Introduction

“WE GOT SOME BAD STUFF OUT HERE!”[1]

It’s January 2020. Houston-based doctor Cedrick Smith blows the whistle on Facebook. He follows the post with some self-help tips: “vigilant hand washing,” “eat whole crushed garlic,” and “sign payment pads with your knuckle.”[2] As a Black director of an urgent care center, Dr. Smith has no disillusion about what is about to unfold in his community in the coming months.

Fast-forward to March 2020. As the coronavirus unassumingly makes its way through the streets of Houston and into the bustling rodeo-going crowds, it lays bare the pronounced health vulnerabilities in communities of color, especially Blacks. By September, Black men and women are 4.6 times more likely to get hospitalized from COVID-19 complications than Whites in any Houston hospital.[3] Across the nation, they similarly face the disproportionate wrath of COVID-19.[4] In Kansas City, Missouri, 40 percent of those infected are Blacks or Latinos, even though those groups make up only 16 percent of the state’s population.[5] Blacks also have the highest nationwide COVID-19 death rates—two or more times higher than the rate for Whites and Asians.[6]

Racial health disparities run deep in America. Blacks have higher rates of diabetes, hypertension, and heart disease than other groups.[7] Black children die from asthma complications 10 times more often than White children.[8] Black adults also face increased risks of dying from cancers.[9] For example, Black women are more likely than White women to die of breast cancer, while Black men are more than twice as likely as Whites to die of prostate cancer.[10]

The rule of law[11] permeates every aspect of society, but its impact on racial health disparities is often poorly understood. Well-designed laws can build strong healthcare systems, produce effective drugs and vaccines, create healthier and safer workplaces, and improve our built and natural environments.[12] In contrast, poorly-designed, -implemented, and -enforced laws can harm marginalized populations.[13] Yet, few studies attempt to evaluate the benefits and harms of laws on health outcomes in these communities. The limited efforts, if any, suffer from lack of data, strong methodology, expertise, and funding. Laws that can bridge the health equity gap are often not adequately scaled, while laws that entrench disparities persist.

This Comment advocates for legal epidemiology as a tool to achieve racial health equity. Legal epidemiology is the scientific study and deployment of laws as a driver in the cause, distribution, and prevention of disease.[14] Primarily developed to evaluate laws, this emerging field plays a key role in health equity advocacy in two ways. First, legal epidemiology produces more scientifically convincing evidence on health disparities.[15] Its methods allow us to look beyond observable disparities and into their root causes. They also reveal how variations in legal designs in different jurisdictions produce different impacts over time on different racial groups. A better understanding of such relationships will enable better design and integration of policies into promoting racial health equity. Second, funders and decision-makers can benefit from more actionable and timely evidence produced by legal epidemiology to deploy time and resources more effectively.[16] Legal epidemiology can help advocates amplify the narrative that health is not only a basic human need—health is a civil right.

This Comment illustrates how legal epidemiology can catalyze racial health equity with three case studies. The first case study describes legal mapping efforts to track legal responses to COVID-19 in 50 states and the District of Columbia.[17] The second case study examines the impact of state preemption of inclusionary zoning policies on health outcomes among different racial groups.[18] Inclusionary zoning laws are local efforts to create affordable housing by requiring or encouraging developers to make certain housing units available at below-market prices. States can override these efforts with preemption laws. Using legal epidemiology tools, researchers compare health outcomes in states with preemption and without preemption laws. They find robust evidence that preemption of inclusionary zoning was negatively associated with health outcomes, particularly for Black adults.

The third case study features a sophisticated policy surveillance, which is another type of legal epidemiology research. The project evaluates the effect of 23 state-level earned income tax credit laws on birth outcomes by race and ethnicity from 1994 to 2013.[19] Although earned income tax credit laws improve birth outcomes in general, after stratifying the impacts by race, the research finds that Black babies experience the biggest outcome improvement compared to babies of other races as a result of the tax credit policy.

As pieces of a puzzle, each case study tells a story. Efforts to generate user-friendly legal databases to track laws across jurisdictions over time are the first steps. They lay a solid foundation for more sophisticated legal epidemiology analyses, as illustrated in the second and third case studies. Policy assessment and surveillance methods help establish the cause-and-effect relationship between laws and racial health outcomes. Together, they unveil the bigger story: race-neutral policies rarely have a race-neutral effect in practice.[20] Whether in a public health emergency, the housing market, or the tax area, laws across sectors can have a profound impact on Blacks’ health. This Comment tells this story to advocate for more widespread adoption of legal epidemiology to further the health equity agenda.

The Comment proceeds in four parts. Part I explains why it is critical to intergrate racial health equity into policy decisions. Part II provides a primer on legal epidemiology and discusses the unique features of the field that distinguishes it from other public health law subfields. Part III presents the case studies on legal epidemiology. Part IV outlines the roles legal epidemiology can play in the health equity advocacy agenda and recommends the next steps to get there.

The Comment uses the definitions of “health equity” and “health disparity” as adopted by Healthy People 2020.[21] “Health equity” means the “attainment of the highest level of health for all people. Achieving health equity requires valuing everyone equally with focused and ongoing societal efforts to address avoidable inequalities, historical and contemporary injustices, and the elimination of health and health care disparities.”[22] “Health disparity” means “a particular type of health difference that is closely linked with social, economic, and/or environmental disadvantage.”[23] Although they are often used interchangeably, health equity implies fairness in the distribution of health, whereas health disparity is a neutral concept that implies measurable difference in the distribution of health.

I. The Need to Integrate Racial Health Equity Considerations into Policy-Making

Law can impact all social determinants of health.[24] The higher a country’s adherence to the rule of law, the better the health of its population.[25] There is extensive literature on how law can lead to racial health disparities. In his address to the World Justice Forum in 2017, Justice Dingake, judge of the High Court of Botswana, outlined four well-documented pathways through which law shapes racial health equity.[26] First, the law can create social conditions that physically and mentally affect individuals and populations.[27] As an example, he cited the “separate but equal” doctrine that allowed racial segregation in housing, health care, education, employment, and transportation for years.[28] Robust evidence has linked racial segregation to health disparities in nursing homes,[29] tobacco use,[30] alcohol consumption,[31] and adoption of healthy habits.[32]

Second, the law may foster or prohibit behaviors that skew distributions of well-being.[33] For example, before the Affordable Care Act was enacted,[34] insurance companies could discriminate against enrollees based on pre-existing conditions. As Black adults face disproportionate rates of chronic health conditions,[35] a lack of protection against such policies hurt them the most.

Third, haphazard enforcement of law can also lead to unequal health outcomes.[36] An egregious example is the water crisis in Flint, Michigan in the 2010s. Despite repeated complaints about tinted and smelly water coming through their water taps, the city did not take any action until Flint residents, especially children, became sick with lead poisoning.[37] A majority of the residents are Black and among the most impoverished of any metropolitan area in the United States.[38] They did not enjoy the same protection of the law and of government provided to other communities. This story highlights the institutional racial injustice perpetuated by under-enforcement of the law.

Finally, the law can directly address health-harming factors such as impoverishment or shortage of affordable housing.[39] The second and third case studies in this Comment will discuss two examples of laws in this category.

Policy expert Emily Benfer adds another pathway: the court systems.[40] The court systems could cause or perpetuate poor health if they “inconsistently apply legal standards and mandates or that do not evaluate individual circumstances in applying them.”[41] Benfer cites examples in eviction courts.[42] Despite tenants’ rights laws, tenants were denied substantive and procedural justice in a majority of cases.[43] Benfer’s framework provides some context to understand the disproportionate health disparities that Black adults and children experience in the housing sector.[44]

Legal epidemiology can move us toward a more equitable health agenda by uncovering the systemic and institutional barriers to health. Senators, representatives, judges, agency administrators, city council members, and the police force make, carry out, and enforce laws every day. Lawyers also play a key part in the process through advisory and advocacy roles. These are actors in position of power. Only after they acknowledge the profound impact of laws on people’s health can health equity considerations be given more weight in policy decisions. With stronger evidence-based recommendations, they can reach more efficient and equitable policies. Legal interventions should be race-conscious and account for the hidden and systemic barriers experienced by Black people.

This Part will briefly describe the historical growth of legal epidemiology in the past 20 years. It will then define the key concepts of legal mapping, legal assessment, and policy surveillence. Key distinct features of legal epidemiology will also be discussed.

