Abstract

BackgroundIdentifying the medical conditions that are associated with poor health is crucial to prioritize decisions for future research and organizing care. However, assessing the burden of disease in the general population is complex, lengthy, and expensive. Claims databases that include self-reported health status can be used to assess the impact of medical conditions on the health in a population.ObjectiveThis study aimed to identify medical conditions that are highly predictive of poor health status using claims databases.MethodsTo determine the medical conditions most highly predictive of poor health status, we used a retrospective cohort study using 2 US claims databases. Subjects were commercially insured patients. Health status was measured using a self-report health status response. All medical conditions were included in a least absolute shrinkage and selection operator regression model to assess which conditions were associated with poor versus excellent health.ResultsA total of 1,186,871 subjects were included; 61.64% (731,587/1,186,871) reported having excellent or very good health. The leading medical conditions associated with poor health were cancer-related conditions, demyelinating disorders, diabetes, diabetic complications, psychiatric illnesses (mood disorders and schizophrenia), sleep disorders, seizures, male reproductive tract infections, chronic obstructive pulmonary disease, cardiomyopathy, dementia, and headaches.ConclusionsUnderstanding the impact of disease in a commercially insured population is critical to identify subjects who may be at risk for reduced productivity and job loss. Claims database studies can measure the impact of medical conditions on the health status in a population and to assess changes overtime and could limit the need to collect prospective collection of information, which is slow and expensive, to assess disease burden. Leading medical conditions associated with poor health in a commercially insured population were the ones associated with high burden of disease such as cancer-related conditions, demyelinating disorders, diabetes, diabetic complications, psychiatric illnesses (mood disorders and schizophrenia), infections, chronic obstructive pulmonary disease, cardiomyopathy, and dementia. However, sleep disorders, seizures, male reproductive tract infections, and headaches were also part of the leading medical conditions associated with poor health that had not been identified before as being associated with poor health and deserve more attention.

Highlights

  • Knowing which medical conditions are associated with perceived poor health is crucial to identify unmet needs and prioritize decisions for future research and interventions

  • The leading medical conditions associated with poor health were cancer-related conditions, demyelinating disorders, diabetes, diabetic complications, psychiatric illnesses, sleep disorders, seizures, male reproductive tract infections, chronic obstructive pulmonary disease, cardiomyopathy, dementia, and headaches

  • Leading medical conditions associated with poor health in a commercially insured population were the ones associated with high burden of disease such as cancer-related conditions, demyelinating disorders, diabetes, diabetic complications, psychiatric illnesses, infections, chronic obstructive pulmonary disease, cardiomyopathy, and dementia

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Summary

Introduction

Knowing which medical conditions are associated with perceived poor health is crucial to identify unmet needs and prioritize decisions for future research and interventions. The Global Burden of Disease Study (GBD) created a framework for integrating and analyzing information on mortality and population health to compare the importance of diseases as measured by their impact on premature death and disability in different populations [3] It requires assessing both the prevalence of each condition of interest and the impact of such conditions on a person’s https://publichealth.jmir.org/2020/1/e13018 XSLFO RenderX. The IBM MarketScan Health Risk Assessment (HRA) Database has self-reported health status information and can be linked to another IBM database—MarketScan Commercial Claims and Encounters (CCAE)—which contains data on health insurance claims of commercially insured individuals This linkage allows researchers to efficiently study the burden of disease in a real-world setting in the employed population. Claims databases that include self-reported health status can be used to assess the impact of medical conditions on the health in a population

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