Dataset Information

Estimation of the Prevalence of Amyotrophic Lateral Sclerosis in the United States Using National Administrative Healthcare Data from 2002 to 2004 and Capture-Recapture Methodology.

ABSTRACT: BACKGROUND:National administrative healthcare data may be used as a case-finding method for prevalence studies of chronic disease in the United States, but the completeness of ascertainment likely varies depending on the disease under study. METHODS:We used 3 case-finding sources (Medicare, Medicaid, and Veterans Administration data) to estimate the prevalence of amyotrophic lateral sclerosis (ALS) in the United States for 2002-2004, and applied the capture-recapture methodology to estimate the degree of under-ascertainment when relying solely on these sources for case identification. RESULTS:Case-finding completeness was 76% overall and did not vary by race, but was lower for males (77%) than for females (88%), and lower for patients under age 65 (66%) than patients over age 65 (79%). The uncorrected ALS prevalence ratio was 2.8/100,000 in 2002, 3.3/100,000 in 2003, and 3.7/100,000 in 2004. After correcting for under-ascertainment, the annual prevalence increased by approximately 1 per 100,000 to 3.7/100,000 in 2002 (95% CI 3.66-3.80), 4.4/100,000 in 2003 (95% CI 4.34-4.50), and 4.8/100,000 in 2004 (95% CI 4.76-4.91). CONCLUSIONS:Federal healthcare claims databases ascertained are a very efficient method for identifying the majority of ALS-prevalent cases in the National ALS Registry, and may be enhanced by having patients self-register through the registry web portal.

SUBMITTER: Nelson LM

PROVIDER: S-EPMC6250049 | biostudies-literature | 2018

REPOSITORIES: biostudies-literature

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Estimation of the Prevalence of Amyotrophic Lateral Sclerosis in the United States Using National Administrative Healthcare Data from 2002 to 2004 and Capture-Recapture Methodology.

Nelson Lorene M LM Topol Barbara B Kaye Wendy W Williamson David D Horton D Kevin DK Mehta Paul P Wagner Todd T

Neuroepidemiology 20180809 3-4

<h4>Background</h4>National administrative healthcare data may be used as a case-finding method for prevalence studies of chronic disease in the United States, but the completeness of ascertainment likely varies depending on the disease under study.<h4>Methods</h4>We used 3 case-finding sources (Medicare, Medicaid, and Veterans Administration data) to estimate the prevalence of amyotrophic lateral sclerosis (ALS) in the United States for 2002-2004, and applied the capture-recapture methodology t ...[more]

PMID: 30092573

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Project description:Estimating population sizes has long been of interest, from the estimation of the human or ecological population size within regions or countries to the hidden number of civilian casualties in a war. Total enumeration of the population, for example, via a census, is often infeasible or simply impractical. However, a series of partial enumerations or observations of the population is often possible. This has led to the ideas of capture-recapture methods, which have been extensively used within ecology to estimate the size of wildlife populations, with an associated measure of uncertainty, and are most effectively applied when there are multiple capture occasions. Capture-recapture ideology can be more widely applied to multiple data-sources, by the linkage of individuals across the multiple lists. This is often referred to as Multiple Systems Estimation (MSE). The MSE approach has been preferred when estimating "capture-shy" or hard-to-reach populations, including those caught up in the criminal justice system; or homeless; or trafficked; or civilian casualties of war. Motivated by a range of public policy applications of MSE, each briefly introduced, we discuss practical problems with potentially substantial methodological implications. They include: "period" definition; "case" definition; when an observed count is not a true count of the population of interest but an upper bound due to mismatched definitions; exact or probabilistic matching of "cases" across different lists; demographic or other information about the "case" which may influence capture-propensities; required permissions to access extant-lists; list-creation by research-teams or interested parties; referrals (if presence on list A results - almost surely - in presence on list B); different mathematical models leading to widely different estimated population sizes; uncertainty in estimation; computational efficiency; external validation; hypothesis-generation; and additional independent external information. Returning to our motivational applications, we focus on whether the uncertainty which qualified their estimates was sufficiently narrow to orient public policy; and, if not, what options were available and/or taken to reduce the uncertainty or to seek external validation. We also consider whether MSE was hypothesis-generating: in the sense of having spawned new lines of inquiry.

