider copula models from Archimedean and Elliptical families, and marginal distributions of Gamma and Inverse Gaussian regression models. The simulated results from independent model, which is obtained from fitting regression models separately to each claim category, and dependent model, which is obtained from fitting copula models to all claim categories, are compared. The results show that the dependent model using Frank copula is the best model since the risk premiums estimated under this model are closely approximate to the actual claims experience relative to the other copula models.

  5. Claims settlement in insurance contracts from a consumer protection perspective in Cameroon

    Directory of Open Access Journals (Sweden)

    Comfort Fuah Kwanga

    2017-10-01

    Full Text Available Everyone in the society is faced with the possibility of one or more hazards that are part of life will sooner or later befall him and may occasion some loss. This misfortune is uncertain as to the time and period when it will occur and this amongst others include: fire outbreak, accident, and even death. This necessitates the need for people to go for insurance policies which suit their various needs in order to permit compensation in case of loss. Most consumers of insurance products are “short changed” in the process because very few take the trouble to read through their insurance policies in order to ascertain and understand the terms and conditions. The result is that most often when a claim arises and it is discovered that the loss is not covered by the terms of the insurance contract, there is the tendency of blaming the insurance companies. This paper posits that: there are of course some “bad eggs” in the industry who manipulate consumers. However, the paper holds that this unpleasant quagmire is often due to lack of understanding of the terms of insurance contracts in general and consumer apathy in particular. The essence of this study is to re-iterate the need to communicate the rules of the insurance game, thereby minimizing some of the misunderstanding and problems faced by consumers.

  6. Nonparametric Fine Tuning of Mixtures: Application to Non-Life Insurance Claims Distribution Estimation

    Science.gov (United States)

    Sardet, Laure; Patilea, Valentin

    When pricing a specific insurance premium, actuary needs to evaluate the claims cost distribution for the warranty. Traditional actuarial methods use parametric specifications to model claims distribution, like lognormal, Weibull and Pareto laws. Mixtures of such distributions allow to improve the flexibility of the parametric approach and seem to be quite well-adapted to capture the skewness, the long tails as well as the unobserved heterogeneity among the claims. In this paper, instead of looking for a finely tuned mixture with many components, we choose a parsimonious mixture modeling, typically a two or three-component mixture. Next, we use the mixture cumulative distribution function (CDF) to transform data into the unit interval where we apply a beta-kernel smoothing procedure. A bandwidth rule adapted to our methodology is proposed. Finally, the beta-kernel density estimate is back-transformed to recover an estimate of the original claims density. The beta-kernel smoothing provides an automatic fine-tuning of the parsimonious mixture and thus avoids inference in more complex mixture models with many parameters. We investigate the empirical performance of the new method in the estimation of the quantiles with simulated nonnegative data and the quantiles of the individual claims distribution in a non-life insurance application.

  7. Outcomes of direct pulp capping: interrogating an insurance database.

    Science.gov (United States)

    Raedel, M; Hartmann, A; Bohm, S; Konstantinidis, I; Priess, H W; Walter, M H

    2016-11-01

    To evaluate the effectiveness of direct pulp capping under general practice conditions. It was hypothesized that direct pulp capping is an effective procedure in the majority of cases and prevents the need for root canal treatment or extraction. Claims data were collected from the digital database of a major German national health insurance company. Only patients who had been insurance members for the entire 3 year period 2010 to 2012 were eligible. Kaplan-Meier survival analyses were conducted for all teeth with direct pulp capping. Success was defined as not undergoing root canal treatment. Survival was defined as not undergoing extraction. Differences between survival functions were tested with the log rank test. A total of 148 312 teeth were included. The overall success rate was 71.6% at 3 years. The overall survival rate was 95.9% at 3 years. The success rates for single-rooted teeth (71.8%) and multirooted teeth (71.5%) were similar although significantly different (P 85 years.). After direct pulp capping, more than two-thirds of the affected teeth did not undergo root canal treatment within 3 years. Although this study has the typical limits of a claims data analysis, it can be concluded that direct pulp capping is an effective intervention to avoid root canal treatment and extraction in a general practice setting. © 2015 International Endodontic Journal. Published by John Wiley & Sons Ltd.

