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Biocultural and social determinants of ill health and early mortality in a New Mexican paediatric autopsy sample

Published online by Cambridge University Press:  15 April 2024

Lexi O’Donnell*
Affiliation:
College of Population Health, University of New Mexico Health Sciences Center, Albuquerque, NM, USA Department of Anthropology, University of New Mexico, Albuquerque, NM, USA
John J. Green
Affiliation:
Southern Rural Development Center and Department of Agricultural Economics, Mississippi State University, Starkville, USA
Ethan C. Hill
Affiliation:
Division of Physical Therapy, Department of Orthopaedics and Rehabilitation, University of New Mexico School of Medicine, Albuquerque, NM, USA
Michael J. O’Donnell Jr.
Affiliation:
Bureau of Business and Economic Research, University of New Mexico, Albuquerque, NM, USA
*
Corresponding author: Lexi O’Donnell; Email: [email protected]
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Abstract

Illness and mortality have social origins, and infants and children are especially susceptible to the impacts of adverse social experiences. Early-life stress (ELS) – physiological disruptions suffered by a developing organism – is incorporated into human biology through embodiment. This paper examines whether children who lived and died in New Mexico (2011–2019) embodied social determinants of health. Data were collected from 780 postmortem computed tomography scans in conjunction with data from field notes and autopsy reports for individuals aged 0.5–20.99 years from New Mexico. Variables included in linear/logistic regressions are the per cent of families in poverty by ZIP code and year, housing type (trailer/mobile home, apartment, house), rural/urban residence areas, and race/ethnicity. Health outcome variables are age at death, respiratory conditions, growth stunting and arrest, and porous cranial lesions. Intersections of poverty, housing disparities, and race/ethnicity are examined to understand whether children from New Mexico incorporated ELS into their biology.

Results

Hispanic children have higher odds of growth stunting than non-Hispanic White children. Native American children die younger and have higher odds of respiratory diseases and porous lesions than Hispanic and non-Hispanic Whites. Rural/urban location does not significantly impact age at death, but housing type does. Individuals who lived in trailers/mobile homes had earlier ages at death. When intersections between housing type and housing location are considered, children who were poor and from impoverished areas lived longer than those who were poor from relatively well-off areas.

Conclusions

Children’s health is shaped by factors outside their control. The children included in this study embodied experiences of social and ELS and did not survive to adulthood. They provide the most sobering example of the harm that social factors (structural racism/discrimination, socioeconomic, and political structures) can inflict.

Type
Research Article
Creative Commons
Creative Common License - CCCreative Common License - BY
This is an Open Access article, distributed under the terms of the Creative Commons Attribution licence (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted re-use, distribution and reproduction, provided the original article is properly cited.
Copyright
© The Author(s), 2024. Published by Cambridge University Press

Research highlights

Hispanic children have higher odds of growth stunting. Native American children from New Mexico have younger ages at death than non-Hispanic White and Hispanic children. They also have higher odds of having respiratory conditions. Results indicate that children embody social determinants of health.

Introduction

The society humans live in acts to shape their health and well-being (Krieger, Reference Krieger1999, Reference Krieger2005, Reference Krieger, Berkman, Kawachi and Glymour2014; Waitzkin, Reference Waitzkin1981). Infants, children, and adolescents are especially affected by social factors (Garg et al., Reference Garg, Toy, Tripodis, Silverstein and Freeman2015). Their health and well-being are shaped not just by parenting and parents’ socioeconomic status (SES) but by personal circumstances and other factors (Coll-Seck et al., Reference Coll-Seck, Clark, Bahl, Peterson, Costello and Lucas2019; Marmot, Reference Marmot2005; Viner et al., Reference Viner, Ozer, Denny, Marmot, Resnick, Fatusi and Currie2012).

Humans are biocultural beings, incorporating all aspects of the lived experience into biology through the process of embodiment (Gravlee, Reference Gravlee2009; Krieger, Reference Krieger2005). Embodiment begins at the level of the oocyte when stress experienced by the mother (exposures to infections, psychosocial stress, and environmental toxins) is incorporated into its biology (Barker, Reference Barker1995; Gluckman et al., Reference Gluckman, Hanson and Beedle2007). Embodiment is observable in population-level patterns of health, disease, and overall well-being (Krieger, Reference Krieger1999, p. 296). Social determinants of health (SDOH), including inadequate housing, lack of access to necessary resources, and poverty, may have lasting impacts across the life course (from conception to death). Infants are born carrying evidence of their mother’s stress, but they also incorporate their own experiences into their body as they grow and develop. Early-life stress [ELS] (in-utero, infancy, childhood), imposed by societal pressures external to the child, also shapes health outcomes later in life (Barker et al., Reference Barker, Godfrey, Gluckman, Harding, Owens and Robinson1993; Barker et al., Reference Barker, Osmond and Law1989). While every child who experiences adverse physical and social conditions is at risk, some have increased burdens.

Society can buffer people from poor health (by buffering them from stress), or it can introduce stressors that create ill health (Schell, Reference Schell1997). Health-damaging experiences can result from inequitable and poor political, economic, and social policies (Marmot et al., Reference Marmot, Friel, Bell, Houweling, Taylor and Health2008, p. 1661). Health-damaging conditions – poverty, childhood abuse, racism, lack of access to healthcare, and poor housing conditions (Gottlieb et al., Reference Gottlieb, Hessler, Long, Amaya and Adler2014; Morone, Reference Morone2017; Trent et al., Reference Trent, Dooley, Dougé, Cavanaugh, Lacroix, Fanburg, Rahmandar, Hornberger, Schneider and Yen2019) – do not exist in a vacuum; they are interrelated, and their effects are cumulative (Beck et al., Reference Beck, Henize, Kahn, Reiber, Young and Klein2014). For example, impoverished families experience other issues, including food insecurity, housing instability, and lack of access to transportation and health care (Morone, Reference Morone2017). The probability of poorer health outcomes increases with risk exposures (Gottlieb et al., Reference Gottlieb, Hessler, Long, Amaya and Adler2014; Morone, Reference Morone2017).