Legal epidemiology has transformed over the past 20 years.[46] In 2000, the field gained major traction with the founding of the Public Health Law Program by the Centers for Disease Control and Prevention,[47] which played a key role in developing trainings and competency models for legal epidemiology.[48] Through the Public Health Law Academy, it offers free training to build internal capacity in state, tribal, and local health departments.[49] Funders are increasingly interested in this field. From 2009 to 2016, the Robert Wood Johnson Foundation funded more than 80 research grants, enabled the production of a textbook on public health law research methods, and advanced the practice of policy surveillance.[50] Most recently, Scott Burris, one of the leading advocates for the field, published a new public health law textbook dedicating an entire section on evaluation and legal epidemiology.[51] Advocacy organizations are also embracing legal epidemiology. For example, ChangeLabSolutions is a national think-tank dedicated to advancing equitable laws and policies by prioritizing communities at highest risks for poor health.[52] The organization has partnered with the Public Health Law Academy to offer training resources[53] and co-authored multiple reports that feature legal epidemiology studies.[54]

Legal mapping is the primary research approach in legal epidemiology. It includes two methodologies: (1) policy surveillance and (2) policy assessment.[55] According to the Center for Policy Surveillance Program:

“Policy surveillance is the systematic, scientific collection and analysis of laws of public health significance. It is a form of legal mapping that creates data suitable for use in rigorous evaluation studies. Policy surveillance addresses the chronic lack of readily accessible, nonpartisan information about status and trends in health legislation and policy, it provides the opportunity to build policy capacity in the public health workforce, and it can speed the diffusion of innovation.”[56]

Policy surveillance and policy assessment share similar tools and methods, but they have a key distinguishing feature.[57] Policy surveillance can evaluate laws across jurisdictions and over time.[58] In contrast, a policy assessment only tracks law across jurisdictions at one point in time.[59] The primary goal of both methodologies is to produce objective data to evaluate laws across jurisdictions.

Three characteristics distinguish legal epidemiology as a distinct subfield in public health law.[60] First, the work is inherently interdisciplinary.[61] This is different from a typical public health law project, which can be undertaken by a team trained entirely in one or two disciplines. Any legal epidemiology project requires a team with legal, health or social science, and statistical background.[62] This mix of expertise is required because a legal epidemiology inquiry often encompasses multiple domains.

Take an evaluation study of stay-at-home orders and their effectiveness in controlling COVID-19, for example. It is straightforward to compare the numbers of cases in localities with and without orders. One can also examine the effects over time by comparing the number of cases in one locality before and after an order took effect. However, such information is barely “scratching the surface.” The difficulty of this inquiry comes from local variations in the design of the stay-at-home orders, the timing of the orders, modifications to orders over time, local enforcement mechanisms, compliance rates, and pre-existing characteristics of the local communities. Some localities have strict stay-at-home orders with very limited exceptions, while some have orders with more exceptions.[63] Some may start with a strict order but gradually relax it.[64] How do we know which approach is superior? Which one should policymakers adopt to yield the most impact for their local community at the least cost? A third equally important question is whether these variations affect different sub-groups differently.

Legal epidemiology combines multiple disciplines to build a more sophisticated model to answer these questions. Take the above stay-at-home order inquiry as an example. Legal and health experts can work together to create a coding scheme or codebook.[65] Like studying a complex concept such as depression, by breaking it into concrete predicting variables measurable on a Likert scale,[66] researchers can dissect a stay-at-home order by looking at its various features or predicting variables.[67] For example, it is reasonable to hypothesize that the more exceptions a stay-at-home order allows, the less effective it is to control COVID-19 cases. Therefore, the exception types to an order are a predicting variable that should be coded. Exception types could be classified into common categories such as “engaging in essential business activities,” “obtaining necessary supplies,” “accessing emergency services,” or “attending religious services.” At this step, a subject matter expert is often consulted to assess how to classify the exception types. Then, coding begins with reading raw texts of an order and assigning a numerical value to each category.[68] The standardized numerical data now allow researchers to compare different orders across jurisdictions and over time.

A legal epidemiology project also needs team members with health or social science backgrounds to generate hypotheses and identify health outcome data.[69] They also help generate the codebook by selecting the features of the law that most likely impact health outcomes. Finally, strong statistical expertise is required to overlay the legal data with health outcomes data while controlling for state-level and individual-level confounding variables.[70] In the stay-at-home order example, confounding variables could be concurrent mask orders, social distancing recommendations, or certain health characteristics of the communities that make them more or less susceptible to COVID-19.

The second unique characteristic of legal epidemiology is its ability to produce rigorous causal inference to justify laws and policies.[71] As a science cliché, correlation is not causation. Yet most legal evaluation studies are about correlations. They stop shy at making explicit causal inferences to inform—and convince—the lay public, advocates, and policymakers. Legal epidemiology changes the game. Categorization or standardized coding of the legal texts is one strategy to strengthen the causal inference.[72] Comparing the data across jurisdictions rather than just in one locality or repeating the measures at different points in time strengthens the causal inference.[73] Another strategy is the use of redundant coding.[74] With this method, at least two people code the same legal text based on an agreed codebook, compare the results, and discuss the differences to minimize biases and errors.[75] Random audit is another quality control method.[76] Additionally, examining multiple affected subgroups such as by race and ethnicity and testing multiple outcomes can rule out confounding variables and strengthen causation.[77] Although these strategies may require intensive resources and expertise, a properly designed study can produce evaluations of legal interventions with overall levels of validity and strength of causal inference comparable with randomized trials.[78]

Lastly, another distinctive contribution of legal epidemiology is the development of software for measuring law.[79] Major projects include the LawAtlas and the Prescription Drug Abuse Policy System.[80] Established in 2014, LawAtlas.org is the flagship policy surveillance tracking tool of the Center for Public Health Law Research at Temple University’s Beasley School of Law.[81] The website provides impressive color-coded maps of laws in 50 states on various topics, filtered by key characteristics of each law. It also features a global map of international laws on important global health topics. The database enables a quick visual comparison of national and regional health policy differences.[82] The Prescription Drug Abuse Policy System was created with funding from the National Institute on Drug Abuse to track key state laws related to prescription drug abuse.[83] The website also provides an interactive experience in which users can view the trends in drug-related laws across 50 states.[84]

This Part features three case studies that utilize legal epidemiology to explore racial health disparities in Black communities. Each case study is selected to illustrate a different legal epidemiology method. The first case study describes legal mapping efforts to track legal responses to COVID-19 in 51 jurisdictions. The second case study is an example of a legal assessment. It examines the impact of state preemption of inclusionary zoning policies on health outcomes in different racial groups. The third case study highlights a sophisticated policy surveillance research, looking at the effect of 23 state-level earned-income tax credit laws on birth outcomes by race and ethnicity from 1994 to 2013.

The first case of COVID-19 in the United States was confirmed by the Centers for Disease Control and Prevention on January 21, 2020.[85] Ever since, governments at every level have taken varying degrees of legal actions to fight this now-declared global pandemic. Washington was the first state to declare a state of emergency,[86] directing its agencies to use all resources necessary to prepare for and respond to the outbreak.[87] By March 16, 2020, all 50 states and the District of Columbia had issued an emergency declaration.[88] By July 1, 2020, states have enacted more than 1,000 legal interventions.[89] In addition, an estimate of more than 850 counties and 500 cities have added their own local responses.[90] A non-exhaustive list of responses include emergency declarations, travel restrictions, stay-at-home orders, business closures, gathering bans, elective medical procedure restrictions, temporary policies regarding the operation of correctional facilities, and face mask requirements. They are not static interventions but have evolved rapidly in response to pandemic trends and other local factors.[91] Overall, in a short time period, the sheer amount of legal responses at the federal, state, and local level to COVID-19 has been unprecedented in American history.

Efforts to systematically track this massive amount of new laws have been underway. One of the most comprehensive listings of these efforts is the “COVID-19 Legal Research Resources” page at LawAtlas.org.[92] The page is maintained and updated by the Center for Public Health Law Research at Temple University’s Beasley School of Law.[93] As of January 21, 2022, the page lists five databases tracking responses at the national and international level, nine at the state level, one at the county level, one at the municipal level, and eight at multi-jurisdictional level.[94] It also lists 20 databases tracking various policy topics related to COVID-19 such as eviction and foreclosure moratoriums, healthy food access policies during the pandemic, prison policies, or abortion access.[95] Some databases offer an interactive experience in which users can choose which COVID-19 data to display on a color-coded U.S. map.[96]

It is worth highlighting the Center for Public Health Law Research’s longitudinal dataset because it was created using rigorous policy surveillance methods. The data explore mitigation policies at the state level in 51 states and the District of Columbia from January 20, 2020 through July 1, 2020.[97] For data collection, the researchers identified and collected the raw legal texts of all emergency declarations and relevant mitigation policies from government websites and other tracking databases.[98] For coding, the team consulted with several subject matter experts with a mix of legal, medical, public health, and research expertise to conceptualize the numerous mitigation policies into concrete, measurable variables.[99] For example, to explore travel restriction, the team examines three variables: whether there is a restriction (presence), types of travelers, and types of mandate.[100] To determine the presence of restriction, the coding question is whether this state places a restriction on travelers.[101] If yes, the team dived deeper by asking what types of travelers are restricted. The answer categories include “all people entering the state,” “travelers from specified states,” or “travelers from specified countries,” “general international travelers,” or “all air travelers.”[102] The team would further examine the types of mandate for travelers, which include categories such as “travelers must self-quarantine,” “travelers must inform others of travel,” “checkpoint must be established,” and “travel requirement must be posted.”[103] Quality control methods such as redundant coding[104] and a final review by a supervisor are built into the coding process.[105] The data are displayed in an interactive, color-coded U.S. map and are available for download at LawAtlas.org.[106]

The systematic tracking of COVID-19 legal responses is important at two levels. At the surface level, it provides policy-makers timely feedback on the impact of policy interventions on pandemic trends. Some databases such as the American Enterprise Institute COVID-19 Tracker allows viewing of nearly real-time county-level trends, demographic analysis, and test results for COVID-19.[107] During a public health emergency, this is arguably the best data available to policymakers. As early as August 2020, these databases allowed researchers to assess various impacts of legal responses on health and social outcomes and make concrete policy recommendations.[108]

At the deeper level, legal mapping enables finer statistical models to examine characteristics of each legal intervention. While the high-level correlation between legal interventions and health outcomes could be useful, it does not tell the full story. Not all travel restrictions or stay-at-home orders are created equal. Their requirements vary substantially from jurisdiction to jurisdiction. The local context in which they are applied also varies. To evaluate these policies, more rigorous policy surveillance methods are required.