  8. Predicting number of hospitalization days based on health insurance claims data using bagged regression trees.

    Science.gov (United States)

    Xie, Yang; Schreier, Günter; Chang, David C W; Neubauer, Sandra; Redmond, Stephen J; Lovell, Nigel H

    2014-01-01

    Healthcare administrators worldwide are striving to both lower the cost of care whilst improving the quality of care given. Therefore, better clinical and administrative decision making is needed to improve these issues. Anticipating outcomes such as number of hospitalization days could contribute to addressing this problem. In this paper, a method was developed, using large-scale health insurance claims data, to predict the number of hospitalization days in a population. We utilized a regression decision tree algorithm, along with insurance claim data from 300,000 individuals over three years, to provide predictions of number of days in hospital in the third year, based on medical admissions and claims data from the first two years. Our method performs well in the general population. For the population aged 65 years and over, the predictive model significantly improves predictions over a baseline method (predicting a constant number of days for each patient), and achieved a specificity of 70.20% and sensitivity of 75.69% in classifying these subjects into two categories of 'no hospitalization' and 'at least one day in hospital'.

  9. Technical evaluation of methods for identifying chemotherapy-induced febrile neutropenia in healthcare claims databases

    OpenAIRE

    Weycker Derek; Sofrygin Oleg; Seefeld Kim; Deeter Robert G; Legg Jason; Edelsberg John

    2013-01-01

    Abstract Background Healthcare claims databases have been used in several studies to characterize the risk and burden of chemotherapy-induced febrile neutropenia (FN) and effectiveness of colony-stimulating factors against FN. The accuracy of methods previously used to identify FN in such databases has not been formally evaluated. Methods Data comprised linked electronic medical records from Geisinger Health System and healthcare claims data from Geisinger Health Plan. Subjects were classifie...

  10. Pluvial, urban flood mechanisms and characteristics - Assessment based on insurance claims

    Science.gov (United States)

    Sörensen, Johanna; Mobini, Shifteh

    2017-12-01

    Pluvial flooding is a problem in many cities and for city planning purpose the mechanisms behind pluvial flooding are of interest. Previous studies seldom use insurance claim data to analyse city scale characteristics that lead to flooding. In the present study, two long time series (∼20 years) of flood claims from property owners have been collected and analysed in detail to investigate the mechanisms and characteristics leading to urban flooding. The flood claim data come from the municipal water utility company and property owners with insurance that covers property loss from overland flooding, groundwater intrusion through basement walls and flooding from the drainage system. These data are used as a proxy for flood severity for several events in the Swedish city of Malmö. It is discussed which rainfall characteristics give most flooding and why some rainfall events do not lead to severe flooding, how city scale topography and sewerage system type influence spatial distribution of flood claims, and which impact high sea level has on flooding in Malmö. Three severe flood events are described in detail and compared with a number of smaller flood events. It was found that the main mechanisms and characteristics of flood extent and its spatial distribution in Malmö are intensity and spatial distribution of rainfall, distance to the main sewer system as well as overland flow paths, and type of drainage system, while high sea level has little impact on the flood extent. Finally, measures that could be taken to lower the flood risk in Malmö, and other cities with similar characteristics, are discussed.

  11. Adult attention-deficit hyperactivity disorder: A database analysis of South African private health insurance

    Directory of Open Access Journals (Sweden)

    Renata Schoeman

    2017-01-01

    Full Text Available Background: Adult attention-deficit hyperactivity disorder (ADHD is a chronic, costly and debilitating disorder. In South Africa (SA, access to funding for care and treatment of ADHD is limited, and research is lacking. Aim: This study aimed to establish the current situation with regard to the psychiatric management of and funding for treatment of adult ADHD in the private sector in SA. Methods: A diagnostically refined retrospective claims database analysis was conducted. We examined the prevalence, costs and funding profile of claims over a 2-year period for adult beneficiaries with possible ADHD of a large medical administrator in SA. Results: The prevalence of adult ADHD was lower than published international rates. The presence of adult ADHD increased the prevalence of comorbidity and doubled the health care costs of beneficiaries. Contrary to public belief, comorbidities (including their medicine costs rather than psychiatric services or medicines were the main cost drivers. Conclusion: The current private health insurance funding model for ADHD limits access to funding. This affects early diagnosis and optimal treatment, thereby escalating long-term costs. Improved outcomes are possible if patients suffering from ADHD receive timely and accurate diagnosis, and receive chronic and comprehensive care. Balanced regulation is proposed to minimise the risk to both medical schemes and patients. A collaborative approach between stakeholders is needed to develop an alternative cost-effective funding model to improve access to treatment and quality of life for adults with ADHD in SA.