This study examines whether children who lived and died in New Mexico incorporated social stressors and inequity into their bodies [embodiment] and how those stressors shaped their ultimate health outcomes. While their lives were short, their skeletons and bodies may act as a record of how they experienced SDOH. Because SDOH are embodied (Krieger, Reference Krieger, Berkman, Kawachi and Glymour2014; Krieger and Davey Smith, Reference Krieger and Davey Smith2004), the effects of SDOH can be detected on the skeleton (Harrod et al., Reference Harrod, Martin, Fields, Klaus, Harvey and Cohen2017; Klaus et al., Reference Klaus, Harvey and Cohen2017; O’Donnell and Edgar, Reference O’Donnell and Edgar2020). SDOH can lead to elevated burdens of chronic and acute illnesses and traumatic injury (Cassidy et al., Reference Cassidy, Lambropoulos, Enters, Gourlay, Farahzad and Lal2013; Foote et al., Reference Foote, Singleton, Holman, Seeman, Steiner, Bartholomew and Hennessy2015; Gottlieb et al., Reference Gottlieb, Hessler, Long, Amaya and Adler2014).

Child health in New Mexico

Compared to other states, New Mexico ranks poorly (50th in the nation) for childhood well-being (NMVoices, 2020). Here, well-being is ranked using multiple metrics, including 1) health (environmental health, substance use, weight, mortality rates, others), 2) family and community (teen birth rates, adult education, and others), 3) education (enrolment and attendance, reading/math proficiency, graduation from high school), and 4) socioeconomic status (housing costs, food insecurity, family income, poverty, parental employment). These low ranks may be driven by characters unique to the state, including population diversity, rural geography, low population density, and relatively low socioeconomic status (herein, SES) (Malik et al., Reference Malik, Booker, Brown, McClain and McGrath2014; New Mexico Department of Health, 2012).

In New Mexico, 62% of children are Hispanic, 22% are non-Hispanic White, and 11% are Native American (NMVoices, 2023). Twenty-five per cent of New Mexican children were living in poverty (familial income at or below $21,330) between 2018 and 2019 (NMVoices, 2020). The median household income in New Mexico is $54,020, lower than the United States median of $69,021 (NMVoices, 2023). Finally, child and teen death rates are high in New Mexico, 43 per 100,000 (compared to the U.S. average of 30 per 100,000). When these are split into children (0–14 years) and teens (15–19 years), the death rates per 100,000 are 49.8 and 90.5, respectively (NMVoices, 2023). While this information comes from more recent NMVoices reports, the trends reported have remained consistent across recent years (NMVoices, 2010).

All New Mexican children experience increased risks for poor health (NMVoices, 2020), but children who reside in tribal areas (on and off reservation trust lands) fare worse. Child poverty rates, death rates, restriction and lack of resource access, and other issues are heightened in tribal areas (NMVoices, 2020, p. 66). Native Americans who live away from tribal lands have poorer outcomes than individuals in other racial/ethnic groups.

Race, a social construct with biological consequences

Because race/ethnicity is a prominent feature of this paper, some definitions and discussion are necessary. ‘Race’ is a social construct composed of physical and cultural features (Blakey, Reference Blakey1999; Jablonski, Reference Jablonski2012; Krieger, Reference Krieger, Whitmarsh and Jones2010; Marks, Reference Marks1996). As such, concepts and definitions of ‘race’ and ‘racial’ identity are fluid, changing across time and space and between nations (Alba, Reference Alba1990; Harris and Sim, Reference Harris and Sim2002; Lieberson and Waters, Reference Lieberson and Waters1993). Despite this, race shapes social experiences, health/well-being, and biology (Gravlee, Reference Gravlee2009). Race’s complex relationship with biology and health is enabled, in part, by other social and political constructs: racism, discrimination, and structural violence. Through embodiment, SDOH can be expressed biologically (de la Cova, Reference de la Cova2012; Gravlee, Reference Gravlee2009; Krieger, Reference Krieger, Berkman, Kawachi and Glymour2014).

Indigenous people experience high rates of infant and child mortality, low life expectancy at birth, malnutrition and growth retardation, high infectious disease burdens, and health issues, including diabetes and cardiovascular disease (Gracey and King, Reference Gracey and King2009). These problems may stem partly from the cascade effects of colonization: poverty, poor living conditions, lack of access to healthcare, governmental indifference, and neglect, among others (Gracey and King, Reference Gracey and King2009).

Just as the impacts of colonization are passed down from mother to child, to grandchild to great-grandchild, etc., embodied ELS experiences may be passed intergenerationally. Social and environmental conditions experienced by oocytes and embryos, infants, children, and adolescents, acting in concert with the experiences of their mothers and grandmothers, can lead to worse health outcomes for adults, culminating in premature mortality (Barker, Reference Barker1995; Braveman and Gottlieb, Reference Braveman and Gottlieb2014; Gluckman and Hanson, Reference Gluckman, Hanson, Wintour and Owens2006; Thayer and Kuzawa, Reference Thayer and Kuzawa2011).

Paper objectives and hypotheses

Epidemiologists and osteologists have long recognized that social and ELS experiences are written in our bones (Beatrice and Soler, Reference Beatrice and Soler2016; Geber, Reference Geber2014; Krieger and Davey Smith, Reference Krieger and Davey Smith2004). Skeletal evidence for ELS included here includes porous cranial lesions (PCLs) and growth stunting/arrest. PCLs of the orbit (cribra orbitalia) and vault bones (porotic hyperostosis) typically form in children between six months and eight years of age (O’Donnell et al., Reference O’Donnell, Buikstra, Hill, Anderson and O’Donnell2023; Watts, Reference Watts2013), indicating health disturbances suffered by a child (Stuart-Macadam, Reference Stuart-Macadam1985).

This paper examines the evidence for embodiment of social experiences through investigation of the relationships between SES, rural-urban residence areas, and race/ethnicity to respiratory disease, age at death, and skeletal manifestations of stress (growth stunting, growth arrest lines, PCLs).