The usefulness of policy surveillance methods to policymaking is analogous to that of genetic sequencing technologies to personalized medicine. Thanks to genetic sequencing technologies, doctors can tailor the most effective regimen with the least side effects for a patient based on the patient’s unique genetic make-ups instead of a one-size-fit-all treatment plan.[109] In the case of COVID-19, every legal response comes with “side effects” — economic and social costs. Every decision is a trade-off. For example, a travel restriction could get the pandemic under control faster and save more lives, but it also imposes high burden on individuals’ mental and physical health and the economy. How strict should a travel restriction be? While banning all travelers into a state may intuitively sound like the most protective measure, it is possible that only banning travelers from certain states can be just as effective without as much economic cost. A more sophisticated analysis would be looking at a combination of responses at the same time. For example, would it be more effective to ban all travelers into the state while having a stay-at-home order allowing people to engaging in many activities, or to ban only travelers from certain states while having a stricter stay-at-home order allowing people to engage in less activities? An even more sophisticated inquiry would be exploring a combination of responses at the same time while examining how they impact sub-groups stratified by age or race and ethnicity. A solid dataset properly collected and coded by the most relevant features of the law, coupled with advanced statistical methods, can enable sophisticated evaluation models based on health outcomes. This information would be critical for the next pandemics.

B. Case Study 2: Assessing State Preemption of Inclusionary Zoning Policies and Health Outcomes of Black Adults

The next case study illustrates how legal epidemiology research can be applied to explore racial health equity. It is an example of a policy assessment, tracking the impact of state preemption of inclusionary zoning policies on health outcomes of Black adults across six states at one point in time. First, an overview of the problem—the affordable housing crisis and its impact of the health of Black communities—will be provided. Next, the legal interventions, including inclusionary zoning and preemption policies, will be described. Finally, the case study will be featured.

1. The Housing Crisis’ Disproportionate Impact on Black’s Health

Many nlow-income families are facing an unprecedented affordable housing[110] crisis. On the rental market, there are only 35 affordable housing units available for every 100 renters with incomes at or below poverty line.[111] Nearly 20 million renter households, or 47.5 percent of all renter households, were rental cost-burdened.[112] Rental cost burden is defined as households who pay over 30 percent of their income towards rent.[113] It is also increasingly difficult for a lower income family to own a home.[114] In Austin, Texas, a family earning 75 percent of the area median income cannot afford 90 percent of the houses listed in 2015.[115] They could afford only 3 percent of the two-bedroom houses listed and 3 percent of the three-bedroom houses.[116]

The COVID-19 pandemic put minority renters and homeowners in a more vulnerable spot than ever before. People of color are twice as likely to be low-income, renters, and rental cost-burdened.[117] In a recent report, 43 percent of Black, 40 percent of Hispanic, and 32 percent of Asian households are rental cost-burdened, compared with 25 percent of White households.[118] Following the economic shutdown, 23 percent of Black renters were behind on their rents by late September, whereas only about ten percent of White renters were behind.[119] Similarly, just seven percent of White homeowners were behind on mortgage payments, but the share was more than double for Black owners at 17 percent. [120]

Blacks are also being evicted at higher rates. According to a joint report from the Massachusetts Institute of Technology and Vida Urbana, over one-third of eviction filings in Boston, Massachusetts, occurred in neighborhoods with high concentration of Black residents during the pandemic.[121] Even controlling for factors such as income, race remains a predictor in eviction filings: they are more likely to occur in census tracts where there is a larger proportion of Black renters.[122]

Housing is a social determinant of health through intersecting pathways.[123] For example, sub-standard housing conditions such as pest infestation, mold, and lead paint causally relate to respiratory diseases, neurological disorders, and other conditions.[124] Housing affordability is another contributing factor. Families who spend a large share of their monthly budgets on housing tend to make trade-offs by spending less on food and medical care.[125] Their children are more likely to suffer from food insecurity.[126] They are also more likely to live in sub-standard housing or neighborhoods with fewer health-relevant resources. Unaffordable housing has been associated with poor mental health outcomes, poor self-rated health status, hypertension, arthritis, and medication non-adherence.[127]

2. State Preemption of Inclusionary Zoning Policies

Inclusionary zoning laws are local efforts to increase affordable housing. They are responses to widening racial disparities amidst the housing crisis.[128] The laws may comprise different features. Typically, local governments could mandate private developers to reserve a portion of housing units at below-market price for income-qualified families.[129] Alternatively, they could make inclusionary zoning voluntary and incentivize developers to provide below-market-price units.[130] Developers may “opt out” by paying a fee instead of participating in the program.[131] Some local governments also offer density bonuses which allow developers to build more market-rate units than they typically would be allowed to offset the costs of providing affordable units.[132] Inclusionary zoning laws have been growing in popularity with 866 jurisdictions having enacted some form of such policies since 2016.[133]

State preemption of inclusionary zoning laws are also on the rise. These are state laws that prohibit local governments from regulating rent or housing prices through zoning ordinances.[134] By 2017, 11 states had preemption laws that either prohibit mandatory inclusionary zoning or limit voluntary programs.[135] In Texas, for example, a city like Houston cannot require developers to reserve 20 percent of residential lots in a subdivision to be sold to low-income families at below-market price because of state preemption.[136] However, the law is silent on rental units and expressly permits voluntary programs. [137] Therefore, the city can create incentive programs, negotiate contract commitment, or offer density bonus to developers in exchange for more affordable housing units.

Inclusionary zoning laws have yielded mixed results. In some jurisdictions, they appear to increase affordable housing, while other jurisdictions have seen little effects.[138] From a health perspective, however, it is reasonable to hypothesize that preemption laws that block local initiatives to address the affordable housing crisis adversely impact health outcomes and widen health disparities.[139] Because some have criticized state-preemption in general as harmful to health[140], and because Blacks face poorer health at baseline, it can be further hypothesized that state preemption of inclusionary zoning may disproportionately affect the health of Blacks.

3. Policy Assessment and Evidence of Racial Health Disparities Linked to State Preemption

A team from the University of Memphis conducted a policy assessment to test these hypotheses. They used preemption data collected by the Policy Surveillance Program of the Center for Public Health Law Research to compare health outcomes for adults in states with preemption and without preemption.[141] The preemption dataset were systematically collected and coded by a transdisciplinary team.[142] The data collection and coding protocol has been documented.[143] The main predictor of health outcomes is the presence of preemption policy as of August 1, 2019, in a state.[144] Health outcomes include (1) delaying medical care because of cost and (2) poor or fair self-rated health status.[145] To build statistical models to overlay health outcome and preemption data, the team controls for individual-level variables, such as health insurance status or income, and state-level variables, such as per capita gross domestic product, new housing building permits per 1,000 residents, and net population growth.[146] Another feature of legal epidemiology employed in the study is the creation of separate statistical models for each racial and ethnic group. Repeating the measures in subgroups can “aid in the understanding of causal mechanisms and the development of more targeted policy interventions.”[147] It allows researchers to ascertain differential effects of preemption on different racial or ethnic subgroups.

The study produced impactful data. Black adults living in states with preemption are more likely to delay medical care because of cost compared to Whites and Hispanics.[148] They also reported delaying medical care and poor self-rated health status more frequently in preemption states, compared to states without preemption.[149] All adults regardless of race and ethnicity reported fair or poor self-rated health status at higher rates in states with preemption than states without preemption.[150] Overall, state preemption of inclusionary zoning is negatively associated with health outcomes, particularly for Black adults.

This is the first and only study that produces disparate impact evidence of state preemption of inclusionary zoning on health. Despite being considered race-neutral, preemption of inclusionary zoning is in a long line of policies that disparately affect Black communities. Given that most laws are difficult to repeal or overturn, acknowledging that a law hurts the health of a certain group more than others is already many steps too late. To strive toward health equity, jurisdictions that consider adopting preemption laws must be race-conscious and take into account the lessons learned from early policy assessments such as this one.