  12. Modeling the Malaysian motor insurance claim using artificial neural network and adaptive NeuroFuzzy inference system

    Science.gov (United States)

    Mohd Yunos, Zuriahati; Shamsuddin, Siti Mariyam; Ismail, Noriszura; Sallehuddin, Roselina

    2013-04-01

    Artificial neural network (ANN) with back propagation algorithm (BP) and ANFIS was chosen as an alternative technique in modeling motor insurance claims. In particular, an ANN and ANFIS technique is applied to model and forecast the Malaysian motor insurance data which is categorized into four claim types; third party property damage (TPPD), third party bodily injury (TPBI), own damage (OD) and theft. This study is to determine whether an ANN and ANFIS model is capable of accurately predicting motor insurance claim. There were changes made to the network structure as the number of input nodes, number of hidden nodes and pre-processing techniques are also examined and a cross-validation technique is used to improve the generalization ability of ANN and ANFIS models. Based on the empirical studies, the prediction performance of the ANN and ANFIS model is improved by using different number of input nodes and hidden nodes; and also various sizes of data. The experimental results reveal that the ANFIS model has outperformed the ANN model. Both models are capable of producing a reliable prediction for the Malaysian motor insurance claims and hence, the proposed method can be applied as an alternative to predict claim frequency and claim severity.

  13. Estimation of National Colorectal-Cancer Incidence Using Claims Databases

    International Nuclear Information System (INIS)

    Quantin, C.; Benzenine, E.; Hagi, M.; Auverlot, B.; Cottenet, J.; Binquet, M.; Compain, D.

    2012-01-01

    The aim of the study was to assess the accuracy of the colorectal-cancer incidence estimated from administrative data. Methods. We selected potential incident colorectal-cancer cases in 2004-2005 French administrative data, using two alternative algorithms. The first was based only on diagnostic and procedure codes, whereas the second considered the past history of the patient. Results of both methods were assessed against two corresponding local cancer registries, acting as “gold standards.” We then constructed a multivariable regression model to estimate the corrected total number of incident colorectal-cancer cases from the whole national administrative database. Results. The first algorithm provided an estimated local incidence very close to that given by the regional registries (646 versus 645 incident cases) and had good sensitivity and positive predictive values (about 75% for both). The second algorithm overestimated the incidence by about 50% and had a poor positive predictive value of about 60%. The estimation of national incidence obtained by the first algorithm differed from that observed in 14 registries by only 2.34%. Conclusion. This study shows the usefulness of administrative databases for countries with no national cancer registry and suggests a method for correcting the estimates provided by these data.

  14. Resolving Malpractice Claims after Tort Reform: Experience in a Self-Insured Texas Public Academic Health System.

    Science.gov (United States)

    Sage, William M; Harding, Molly Colvard; Thomas, Eric J

    2016-12-01

    To describe the litigation experience in a state with strict tort reform of a large public university health system that has committed to transparency with patients and families in resolving medical errors. Secondary data collected from The University of Texas System, which self-insures approximately 6,000 physicians at six health campuses across the state. We obtained internal case management data for all medical malpractice claims closed during 1 year before and 6 recent years following the enactment of state tort reform legislation. We retrospectively reviewed information about malpractice claimants, malpractice claims, and the process and outcome of dispute resolution. We accessed an internal case management database, supplemented by both electronic and paper records compiled by the university's Office of General Counsel. Closed claims dropped from 244 in 2001-2002 to an annual mean of 96 in 2009-2015, closures following lawsuits from 136 in 2001-2002 to an annual mean of 28 in 2009-2015, and paid claims from 60 in 2001 to an annual mean of 20 in 2009-2015. Patterns of resolution suggest efforts by the university to provide some compensation to injured patients in cases that were no longer economically viable for plaintiffs' lawyers to litigate. The percentage of payments relating to cases in which lawsuits had been filed decreased from 82 percent in 2001-2002 to 47 percent in 2009-2012 and again to 29 percent in 2012-2015, although most paid claimants were represented by attorneys. Unrepresented patients received payment in 13 cases closed in 2009-2012 (22 percent of payments; mean amount $60,566) and in 24 cases closed in 2012-2015 (41 percent of payments; mean amount $109,410). Even after tort reform, however, claims that resulted in payment remained slow to resolve, which was worsened for claimants subject to Medicare secondary payer rules. Strict confidentiality became a more common condition of settlement, although restrictions were subsequently relaxed