Due to intersecting issues confronted by socioeconomically disadvantaged individuals and those from rural areas (Del Rio et al., Reference Del Rio, Hargrove, Tomaka and Korc2017; James et al., Reference James, Moonesinghe, Wilson-Frederick, Hall, Penman-Aguilar and Bouye2017), the expectations are overlapping. Expectations are that individuals from lower SES/rural areas will die earlier (1) and have higher odds of skeletal changes associated with stress (PCLs of the orbit/vault, Harris lines (growth arrest), and growth stunting) (2). Some issues experienced by lower SES/rural residents are amplified in individuals from historically underrepresented groups.

Subjects and methods

Data include information from 780 individuals whose resident state was New Mexico; all individuals died in 2011–2019 between 0.5 and 20.9 years of age. All underwent postmortem computed tomography (PMCT) at the New Mexico Office of the Medical Investigator (OMI). This research includes decedent data and is not under the purview of IRB. Research requests for access to PMCT and associated reports were submitted and approved by the research/ethics board at OMI in 2019, 2020, and 2023. For greater detail, see O’Donnell et al. (Reference O’Donnell, Buikstra, Hill, Anderson and O’Donnell2023); O’Donnell et al. (Reference O’Donnell, Hill, Anderson and Edgar2020); O’Donnell et al. (Reference O’Donnell, Hill, Anderson and Edgar2022). Table 1 provides variable descriptions.

Table 1. Descriptions of Each Variable used in Analysis

Data on health conditions, race/ethnicity, and cadaver length were recorded from autopsy reports and associated paperwork. Also included are the percentage of families in poverty by ZIP Code Tabulation Area; the Rural-Urban Commuting Area Codes (RUCA) were used to determine the rurality of a place.

Limitations

A potential limitation relates to the study of the dead. Skeletal samples are not entirely representative of the living population (Wood et al., Reference Wood, Milner, Harpending and Weiss1992). At any time, there are more people at risk of becoming ill and dying than who actually died (Wood et al., Reference Wood, Milner, Harpending and Weiss1992). So, researchers who study the dead [as in this study] likely only see those individuals at greatest risk of illness or death (Vaupel et al., Reference Vaupel, Manton and Stallard1979). Further, while sample bias may exist in who arrives at OMI, this sample is demographically representative of New Mexican children who die (O’Donnell and colleagues Reference O’Donnell, Hill, Anderson and Edgar2020; Reference O’Donnell, Hill, Anderson and Edgar2022).

Analytical methods

All analyses were done using Stata15. Multi-level mixed-effects regression (logistic and linear) was used to test for a random effect by ZIP code. Age at death was the dependent variable in linear regression, examining whether SDOH (e.g., housing type) and stress indicators (e.g., Harris lines) are associated with premature death. To further interrogate the relationships between skeletal indicators of stress and SDOH, logistic regressions were done using each indicator of stress as a dependent variable. Interaction terms for the per cent families in poverty variable [levels 0 (0–20% families in poverty) and 1 (>20% families in poverty)] and home type (trailer/mobile home, house, apartment) are included, alongside age and sex.

Predictive margins (probabilities) were estimated, and plots were used to examine interactions. Plots and tables for predicted margins provide all levels, including the base level (comparison category). Bases are as follows: race/ethnicity: Native American; rural/urban: metropolitan; housing type: house; SES: 0–20% poverty]. Sample sizes for some respiratory conditions are not large enough to be explored independently. Pneumonia, bronchitis, and asthma were examined separately. Following those analyses, respiratory conditions were pooled together. See Table 2 for descriptive statistics.

Table 2. Descriptive Statistics for the Sample.

Notes: Health outcome variables have the number of individuals scorable for each condition, including the number of individuals with each condition.

Results

Results of linear and logistic regression are provided in forest plots (Fig. 2). For full models, see Table 3; predictive margins are in Table 3 and Figs. 36. No random effects by ZIP code were noted (the likelihood ratio test was insignificant for all models, indicating no difference between ordinary logistic regression and mixed-effect logistic regression). So, all results presented are for ordinary logistic/linear regression.

Figure 1. PMCT images of porous cranial lesions of the orbit (cribra orbitalia) and vault (porotic hyperostosis) and Harris lines. (a) Orbital and vault lesions present with marrow expansion; (b) Orbital and vault lesions present without expansion; (c) absence of PCLs; (d) Harris lines (marked with arrows) as observed in a single individual. The far-left image is a radiograph, and the others are from PMCT. Harris lines are observable in both radiographs and PMCT.

Figure 2. Forest plots of linear regression results (a) and logistic regression results (b–f) for each variable. Included are coefficient estimates (a) and odds ratios (b–f) and 95% confidence intervals. The vertical line is at 0 (a) and 1 (b–d).

Table 3. Regression Results for Models 1–6.

Notes: Standard errors are in parentheses.

Model 1 is a Linear Regression with Age at Death as the Dependent Variable; Models 2–6 are Logistic Regressions with Odds Ratios Presented. Significant Values Are in Bold, Standard Errors are in Parentheses.

*** P < .01,

** P < .05,

* P < .1

Figure 3. Margins plots for interaction terms from Table 4 (for age at death). The left plot shows predictive margins for housing type and poverty, and the right shows predictive margins for housing type and race/ethnicity.

Figure 4. Margins plots for interaction terms from Table 4 for stunting [Model 2] (a, b) and Harris Lines [Model 3] (c, d). The left plots (a, c) show predictive margins for housing type and poverty, and the right plots (b, d) show predictive margins for housing type and race/ethnicity.

Figure 5. Margins plots for interaction terms from Table 4 for orbital lesions [Model 4] (a, b) and vault lesions [Model 5] (c, d). The left plots (a, c) show predictive margins for housing type and poverty, and the right plots (b, d) show predictive margins for housing type and race/ethnicity.

Figure 6. Margins plots for interaction terms from Table 4 for respiratory conditions [Model 6]. Left plot (a) shows predictive margins for housing type and poverty, and right (b) shows predictive margins for housing type and race/ethnicity.

