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Estimating prevalence of post-war health disorders using multiple systems data.

ABSTRACT: Effective surveillance on the long-term public health impact due to war and terrorist attacks remains limited. Such health issues are commonly under-reported, specifically for a large group of individuals. For this purpose, efficient estimation of the size or undercount of the population under the risk of physical and mental health hazards is of utmost necessity. A novel trivariate Bernoulli model is developed allowing heterogeneity among the individuals and dependence between the sources of information, and an estimation methodology using a Monte Carlo-based EM algorithm is proposed. Simulation results show the superiority of the performance of the proposed method over existing competitors and robustness under model mis-specifications. The method is applied to analyse two real case studies on monitoring amyotrophic lateral sclerosis (ALS) cases for the Gulf War veterans and the 9/11 terrorist attack survivors at the World Trade Center, USA. The average annual cumulative incidence rate for ALS disease increases by 33% and 16% for deployed and no-deployed military personnel, respectively, after adjusting the undercount. The number of individuals exposed to the risk of physical and mental health effects due to WTC terrorist attacks increased by 42% . These results provide interesting insights that can assist in effective decision-making and policy formulation for monitoring the health status of post-war survivors.

SUBMITTER: Bhuyan P

PROVIDER: S-EPMC11208173 | biostudies-literature | 2024 Jun

REPOSITORIES: biostudies-literature

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Estimating prevalence of post-war health disorders using multiple systems data.

Bhuyan Prajamitra P Chatterjee Kiranmoy K

Scientific reports 20240626 1

Effective surveillance on the long-term public health impact due to war and terrorist attacks remains limited. Such health issues are commonly under-reported, specifically for a large group of individuals. For this purpose, efficient estimation of the size or undercount of the population under the risk of physical and mental health hazards is of utmost necessity. A novel trivariate Bernoulli model is developed allowing heterogeneity among the individuals and dependence between the sources of inf ...[more]

PMID: 38926444

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Project description:BackgroundReliable epidemiological data on Alzheimer's disease are scarce. However, these are necessary to adapt healthcare policy in terms of prevention, care and social needs related to this condition. To estimate the prevalence rate in the Alpes-Maritimes on the French Riviera, with a population of one million, we present a capture-recapture procedure applied to cases of Alzheimer's disease, based on two epidemiological surveillance systems.MethodsTo estimate the total number of patients affected by Alzheimer's disease, a capture-recapture study included a cohort of patients with Alzheimer's disease or receiving medications only eligible for use for this condition, recorded by a specific health insurance information system (Health Insurance Cohort, HIC), and those registered in the French National Alzheimer's Data Bank ("Banque Nationale Alzheimer", BNA) in 2010 and 2011. We applied Bayesian estimation of the Mt ecological model, taking into account age and gender as covariates, i.e. factors of inhomogeneous catchability.ResultsOverall, 5,562 patients with Alzheimer's disease were recorded, of whom only 856 were common to both information systems. Mean age and F/M sex ratio differed between BNA and HIC surveillance systems, 81 vs 84 years and 2.7 vs 3.2, respectively. A Bayesian estimation, with age and gender as covariates, yields an estimate of 15,060 cases of Alzheimer's disease [95%HPDI: 14,490-15,630] in the Alpes-Maritimes. The completeness of the HIC and BNA databases were respectively of 25.4% and 17.2%. The estimated prevalence rate among the population over 65 years old was 6.3% in 2010-2011.ConclusionsThis study demonstrates that it is possible to determine the number of subjects affected by Alzheimer's disease in a geographical unit, using available data from two existing surveillance systems in France, i.e. 15,060 cases in the Alpes-Maritimes. This is the first stage of a population-based approach in view of adapting available resources to the population's needs.

Project description:ImportancePrevalence estimates of substance use disorders in the US rely on general population surveys. However, major population groups, such as homeless individuals and institutionalized individuals, are not captured by these surveys, and participants may underreport substance use.ObjectiveTo estimate the prevalence of substance use disorders in the US.Design, setting, and participantsThe benchmark multiplier method was used to estimate the prevalence of alcohol, cannabis, opioid, and stimulant use disorders based on data from the Transformed Medicaid Statistical Information System (T-MSIS) (the benchmark) and the National Survey on Drug Use and Health (NSDUH) (the multiplier) for 2018 and 2019. T-MSIS collects administrative data on Medicaid beneficiaries 12 years and older with full or comprehensive benefits. NSDUH is a nationally representative annual cross-sectional survey of people 12 years and older. Data were analyzed from February to June 2022.Main outcomes and measuresPrevalence of substance use disorders was estimated using the benchmark multiplier method based on T-MSIS and NSDUH data. Confidence intervals for the multiplier method estimates were computed using Monte Carlo simulations. Sensitivity of prevalence estimates to variations in multiplier values was assessed.ResultsThis study included Medicaid beneficiaries 12 years and older accessing treatment services in the past year with diagnoses of alcohol (n = 1 017 308 in 2018; n = 1 041 357 in 2019), cannabis (n = 643 737; n = 644 780), opioid (n = 1 406 455; n = 1 575 219), and stimulant (n = 610 858; n = 657 305) use disorders and NSDUH participants with 12-month DSM-IV alcohol (n = 3390 in 2018; n = 3363 in 2019), cannabis (n = 1426; n = 1604), opioid (n = 448; n = 369), and stimulant (n = 545; n = 559) use disorders. The benchmark multiplier prevalence estimates were higher than NSDUH estimates for every type of substance use disorder in both years and in the combined 2018 to 2019 sample: 20.27% (95% CI, 17.04-24.71) vs 5.34% (95% CI, 5.10-5.58), respectively, for alcohol; 7.57% (95% CI, 5.96-9.93) vs 1.68% (95% CI, 1.59-1.79) for cannabis; 3.46% (95% CI, 2.97-4.12) vs 0.68% (0.60-0.78) for opioid; and 1.91% (95% CI, 1.63-2.30) vs 0.85% (95% CI, 0.75-0.96) for stimulant use disorders. In sensitivity analyses, the differences between the benchmark multiplier method and NSDUH estimates persisted over a wide range of potential multiplier values.Conclusions and relevanceThe findings in this study reflect a higher national prevalence of substance use disorders than that represented by NSDUH estimates, suggesting a greater burden of these conditions in the US.

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Estimating prevalence of post-war health disorders using multiple systems data.

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Estimating prevalence of post-war health disorders using multiple systems data.

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