Insurance instability has many perverse outcomes; we find that one of these is a change in utilization patterns. Our estimates indicate that ER use, office visits and hospitalizations rise between 10% and 36% and that use of prescription medications falls by 19% among the unstably insured compared to those with consistent Medicaid coverage. Lack of a continuous source of coverage may cause individuals to overuse expensive sources of care like the ER or put off seeing a doctor until their health deteriorates enough to warrant an inpatient episode. Conversely, patients without coverage are often unable to afford required prescription medications, and this may in turn lead to an inpatient or ER episode. Alternately, those that are stably insured in Medicaid may be distinct from those that are not as indicated by the descriptive characteristics, in that they may be eligible based on a disability or other chronic condition, impacting both their utilization patterns and ease of enrollment relative to those who qualify based on income and assets.
In a study looking at insurance instability among HIV patients, Smith et al.[22
] find that changes in health insurance coverage are associated with lower drug utilization. Beyond this study, to our knowledge, there are no other studies evaluating the effect of inconsistent coverage on prescription drug utilization. Our finding of lower prescription drug utilization for individuals with multiple transitions is not surprising and may be affected by a number of issues including the individual not filling prescriptions while transitioning between insurance types/plans, formulary issues that may restrict use, higher cost-sharing among private plans compared to Medicaid, and treatment/prescribing patterns of new providers when a provider change may be necessary. Alternately, as stated above it may be due to underlying population characteristics of those consistently enrolled.
Multiple transitions to and from public programs may have significant implications for health care costs and quality. Administrative costs may be higher for individuals who undergo multiple insurance transitions. Health plans and providers may lose anticipated revenue or incur costs that they may not otherwise incur. Managing and monitoring care and measuring the quality of care also becomes more difficult. Additionally, a recent study suggests that individuals who lose continuity of care tend to feel unsafe if they do not get to see their usual physician[23
]. If individuals with continuous coverage do differ on their basis of eligibility, which cannot be determined using MEPS, lessons can be learned about the potential benefits of continuous enrollment evident even in comparison to a population that likely has more chronic health problems. These relationships should be disentangled in further work.
An interesting feature of our findings is the role played by unobserved factors. Adding parameterized versions of time-varying individual variables changes the estimates in certain cases. Moreover, the dynamic specification reveals that initial conditions play an important role in all cases. We believe these are a proxy for time-dependent unobserved effects; for example, certain individuals may be more likely to utilize health care than others and a dynamic model is necessary to capture this factor. These differences in effects are observed when the static specification is changed to a dynamic specification.
As with any observational study, our study has a number of limitations. We were not able to model the directionality of change and the type of insurance the individual transitioned into. We did not have information on why insurance transitions took place which may affect utilization behavior. For example, an individual transitioning due to a job change may have different utilization patterns than one transitioning due to administrative issues or one transitioning due to pregnancy. Another limitation is that we did not have information on the state of residence and thus were not able to control for differing renewal policies across states. Finally, all of the data used is based on self-report. In our analysis we include those who may erroneously report that they have Medicaid (false positives) and omit those that fail to report that they are covered (false negatives). Prior work has shown, however, that the size of these errors is likely small[24
From a methodological standpoint, Poisson regression has well-known weaknesses, in particular when data are overdispersed. The negative binomial (NB) model is often used as an alternate model in these situations, but as Cameron and Trivedi[25
] point out, it does not help when the conditional mean is poorly specified and it is less robust to distributional misspecification than the Poisson model. Moreover, in our case, Wooldridge's[18
] approach for dynamic panels has only been developed for Poisson models so we use this specification in our analysis.
If the effects we have found are indeed causal, there are potentially large gains to instituting programs or policies that provide consistent coverage across transitional life events. Increased utilization of health care in circumstances where individuals lose Medicaid coverage as a result of purely administrative reasons is wasteful from both an individual and societal perspective. It can also be argued that similarly wasteful are the transitions that occur among those that are at the border of eligibility and thus regularly transition on and off Medicaid. Moreover, our results suggest that providing better access to prescription medication may be a potentially effective method for maintaining health for those in transition. This in turn may reduce the need for expensive emergent care.