Age at death

Rural/urban location does not significantly impact age at death. Trailers/mobile homes or apartments in higher-poverty areas are associated with older ages at death than in lower-poverty areas (Fig. 3a, Table 3, Model 1). On average, Native Americans live three fewer years than Hispanic or non-Hispanic White children (Figs. 2a and 3b; Table 3, Model 1).

Growth stunting and arrest (Harris lines)

Growth stunting is associated with higher odds of Harris lines. Hispanic individuals were more likely to be growth-stunted (Fig. 2b, Table 3, Model 2). Hispanic individuals who lived in trailers/mobile homes have a higher probability of growth stunting versus all other groups (Fig. 4b, Table 4). For other groups, housing type, rural-urban location, and percentage of families in poverty do not affect growth stunting (Fig, 4a and 4b, Table 3).

Table 4. Estimated Margins at Means for Interactions by Regression Model. Delta-Method Standard Errors Are in Italics, P-Values are Underlined, Significant Results Are in Bold. Base Levels: Race/ethnicity – Native American; Manner of Death – Natural; Rural-Urban – Metropolitan; Per cent Families in Poverty 0–20%; home Type – House. Age at Death Is the Mean Age at Death, All Others Are Probabilities. Age at Death (Model 1), Stunting (Model 2), Harris Lines (Model 3), Cribra Orbitalia (Model 4), Porotic Hyperostosis (Model 5), and Respiratory Conditions (Model 6).

* Note: Harris Lines margins were estimated for a regression that did not include respiratory illness, as small sample sizes made certain margins inestimable.

Native American, Hispanic, and White individuals have similar probabilities of having growth arrest (Fig. 2c). Housing type, rural-urban location, and per cent of families in poverty do not significantly affect the presence of Harris lines (Fig. 4c and 4d, Table 3).

Orbital and vault lesions

Native Americans have a higher probability (0.40) of having orbital lesions than Hispanic individuals (who have lower odds of orbital lesions, Fig. 2d), regardless of housing type (Fig. 5b, Table 4, Model 4). Housing type and per cent of families in poverty in the ZIP code alone do not explain the presence of PCLs (Tables 2 and 3, Models 4, 5). When interaction terms are included, individuals who live in trailers in areas with lower poverty have higher probabilities of having orbital lesions (Fig. 5a). These differences are not as pronounced for vault lesions (Fig. 5c and 5d).

Respiratory conditions

PCLs are associated with higher odds of respiratory conditions (Figs. 2d, 2e and 5a–5d, Table 3). Individuals who live in trailers/mobile homes and apartments in areas with relatively low poverty have increased probabilities of having respiratory conditions (Fig. 6a, Table 4, Model 6). Native Americans have a higher probability of having respiratory conditions, regardless of housing (Fig. 6b, Table 4), than Hispanic or White individuals.

Discussion

The culture constructed by adults can and does impact the lives, health, and well-being of infants, children, and adolescents. Ill health is driven by complex interactions of genetic, epigenetic, behavioural, social, environmental, economic, and political forces (Schell, Reference Schell1997; Waitzkin, Reference Waitzkin1981). Children’s social and physical well-being is shaped by forces outside their control. The idea that social factors impact health is not new. Engels (1844) noted that inequity has consequences over the life course, ‘Common observation shows how the sufferings of childhood are indelibly stamped on the adults’ (Krieger and Davey Smith, Reference Krieger and Davey Smith2004, p. 94). However, Engels assumed that children who suffer survive to adulthood, which is not always true. The sufferings of childhood are stamped onto children who die as well.

When we control for SES and rural/urban residence, disparities between Native American, Hispanic, and non-Hispanic White children persist (see also Braveman, Reference Braveman2012), meaning that differences between the groups cannot be explained by SES or rural life alone. This study finds that Hispanic children have higher odds of growth stunting. In contrast, Native American children have earlier ages at death, higher odds of respiratory diseases, and higher odds of PCLs than non-Hispanic White children. These are all indications of how marginalization, discrimination, and structural forces are embodied, even in lives that were cut short. Inequity literally ‘gets under the skin’.

Age at death

Early death is the ultimate outcome of poor health (O’Donnell and Edgar, Reference O’Donnell and Edgar2020), and people who die as children are among the least ‘healthy’. They likely experienced increased risks for illness and early death than other individuals from their age and social cohorts (e.g., increased frailty) (Vaupel et al., Reference Vaupel, Manton and Stallard1979; Wood et al., Reference Wood, Milner, Harpending and Weiss1992). The longevity of children in this study may be partly determined by the inheritance of frailty from their parents (e.g., Vaupel, Reference Vaupel1988), but also by interactive factors, including intrauterine and ELS experiences, such as illness, poor quality environment, or low socioeconomic status. This study finds that Native American children die earlier than non-Hispanic White and Hispanic children. Other research has similar findings, but in adults, on average Native Americans die earlier [13 years (women) and 12 years (men)] than White individuals (Gorzig et al., Reference Gorzig, Feir, Akee, Myers, Navid, Tiede and Matzke2022).

Indigenous people experience high rates of infant and child mortality, low life expectancy at birth, malnutrition and growth retardation, high infectious disease burdens, and other health issues (Gracey and King, Reference Gracey and King2009). These problems may stem partly from the cascade effects of colonization: poverty, poor living conditions, lack of access to healthcare, governmental indifference, and neglect, among others (Gracey and King, Reference Gracey and King2009). Results of the present study demonstrate that SDOH have deleterious effects not just on adult health outcomes but also on childhood health outcomes.

Respiratory illness and porous cranial lesions

While other studies indicate that living in trailer/mobile homes is associated with an increased likelihood of having respiratory conditions and increased morbidity (Gan et al., Reference Gan, Sanderson, Browning and Mannino2017; Salo et al., Reference Salo, Wilkerson, Rose, Cohn, Calatroni, Mitchell, Sever, Gergen, Thorne and Zeldin2018), no impacts of housing type on respiratory illness were noted in the present analyses. Instead, results show that Native Americans have higher probabilities of having respiratory conditions, regardless of housing type.

