Keywords

FormalPara Personal Stories

I stood under a tent in the rain-soaked parking lot to collect information from food pantry visitors, taking dozens of English and Spanish ID cards, some plastic, some fragile paper. I needed to quickly gather crucial information from every participating household. While dodging raindrops, the paper form I scribbled information on slowly became saturated. Pursuing brevity, I used shorthand for nearby towns and dashes for no information. Through an oft-used KN-95 mask, I shouted over the rain in Spanish and English, “How many? “Cuántos personas?” straining to hear the shouted answers through many accents and rain hammering on the tent above me, contorting words. Tres became seis, and street names I drove on daily became unrecognizable. I tried my best to transcribe it all. By closing time, I had four soggy pages of attendee information, each record listed with questionable legibility. I walked inside, submitted the records, and breathed relievedly, seeing the tacos al pastor awaiting volunteers. I forgot about the records written in my rain-addled shorthand and enjoyed my meal. Aida Guhlincozzi

The COVID-19 pandemic meant balancing either—in fear—avoiding healthcare systems or—in need—going to healthcare systems. I faced this difficult choice when accompanying a family member to the emergency department. The room was crowded, people waiting to be seen for over 10 h, including us. In the triage room, the nurse spoke mostly to me, a common action when clinicians hear my family member’s accent. I tried redirecting them to speak directly to my family member to no avail. When the nurse entered the information, I noticed the demographics section. Race: Black/African American, Ethnicity: Not Hispanic/Latino. That was incorrect, but a consistent experience of poor demographic data collection anytime my family interacted with any healthcare system. I wanted to correct the nurse, “Black AND Latino”, but we had more pressing matters at hand. Still, I knew the implications of this mistake—notably, inaccurate reports on healthcare utilization. Another misunderstanding is who is being affected by COVID-19 when supposedly “self-reported” demographics are inaccurately observer-reported. This incident clarified the nuance of reported race and ethnicity data and who is and is not made visible during data collection. Deshira Wallace

Introduction

Collecting accurate health data is always challenging, but doing so on historically marginalized populations can be especially difficult. Fine-tuning our data collection methods, including our questions, can improve our data quality and lead to better solutions. When looking at the issues of a group that is racialized or othered by structural racism, questions frequently focus on what a person’s self-reported race or ethnicity is. Yet, responses to these questions are broad and can be interpreted in various ways depending on context and lived experiences.

Further, researchers should interrogate why they need to know race and ethnicity. Race should not be used as a proxy for the effects of systemic racism or suiting a false notion of biological race (Lett et al., 2022). Questioning how we discuss the “Latine”Footnote 1 community and their COVID-19 experience is central to this chapter because too often, only white Latines are conceptualized as “Hispanic or Latino.”

For example, Cecilia Márquez (2019) applies the racialized and geographic realities of Latines within the context of legalized racial segregation (i.e., Jim Crow) in the contemporary US South. Juan Crow, appeared in 2008 as a response to the racist, anti-immigrant laws affecting Latines in the South. Márquez critiques the term Juan Crow for erasing the historical and contemporary presence of Afro-Latines in the South, who were subjugated to Jim Crow – a US version of apartheid – as racialized Black people experiencing Black-specific oppression in the past and continuing to be impacted in the present. This framing of Juan Crow as an immigrant and mestizo issue reifies the idea that Black and Latine peoples are categorically “separate groups living under discrete systems of racial control” and that Latines across different geographies in the U.S. are always “new arrivals” or forever-immigrants (Márquez, 2019). Laws constructed to define racialized space by the state went beyond Jim Crow, they included racial covenants, which were written in property deeds to explicitly not sell homes to non-white people for the preservation of white spaces (Gibbons, 2018). Additionally, “redlining” which legitimized the practice of maintained racial segregation through real estate practices and government programs restricting access to home loans and purchasing for non-white, but specifically for Black individuals (Gibbons, 2018).