This observational study is only the starting point.[151] More legal epidemiology research will be needed to design effective solutions to rectify the existing disparities. In states that preempt mandatory inclusionary zoning laws like Texas, more evaluation is needed to determine the most effective voluntary programs given the heterogeneity of local programs.[152] The interactions between preemption and broader land use regulations and their combined impacts on health outcomes should also be studied.[153]

C. Case Study 3: Evaluating State-Level Earned Income Credit Laws and Birth Outcomes by Race

The third case study examines the impact of 23 state-level earned income tax credit laws on birth outcomes by race and ethnicity from 1994 to 2013. This case study features the most sophisticated application of policy surveillance. It evaluates a law across jurisdictions and over time, capturing not only the different policy dimensions of the law in 23 states, but also the changes to the law in each state over 19 years. It ends on the high note that some laws, if adequately scaled, can have a positive impact on health equity.

The section proceeds as follows. First, the problem of racial disparities in birth outcomes will be described. Second, various dimentions of the intervention—state earned income tax credit laws—will be explained. Finally, the case study will be featured.

1. Striking Racial Disparities in Birth Outcomes

There are striking and persistent disparities in birth outcomes by a mother’s race in the United States. A Black baby born in America is twice as likely to die in the first year of life compared to a White baby.[154] In Texas, the year 2018 marked a milestone when infant mortality rate reached a historic low of 5.5 deaths per 1,000 live births.[155] This number exceeded the Healthy People 2020 goal of 6 deaths per 1,000 live births.[156] Yet lurking behind this winning number is a shocking story: the infant mortality rate for Black babies in Texas has consistently hovered around 11,[157] only slightly below the rate of 12.6 for babies born in Mexico.[158] Across the country, Black infants also have a 50 percent higher risk of being born preterm (before 37 weeks of gestation) and are almost twice as likely to be born with low birth weight.[159] In a now classic study of racial health disparities, Africa-born Black mothers and US-born White mothers in Illinois in the 1990s gave birth to babies of comparable weights, but US-born Black mothers gave birth to substantially smaller babies than US-born White mothers, suggesting that the disparity was not attributable to genetics.[160] Just being born Black in the US alone is a risk factor associated with low birth weight.

Closing the disparities in birth outcomes requires more than improving healthcare quality and access.[161] In term of quality, the United States is among countries with the most well-developed public healthcare systems.[162] Prenatal care has been available in the U.S. since 1911.[163] In term of access, under the Affordable Care Act, the uninsured rate and number of uninsured people declined to a historic low by 2016.[164] Approximately two thirds of minority women get access to prenatal care in the first trimester, which is not significantly less than the rate for all U.S. women.[165]

To explain persistent racial disparities despite the progress, one model by Lu and Halfon proposes a life course perspective approach.[166] This model conceptualizes birth outcomes as a product not only of nine months of pregnancy, but also the entire prior life course of the mother.[167] Disparities are consequences of both differential exposures during pregnancy and throughout mothers’ life span. Income inequality, educational achievement gaps, housing disparities, and negative environmental exposures may have all contributed to birth outcomes.[168] Although the causal pathways contributing to birth outcomes are complex, this theory helps explain, at least partly, why interventions focusing on prenatal care and pregnancy often fail to reduce population-level disparities in birth outcomes.

2. Positive Impact of State-Level Earned Income Tax Credit Laws on Birth Outcomes

Designed to reward work, the federal Earned Income Tax Credit program helps low-to moderate-income families by giving them a credit to reduce the taxes owed or to increase the tax refund.[169] A family must earn a positive income and meet certain criteria to be eligible for credit.[170] The equity goals behind family economic security policies such as the federal tax credit program underscore a similar rationale to Lu’s and Halfon’s life course perspective. By increasing a family’s income via a tax credit, we can expect a positive impact not only on their financial well-being but also on health and even birth outcomes. In fact, a $1,000 income increase due to federal tax credit has been shown to reduce low birth weight by 2 to 3 percent.[171]

State earned income tax credit laws are local supplemental programs to the federal earned-income tax credits.[172] These state programs widely vary in design and implementation. Twenty-eight states and the District of Columbia offer their own tax credit program.[173] In all but six states—Delaware, Hawaii, Ohio, Oklahoma, South Carolina, and Virginia—earned income tax credit laws are refundable like the federal program.[174] If a refundable credit exceeds a taxpayer’s state income tax, the taxpayer receives the excess amount as a payment from the state. In contrast, a nonrefundable credit can only offset state income taxes, so low-income families with little taxable income would not benefit significantly from the program. All states except for Minnesota set their credits as a percentage of the federal credit.[175] State credits as a percentage of the federal credit ranged from 3 percent in Montana to a nonrefundable 62.5 percent in South Carolina.[176] The highest refundable credit is in the District of Columbia at 40 percent.[177]

Currently, it remains unknown whether state earned income tax credit laws could help reduce disparities in birth outcomes by race and ethnicity. If yes, the next question is the magnitude of the impact. So far, the federal program has been shown to improve birth outcomes.[178] The positive impact of state-level earned income tax credit laws on birth outcomes has also been documented.[179] Given the previous studies, it could be hypothesized that Black babies would benefit more from earned income tax credit expansions than babies of other races and ethnicities.

3. Evidence of State-Level Earned Income Tax Credit Expansions Closing the Gap in Birth Outcomes

A team from Emory University’s Rollins School of Public Health studied the impact of earned income tax credit laws in 23 states on birth outcomes by race and ethnicity from 1994 to 2013. [180] Given the heterogeneity of these laws, the team had to systematically collect and document tax credit policies in each state, including all changes that occurred from 1994 to 2013. The same step was repeated for all 50 states and Washington DC. They identified 80 changes in state earned income tax credit laws in 19 years.[181] These changes represented 34 shifts in policy category across 23 states over the study period.[182]

Then, they developed a codebook and detailed coding protocol to capture important tax credit policy dimensions, including eligibility criteria, refundability, and amount of the credit.[183] Quality control procedures included blinded independent coding of a random sample of items by two trained legal researchers. All coders were closely supervised by a senior attorney.[184] The states were sorted into five categories:

(1) states having no earned income tax credit law (reference category);
(2) states having an earned income tax credit law, non-refundable payments, and payments less than ten percent of the federal amount;
(3) states having an earned income tax credit law, refundable payments, and payments less than ten percent of the federal amount;
(4) states having an earned income tax credit law, non-refundable payments, and payments of ten percent or more of the federal amount; and
(5) states having an earned income tax credit law, refundable payments, and payments of ten percent or more of the federal amount.[185]

Category (1) includes states with no program. Category (2) represents the states with the least generous tax credit programs, whereas category (5) represents the states with the most generous programs.

Next, the team identified and collected infant health outcomes and maternal characteristics from the U.S. National Vital Statistics System.[186] Finally, the statisticians developed a model to merge tax credit laws data, health outcomes, and maternal characteristics. The model also enabled them to control for time trends and state differences that might also affect birth outcomes.[187] This is the most sophisticated model featured out of the three case studies because it utilized key legal epidemiology tools, including rigorously categorizing legal data, testing multiple outcomes, and repeating the measures on multiple subgroups of mothers by race and ethnicity, age, marital status, and education.[188] All of these strategies help strengthen the causal inference between earned income tax credit laws and birth outcomes.

The results confirm the research hypothesis. In all states with the program, Black mothers experienced larger reductions in the rate of low birth weight and increases in gestation duration. The amount of low-birth-weight reductions ranged from 5–11.7 percent with a mean of 12 percent for Black mothers, compared to 3.3–11.7 percent with a mean of 6 percent for White mothers.[189] States with the most generous policy see the most dramatic improvements for Black mothers. Average gestation weeks improved at a slightly larger magnitude for Black mothers in states offering generous credits. On average, Black mothers experienced 0.38–0.46 percent longer gestation weeks, compared to 0.17–0.41 percent longer gestation weeks for White mothers.[190]

While the positive health effects of federal and state earned income tax credit laws have been documented, this study was the first to provide evidence on its impact on Black infants and mothers. When states offer more generous credits, Black mothers and babies are less likely to experience low birth weight and pre-term birth.[191] The study produced actionable data to secure the political will to reduce health inequity for Blacks. Earned income tax credit law is scalable, and the 20 states without their own earned income tax credit program should enact one. States with less generous program could consider expanding benefits, while states with the most generous benefits should not undermine the program’s success with budget cuts.[192]

VI. Recommendations

Capacity building should be the key to unlock the potentials of legal epidemiology. Although many studies are currently labeled as legal epidemiology studies, they are not truly so.[193] Examples of rigorous policy assessment and policy surveillance projects are rare. A 2015 scan of 158 articles labeled as legal epidemiology and authored by the Centers for Disease Control and Prevention (CDC) staff shows that most focus on “traditional” public health law interventions such as vaccination or occupational health regulations.[194] In contrast, legal epidemiology promises to deliver the most impact on uncovering the incidental health effects of non-health related policies. Among 158 articles, only three explored such policies[195].

The scan also suggests that legally trained authors are not typically included on the CDC’s legal epidemiology research teams. Even in mapping studies, which require the skills for which lawyers are trained, authors with J.D. degrees were listed on only seven of the 15 studies for which author credentials were provided. Even at a leading institution for the practice of legal epidemiology like the CDC, human resources are still scare.