PCLs are markers of ELS that manifest on the skeleton. The causes of PCLs are complex and poorly understood (Brickley, Reference Brickley2018), but recently, PCLs have been associated with respiratory infections (O’Donnell et al., Reference O’Donnell, Hill, Anderson and Edgar2020). Not only do Native American children in this study have younger ages at death and higher odds of respiratory illness but they also have higher odds of PCLs. So, PCLs may mark the presence of infection in children and higher odds of infection for Native American children in this study.

The development of respiratory illness is driven by the interactions of multiple social and environmental factors, which are often compounded for individuals of lower SES (Thakur et al., Reference Thakur, McGarry, Oh, Galanter, Finn, Burchard and Committee2014). Poorer people and children have a lower ability to reduce their exposure to conditions that increase the likelihood of developing respiratory disease (Isaacs and Schroeder, Reference Isaacs and Schroeder2004), partly because they have little control over altering their environments.

Native American children in this study may have experienced more exposures that could have impacted their risk of developing respiratory disease than Hispanic and non-Hispanic White children. Southwest Native Americans have higher morbidity and mortality from infectious respiratory diseases compared to other groups (Sutcliffe et al., Reference Sutcliffe, Grant, Reid, Douglass, Weatherholtz, Hubler, Quintana, Reid, Yazzie and Santosham2019). (Groom et al., Reference Groom, Hennessy, Singleton, Butler, Holve and Cheek2014). While disparities in respiratory illness rates between Native Americans and the general population have decreased over time, they still exist (Burki, Reference Burki2021; Foote et al., Reference Foote, Singleton, Holman, Seeman, Steiner, Bartholomew and Hennessy2015; Groom et al., Reference Groom, Hennessy, Singleton, Butler, Holve and Cheek2014). Proposed reasons for the observed disparities include social and environmental factors: exposure to indoor smoke or mould, crowding in the household, poverty, and lack of household water security (Ali et al., Reference Ali, Shimeka and Kassaw2022; Deitz and Meehan, Reference Deitz and Meehan2019, p. 2; Findling et al., Reference Findling, Blendon, Benson and Miller2020; Foote et al., Reference Foote, Singleton, Holman, Seeman, Steiner, Bartholomew and Hennessy2015; Liu et al., Reference Liu, Lim, Chen, Strak, Wolf, Weinmayr, Rodopolou, de Hoogh, Bellander and Brandt2022; Seltenrich, Reference Seltenrich2012; Smit, Reference Smit2022). These factors may also impact PCL formation processes.

The above results may be driven partly by environmental exposures in the household. First, housing shortages and poverty may lead to household overcrowding, a risk factor for infections and underlying health conditions (Kamis et al., Reference Kamis, Stolte, West, Fishman, Brown, Brown and Farmer2021; Krieger et al., Reference Krieger, Waterman and Chen2020; Simpson et al., Reference Simpson, Jones, Davies and Cushing1995).

Second, while many households on tribal lands use gas/electricity for heating and cooking, solid fuels (e.g., coal, wood) are still used for cooking or heating (Bunnell et al., Reference Bunnell, Garcia, Furst, Lerch, Olea, Suitt and Kolker2010; Hadeed et al., Reference Hadeed, O’Rourke, Canales, Joshweseoma, Sehongva, Paukgana, Gonzalez-Figueroa, Alshammari, Burgess and Harris2021; Morris et al., Reference Morris, Morgenlander, Coulehan, Gahagen and Arena1990). Rates of solid fuel use are highest in the Southwest United States, including New Mexico (Hadeed et al., Reference Hadeed, O’Rourke, Canales, Joshweseoma, Sehongva, Paukgana, Gonzalez-Figueroa, Alshammari, Burgess and Harris2021). Burning solid fuels increases levels of particulate matter (e.g., PM2.5, PM10) (Hadeed et al., Reference Hadeed, O’Rourke, Canales, Joshweseoma, Sehongva, Paukgana, Gonzalez-Figueroa, Alshammari, Burgess and Harris2021; Seltenrich, Reference Seltenrich2012). Exposure to PM is complex; for example, the quality of the wood stove impacts the amount of PM2.5 pollution in a home (Walker et al., Reference Walker, Noonan, Semmens, Ware, Smith, Boyer, Erdei, Hopkins, Lewis and Belcourt2021). A study of indoor PM10 concentrations and respiratory infections in children from the Navajo Nation found that homes heated with solid fuel (e.g., wood, coal) had higher PM10 than those heated through other means (Robin et al., Reference Robin, Lees, Winget, Steinhoff, Moulton, Santosham and Correa1996). This result is repeated in other studies (Bunnell et al., Reference Bunnell, Garcia, Furst, Lerch, Olea, Suitt and Kolker2010; Hadeed et al., Reference Hadeed, O’Rourke, Canales, Joshweseoma, Sehongva, Paukgana, Gonzalez-Figueroa, Alshammari, Burgess and Harris2021). In homes cooking with wood, children had increased risks of acute lower respiratory tract infections (including pneumonia and bronchitis) (Robin et al., Reference Robin, Lees, Winget, Steinhoff, Moulton, Santosham and Correa1996). Other work implicates woodsmoke in increased paediatric risk of lower respiratory tract infections (Honicky et al., Reference Honicky, Osborne and Akpom1985; Morris et al., Reference Morris, Morgenlander, Coulehan, Gahagen and Arena1990).

Finally, many tract homes on tribal lands were built with funding from The Department of Housing and Urban Development (HUD) and the Bureau of Indian Affairs (BIA) between the 1950s and 1970s (Seltenrich, Reference Seltenrich2012). These homes were built according to federal rather than local or state codes, which may render them more prone to developing indoor mould (Seltenrich, Reference Seltenrich2012). Mould or dampness in the home is associated with childhood pneumonia (Dales et al., Reference Dales, Zwanenburg, Burnett and Franklin1991; Lu et al., Reference Lu, Yang, Liu, Liao, Li and Liu2022).