Yet, as the law constructs racialized stratification through geography in the United States, it also does so in health and well-being. This was experienced during the COVID-19 pandemic, which has unevenly impacted the US population through the laws and policies passed to mitigate the pandemic’s effects (Asad & Clair, 2018). The law’s role in emergency response can contribute to the inequities experienced by minoritized communities, immigrants, and those living in congregated spaces (Parmet et al., 2021). Joseph (2017) found a similar pattern in the healthcare-seeking behaviors of various Latine groups in Boston. Dependent on whether they were racialized as white, Black, or “Latino” was the likelihood of being profiled on immigration and healthcare treatment status (Joseph, 2017). This is only one example of the type of nuance critical to capturing health and healthcare access research, especially as it relates to COVID-19 and the varied Latine experience of the pandemic.

In this chapter, we briefly discuss who in the Latine population is counted, and how, and why this leads to flawed data collection on Latine health and healthcare experiences. We summarize the available COVID-19 data and the societal structures contributing to COVID-19 disparities. To support new data collection on this population and speak more broadly to the use of race and ethnicity in health and healthcare data, we provide a framework for potential ways of asking about the Latine population and conclude with future directions.

Latine Community: Who “Counts” and Why?

We begin our exploration of data collection with the foundational practice of defining the population. According to the US construction of Latines, “Hispanic” or “Latino” refers to a geopolitical grouping of “Cuban, Mexican, Puerto Rican, South or Central American, or other Spanish culture or origin regardless of race” (Census, 2021).

The US Census is important framing in the discussion of public health because socially constructed ethnoracial groups in the United States have been legitimized by the Census since the late 1700s (Zuberi, 2001). The Census currently uses national origin (or place of ancestral migration), tribal affiliation and membership, and physical characteristics (i.e., skin color) to categorize race. The model of counting individuals for population estimates based on demographic criteria can change over time and place due to sociopolitical contexts. One example is how nations can change in size and territory, which can lead to differences in national identity. Yet, how public health conducts population-level and community-level studies is closely tied to this Census operationalization, even if it does not reflect the complexities of race and ethnicity. Instead, the questions posed in the Census are, “Is this person of Hispanic, Latino, or Spanish origin?” and “What is this person’s race?” (US Census, 2010, 2011). These questions are not always simple to respond to for Latines. The complexities of each question are explored briefly below.

First, the “ethnicity” question ties the nation-state as a descriptor for the ethnic group. This assumption of ethnicity as rooted in nation-states extends the settler-colonial project of Latin American countries and flattens the Indigenous identities within these countries (Cardenas, 2018). According to US Census records, the Latine “ethnic” group, makes up approximately 18.5% of the population; however, the question remains if current conceptualization and enumeration is reflective of who the purported Latine populations are.

To address this issue, we explore race as the next domain. Clara Rodriguez (2000) noted that at the time, the conceptualization of race for the typical Latine person was the equivalent to “la raza”, which is a continuum and reflective of racial mixture, thereby having no categorically distinct races. Rodriguez argues that it is this disconnect between raza as operationalized in Latin America and race as operationalized in the United States that causes this confusion. For example, in the 1990 Census, 43.5% of the self-identified “Hispanic” population elected “other race” when asked to fill in their self-identified race. This was similar in 1980 (~40%), 42.2% in 2000, and 36.7% in 2010 (US Census, 2001). Note that “other race” often came in second to self-identified “white” race for the Latine population, which increased over this same timeframe. This does provoke the notion that while Latines hypothetically ascribe to a non-racial ideal, they will still endorse whiteness over other ethnoracial options. This perceived confusion about Latine ethnoracial positioning appears often during these Census reports, with one 2010 Census headline printing that over 75% of Puerto Ricans on the Island of Puerto Rico checked “white” for race, leading to think pieces of Latines, or in this case Puerto Ricans, understanding whiteness differently (US Census, 2010). Whereas results from the 2020 Census – in which only 17% of Puerto Ricans on the island endorsed white – only resulted in more dialogue about Puerto Ricans’ self-identity and their racialized experiences (Jensen et al., 2021; Ortiz-Blanes, 2021). However, although endorsement of white-only labels has shifted over time, what was evident in the data was the consistent action of respondents positioning themselves away from endorsing Black or Indigenous self-identities, which are supposedly part of Latine culture (López Oro, 2021).