Law schools and joint degree programs between law schools and schools of public health (or schools of medicine) could fill in the expertise gap. Health law curriculum should consider incorporating at least two credit hours of training on legal evaluation and legal epidemiology methods. The objectives of this training are two-folds. First, it gives future law practioners, advocates and policy-makers an appreciation on the profound impact that seemingly neutral laws can have on health and health disparity. As discussed in Part I, legal interventions cannot afford to be race-neutral. At the minimum, decision-makers must consider the hidden and systemic barriers experienced by various racial groups.

Second, the training should provide students with the basic tools to conduct a legal epidemiology project. The preferred pedagogical methods could be group-based simulation or experiential projects because of the collaborative and interdisciplinary nature of legal epidemiology. An inter-curriculum course offered within a joint J.D./M.P.H. or J.D./M.D. degree program would provide an ideal environment to implement such a training. This is because the program brings together students from various backgrounds, including law, health sciences, and biostatistics.

B. Knowledge Is Power: Setting a New Research and Advocacy Agenda for Health Equity

Racial justice in America has reached an inflection point in 2020. The COVID-19 pandemic and the Black Lives Matter movement following the death of George Floyd catapulted deeply-rooted health disparities and racial injustice issues to the forefront. Long called a “forgotten aim,”[196] health equity has been a lofty but elusive mission still in search of effective solutions. At the forefront, political will is urgently needed to bring about real transformations. But America has come to realize that the fight is not just about passing a new health law; it is not just about increasing budget for a certain disease that disproportionately affects Black people; it is not just about advocating for more health education and awareness for Blacks; it is also not just about improving the quality and access to the health system. The fight extends beyond healthcare. It is about understanding and acknowledging the causal pathways through which institutions such as housing, transportation, court, or tax system shape the health experiences of marginalized communities—both negatively and positively—and securing the political will to prioritize health equity in law-making across sectors.

Legal epidemiology is spotlighting these causal pathways. The three case studies feature collaborations among teams with J.D., M.P.H., Ph.D., M.S., and M.D. degrees. The work came from schools of law school, public health, arts and sciences, and from advocacy organizations. The teams addressed topics in public health emergency responses, housing regulations, and family economic security policies. The interdisciplinary collaborations and range of topics underscore the broad, interdisciplinary nature of health disparity issues. Although the laws and policies discussed are race-neutral and are not related to health care, they have a profound impact on health.

As illustrated in the case studies, legal epidemiology can help guide the research and advocacy agenda to advance health equity.[197] First, more policy assessment and surveillance studies will be needed to monitor and evaluate laws. For example, tracking legal responses to COVID-19 was a timely effort to help design effective legal solutions, especially during a time of crisis when the country experienced almost 3000 COVID-19 deaths per day[197] and executive leadership needed strong policy guidance more than ever.

Second, policy assessment and surveillance can help design more equitable legal solutions and enforcement mechanisms. The study of state preemption of inclusionary zoning reveals that the rise of preemption could perpetuate racial health disparities. This impact should be mitigated by considering alternatives to preemption or securing local commitment to providing more affordable housing. In contrast, laws such as state earned income tax credits are effective policy tools to address persistent racial disparities in birth outcomes. They should be scaled.

Third, if properly disseminated and amplified through advocacy networks, legal epidemiology findings can engage communities, forge partnerships, and build political will. In the past, most legal assessments have been about observable correlations between a law in one locality and health outcomes. Despite providing very valuable information, they stop shy at making explicit causal inferences to inform—and convince—policy-makers about the direct impact of their decisions on people’s health. By strengthening the causal inferences, legal epidemiology provides advocates with enhanced and convincing narratives on health equity.

  1. Story borrowed from Alex Samuels, Black Texans Already Face Health Care Disparities. The Coronavirus Is Making It Worse, The Tex. Tribune (Apr. 29, 2020), https://www.texastribune.org/2020/04/29/black-texas-coronavirus-health-care-disparities/.

  2. Said Rick, WE GOT SOME BAD STUFF OUT HERE!, Facebook (Jan. 23, 2020), https://www.facebook.com/lilsmittygolf/posts/10219164814605761.

  3. Shanley Pierce, Texas Studying COVID-19’s Uneven Impact on Communities of Color, TMC News (Sept. 2, 2020), https://www.tmc.edu/news/2020/09/texas-studying-covid-19s-uneven-impact-on-communities-of-color/.

  4. Statistics are from 2020. See Richard A. Oppel et al., The Fullest Look Yet at the Racial Inequity of Coronavirus, N.Y. Times (July 5, 2020), https://www.nytimes.com/interactive/2020/07/05/us/coronavirus-latinos-african-americans-cdc-data.html.

  5. Id.

  6. APM Research Lab Staff, COVID-19 Deaths Analyzed by Race and Ethnicity, APM Research Lab (Dec. 10, 2020), https://www.apmresearchlab.org/covid/deaths-by-race (Updated March 5, 2021) (the mortality gaps became even wider when data were adjusted by age).

  7. See Ctrs. for Disease Control & Prevention, CDC Health Disparities and Inequalities Rep. — United States, 2013 101, 146, 158 (Nov. 22, 2013), https://www.cdc.gov/minorityhealth/CHDIReport.html [hereinafter, Disparities and Inequities 2013 Report].

  8. Office of Minority Health, Asthma and African Americans, https://minorityhealth.hhs.gov/omh/browse.aspx?lvl=4 (last visited Jan. 3, 2021).

  9. National Cancer Institute, Cancer Disparities, https://www.cancer.gov/about-cancer/understanding/disparities (last visited Jan. 3, 2021).

  10. Id.

  11. As used in this Comment, the term “law” encompasses the legal instruments such as statutes, treaties, ordinances as well as agencies’ rules and regulations. See Lawrence O Gostin et al., The Legal Determinants of Health: Harnessing the Power of Law for Global Health and Sustainable Development, 393 Lancet 1857, 1857 (2019).

  12. Examples of such laws include the Federal Food, Drug, and Cosmetic Act of 1938, the Fair Packaging and Labeling Act of 1967, the Clean Air Act of 1969, and the Safe Drinking Water Act of 1974.

  13. See, e.g., LaShyra T. Nolen et al., How Foundational Moments in Medicaid’s History Reinforced Rather Than Eliminated Racial Health Disparities, Health Aff. Blog (Sept. 1, 2020), https://www.healthaffairs.org/do/10.1377/hblog20200828.661111/full/ (finding that under-enforcement of Medicaid desegregation requirements in the 1960s led to Black nursing home residents being at greater risks than White residents to be in facilities with serious deficiencies and suboptimal care); see also Am. Civil Liberties Union, Silenced: How Nuisance Ordinances Punish Crime Victims in New York 3–4 (2015), https://www.aclu.org/report/silenced-how-nuisance-ordinances-punish-crime-victims-new-york (finding that nuisance ordinances that penalize tenants based on police response occurring on a property deter crime victims from reporting crime and frequently lead to evictions or other harmful penalties for victims who do call 911 in an emergency. These ordinances disproportionately affect Black residents.).

  14. Tara Ramanathan et al., Legal Epidemiology: The Science of Law, 45 J. L. Med. Ethics 69, 69 (2017).

  15. Michelle M. Mello, Peering Into Hidden Worlds: The Past And Future Of Legal Epidemiology Foreword, 92 Temp. L. Rev. 837, 838-39 (2020).

  16. See id. at 842.

  17. See infra Part III(A).

  18. See infra Part III(B).

  19. See infra Part III(C).

  20. Not unique to health, this proposition has been at the heart of the Black Lives Matter and other racial justice movements. Ostensibly race-neutral policies such as policing policies and sentencing laws are allegedly key sources of racial inequality. For more discussion, see, e.g., Nazgol Ghandnoosh, The Sentencing Project, Black Lives Matter: Eliminating Racial Inequity in the Criminal Justice System (2015).

  21. Healthy People 2020 is an initiative led by Office of Disease Prevention and Health Promotion, U.S. Department of Health and Human Services. Starting in 2010, Healthy People provided science-based, 10-year national objectives for improving the health of all Americans. See generally ODPHP, About Healthy People, https://www.healthypeople.gov/2020/About-Healthy-People (last visited Jan. 3, 2021).

  22. Office of Disease Prevention and Health Promotion, Disparities, HealthyPeople.gov, https://www.healthypeople.gov/2020/about/foundation-health-measures/Disparities#5 (last visited Jan. 3, 2021).

  23. Id.

  24. Gostin et al., supra note 11.

  25. 25 See, e.g., Angela Maria Pinzon-Rondon et al., Association of Rule of Law and Health Outcomes: an Ecological Study, BMJ Open (2015), doi: 10.1136/bmjopen-2014-007004 (finding that the rule of law correlated with infant mortality rate, maternal mortality rate, life expectancy, and cardiovascular disease and diabetes mortality rate).

  26. See O. B. K. Dingake, The Rule of Law as a Social Determinant of Health, 19 Health Hum. Rts. 295, 295, 298 (2017).

  27. Id. at 298.

  28. Id.

  29. See generally, Deborah S. Mack et al., Racial Segregation Across U.S. Nursing Homes: A Systematic Review of Measurement and Outcomes, 60(3) Gerontologist e218-31 (2020), doi:10.1093/geront/gnz056.