  15. Estimating Total Claim Size in the Auto Insurance Industry: a Comparison between Tweedie and Zero-Adjusted Inverse Gaussian Distribution

    Directory of Open Access Journals (Sweden)

    Adriana Bruscato Bortoluzzo

    2011-01-01

    Full Text Available The objective of this article is to estimate insurance claims from an auto dataset using the Tweedie and zero-adjusted inverse Gaussian (ZAIG methods. We identify factors that influence claim size and probability, and compare the results of these methods which both forecast outcomes accurately. Vehicle characteristics like territory, age, origin and type distinctly influence claim size and probability. This distinct impact is not always present in the Tweedie estimated model. Auto insurers should consider estimating total claim size using both the Tweedie and ZAIG methods. This allows for an estimation of confidence interval based on empirical quantiles using bootstrap simulation. Furthermore, the fitted models may be useful in developing a strategy to obtain premium pricing.

  16. Validation of the diagnosis canine epilepsy in a Swedish animal insurance database against practice records

    DEFF Research Database (Denmark)

    Heske, Linda; Berendt, Mette; Jäderlund, Karin Hultin

    2014-01-01

    Canine epilepsy is one of the most common neurological conditions in dogs but the actual incidence of the disease remains unknown. A Swedish animal insurance database has previously been shown useful for the study of disease occurrence in companion animals. The dogs insured by this company...... represent a unique population for epidemiological studies, because they are representative of the general dog population in Sweden and are followed throughout their life allowing studies of disease incidence to be performed. The database covers 50% of all insured dogs (in the year 2012) which represents 40......% of the national dog population. Most commonly, dogs are covered by both veterinary care insurance and life insurance. Previous studies have shown that the general data quality is good, but the validity of a specific diagnosis should be examined carefully before using the database for incidence calculations...

  17. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, WILSON COUNTY, NORTH CAROLINA

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — This Flood Insurance Study was produced through a cooperative partnership between the State of North Carolina and FEMA. The North Carolina Floodplain Mapping...

  18. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, GREENE COUNTY, NORTH CAROLINA

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — This Flood Insurance Study was produced through a cooperative partnership between the State of North Carolina and FEMA. The North Carolina Floodplain Mapping...

  19. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, EDGECOMBE COUNTY, NORTH CAROLINA

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — This Flood Insurance Study was produced through a cooperative partnership between the State of North Carolina and FEMA. The North Carolina Floodplain Mapping...

  20. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, Scotland County, North Carolina

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — This Flood Insurance Study was produced through a cooperative partnership between the State of North Carolina and FEMA. The North Carolina Floodplain Mapping...

  1. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, HALIFAX COUNTY, NORTH CAROLINA

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — This Flood Insurance Study was produced through a cooperative partnership between the State of North Carolina and FEMA. The North Carolina Floodplain Mapping...

  2. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, Franklin County, NORTH CAROLINA

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — This Flood Insurance Study was produced through a cooperative partnership between the State of North Carolina and FEMA. The North Carolina Floodplain Mapping...

  3. Database and Registry Research in Orthopaedic Surgery: Part I: Claims-Based Data.

    Science.gov (United States)

    Pugely, Andrew J; Martin, Christopher T; Harwood, Jared; Ong, Kevin L; Bozic, Kevin J; Callaghan, John J