Growth stunting & arrest

A population’s average stature reflects interactions between nutrition, the physical environment, and exposure to disease and illness (Carson, Reference Carson2011; Steckel, Reference Steckel1995). Growth stunting stems from varied causes, including inadequate nutrition (Iriart et al., Reference Iriart, Boursaw, Rodrigues and Handal2013; Iriart et al., Reference Iriart, Handal, Boursaw and Rodrigues2011), frequent infections in early life (Frongillo Jr., Reference Frongillo1999), and intrauterine growth retardation (Dewey and Begum, Reference Dewey and Begum2011). Likewise, growth arrest lines (Harris lines), which indicate temporary halting of growth, are associated with nutrient deficiency (Geber, Reference Geber2014; Harris, Reference Harris1931), infectious diseases (Hewitt et al., Reference Hewitt, Westropp and Acheson1955), trauma (Kennedy et al., Reference Kennedy, Irwin and Huntley2014), and shorter life spans (Nowak and Piontek, Reference Nowak and Piontek2002).

In this study, Hispanic children are more likely to be growth-stunted compared to non-Hispanic White children. Achieved stature is indicative of individual health and is determined not just by genetic predisposition but by environmental and social factors (Steckel, Reference Steckel1995, p. 1903). In developed countries, children who live in impoverished conditions are more likely to be stunted (Jansen and Hazebroek-Kampschreur, Reference Jansen and Hazebroek-Kampschreur1997). Short stature in adults is associated with poor health outcomes, including heart disease (Forsen et al., Reference Forsen, Eriksson, Qiao, Tervahauta, Nissinen and Tuomilehto2000).

Hispanic children are reported to have a higher prevalence of stunting than non-Hispanic Whites in other works (Iriart et al., Reference Iriart, Boursaw, Rodrigues and Handal2013; Iriart et al., Reference Iriart, Handal, Boursaw and Rodrigues2011). Hispanic children likely suffer from malnutrition and obesity (the ‘nutritional double burden’): they have high-calorie diets, which are low in necessary nutrients, which may, in turn, lead to growth stunting and obesity (Iriart et al., Reference Iriart, Boursaw, Rodrigues and Handal2013). In New Mexico (2017–2018), 33% of Hispanic children were obese, compared to 26% of Native American children and 24% of non-Hispanic White children (NMVoices, 2020). The results of the present study may reflect interacting factors including poverty and food insecurity. However, when confounding effects (SES, housing type, some health indicators) are included in analyses, they do not entirely ‘erase’ the impact of race/ethnicity on stunting. Following the conclusions made by Iriart and colleagues, we interpret this as evidence for inequities suffered by Hispanic children, especially when compared with non-Hispanic White children.

Housing type, per cent families in poverty

Housing is an indirect correlate of health and well-being, and housing type can be used as a proxy for SES (Juhn et al., Reference Juhn, Beebe, Finnie, Sloan, Wheeler, Yawn and Williams2011; Shaw, Reference Shaw2004). Unlike permanent structures (houses), the value of trailers/mobile homes depreciates over time, and they have no equity value (Latimer and Woldoff, Reference Latimer and Woldoff2010; Twiss and Mueller, Reference Twiss and Mueller2004). Overall, the results of this study show that individuals who lived in trailers/mobile homes had earlier ages at death than those who lived in other types of housing. Living in trailers/mobile homes is associated with increased morbidity and lack of access to necessary resources (e.g., reliable water services) (Gan et al., Reference Gan, Sanderson, Browning and Mannino2017; Pierce and Jimenez, Reference Pierce and Jimenez2015; Salo et al., Reference Salo, Wilkerson, Rose, Cohn, Calatroni, Mitchell, Sever, Gergen, Thorne and Zeldin2018).

When we consider intersections between housing type and poverty levels, a different pattern emerges. While the results of this study indicate that individuals who live in trailers/mobile homes and apartments in relatively higher SES areas live fewer years than their counterparts who live in houses, this result does not hold for those who live in relatively poorer areas. People who live in poorer areas may have access to resources due to governmental outreach that does not exist in areas with lower poverty. In those areas, poor people may be further marginalized and experience the greatest restrictions to resource access. Another possibility is that people benefit from living near and around individuals of similar circumstances (Kirby, Reference Kirby2008, p. 344). If this is the case, the community may act as a buffer from negative external inputs.

Significance

This paper demonstrates how the complex interplay between social, cultural, environmental, and biological forces can impact children’s health outcomes. By understanding how political, environmental, and social forces work in concert to create inequity and induce poor health, we can perhaps intervene before a premature death. Children who live in similar circumstances to those in this sample are alive now. They will either die or live into adulthood and suffer the effects of their adverse childhood experiences. The society within which we operate can buffer children from sickness and death, or it can induce ill health and poor outcomes (Braveman, Reference Braveman2012; Gravlee, Reference Gravlee2009; Krieger, Reference Krieger, Berkman, Kawachi and Glymour2014). In the children in this sample, we see evidence for society’s buffering capabilities and the harmful effects of systems not created for the individuals operating within them.

Conclusions

Evidence for inequity and the effects of the SDOH exists in deceased children from New Mexico. We find that Hispanic children are more likely to be growth-stunted than non-Hispanic White children. This may be related to inadequate nutrition stemming from inequities in resource access suffered by Hispanic children. Native American children die earlier and are more likely to have skeletal indicators of stress (orbital lesions) and respiratory infections than non-Hispanic White and Hispanic children. These results speak to the increased risks of morbidity and mortality faced by Native Americans in the United States. They also demonstrate that children embody ELS, a process which is likely induced by the interplay between various SDOH.