  30. See generally, Richard W. Pollay et al., Separate, But Not Equal: Racial Segmentation in Cigarette Advertising, 21 J. Advertisement 45 (1992).

  31. See generally, Luisa N. Borrella et al., Racial Discrimination, Racial/Ethnic Segregation, and Health Behaviors in the CARDIA Study, 18(3) Ethnicity & Health 227 (2013).

  32. Id.

  33. See Dingake, supra note 26, at 298.

  34. 45 C.F.R. § 147.108.

  35. See supra notes 7-9.

  36. Dingake, supra note 26, at 298.

  37. Merrit Kennedy, Lead-Laced Water In Flint: A Step-By-Step Look At The Makings Of A Crisis, NPR (Apr. 20, 2016, 6:39 PM ET), https://www.npr.org/sections/thetwo-way/2016/04/20/465545378/lead-laced-water-in-flint-a-step-by-step-look-at-the-makings-of-a-crisis.

  38. Id.

  39. Dingake, supra note 26, at 298.

  40. See Emily A. Benfer, Health Justice: A Framework (and Call to Action) for the Elimination of Health Inequity and Social Injustice, 65 Am. U.L. Rev. 275, 306-07 (2015).

  41. Id. at 307.

  42. Id.

  43. Although there are laws to protect tenants from forcible entry and “self-help” evictions, tenants’ rights were often not adequately protected. Landlords were rarely required to meet the burden of proof necessary to support an order of possession. Cases should be dismissed if the landlord is not present, but they were only dismissed in sixty percent of cases when a landlord failed to appear. In another example, a judge must determine whether the tenant violated the law or breached the rental agreement. To do so, a judge may review testimony from both parties, but both parties were sworn in and asked to take an oath to tell the truth in only eight percent of cases. These examples illustrate the inconsistency in applying standards of justice. See id. at 309-10.

  44. See infra part III(B)(1).

  45. The analogy of legal epidemiology as a microscope to “peer into hidden worlds” was first introduced in Michelle M. Mello, Peering Into Hidden Worlds: The Past And Future Of Legal Epidemiology Foreword, 92 Temp. L. Rev. 837, 838 (2020).

  46. See Scott Burris et al., The Growing Field of Legal Epidemiology, 26 J. PUB. HEALTH MGMT. PRAC. 2 Supp. S4, S4 (2020) (adding that rigorous research on the health effects of legal interventions goes back at least 50 years).

  47. Id. (citing Richard A. Goodman et al., Law and Public Health at CDC, 55 MMWR Suppl. (Dec. 22, 2006), https://www.cdc.gov/mmwr/preview/mmwrhtml/su5502a11.htm).

  48. The Legal Epidemiology Competency Model (LECM) Version 1.0 establishes standards for practitioners to ensure that legal epidemiology deliverables are competency-based. It also helps guide job description, career planning and evaluation of current practitioners, and curriculum for students. The LECM is organized into three major domains, including (1) general legal epidemiology competencies, (2) legal mapping, and (3) legal evaluation. The first domain addresses knowledge and basic research and epidemiology skills needed to conduct and translate different types of legal epidemiology studies. The second domains focuses on legal mapping studies, including trainings on how to collect valid and reliable legal or health data. The third domain focuses on legal evaluation, including more advanced methods to study potential associations between health and law. See The Legal Epidemiology Competency Model Version 1.0, Ctrs. for Disease Control & Prevention, https://www.cdc.gov/phlp/publications/topic/resources/legalepimodel/index.html (last visited Jan. 5, 2021).

  49. Public Health Law Academy, Ctrs. for Disease Control & Prevention, https://www.cdc.gov/phlp/publications/topic/phlacademy.html (last visited Jan. 5, 2021).

  50. Burris et al., supra note 46 (citing Charles Tremper et al., Measuring Law for Evaluation Research, 34 Evaluation Rev. 242 (2010)); See generally, Scott Burris et al., Policy Surveillance: a Vital Public Health Practice Comes of Age, 41 J Health Pol. Pol’y L. 1151 (2016)).

  51. See Scott Burris et al., Scientific Evaluation in Transdisciplinary Public Health Research and Practice, in The New Public Health Law 265-80 (2018).

  52. Who We Are, ChangeLab Solutions, https://www.changelabsolutions.org/who-we-are (last visited Jan. 2, 2021).

  53. Legal Epidemiology Resources, ChangeLab Solutions, https://www.changelabsolutions.org/good-governance/phla/legal-epidemiology-resources (last visited Jan. 2, 2021).

  54. See, e.g., Scott Burris et al., Pub. Health l. Watch, Assessing Legal Responses to COVID-19 (Aug. 2020), https://www.debeaumont.org/wp-content/uploads/2020/08/Assessing-Legal-Responses-to-COVID-19-APHA-de-Beaumont.pdf (hereinafter, Assessing Legal Responses to COVID-19).

  55. ChangeLab Solutions, Introduction to Legal Mapping, Youtube at 11:50:00-13:40:00 (June 3, 2019), https://www.youtube.com/watch?v=svDFKAuSKaI&t=713s.

  56. Policy Surveillance Program, Ctr. Pub. Health L. Res., http://publichealthlawresearch.org/content/policy-surveillance-program (last visited Jan. 6, 2021).

  57. See ChangeLab Solutions, supra note 55.

  58. Id.

  59. Additionally, legal mapping can also include conventional research methods commonly used in legal scholarship such as legal scan and legal profile. A legal scan answers broad questions such as the number of states that have certain laws, whereas a legal profile examines an in-depth topic across jurisdictions. ChangeLab Solutions, supra note 55.

  60. See Scott Burris et al., The Growing Field of Legal Epidemiology, 26 J. PUB.HEALTH MGMT. PRAC. 2 Supp. S4, S5 (2020) (arguing that legal epidemiology emerged out of a gap between the public health and the legal communities. Public health lawyers were usually not trained or supported to participate in empirical research, and scientists who studied the health effects of laws often lacked legal expertise or a sense that they were part of a “field” of public health law research. Recent developments in legal epidemiology attempt to merge the two fields.).

  61. Id.; ChangeLab Solutions, supra note 55, at 13:44:00.

  62. ChangeLab Solutions, supra note 55, at 13:44:00.

  63. See Amanda Moreland et al., Timing of State and Territorial COVID-19 Stay-at-Home Orders and Changes in Population Movement — United States, March 1–May 31, 2020, 69 MMWR 1198, 1200 (Sept. 4, 2020), https://www.cdc.gov/mmwr/volumes/69/wr/mm6935a2.htm#F1_down (providing a summary of type and duration of COVID-19 state and territorial stay-at-home orders by jurisdiction from March 1 to May 31, 2020).

  64. Id.

  65. ChangeLab Solutions, supra note 55, at 14:30:00, 33:40:00.

  66. See, e.g., Beck Depression Inventory (BDI), Am. Psychol. Ass’n (Jan. 2011), https://www.apa.org/pi/about/publications/caregivers/practice-settings/assessment/tools/beck-depression.

  67. Moreland et al., supra note 63.

  68. ChangeLab Solutions, supra note 55, at 44:39:00 (stressing the importance of legal expertise in reading and recording the raw legal texts as they are written. The role of the coder in this research is not to interpret the laws, but to create objective data for comparison.).

  69. ChangeLab Solutions, supra note 55, at 13:40:00.

  70. Id.; see case study in Part III(C) for an example.

  71. Scott Burris et al., The Growing Field of Legal Epidemiology, 26 J. PUB. HEALTH MGMT. PRAC. 2 Supp. S4, S5 (2020).

  72. Id.

  73. Id.

  74. ChangeLab Solutions, supra note 55, at 28:12:00.

  75. Id.

  76. ChangeLab Solutions, supra note 55, at 50:26:00.

  77. Burris et al., supra note 71, at S5.

  78. Id.

  79. Id. at S6.

  80. The Policy Surveillance Program, http://lawatlas.org/ (last visited Jan. 8, 2021); Prescription Drug Abuse Policy System, http://pdaps.org/ (last visited Jan. 8, 2021).

  81. About Lawatlas.org, The Policy Surveillance Program, http://lawatlas.org/page/lawatlas-about (last visited Jan. 8, 2021).

  82. Some example topics include alcohol, tobacco and other drugs, chronic disease, disabilities, environmental health, food safety, and health communication and information technology. Each topic features several laws. For example, under Reproductive And Sexual Health, the project examines global abortion laws relating to self-managed abortion, procedural protections in reproductive health care conscience laws, and state abortion laws. Under Global Abortion Laws relating to Self-Managed Abortion, users can view a world map of abortion laws by country, filtered by features such as “Does the country regulate abortion by law?” (yes or no), “Under which grounds, if any, is abortion permissible under the law?” (economic reasons, social reasons, fetal impairment etc.). For a discussion of how to better understand abortion laws using legal epidemiology, see Patty Skuster, Legal Epidemiology for A Clearer Understanding of Abortion Laws and Their Impact, 92 Temp. L. Rev. 917 (2020).

