Sample and Data
We analyzed data from the Medical Expenditure Panel Survey (2006-2015), a nationally representative survey of the civilian non-institutionalized population, for 32,919 Medicare beneficiaries 65 years and older. We linked respondents’ data in MEPS annual files to their respective records from the Medical Conditions files, and then pooled linked datasets for years 2006-2015. Our data cover three distinct periods with respect to the ACA: pre-ACA (2006-2010), implementation period of ACA provisions relevant to older adults with MCCs (2011-2013), and post-ACA (2014-2015).
Measures
Outcomes: As primary outcomes, we first documented the prevalence of having any (at least one) emergency department (ED) visit, hospital inpatient visit, and overnight inpatient stay. As secondary outcomes, we analyzed counts of ED visits, inpatient visits, and LOS (total and average).
Chronic Conditions: We identified chronic conditions by using the definitions developed by Hwang and colleagues, and adopted by the Agency for Healthcare Research and Quality [25, 26], applied to International Classification of Diseases 9th Revision (ICD-9) 3-digit codes in the MEPS Medical Conditions files. We then calculated the total number of unique chronic conditions for each respondent, and categorized them as having 0, 1, 2, 3, 4, or 5+ chronic conditions. Those with ≥2 conditions were classified as having MCCs.
Covariates: Our analysis used data about respondents’ characteristics known to be related to ED visits, inpatient services, and having MCCs. Respondent sociodemographic characteristics included age, gender, race/ethnicity, language, marital status, region, income relative to the federal poverty line (FPL), and education. To measure respondents’ health status, we included self-rated general and mental health, activity limitations (physical and cognitive), and what chronic condition(s) they had (e.g., high blood pressure, diabetes, heart disease, stroke, and asthma). We also considered respondents’ access to care including types of payer (i.e., Medicaid, private insurance), having a usual source of care, receiving needed medical care, and getting needed prescription drugs. These factors are key determinants of ED use and hospitalization.
Statistical Analysis
The goal of our analysis was to provide an update of where levels of ED visits and inpatient stays stand among older adults with MCCs following relevant ACA reforms, relative to the pre-ACA period. In our statistical models, this was accomplished by interacting a period indicator (pre-ACA = 0, post-ACA = 1) with chronic condition categories (having 5+, 4, 3, 2, 1, vs. 0), while including the main effects of these variables as well as the aforementioned confounding covariates. Since we are interested in the specific association of having MCCs with ED/inpatient utilization, we adjusted for potential confounding by the particular conditions respondents have had as well as the key predisposing/enabling sociodemographic and access-related factors, which are likely related to both the need/ability to use the ED/hospital and developing MCCs in the first place.
We analyzed binary outcomes (prevalence of having ≥1 utilization event [i.e., visit or night]) in logit models. For count outcomes, we used two-part, logit-negative binomial models, with the negative-binomial part estimated conditional on having ≥1 event. Two-part models (known as hurdle models for count data) handle the severely right-skewed nature of count distributions, with a concentrated mass of zeros on the left-hand side of the distribution and a very long right tail [27]. Two-part models also allow recovering population-average estimates of change in outcome levels from the entire sample, as opposed to conditional estimates obtained from models fit only to the subsample with ≥1 event [28]. After estimating each of our logit and two-part models, we recovered the adjusted, average marginal probability (of having ≥1 event) and count of events, by ACA period and MCC category. Finally, we estimated the pre-post-ACA changes in probabilities and counts for each MCC category. All estimates were adjusted for complex survey design, and were made nationally representative using survey weights. All analyses were performed in Stata 14.2 (StataCorp, College Station, TX).