Children’s health is shaped by factors that they cannot control. The children included in this study did not survive to adulthood, and they provide the most sobering example of the harm that social constructs (race/ethnicity, SES, and place) can inflict. While race is a social construct, it has real, tangible, and sometimes devastating biological consequences. Social constructs become biology (Gravlee, Reference Gravlee2009; O’Donnell and Edgar, Reference O’Donnell and Edgar2020), and illness and early mortality have social origins (Waitzkin, Reference Waitzkin1981). We can prevent early mortality for children and adults by improving health through the improvement of our social systems. One way to do this is to make systems equitable and resources more accessible. In the paraphrased words of Rudolf Virchow (1821–1902), to remedy physical ills, our social structures must be remodelled. It is time that we remedy our social structures.

Data availability statement

Because cases for some individuals included in this study are still open and active, data used in each analysis are available to Bona fide researchers only upon email request to the corresponding author. Some data will be presented in abstracted format only to ensure the anonymity of each individual in the sample.

Funding statement

J. Green and the Southern Rural Development receives funding support from the U.S. Department of Agriculture, National Institute of Food and Agriculture.

Competing interests

The authors have no conflicts of interest to report.

Ethical standard

We submitted proposals requesting to collect and analyse the data included here to the New Mexico Office of the Medical Investigator review board. All research requests were approved. We protect individual anonymity through the abstraction of some data.

