The U.S. COVID-19 County Policy Database: a novel resource to support pandemic-related research

The COVID-19 pandemic has resulted in over 1 million deaths in the U.S. as of May 2022 [1]. It has also caused tremendous financial hardship with nearly 15% unemployment at its peak in April 2020 [2], and millions falling into poverty [3]. It is increasingly recognized that health and social policies played a role in both alleviating and exacerbating the health and economic consequences of the pandemic. For example, mandated business closures likely contributed to decreased levels of transmission [4, 5], but also job and income loss that disproportionately affected low-income groups and women [6]. Meanwhile, economic policies like eviction moratoria ensured that families had the resources to stay healthy at home [7].

With limited coordination of federal guidance in COVID-19 policymaking relative to other high-income countries [8, 9], there has been substantial variation in state- and county-level COVID-19-related policies. The COVID-19 U.S. State Policy database at Boston University has systematically documented longitudinal variation in state policies on closures, shelter-in-place orders, housing protections, and more [10]. Similarly, the Oxford COVID-19 Government Response Tracker documents national-level policies over time and collects subnational data for countries with substantial local variation (including the U.S.) [11]. This database has documented substantial state-level variation in the comprehensiveness of containment and closure policies. Researchers have subsequently examined state policies’ effects on a variety of health and related outcomes, including whether minimum wage and paid sick leave policies affected food insufficiency during the pandemic [12].

In contrast, there has been little systematic documentation of county-level policies in the U.S., despite the fact that counties have been deeply involved in COVID-19 policymaking since early in the pandemic and that there are substantial differences in county-level COVID-19 rates even within a given state [13]. For example, six counties in the San Francisco Bay Area issued the first coordinated shelter-in-place orders in the U.S. on March 16, 2020 even before an analogous state policy was implemented [14]. A few studies have undertaken assessments of county policies for a handful of counties or for single type of policy (e.g., mask requirements) [15, 16]. Other studies seeking to explain differences in COVID-19-related outcomes have focused on county demographic characteristics such as population density or racial/ethnic composition, despite acknowledging the importance of policymaking [17, 18]. Without systematic and comprehensive documentation of county-level policies, it will be difficult to evaluate policies’ effects on health and economic outcomes.

In this study, we sought to fill this gap by gathering data on a range of county-level COVID-19-related policies from 171 counties in 7 states as the first phase of the new U.S. COVID-19 County Policy (UCCP) Database. We then characterize these policies both within and across states, testing the hypothesis that there is geographic variation in both the number and stringency of policies passed at the county and state levels.

Overall, policy data were most readily available for states and urban and suburban counties. Rural county data collection was more challenging since policies often were not documented or implemented, or websites were not updated regularly. Policy information was most often derived from health department and other government websites.

Assembling data from a variety of government and media websites, the U.S. COVID-19 County Policy (UCCP) Database is among the first to provide granular data on local governments’ policy responses to the COVID-19 pandemic. In the current study, we present findings from the first phase of data collection, including data from 171 counties across 7 states in the U.S. We document variation across and within states on 22 types of containment, economic, and public health policies, finding that there were important geographic differences in the types and comprehensiveness of policies to address the COVID-19 pandemic’s health and economic consequences. This adds to a body of literature that has examined variation in COVID-19 policymaking globally across countries and domestically at the state level [11, 24, 25], and suggests that future research examining the impact of policies on health during the pandemic needs to consider local variation that may result in substantial heterogeneity within states.

Several factors may explain the differences in county-level policymaking. These include health-related factors (like COVID-19 case rates, vaccines, or underlying morbidity and age distributions that may spur county policymakers to act), or sociodemographic characteristics (like population density or urbanicity that may have heightened concern for transmission). Local economic factors—e.g., unemployment or poverty rates—may have also driven policymaking around economic support for local residents. Alternatively, a stronger history of public health policymaking—e.g., around HIV/AIDS in the San Francisco Bay Area—may have led to more active COVID-19-related policymaking. Prior work has examined state characteristics associated with public health policymaking to address obesity [26], and future work could similarly examine the predictors of COVID-19-related policymaking.

It is also likely that the variation in policymaking in this study resulted in county variation in health outcomes. Studies before COVID-19 have often demonstrated the impacts of public health and economic policies on health and social outcomes [27‐29], and a handful of studies during the COVID-19 pandemic have addressed similar questions, e.g., finding that state shelter-in-place policies affected mobility [30]. This is an important area of future research, to inform policymaking during the later phases of this pandemic and future crises. Notably, research on the health impacts of COVID-19-related policies will be complicated by the collinearity of some policies (demonstrated here) and confounding by other unobserved place-based factors.

While patterns of policy comprehensiveness were similar when using county-only versus imputed county-plus-state data, the number of policies per county in each state varied much more. Specifically, some states were more likely to have policies enacted at the state level rather than by counties. There are several possible explanations for these findings. During this phase of the pandemic, these counties—which tend to be located in more rural states—may have been experiencing more limited transmission relative to counties in more urban states, such that county policymakers felt less urgency to act. Alternately, these counties are also located in states that are more right-leaning on indicators of political conservativeness [21], and which have been previously documented to have a less forceful policy response to COVID-19 in part perhaps due to a political philosophy supporting a more limited role of government [24]. Relatedly, these states are more likely to engage in preemption of local policies, in which state governments restrict county governments’ abilities to act on a given issue [31]. We did not capture state pre-emption policies, but pre-emption is known to affect other types of health policies like tobacco control and firearm safety [31]. Future research can examine the extent to which this may have played a role in constraining county COVID-19-related policies. Interestingly, these same states also tend to be bigger proponents of federalism, in which authority over local affairs is ceded to states rather than the federal government, including for public health issues [32]. It appears that the same logic does not extend to counties being afforded more flexibility when managing their own local affairs, despite the proliferation of local public health agencies in recent decades [32].

This study has several strengths, including the collection of policy data spanning 171 diverse counties in 7 states. We collected 22 policies across numerous domains, painting a detailed portrait of the policy landscape during this period of active policymaking and COVID-19 transmission. This study also has limitations. Counties were selected for inclusion based on the criteria of the parent project (see Supplement), and therefore do not constitute a generalizable or representative sample nor cover all 50 states. Similarly, data were collected in January-March 2021, and do not capture earlier or later phases of the pandemic. Nevertheless, even in this sample that is limited in geographic and temporal scope, we demonstrate substantial variation, and provide evidence that such data collection endeavors of county policies are critical to understanding how the pandemic has unfolded. Moreover, in ongoing work, we are conducting retrospective longitudinal weekly data collection for the period 2020–2021 from a larger swath of U.S. counties in all 50 states and Washington D.C. to fill this gap, and the current database will be updated with new data as it becomes available. Additionally, while the initial data collection presented here involved 22 policies hypothesized to be important for health, in ongoing work we are collecting additional data on a more robust set of social and economic support policies that may be important for addressing health disparities. Finally, policies are not always implemented or enforced as intended; importantly, we did not intend to capture actual enforcement or on-the-ground conditions, but rather the policies that were passed by governments during this time.

This study provides among the first evidence of variation in county-level COVID-19-related policies across multiple states, demonstrating substantial differences that reflect an active and diverse policymaking landscape. Assembling such data is the first step in developing evidence on how these policies contributed to and may continue to influence pandemic-related health and socioeconomic outcomes and disparities therein, and this study thereby informs future research on the health impacts of these policies. We encourage other investigators to leverage these data, which we are making publicly available, to advance research in this realm.