    2015-08-05

    The use of large-scale national databases for observational research in orthopaedic surgery has grown substantially in the last decade, and the data sets can be grossly categorized as either administrative claims or clinical registries. Administrative claims data comprise the billing records associated with the delivery of health-care services. Orthopaedic researchers have used both government and private claims to describe temporal trends, geographic variation, disparities, complications, outcomes, and resource utilization associated with both musculoskeletal disease and treatment. Medicare claims comprise one of the most robust data sets used to perform orthopaedic research, with >45 million beneficiaries. The U.S. government, through the Centers for Medicare & Medicaid Services, often uses these data to drive changes in health policy. Private claims data used in orthopaedic research often comprise more heterogeneous patient demographic samples, but allow longitudinal analysis similar to that offered by Medicare claims. Discharge databases, such as the U.S. National Inpatient Sample, provide a wide national sampling of inpatient hospital stays from all payers and allow analysis of associated adverse events and resource utilization. Administrative claims data benefit from the high patient numbers obtained through a majority of hospitals. Using claims, it is possible to follow patients longitudinally throughout encounters irrespective of the location of the institution delivering health care. Some disadvantages include lack of precision of ICD-9 (International Classification of Diseases, Ninth Revision) coding schemes. Much of these data are expensive to purchase, complicated to organize, and labor-intensive to manipulate--often requiring trained specialists for analysis. Given the changing health-care environment, it is likely that databases will provide valuable information that has the potential to influence clinical practice improvement and health policy for

  4. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, Northampton County, Virginia (And Incorporated Areas)

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  5. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, CHARLES CITY COUNTY, VA, USA

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  6. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, JACKSON COUNTY, TEXAS (AND INCORPORATED AREAS)

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  7. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, ALBANY COUNTY, WYOMING (AND INCORPORATED AREAS)

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  8. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, EAU CLAIRE COUNTY PMR, WISCONSIN, USA

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk;...

  9. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, MOHAVE COUNTY, ARIZONA (AND INCORPORATED AREAS)

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  10. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, NAVARRO COUNTY, TEXAS (AND INCORPORATED AREAS)

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  11. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, McCURTAIN COUNTY, OKLAHOMA AND INCORPORATED AREAS

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  12. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, COMMONWEALTH OF PUERTO RICO, PUERTO RICO

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  13. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, KENDALL COUNTY AND INCORPORATED AREAS, TEXAS, USA

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  14. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, DALE COUNTY, ALABAMA AND INCORPORATED AREAS

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  15. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, SEVIER COUNTY, TENNESSEE (AND INCORPORATED AREAS)

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  16. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, GRAINGER COUNTY, TENNESSEE AND INCORPORATED AREAS

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  17. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, TAYLOR COUNTY, TX AND INCORPORATED AREAS

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  18. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, CHURCHILL COUNTY, NEVADA (AND INCORPORATED AREAS)

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  19. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, COMAL COUNTY AND INCORPORATED AREAS, TEXAS, USA

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  20. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, FORSYTH COUNTY, GEORGIA, AND INCORPORATED AREAS

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk Information And supporting data used to develop the risk data. The primary risk;...

  1. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, ARLINGTON COUNTY, VA, USA, AND INCORPORATED AREAS

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  2. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, SANTA FE COUNTY, NEW MEXICO

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  3. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, CAPE GIRARDEAU COUNTY, MISSOURI (AND INCORPORATED AREAS)

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  4. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, Otero COUNTY and Incorporated Areas, NEW MEXICO, USA

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  5. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, Lee County, Alabama and Incorporated Areas

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  6. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, CLARKE COUNTY, MISSISSIPPI AND INCORPORATED AREAS

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  7. DIGITAL FlOOD INSURANCE RATE MAP DATABASE, CHESTER COUNTY, SC, USA

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts Flood risk information and supporting data used to develop the risk data. The primary risk...

  8. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, JONES COUNTY, MISSISSIPPI AND INCORPORATED AREAS

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  9. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, VALENCIA COUNTY, NEW MEXICO, USA

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  10. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, CHESTER COUNTY, TENNESSEE AND INCORPORATED AREAS

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  11. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, MCNAIRY COUNTY, TENNESSEE AND INCORPORATED AREAS

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  12. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, SUMNER COUNTY, TENNESSEE AND INCORPORATED AREAS

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  13. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, MEIGS COUNTY, TENNESSEE AND INCORPORATED AREAS

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  14. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, LEWIS COUNTY, TENNESSEE AND INCORPORATED AREAS

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  15. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, JACKSON COUNTY, TENNESSEE AND INCORPORATED AREAS

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  16. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, SEQUATCHIE COUNTY, TENNESSEE AND INCORPORATED AREAS