  83. Prescription Drug Abuse Policy System, http://pdaps.org/ (last visited Jan. 8, 2021).

  84. For example, drug induced homicide laws establish criminal liability for individuals who sell controlled-substances to another individual who dies as a result. These laws vary from state to state in how they are classified, how they are sentenced, and what elements need to be proven. The site displays drug induced homicide laws in 50 states, broken down into six features: 1. Does the state have a specific drug induced homicide law? (yes or no); 2. How does the statute classify the charge brought against the accused? (includes manslaughter or no data); 3. Is there a mandatory minimum sentence? (yes or no); 3.1. If yes, what is the minimum incarceration period? (choices ranging from 13-24 months to life in prison); 4. Is there a mandatory maximum sentence? (yes or no), 4.1. If yes, what is the maximum incarceration period? (choices ranging from 109-120 months to death); 5. Are there mitigating factors that influence sentencing for this statute? (yes or no); and 6. What are the causation requirements in place under the statute? (but-for cause, “proximate” cause, or “contributed to” cause). Drug Induced Homicide Laws, Prescription Drug Abuse Policy System, http://pdaps.org/datasets/drug-induced-homicide-1529945480-1549313265-1559075032 (last visited Jan. 8, 2021).

  85. Press Release, Ctrs. for Disease Control & Prevention, First Travel-related Case of 2019 Novel Coronavirus Detected in United States (Jan. 21, 2020), https://www.cdc.gov/media/releases/2020/p0121-novel-coronavirus-travel-case.html.

  86. Lindsay K. Cloud et al., A Chronological Overview of the Federal, State, and Local Response to COVID-19, in Assessing Legal Responses to COVID-19 10, 12 (Aug. 2020).

  87. Inslee Issues COVID-19 Emergency Proclamation, Wash. Governor Jay Inslee (Feb. 29, 2020), https://www.governor.wa.gov/news-media/inslee-issues-covid-19-emergency-proclamation.

  88. Cloud et al., supra note 86.

  89. Id. at 11.

  90. Id. at 18.

  91. Id. at 15 (discussing, for example, how states began to relax restrictions in April when the White House issued guidelines for reopening. This has been a process because states began to lift stay-at-home orders in various phases and at different timeline.).

  92. COVID-19 Legal Research Resources, The Policy Surveillance Program, http://lawatlas.org/page/covid19-legal-research-resources (last visited Jan. 21, 2022).

  93. Id.

  94. See id.

  95. See id.

  96. See, e.g., COVID-19 Tracker, Am. Enterprise Inst., https://www.aei.org/covid-2019-action-tracker/ (last visited Jan. 10, 2021).

  97. COVID-19: State Emergency Declarations & Mitigation Policies, The Policy Surveillance Program, https://lawatlas.org/datasets/covid-19-emergency-declarations (last visited Jan. 10, 2021).

  98. The Policy Surveillance Program, Research Protocol for COVID-19: State Emergency Declarations & Mitigation Policies – Batch 5 of 5 3-4 (July 2020) (hereinafter Research Protocol; protocol available for download at https://lawatlas.org/datasets/covid-19-emergency-declarations).

  99. Id. at 3.

  100. See id. at 8-12; COVID-19: State Emergency Declarations & Mitigation Policies, The Policy Surveillance Program, https://lawatlas.org/datasets/covid-19-emergency-declarations (last visited Jan. 10, 2021).

  101. Research Protocol, supra note 98, at 8-12.

  102. Id.

  103. Id. at 9 (providing guidance on how to consistently select the answer categories).

  104. See infra Part III.

  105. Research Protocol, supra note 98, at 13-14.

  106. The coding and visualization of the data was created in MonQcle, an online software platform built specifically for policy surveillance research. MonQcle, https://monqcle.com/ (last visited Jan. 10, 2021); Research Protocol, supra note 98, at 5.

  107. COVID-19 Tracker, Am. Enter. Inst., https://www.aei.org/covid-2019-action-tracker/ (last visited Jan. 10, 2021).

  108. See generally, Assessing Legal Responses to COVID-19 (concluding that legal responses in the United States have failed at multiple levels in light of rising cases and deaths compared to other countries. In 36 expert assessments, the report highlights the failure to prevent racial and economic disparities. In some cases, COVID-19 responses have aggravated them. Despite the available resources, response efforts were impeded by budget cuts and political interference, with ample legal authority not properly being used. There was also failure of executive leadership and implementation at the top and in many states and cities.).

  109. Michele Araújo Pereira et al., Chapter 13: Application of Next-Generation Sequencing in the Era of Precision Medicine, in Applications of RNA-Seq and Omics Strategies: From Microorganisms to Human Health (Fabio Marchi et al. eds., Sept. 13, 2017), https://doi.org/10.5772/intechopen.69337.

  110. An affordable home is defined as a home that costs less than 30 percent of household income. Nat’l low-income housing Coal., The Gap - A Shortage of Affordable Homes 3 (Mar. 2017), http://nlihc.org/sites/default/files/Gap-Report_2017.pdf

  111. Dayna Bowen Matthew, Health and Housing: Altruistic Medicalization of America’s Affordability Crisis, L. Contemp. Probs. 161, 171 (2018) (citing Nat’l low-income housing Coal., The Gap - A Shortage of Affordable Homes 6 (Mar. 2017)).

  112. Emily Benfer et al., The COVID-19 Eviction Crisis: An Estimated 30-40 Million People in America Are at Risk, Aspen Ins. (Aug. 7, 2020), https://www.aspeninstitute.org/blog-posts/the-covid-19-eviction-crisis-an-estimated-30-40-million-people-in-america-are-at-risk/.

  113. Id. (adding that among renter households below the poverty line, one in four spending over 70 percent of their income toward housing costs. Before the pandemic, eviction occurred frequently across the country.).

  114. See Matthew, supra note 111, at 171-72.

  115. Id. at 172.

  116. Id.

  117. Benfer et al., supra note 112.

  118. Joint Ctr. Housing Stud. Harv. U., The State of the Nation’s Housing 2020 5 (2020), https://www.jchs.harvard.edu/sites/default/files/reports/files/Harvard_JCHS_The_State_of_the_Nations_Housing_2020_Report_Revised_120720.pdf.

  119. Id. at 2.

  120. Id. at 3.

  121. David Robinson & Justin Steil, Vida Urbana, Evictions in Boston: The Disproportionate Effects of Forced Moves on Communities of Color 36 (2020), https://www.bostonevictions.org/.

  122. Id. at 90; see Benfer et al., supra note 112 (discussing the disproportionate evictions of Black residents before the pandemic).

  123. Carolyn B. Swope & Diana Hernández, Housing as a Determinant of Health Equity: A Conceptual Model, Soc. Sci. Med. 1, 8-9 (Dec. 2019), doi:10.1016/j.socscimed.2019.112571 (outlining four housing pillars that are associated with health outcomes: 1) cost, 2) conditions, 3) consistency, and 4) context. Cost represents housing affordability. Conditions encompass the adequacy of physical hardware and environmental conditions of the building and unit. Consistency describes residential stability and residents’ ability to remain in their home for as long as they desire. Finally, context characterizes the presence of positive or adverse health-relevant resources in the surrounding neighborhood.).

  124. Id. at 10-11.

  125. Matthew, supra note 111, at 176.

  126. Id.

  127. Id. at 178 (citing Craig E. Pollack et al., Housing Affordability and Health Among Homeowners and Renters, 39 AM. J. PREVENTIVE MED. 515, 519-520 (2010)).

  128. Kriti Ramakrishnan et al., Urban Inst., Inclusionary Zoning: What Does the Research Tell Us about the Effectiveness of Local Action? 1, https://www.urban.org/sites/default/files/publication/99647/inclusionary_zoning._what_does_the_research_tell_us_about_the_effectiveness_of_local_action_2.pdf

  129. Id. at 2.

  130. Id.

  131. Id.

  132. Common Incentives and Offsets in Inclusionary Housing Policies, Nat’l Housing Conf., https://nhc.org/policy-guide/inclusionary-housing-the-basics/common-incentives-and-offsets-in-inclusionary-housing-policies/ (last visited Jan. 13, 2021).

  133. Emily Thaden & Ruoniu Wang, Lincoln Inst., Inclusionary Housing in the United States: Prevalence, Impact, and Practice 11 (Sept. 2017).

  134. See Courtnee Melton-Fant, Relationship Between State Preemption of Inclusionary Zoning Policies and Health Outcomes: Is There Disparate Impact Among People of Color?, 30 J. Housing Pol’y Debate 1056, 1057 (2020).

  135. Richard Schragger, State Preemption of Local Laws: Preliminary Review of Substantive Areas 11 (2017). These states include Arizona (2015), Colorado (2010), Texas (2005), Arkansas (2003), Massachusetts (2003), Oregon (1999), Tennessee (1996), New Hampshire (1991), New Jersey (1975), Rhode Island, and North Carolina.

