The Burden of Medical Debt Faced by Households with Chronic Health Conditions in the United States

Several studies have documented that patients with chronic conditions face substantial financial hardships, such as high medical bills, compared to those without chronic conditions, mainly because of high out-of-pocket costs. This study examines the effect of chronic health conditions on medical debt while accounting for potential endogeneity issues.

From a theoretical standpoint, this study starts with Becker’s household production model (1981) to hypothesize that household members are altruistic and jointly maximize utility given the household’s preferences, income, assets, and budget set. Faced with a chronic health condition, households produce health in a manner that is consistent with the Grossman model (1972) by using a combination of market and non-market inputs. Depending on insurance coverage and treatment costs, the household may face high out-of-pocket costs (the actual price of receiving care). Based on the permanent income hypothesis, households that experience high out-of-pocket costs may have to borrow, hence accumulating medical debt, to smooth consumption. This paper considers three potential mechanisms through which chronic health conditions may have an effect on medical debt: financial resources (employment, income and wealth), insurance and out-of-pocket costs.

This study uses data from the 2011 Panel Study of Income Dynamics (PSID), the first year in which unsecured debt was divided into several categories such as credit card debt, student loans, medical debt, legal bills and debt from relatives. The sample is restricted to 4,758 households with heads who are between 18 and 65 years old. The Healthcare Cost and Utilization Project Chronic Condition Indicator is used to measure chronic health conditions reported by either the head of the household or his/her spouse.

We use several OLS and tobit model specifications and find a positive and significant association between the number of chronic conditions and medical debt. For instance, the elasticity of chronic conditions on medical debt ranges from 0.85 to 1.42 (p<0.001) for tobit models (expected log of medical debt evaluated at the means, given that debt has not been censored). Subsample analyses conditional on households with any secured and unsecured debt show similar results. Additionally, inverse probability weighting propensity score models show elasticities of chronic conditions on medical debt ranging from 0.97 to 0.70 (p<0.001). These models account for systematic differences on observables between households reporting a diagnosis of chronic health condition compared to those with no chronic health condition. We plan to use instrumental variables to address potential omitted variables bias and stratify analyses by income quartiles and insurance types to address heterogeneity in findings. Finally, counterfactual policy simulation of the ACA Medicaid expansion in reducing medical debt associated with chronic conditions will be conducted.

Findings will contribute to the growing literature on medical debt and the broader literature of health and socioeconomic status (SES) while accounting for issues of endogeneity. This analysis will help educate and guide policymakers, patients, physicians, and public health professionals in choosing effective treatment options for chronic health conditions.