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Data Processing Improvements for Estimates of Health Insurance Coverage in the Current Population Survey Annual Social and Economic Supplement.

ABSTRACT: Estimates of health insurance coverage in the United States rely on household-based surveys, and these surveys seek to improve data quality amid a changing health insurance landscape. We examine postcollection processing improvements to health insurance data in the Current Population Survey Annual Social and Economic Supplement (CPS ASEC), one of the leading sources of coverage estimates. The implementation of updated data extraction and imputation procedures in the CPS ASEC marks the second stage of a two-stage improvement and the beginning of a new time series for health insurance estimates. To evaluate these changes, we compared estimates from two files that introduce the updated processing system with two files that use the legacy system. We find that updates resulted in higher rates of health insurance coverage and lower rates of dual coverage, among other differences. These results indicate that the updated data processing improves coverage estimates and addresses previously noted limitations of the CPS ASEC.

SUBMITTER: Berchick ER

PROVIDER: S-EPMC10232008 | biostudies-literature | 2022 Apr

REPOSITORIES: biostudies-literature

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Data Processing Improvements for Estimates of Health Insurance Coverage in the Current Population Survey Annual Social and Economic Supplement.

Berchick Edward R ER Jackson Heide H

Medical care research and review : MCRR 20210323 2

Estimates of health insurance coverage in the United States rely on household-based surveys, and these surveys seek to improve data quality amid a changing health insurance landscape. We examine postcollection processing improvements to health insurance data in the Current Population Survey Annual Social and Economic Supplement (CPS ASEC), one of the leading sources of coverage estimates. The implementation of updated data extraction and imputation procedures in the CPS ASEC marks the second sta ...[more]

PMID: 33754889

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Project description:ObjectiveTo improve understanding of the relationship between lack of insurance and risk of subsequent mortality.Data sourcesAdults who reported being uninsured or privately insured in the National Health Interview Survey from 1986 to 2000 were followed prospectively for mortality from initial interview through 2002. Baseline information was obtained on 672,526 respondents, age 18-64 at the time of the interview. Follow-up information on vital status was obtained for 643,001 (96 percent) of these respondents, with approximately 5.4 million person-years of follow-up.Study designRelationships between insurance status and subsequent mortality are examined using Cox proportional hazard survival analysis.Principal findingsAdjusted for demographic, health status, and health behavior characteristics, the risk of subsequent mortality is no different for uninsured respondents than for those covered by employer-sponsored group insurance at baseline (hazard ratio 1.03, 95 percent confidence interval [CI], 0.95-1.12). Omitting health status as a control variable increases the estimated hazard ratio to 1.10 (95 percent CI, 1.03-1.19). Also omitting smoking status and body mass index increases the hazard ratio to 1.20 (95 percent CI, 1.15-1.24). The estimated association between lack of insurance and mortality is not larger among disadvantaged subgroups; when the analysis is restricted to amenable causes of death; when the follow-up period is shortened (to increase the likelihood of comparing the continuously insured and continuously uninsured); and does not change after people turn 65 and gain Medicare coverage.ConclusionsThe Institute of Medicine's estimate that lack of insurance leads to 18,000 excess deaths each year is almost certainly incorrect. It is not possible to draw firm causal inferences from the results of observational analyses, but there is little evidence to suggest that extending insurance coverage to all adults would have a large effect on the number of deaths in the United States.

Project description:ObjectivesTo analyse the potential of the Social Health Insurance (SHI) model to support the achievement of Universal Health Coverage (UHC) in Low and Middle-Income Countries (LMICs) through a policy analysis case study of Kenya's National Health Insurance Fund (NHIF).DesignWe used an adaptation of the policy triangle framework to perform a retrospective policy analysis of Kenya's NHIF, drawing from semistructured interviews and analysis of published documents and grey literature.SettingWe focused on Kenya's NHIF as a case study.ParticipantsWe conducted 21 interviews with key stakeholders including policy experts, healthcare providers and formal and informal sector workers. We then triangulated the interview findings with document analysis.ResultsOnly 17% of Kenya's population are currently covered by the SHI as of 2023. Only 27% of the informal economy is covered by the NHIF, implying very low uptake and/or retention rates. We found little stakeholder engagement in the policy implementation process and minimum adoption of expert advice. Our analysis suggest that political affiliations and positions of power heavily influence health financing policies in Kenya. Purchasing and payment of healthcare was found to be riddled with inefficiencies, including slow bureaucratic reimbursement procedures, little expertise by rural hospital clerks, misappropriations and favouritism of specific private healthcare providers. We also found that group-based parallel schemes and penalty payments for defaulted premiums widened the existing inequity gap in healthcare access.ConclusionAlthough the SHI system is perceived to increase coverage and the quality of health services in Kenya, substantial structural and contextual challenges appear to deter its suitability to finance the attainment of Universal Health Coverage. From Kenya's experience, we identify little informal sector participation, inefficiencies in purchasing and payment of healthcare services, as well lack of political goodwill, as key bottlenecks for the implementation of SHI schemes in LMICs. LMICs adopting SHI need to also implement co-financing arrangements that do not impose on the population to co-finance, strategic purchasing systems, political goodwill and good governance for the SHI systems to be beneficial.

Dataset Information

Data Processing Improvements for Estimates of Health Insurance Coverage in the Current Population Survey Annual Social and Economic Supplement.

Publications

Data Processing Improvements for Estimates of Health Insurance Coverage in the Current Population Survey Annual Social and Economic Supplement.

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