  136. Tex. Local Gov’t. Code § 214.905(a) (“A municipality may not adopt a requirement in any form, including through an ordinance or regulation or as a condition for granting a building permit, that establishes a maximum sales price for a privately produced housing unit or residential building lot.”).

  137. Tex. Local Govt. Code § 214.905(b) (“This section does not affect any authority of a municipality to: (1) create or implement an incentive, contract commitment, density bonus, or other voluntary program designed to increase the supply of moderate or lower-cost housing units; or (2) adopt a requirement applicable to an area served under the provisions of Chapter 373A, Local Government Code, which authorizes homestead preservation districts, if such chapter is created by an act of the legislature.”).

  138. Ramakrishnan et al., supra note 128, at 3 (citing Stockton William et al., Urban Land Inst., The Economics of Inclusionary Development (2016)).

  139. See id. at 6 (providing that in addition to increasing affordable housing units, some evidence suggests that inclusionary zoning policies promote more racially integrated neighborhoods).

  140. See Jennifer L. Pomeranz & Mark Pertschuk, State Preemption: A Significant and Quiet Threat to Public Health in the United States, 107 Am. J. Public Health 900–92 (2017).

  141. Courtnee Melton-Fant, Relationship Between State Preemption of Inclusionary Zoning Policies and Health Outcomes: Is There Disparate Impact Among People of Color?, 30 J. Housing Pol’y Debate 1056, 1058 (2020); State Preemption Laws, The Policy Surveillance Program, https://lawatlas.org/datasets/preemption-project (last visited Jan. 12, 2021).

  142. The Policy Surveillance Program, Research Protocol for State Preemption Project 3, 5-6 (July 2020) (protocol available for download at https://lawatlas.org/datasets/preemption-project).

  143. Id.

  144. Melton-Fant, supra note 141, at 1095.

  145. Id. at 1958-59 (showing health outcome and demographic data from the 2016–2018 Behavioral Risk Factor Surveillance System database).

  146. Id. at 1059 (including state-level variables to account for differences in local economies and housing markets).

  147. Id. (citing Maya Sen & Omar Wasow, Race as a Bundle of Sticks: Designs that Estimate Effects of Seemingly Immutable Characteristics, 19 Ann. Rev. Pol. Sci. 499–522 (2016).).

  148. Id. at 1060.

  149. See id. (Table 2).

  150. Id. at 1059.

  151. Interview with Courtnee Melton-Fant, Assistant Professor, University of Memphis (Jan. 5, 2021) (email on file with author).

  152. Id.

  153. Id.

  154. Heather H. Burris & Michele R. Hacker, Birth Outcome Racial Disparities: A Result of Intersecting Social and Environmental Factors, 41 Seminars perinatology 360, 360 (2017).

  155. Tex. Dep’t of State Health Serv.'s, 2019 Healthy Texas Mothers & Babies Data Book 15 (Nov. 2019), https://dshs.texas.gov/healthytexasbabies/Documents/HTMB-Data-Book-2019-20200206.pdf.

  156. Id.

  157. Id. at 16.

  158. Mortality Rate, Infant (Per 1,000 Live Births) – Mexico, World Bank, https://data.worldbank.org/indicator/SP.DYN.IMRT.IN?locations=MX (last visited Jan. 12, 2021) (2018 number).

  159. Burris & Hacker, supra note 154, at 360.

  160. See generally Richard J. David & James W. Collins, Differing Birth Weight Among Infants of U.S.-Born Blacks, African-born Blacks, and U.S.-born Whites, 337 New Eng. J. Med 1209 (1997).

  161. See Burris & Hacker, supra note 154, at 361.

  162. Countries With the Most Well-Developed Public Health Care Systems, U.S. News World Rep., https://www.usnews.com/news/best-countries/slideshows/countries-with-the-most-well-developed-public-health-care-system (last visited Jan. 15, 2021)

  163. Susan Gennaro et al., Improving Prenatal Care for Minority Women, 41 MCN Am. J. Maternal Child Nurs. 147, 148 (2017), https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4851587/.

  164. Rachel Garfield et al., The Uninsured and the ACA: A Primer - Key Facts About Health Insurance and the Uninsured Amidst Changes to the Affordable Care Act, Kaiser Fam. Found. (Jan. 25, 2019), https://www.kff.org/report-section/the-uninsured-and-the-aca-a-primer-key-facts-about-health-insurance-and-the-uninsured-amidst-changes-to-the-affordable-care-act-how-many-people-are-uninsured/

  165. Compare Gennaro et al., supra note 163, at 2 (reporting 2/3, or 66.67 percent, of minority women receive prenatal care in the first trimester), with Michelle Osterman & Joyce Martin, Timing and Adequacy of Prenatal Care in the United States, 2016, 67 Nat’l Vital Stat. Rep. 1, 1 (2018), https://www.cdc.gov/nchs/data/nvsr/nvsr67/nvsr67_03.pdf (reporting that three out of four U.S. women (77.1 percent) received prenatal care in the first semester in 2016).

  166. Michael C. Lu & Neal Halfon, Racial and Ethnic Disparities in Birth Outcomes: A Life-Course Perspective, 7 Maternal Child Health J. 13, 19 (2013).

  167. Id.

  168. See, e.g., Burris & Hacker, supra note 154, at 361-63 (discussing racial disparities in education, income, environmental exposures, and psychological stressors).

  169. Earned Income Tax Credit (EITC), IRS, https://www.irs.gov/credits-deductions/individuals/earned-income-tax-credit-eitc (last visited Jan. 15, 2021).

  170. Id.

  171. Hilary Hoynes et al., Income, the Earned Income Tax Credit, and Infant Health, 7 Am. Econ. J.: Econ. Pol’y 172, 174 (2015).

  172. How Do State Earned Income Tax Credit Works?, Tax Pol’y Ctr., https://www.taxpolicycenter.org/briefing-book/how-do-state-earned-income-tax-credits-work (last visited Jan. 16, 2021).

  173. Id.

  174. Id.

  175. Id. (providing that Minnesota calculates its credit as a percentage of income).

  176. Id.

  177. Id.

  178. See Hoyes et al., supra note 171.

  179. The more generous tax credits a state offer, the larger improvements in birth weight and gestation weeks were observed in babies. In all states with tax credits programs, the average birth weights increase by a range of 9–27 g, or 0.3%–0.8% increases. Average gestation weeks also increase with more generous state tax credits. Sara Markowitz et al., Effects of State-Level Earned Income Tax Credit Laws in the U.S. on Maternal Health Behaviors and Infant Health Outcomes, 194 Soc. Sci. Med. 67, 72-73 (2017).

  180. Kelli A Komro et al., Effects of State-level Earned Income Tax Credit Laws on Birth Outcomes by Race and Ethnicity, 3 Health Equity 62 (2019).

  181. Id. at 63.

  182. Id.

  183. Id.

  184. Id.

  185. Id.

  186. Id. (providing that infant health outcome indicators include birth weight, probability of low birth weight (< 2500g), and gestation weeks. Maternal characteristics recorded on birth certificates include mother’s age, marital status, education, and race/ethnicity).

  187. Id. at 63-64.

  188. See infra Part II.

  189. Komro et al., supra note 180, at 65.

  190. Id.

  191. Id. at 66.

  192. See generally Erica Williams et al., States Can Adopt or Expand Earned Income Tax Credits to Build a Stronger Future Economy, Ctr. Budget Pol’y Priorities (Mar. 9, 2020), https://www.cbpp.org/research/state-budget-and-tax/states-can-adopt-or-expand-earned-income-tax-credits-to-build-a.

  193. Leila Martini et al., A Scan of CDC-Authored Articles on Legal Epidemiology, 2011-2015, 131 Public Health Rep. 809, 811, 812-13 (2016).

  194. Id.

  195. The three articles explore the possible health effects of medical liability, community redevelopment, and policies on school discipline.

  196. In 2001, the Institute of Medicine published a report entitled “Crossing the Quality Chasm: A New Health System for the 21st Century.” The report set six aims of healthcare: (1) safe, (2) person-centered, (3) timely, (4) effective, (5) efficient, and (6) equitable. In 2003, it also published another report entitled “Unequal Treatment: Confronting Racial and Ethnic Disparities in Health Care,” highlighting racial disparities in care. Twenty years later, although many progresses have been made in the first five aims, equity has lagged behind, leading some advocates to call it “the forgotten aim.” Derek Feeley, Equity: The Forgotten Aim?, Inst. Healthcare Improvement (Aug. 11, 2016), http://www.ihi.org/communities/blogs/equity-the-forgotten-aim; See generally, Inst. Med., Crossing the Quality Chasm: A New Health System for the 21st Century (2001), https://pubmed.ncbi.nlm.nih.gov/25057539/; Brian Smedley et al., Inst. Med., Unequal Treatment: Confronting Racial and Ethnic Disparities in Health Care (2003), https://pubmed.ncbi.nlm.nih.gov/25032386/.

  197. Coronavirus in the U.S.: Latest Map and Case Count, N.Y. Times, https://www.nytimes.com/interactive/2020/us/coronavirus-us-cases.html (last visited Jan. 17, 2021).