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  17. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, PICKETT COUNTY, TENNESSEE AND INCORPORATED AREAS

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  18. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, WEAKLEY COUNTY, TENNESSEE AND INCORPORATED AREAS

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  19. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, MARION COUNTY, TENNESSEE AND INCORPORATED AREAS

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  20. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, DYER COUNTY, TENNESSEE AND INCORPORATED AREAS

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  1. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, UNICOI COUNTY, TENNESSEE AND INCORPORATED AREAS

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  2. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, HANCOCK COUNTY, TENNESSEE AND INCORPORATED AREAS

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  3. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, RHEA COUNTY, TENNESSEE AND INCORPORATED AREAS

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  4. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, PERRY COUNTY, TENNESSEE AND INCORPORATED AREAS

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  5. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, NANTUCKET COUNTY, MASSACHUSETTS (ALL JURISDICTIONS)

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  6. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, ORANGE COUNTY, FLORIDA AND INCORPORATED AREAS

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  7. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, BREVARD COUNTY, FLORIDA AND INCORPORATED AREAS

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  8. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, CANYON COUNTY, IDAHO (AND INCORPORATED AREAS)

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  9. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, San Bernardino COUNTY, CALIFORNIA, USA

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  10. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, PICKENS COUNTY, SOUTH CAROLINA AND INCORPORATED AREAS

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  11. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, EAST BATON ROUGE PARISH, LOUISIANA (AND INCORPORATED AREAS)

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  12. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, RICHLAND COUNTY, SOUTH CAROLINA AND INCORPORATED AREAS

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  13. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, LEWIS COUNTY, WEST VIRGINIA (AND INCORPORATED AREAS)

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  14. DIGITAL FLOOD INSURANCE RATE MAP DATABASE,FREDERICK COUNTY, VA, USA

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  15. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, JEFFERSON COUNTY, NEW YORK, USA

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk Information And supporting data used to develop the risk data. The primary risk;...

  16. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, CANNON COUNTY,TENNESSEE AND INCORPORATED AREAS

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  17. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, CITY AND COUNTY OF CUSTER COUNTY, MONTANA (AND INCORPORATED AREAS)

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  18. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, MCMINN COUNTY, TENNESSEE (AND INCORPORATED AREAS)

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  19. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, LAVACA COUNTY, TEXAS (AND INCORPORATED AREAS)

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  20. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, ULSTER COUNTY, NEW YORK, USA

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  1. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, San Juan COUNTY, New Mexico

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  2. Digital Flood Insurance Rate Map Database for Hot Spring County (and Incorporated Areas), AR, USA

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  3. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, OCONEE COUNTY, SOUTH CAROLINA AND INCORPORATED AREAS

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  4. Digital Flood Insurance Rate Map Database for Brazos County, TX, USA

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  5. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, RANKIN COUNTY, MISSISSIPPI AND INCORPORATED AREAS

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  6. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, CITRUS COUNTY, FLORIDA AND INCORPORATED AREAS

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  7. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, LAKE COUNTY, TENNESSEE AND INCORPORATED AREAS

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  8. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, MADISON COUNTY AND INCORPORATED AREAS, MO, USA

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  9. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, SMITH COUNTY, TEXAS, USA and Incorporated Areas

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  10. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, CALHOUN COUNTY, MISSISSIPPI AND INCORPORATED AREAS

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  11. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, LEWIS COUNTY AND INCORPORATED AREAS, MO, USA

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  12. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, HARRISON COUNTY, MISSISSIPPI AND INCORPORATED AREAS

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  13. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, POWESHIEK COUNTY, IOWA AND INCORPORATED AREAS

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  14. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, DES MOINES COUNTY, IOWA, USA

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  15. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, WARREN COUNTY AND INCORPORATED AREAS, IA, USA

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  16. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, CERRO GORDO COUNTY, IOWA, USA

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  17. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, HANCOCK COUNTY, MISSISSIPPI AND INCORPORATED AREAS

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  18. DRAFT DIGITAL FLOOD INSURANCE RATE MAP DATABASE, LA PAZ COUNTY, AZ

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  19. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, LA PAZ COUNTY, ARIZONA AND INCORPORATED AREAS

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  20. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, BENTON COUNTY, TENNESSEE AND INCORPORATED AREAS

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...