These examples can be interpreted as forms of racialized, geographic, and contextual-specific fluidities in race, and these also demonstrate the challenges of relying on self-identified race questions when the aim is not enumeration but to address structural determinants of health. Further, the argument of Latines not having a set race and being fluid is perpetuated in public health, which has stunted the critical data collection of ethnoracial-focused demographics.

The questions “what is the person’s race?” or the alternative, “what is your race?” can be interpreted varyingly. Wendy Roth (2016) summarized the multidimensionality of measuring the concept of “race” as including racial identity, self-classification, observed race, reflected race, phenotype, and racial ancestry. Most public health studies operationalize race and ethnicity questions only as self-classification questions separated from time, the state, and power. However, according to Charles Mills (1997), what self-classification and similar types of questions get at are how the individual places themselves within the state-constructed racialized hierarchy. This hierarchy in the United States and across the Americas designates white as a class privileged with full personhood and non-white as individuals without full personhood. Thus, according to US Census patterns from the 1980s to today, when Latines across geographies select “white” or “other” as their self-classified race in a Census they are consenting to their own position in the racial order of being white or approximating whiteness rather than ascribe to Blackness or Indigeneity given the sociopolitical implications of aligning with these racialized groups.

Considering the social, political, and historical realities of the United States, the question of who are Latines is dependent on time, geography, and how we consider “race” in both law and practice. The current operationalization of Latine using standardized surveys attempts to simplify and flatten Latine populations as a homogenous group based on geography and language(s). However, public health and social science more broadly will continue to miss the nuances within this group particularly when addressing structural factors that impact health and well-being if we continue to aim to simplify demographic questions without interrogating context. The COVID-19 pandemic demonstrated issues with data collection and with the categorization of ethnoracial groups. Particularly for Latines living across the United States, the COVID-19 data did not highlight the full story of who was affected and how.

COVID-19 and Data on Latine Population

As we consider these critical perspectives of the data we collect regarding Latine populations, how do we understand their experiences of COVID-19? Data from the Center for Disease Control and Prevention (CDC) show trends that the pandemic differentially affected racial and ethnic groups. At the peak of reported cases in January 2021, the highest number of incident cases per 100,000 of the population was among reported “Hispanic” individuals (CDC, 2021). However, it is unclear if the reports were self-reported and/or observer-reported race and ethnicity. At the start of the year, approximately 465.7 cases per 100,000 people were reported among “Hispanics.” This was closely followed by 418.5 per 100,000 cases among American Indigenous groups. However, there is an issue with the percentage of cases that reported race in general. The proportion of the data linked to reported racial or ethnic groups were 62.42%, indicating the potential for error or bias, in the case estimates. These data are also in flux, as case numbers are updated with reports from various regions in the United States.

As of November 2020, Latines accounted for one-third of all confirmed COVID-19 cases, where race/ethnicity data are collected (Rodriguez-Diaz et al., 2020). Latines account for one in five confirmed COVID-19-related deaths (Rodriguez-Diaz et al., 2020). A study of national transmission rates of Latines in the United States found that COVID-19 diagnoses were greater in counties that had a significantly greater share of Latines than the national average (>17.8%) compared to counties with fewer proportions of Latines. Greater cases of COVID-19 cases and deaths were in the Midwestern Latine counties, and greater cases of COVID-19 in the Northeastern counties. County level data were collected from the US Census Bureau ACS and COVID-19 cases and deaths were from USAFacts (Rodriguez-Diaz et al., 2020). Rodriguez-Diaz et al. (2020), a scoping review published in 2021, evaluated 37 articles that focused on the impact of the COVID-19 pandemic on the living conditions of Latines in the United States. The articles were specifically evaluated using social determinants of health framework. Key risk factors were working an “essential” job, living in an area with a high population density of Latine and Black people, overcrowded living conditions, limited English proficiency, and barriers to execute recommended preventive behaviors.