Project description:Population dynamic models combine density dependence and environmental effects. Ignoring sampling uncertainty might lead to biased estimation of the strength of density dependence. This is typically addressed using state-space model approaches, which integrate sampling error and population process estimates. Such models seldom include an explicit link between the sampling procedures and the true abundance, which is common in capture-recapture settings. However, many of the models proposed to estimate abundance in the presence of capture heterogeneity lead to incomplete likelihood functions and cannot be straightforwardly included in state-space models. We assessed the importance of estimating sampling error explicitly by taking an intermediate approach between ignoring uncertainty in abundance estimates and fully specified state-space models for density-dependence estimation based on autoregressive processes. First, we estimated individual capture probabilities based on a heterogeneity model for a closed population, using a conditional multinomial likelihood, followed by a Horvitz-Thompson estimate for abundance. Second, we estimated coefficients of autoregressive models for the log abundance. Inference was performed using the methodology of integrated nested Laplace approximation (INLA). We performed an extensive simulation study to compare our approach with estimates disregarding capture history information, and using R-package VGAM, for different parameter specifications. The methods were then applied to a real data set of gray-sided voles Myodes rufocanus from Northern Norway. We found that density-dependence estimation was improved when explicitly modeling sampling error in scenarios with low process variances, in which differences in coverage reached up to 8% in estimating the coefficients of the autoregressive processes. In this case, the bias also increased assuming a Poisson distribution in the observational model. For high process variances, the differences between methods were small and it appeared less important to model heterogeneity.

Project description:Understanding the influence of individual attributes on demographic processes is a key objective of wildlife population studies. Capture-recapture and age data are commonly collected to investigate hypotheses about survival, reproduction, and viability. We present a novel age-structured Jolly-Seber model that incorporates age and capture-recapture data to provide comprehensive information on population dynamics, including abundance, age-dependent survival, recruitment, age structure, and population growth rates. We applied our model to a multi-year capture-recapture study of polar bears (Ursus maritimus) in western Hudson Bay, Canada (2012-2018), where management and conservation require a detailed understanding of how polar bears respond to climate change and other factors. In simulation studies, the age-structured Jolly-Seber model improved precision of survival, recruitment, and annual abundance estimates relative to standard Jolly-Seber models that omit age information. Furthermore, incorporating age information improved precision of population growth rates, increased power to detect trends in abundance, and allowed direct estimation of age-dependent survival and changes in annual age structure. Our case study provided detailed evidence for senescence in polar bear survival. Median survival estimates were lower (<0.95) for individuals aged <5 years, remained high (>0.95) for individuals aged 7-22 years, and subsequently declined to near zero for individuals >30 years. We also detected cascading effects of large recruitment classes on population age structure, which created major shifts in age structure when these classes entered the population and then again when they reached prime breeding ages (10-15 years old). Overall, age-structured Jolly-Seber models provide a flexible means to investigate ecological and evolutionary processes that shape populations (e.g., via senescence, life expectancy, and lifetime reproductive success) while improving our ability to investigate population dynamics and forecast population changes from capture-recapture data.

Project description:Background and aimsCapture-recapture (CRC) analysis is recommended for estimating the prevalence of problem drug use or people who inject drugs (PWID). We aim to demonstrate how naive application of CRC can lead to highly misleading results, and to suggest how the problems might be overcome.MethodsWe present a case study of estimating the prevalence of PWID in Bristol, UK, applying CRC to lists in contact with three services. We assess: (i) sensitivity of results to different versions of the dominant (treatment) list: specifically, to inclusion of non-incident cases and of those who were referred directly from one of the other services; (ii) the impact of accounting for a novel covariate, housing instability; and (iii) consistency of CRC estimates with drug-related mortality data. We then incorporate formally the drug-related mortality data and lower bounds for prevalence alongside the CRC into a single coherent model.ResultsFive of 11 models fitted the full data equally well but generated widely varying prevalence estimates, from 2740 [95% confidence interval (CI) = 2670, 2840] to 6890 (95% CI = 3740, 17680). Results were highly sensitive to inclusion of non-incident cases, demonstrating the presence of considerable heterogeneity, and were sensitive to a lesser extent to inclusion of direct referrals. A reduced data set including only incident cases and excluding referrals could be fitted by simpler models, and led to much greater consistency in estimates. Accounting for housing stability improved model fit considerably more than did the standard covariates of age and gender. External data provided validation of results and aided model selection, generating a final estimate of the number of PWID in Bristol in 2011 of 2770 [95% credible interval (Cr-I) = 2570, 3110] or 0.9% (95% Cr-I = 0.9, 1.0%) of the population aged 15-64 years.ConclusionsSteps can be taken to reduce bias in capture-recapture analysis, including: careful consideration of data sources, reduction of lists to less heterogeneous subsamples, use of covariates and formal incorporation of external data.

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Dataset Information

Estimation of the Prevalence of Amyotrophic Lateral Sclerosis in the United States Using National Administrative Healthcare Data from 2002 to 2004 and Capture-Recapture Methodology.

Publications

Estimation of the Prevalence of Amyotrophic Lateral Sclerosis in the United States Using National Administrative Healthcare Data from 2002 to 2004 and Capture-Recapture Methodology.

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