introduction
in this paper, we attempt to inform these tradeoffs by examining the effect of sip polices on use of healthcare services. an important empirical challenge with evaluating the impacts of these policies is the endogenous nature of their implementation ( goodman-bacon and marcus 2020 ). while there is variation in the timing of implementation of sip policies across counties, which can be used to identify the effects of these policies on health care utilization, counties might have implemented these policies in response to or concurrently with rising covid cases. therefore, it is important to disentangle the effects of sip policies from the effects of changes in trajectory of the covid-19 pandemic within a county. we address this potential endogeneity of sip policies by non-parametrically controlling for the number of weeks since the first covid-19 case and covid-19 death in each county.
the rest of the paper proceeds as follows. first, we outline the data sources and measures used in the present study. second, we define the methods used for evaluating the impact county-level sip policies on county-level healthcare utilization. third, we report the results. finally, we contextualize the results within the broader literature and present policy implications.
discussion
this study is not without limitations. first, while we use medical claims data from a large and diverse study population that is employed, it represents a subset of individuals with private insurance and it does not include other important populations, such as patients with medicaid, and those lacking insurance. insurers have reported that medicaid enrollment is increasing at a rapid rate when compared to marketplace enrollment, but surprisingly enough slower-than expected ( lucia et al., 2020 ). job loss can reduce health care utilization use through both loss of insurance coverage and reductions in income. due to the mechanical linkage between our study population and employment, our findings, while large, are potentially an underestimate for the declines in the number of visits. second, the safegraph mobile tracking data, comprise of approximately 10 percent of all cellphone users and 6 million locations. thus, our results using those data are limited to the population and those locations covered by the safegraph data. our measures of number of visits are not necessarily tracking healthcare utilization, instead the number of individuals who visit locations under that particular naics code. the decline in the number of visits may be due to declines in employment in the healthcare sector as well. third, we are not able to examine whether care that has been deferred during the early period of the covid-19 pandemic will be deferred until the future or avoided completely. our estimates do not capture potential innovative approaches by providers to ensure patient resumption of preventive care. this limitation is important, given that concerted efforts are being made to increase the use of preventive services such as vaccinations which has exhibited a sharp initial drop after the national emergency declaration. future work must monitor each of the preventive measures that we track in the present study and disparities in the use of preventive services. fourth, the present study does not include measures of healthcare capacity. that said, a recent report by the government accountability office (2020) reports that intensive care unit bed availability data is not currently up to date given that only 60 percent of hospitals have reported their information as of early june 2020 and that 95 to 100 percent are needed for effective analysis ( government accountability office 2020 ). fifth, we do not examine the use of telehealth broken down by the type of provider or cause for the interaction. existing studies have shown that changes in telehealth utilization differ by the provider and the cause for the interaction ( lau et al., 2020 ; patel et al., 2021 ). there is important heterogeneity in the use of telehealth during the pandemic that our study does not quantify. finally, the claims data we use does not include detailed race and ethnicity characteristics of patients. the covid-19 pandemic has had a disproportionate impact on communities of color and other marginalized groups ( khazanchi et al., 2020 ; azar et al., 2020 ). therefore, we are unable to examine differences in healthcare utilization by these key
characteristics.