Beyond actual exposure to COVID-19, and in relation to essential work, Latines represent a disproportionate number of workers in the food industry sector, and in service areas such as construction and housekeeping (Hawkins, 2020). These and similar jobs that could not be transitioned to a “work-from-home” environment, were affected earlier on in the pandemic when physical distancing measures were in place and organizations/companies had to lay off or furlough employees, causing extreme financial strain particularly for financially precarious individuals and families (Salgado de Snyder et al., 2021).

Studies out of New York City (NYC) serve as examples of the use of data to demonstrate health disparities by race and geography. A study by Renelus et al. (2021) examined racial disparities in COVID-19 related hospitalizations and in-hospital mortality. The authors used electronic health record (EHR) data from a single hospital center for hospitalization data and used demographic data from the U.S. Census Bureau and the New York City Department of Health to calculate rates of hospitalization. Ultimately the authors found that Black people were more likely than white people to require hospitalization for COVID-19, and Latine people were more likely to experience in-hospital mortality compared to white people. In another NYC-based study on COVID-19 positivity, hospitalization, and mortality, the study authors used electronic health records from one NYC healthcare system and Census tract data to geocode patients on neighborhood socioeconomic status (Ogedegbe et al., 2020). The results of the study were that Black and Latine patients were more likely to have a positive COVID test than white patients but were less likely to be hospitalized in this healthcare system. Race and ethnicity were the predictor variables, and this study coded self-reported ethnicity as Hispanic regardless of race. If ethnicity was missing, self-reported race was assigned and for patient data with multiple entries for race, the entry with the most information was chosen. Although Ogedegbe et al. (2020) used EHR data and purported to focus on self-reported race and ethnicity data, these data may not have only been self-reported. Rather, these data could include observer-reported data, which could account for differences at different clinical encounters. These data also reference to the paradoxical standardization of Latines having both all races and no race at all.

Lessons learned from these studies in relation to how Latines were affected by COVID include the strict dichotomization of Latines as separate from other ethnoracial groups. Latines are often described as multicultural, which can include multiracial; however, that does not mean that all Latines experience the same racialized experiences, particularly in an environment such as New York City, which is the hub of Black Latine migration from the Caribbean and Central America. Contextualizing COVID-19 data from New York City brings into question why intersectional methods were not used to account for the intersectional identities of a multitude of ethnoracial groups, including Latines. Intersectional methods such as those recommended by Lett et al. (2022) would include incorporating additional data from other sources to contextualize the data and questioning what experiences study participants would have with racism and other biases. Further, the primary question these and similar studies evoke is, do studies that enumerate Latines using one dimension or “race” improve our understanding of health inequities? Or are they simplifying the experiences of Latines to the point of providing incomplete information about health risks and outcomes?

Improving Public Health Data and Experiences of Marginalized Communities

In the discussion of affected race and ethnic groups, the CDC notes there is potential for error in the reported race and ethnicity of COVID-19 cases. In the work of Salgado de Snyder et al. (2021), they describe the only unifying element of “Latinos” is the shared Spanish language. However, we know this ignores the French and Portuguese colonization of Latin American countries. This also entirely erases people from Indigenous nations in Latin America still fighting for their land and resource rights. Still, the geographic-rooted identity of Latines is just as diverse as the languages spoken and racial identities. With 33 nation-states in Latin America, and even more villages, townships, and other unique scales of local identity, there are many ways to diversify and examine the experiences of Latines. Failing to do so leads to gaps in knowledge, coarse details in our data, and an inability to fine-tune health and healthcare access interventions.

McLafferty et al. (2021) present the three P’s of data: perspective, purpose, and privacy. They encourage researchers to ask what the biases are embedded in the data creation and analysis, who is served by the data and results collected, and the analyses conducted, and ask how the data, results, and related analyses impact the privacy the data is collected from.

While we already know some information about Latines, we do not know the data quality. Just as the stories this chapter opened with suggested, data collection contains errors affected by many factors. These include the environments the data were collected in, the humans doing the collection, the tools being used to collect the data, and the humans the data are being collected from. Incorporating these factors into the data collection process can help researchers address these biases, and better understand the nuances of the population being studied.

Data collection and analysis requires interrogating our methods and biases as researchers as well (Gieseking, 2018; Graham, 1999). Considering how perspective, purpose, and privacy can impact the data collection, a broad set of questions when designing a study with data collection may include the following:

  • What do I want to know?

  • What information will help me answer that question?

  • Why do I think this information will help me answer my question?

  • How do I want to retrieve this information?

  • How do I ensure that the data collected will not cause harm?

  • Who is the referent group and why?

  • Where is the referent group located and how would their placement potentially change their experiences from others?

Looking for information on Latines in the United States based on self-reported characteristics is a flawed measure. Racially, Latines in the United States have modified their racial identification to suit their safety, depending on their geographic placement, social status, and socio-economic structure of the societal context they are located within. Further, when we are asking about Latines’ health and wellbeing as researchers, what exactly are we asking? Who is in the cohort we are envisioning? Are they white, Black, or the often-fluctuating concept of “brown”? Are they bilingual, Spanish-speaking, Portuguese-speaking, or speaking another language? What is their immigration status? What is their educational background? The potential answers to these questions alone are numerous. The potential additional questions to include similarly so.

When using data collected by others, where efforts on asking questions around race and ethnicity are limited or constrained, questions to ask may be:

  • What other information has been collected and can data from national censuses, local government structures, and other organizations be consulted to contextualize the information? (Lett et al., 2022)

  • How do these other sets of information provide details on the groups I am interested in asking about?

  • How are these other sets of information able to speak to the structures that impact health and healthcare access for the group I am seeking to learn about?

We cannot always control the quality of the data available for our research. We can use more nuanced and critical frameworks when evaluating that data. For example, many Latin American countries do not collect data for Black or “Afrodescendent” people in their censuses. However, shifting discourse has created opportunities for data collection incorporating Black people and their experiences. By 2010, 16 countries in Latin America had collected this information at one point in time. Using data compiled critically for the United Nations Economic Commission for Latin America and the Caribbean “Social Panorama of Latin America 2016” Report,Footnote 2 this map of Afrodescendent people in Latin America was created (ECLAC, 2017) (Fig. 9.1). While limited in data, and ranging in timeframe, providing details of potential errors allows researchers to make better choices for analysis.

Fig. 9.1
An illustration of a map highlights only Latin American countries with respect to the Afrodescent population.

Latin American countries by total population identified as Afrodescent

Full size image

Other data limitations also exist. The Uncertain Geographic Context Problem (UGCoP), first named in 2012, is how geographic units used in analysis (such as census tracts) are not drawn to match the actual, socially-relevant geographic context. This uncertainty in the geographic context can lead to less accurate findings on environmental health effects, such as assuming good air quality because of where a person lives rather than where they work and spend most of their day (Kwan, 2012). The Neighborhood Effect Averaging Problem (NEAP) considers health research focusing on the residential neighborhood of the population under study, given that mobility can mean a significant amount of time is spent outside the residential neighborhood and thus have a significant impact on health effects from the environment (Kwan, 2018). Geographic research seeks more fine-grained data and collection methods for these problems and more attentively addresses the unique contexts and impacts on health given these variations. Similar efforts are needed in health research on Latines, and the varied impacts on health and healthcare access systemic racism can have on Black, Indigenous, and white Latines.

Conclusion

Between 2019 and 2020, there has been a significant push towards applying health equity in public health research. Whether or not this shift is short-lived or leads to needed change in how we approach public health, one major issue remains the same—our data collection tools have stayed the same since the late 1700s, despite technological advances. Whether we are examining health disparities to record a difference between groups or striving to shift towards a health equity approach to examine why the differences are there in the first place, we must interrogate how we are collecting data. If collecting race and ethnicity data has not significantly changed since the first U.S. Census in 1790, how do we work towards improving the health and quality of life of people? For Latines specifically, continuing to place Latines as a homogenous group linked to an imagined, singular cultural lived experience is a disservice to the diverse and rich narratives that individuals in different Latine communities bring with them.