References

Alba, RD (1990) Ethnic Identity: The Transformation of White America. New Haven, CT: Yale University Press.Google Scholar
Ali, G, Shimeka, A and Kassaw, A (2022) Determinants of community-acquired pneumonia among under-five children in Tehulederie district, Northeast Ethiopia. International Journal of Pediatric Research 8, 096.Google Scholar
Barker, DJP (1995) Fetal origins of coronary heart disease. British Medical Journal 311(6998), 171174.10.1136/bmj.311.6998.171CrossRefGoogle ScholarPubMed
Barker, DJP, Godfrey, KM, Gluckman, PD, Harding, JE, Owens, JA and Robinson, JS (1993) Fetal nutrition and cardiovascular disease in adult life. The Lancet 341(8850), 938941.10.1016/0140-6736(93)91224-ACrossRefGoogle ScholarPubMed
Barker, DJP, Osmond, C and Law, CM (1989) The intrauterine and early postnatal origins of cardiovascular disease and chronic bronchitis. Journal of Epidemiology and Community Health 43(3), 237240.10.1136/jech.43.3.237CrossRefGoogle ScholarPubMed
Beatrice, JS and Soler, A (2016) Skeletal indicators of stress: a component of the biocultural profile of undocumented migrants in Southern Arizona. Journal of Forensic Sciences 61(5), 11641172. https://doi.org/10.1111/1556-4029.13131 CrossRefGoogle Scholar
Beck, AF, Henize, AW, Kahn, RS, Reiber, KL, Young, JJ and Klein, MD (2014) Forging a pediatric primary care–community partnership to support food-insecure families. Pediatrics 134(2), e564e571. https://doi.org/10.1542/peds.2013-3845 CrossRefGoogle ScholarPubMed
Blakey, ML (1999) Scientific racism and the biological concept of race. Literature and Psychology 45(1/2), 29.Google Scholar
Braveman, P (2012) Health inequalities by class and race in the US: what can we learn from the patterns? Social Science & Medicine 74(5), 665667.10.1016/j.socscimed.2011.12.009CrossRefGoogle ScholarPubMed
Braveman, P and Gottlieb, L. (2014). The social determinants of health: it’s time to consider the causes of the causes. Public Health Reports 129(1_suppl2), 1931.10.1177/00333549141291S206CrossRefGoogle ScholarPubMed
Brickley, MB (2018) Cribra orbitalia and porotic hyperostosis: a biological approach to diagnosis. American Journal of Physical Anthropology 167, 896–902.10.1002/ajpa.23701CrossRefGoogle ScholarPubMed
Bunnell, JE, Garcia, LV, Furst, JM, Lerch, H, Olea, RA, Suitt, SE and Kolker, A (2010) Navajo coal combustion and respiratory health near Shiprock, New Mexico. Journal of Environmental and Public Health 2010, 1–14.10.1155/2010/260525CrossRefGoogle ScholarPubMed
Burki, T (2021) COVID-19 among American Indians and Alaska natives. The Lancet Infectious Diseases 21(3), 325326.10.1016/S1473-3099(21)00083-9CrossRefGoogle ScholarPubMed
Carson, SA (2011) Demographic, residential, and socioeconomic effects on the distribution of nineteenth-century African-American stature [Article]. Journal of Population Economics 24(4), 14711491. https://doi.org/10.1007/s00148-010-0324-x CrossRefGoogle Scholar
Cassidy, LD, Lambropoulos, D, Enters, J, Gourlay, D, Farahzad, M and Lal, DR (2013) Health disparities analysis of critically ill pediatric trauma patients in Milwaukee, Wisconsin. Journal of the American College of Surgeons 217(2), 233239. https://doi.org/10.1016/j.jamcollsurg.2013.02.032 CrossRefGoogle ScholarPubMed
Clark, GA and Mack, M (1988) Reliability assessment of transverse lines. Human Biology 60(2), 283291.Google ScholarPubMed
Coll-Seck, A, Clark, H, Bahl, R, Peterson, S, Costello, A and Lucas, T (2019) Framing an agenda for children thriving in the SDG era: a WHO–UNICEF–Lancet commission on child health and wellbeing. The Lancet 393(10167), 109112.10.1016/S0140-6736(18)32821-6CrossRefGoogle Scholar
Cromartie, J (2020) Rural-Urban Commuting Area Codes: Economic Research Service, U.S. Department of Agriculture. URL: https://www.ers.usda.gov/data-products/rural-urban-commuting-area-codes.aspx Google Scholar
Dales, RE, Zwanenburg, H, Burnett, R and Franklin, CA (1991) Respiratory health effects of home dampness and molds among Canadian children. American Journal of Epidemiology 134(2), 196203. https://doi.org/10.1093/oxfordjournals.aje.a116072 CrossRefGoogle ScholarPubMed
de la Cova, C (2012) Patterns of trauma and violence in 19th-century-born African American and Euro-American females. International Journal of Paleopathology 2(2–3), 6168. https://doi.org/10.1016/j.ijpp.2012.09.009 CrossRefGoogle ScholarPubMed
Deitz, S and Meehan, K (2019) Plumbing poverty: mapping hot spots of racial and geographic inequality in US household water insecurity. Annals of the American Association of Geographers 109(4), 10921109.10.1080/24694452.2018.1530587CrossRefGoogle Scholar
Del Rio, M, Hargrove, WL, Tomaka, J and Korc, M (2017) Transportation matters: a health impact assessment in rural New Mexico. International Journal of Environmental Research and Public Health 14(6), 629.10.3390/ijerph14060629CrossRefGoogle ScholarPubMed
Dewey, KG and Begum, K (2011) Long-term consequences of stunting in early life. Maternal & Child Nutrition 7, 518.10.1111/j.1740-8709.2011.00349.xCrossRefGoogle ScholarPubMed
Findling, MTG, Blendon, RJ, Benson, JM and Miller, C (2020) The unseen picture: issues with health care, discrimination, police and safety, and housing experienced by Native American populations in rural America. The Journal of Rural Health. https://doi.org/10.1111/jrh.12517 Google ScholarPubMed
Foote, EM, Singleton, RJ, Holman, RC, Seeman, SM, Steiner, CA Bartholomew, M and Hennessy, TW (2015) Lower respiratory tract infection hospitalizations among American Indian/Alaska Native children and the general United States child population. International Journal of Circumpolar Health 74, 29256. https://doi.org/10.3402/ijch.v74.29256 CrossRefGoogle ScholarPubMed
Forsen, T, Eriksson, J, Qiao, Q, Tervahauta, M, Nissinen, A and Tuomilehto, J (2000) Short stature and coronary heart disease: a 35-year follow-up of the Finnish cohorts of The Seven Countries study. Journal of Internal Medicine 248(4), 326332.10.1046/j.1365-2796.2000.00747.xCrossRefGoogle ScholarPubMed
Frongillo, EA Jr (1999) Symposium: causes and etiology of stunting. The Journal of Nutrition 129(2S Suppl), S529S530.10.1093/jn/129.2.529SCrossRefGoogle ScholarPubMed
Gan, WQ, Sanderson, WT, Browning, SR and Mannino, DM (2017) Different types of housing and respiratory health outcomes. Preventive Medicine Reports 7, 124129.10.1016/j.pmedr.2017.05.018CrossRefGoogle ScholarPubMed
Garg, A, Toy, S, Tripodis, Y, Silverstein, M and Freeman, E (2015) Addressing social determinants of health at well child care visits: a cluster RCT. Pediatrics 135(2), e296e304.10.1542/peds.2014-2888CrossRefGoogle ScholarPubMed
Geber, J (2014) Skeletal manifestations of stress in child victims of the Great Irish Famine (1845–1852): prevalence of enamel hypoplasia, Harris lines, and growth retardation: Skeletal Stress in Child Victims of Famine. American Journal of Physical Anthropology 155(1), 149161. https://doi.org/10.1002/ajpa.22567 CrossRefGoogle ScholarPubMed
Gluckman, PD and Hanson, MA (2006) The developmental origins of health and disease: an overview. In Wintour, EM and Owens, JA (Eds.), Early Life Origins of Health and Disease (pp. 17). Boston, MA: Springer.10.1017/CBO9780511544699CrossRefGoogle Scholar
Gluckman, PD, Hanson, MA and Beedle, AS (2007) Early life events and their consequences for later disease: a life history and evolutionary perspective. American Journal of Human Biology 19(1), 119. https://doi.org/10.1002/ajhb.20590 CrossRefGoogle ScholarPubMed
Gorzig, MM, Feir, D, Akee, R, Myers, S Jr, Navid, M, Tiede, K and Matzke, O (2022) Native American age at death in the USA. Journal of Economics, Race, and Policy 5(3), 194209.10.1007/s41996-021-00095-0CrossRefGoogle ScholarPubMed
Gottlieb, L, Hessler, D, Long, D, Amaya, A and Adler, N (2014) A randomized trial on screening for social determinants of health: the iScreen study. Pediatrics 134(6), e1611e1618.10.1542/peds.2014-1439CrossRefGoogle ScholarPubMed
Gracey, M and King, M (2009) Indigenous health part 1d Determinants and disease patterns. The Lancet 374(9683), 6575.10.1016/S0140-6736(09)60914-4CrossRefGoogle Scholar
Gravlee, CC (2009) How race becomes biology: embodiment of social inequality. American Journal of Physical Anthropology 139(1), 4757. https://doi.org/10.1002/ajpa.20983 CrossRefGoogle ScholarPubMed
Groom, AV, Hennessy, TW, Singleton, RJ, Butler, JC, Holve, S and Cheek, JE (2014) Pneumonia and influenza mortality among American Indian and Alaska Native people, 1990–2009. American Journal of Public Health 104(S3), S460S469.10.2105/AJPH.2013.301740CrossRefGoogle ScholarPubMed
Hadeed, SJ, O’Rourke, MK, Canales, RA, Joshweseoma, L, Sehongva, G, Paukgana, M, Gonzalez-Figueroa, E, Alshammari, M, Burgess, JL and Harris, RB (2021) Household and behavioral determinants of indoor PM2. 5 in a rural solid fuel burning Native American community. Indoor Air 31(6), 20082019.10.1111/ina.12904CrossRefGoogle Scholar
Harris, DR and Sim, JJ (2002) Who is multiracial? Assessing the complexity of lived race. American Sociological Review 64(4), 614627.10.1177/000312240206700407CrossRefGoogle Scholar
Harris, HA (1931) Lines of arrested growth in the long bones in childhood: the correlation of histological and radiographic appearances in clinical and experimental conditions. The British Journal of Radiology 4(47), 561588.10.1259/0007-1285-4-47-561CrossRefGoogle Scholar
Harrod, RP, Martin, DL and Fields, M (2017) Across a spectrum of inequality: hierarchy, health and culturally sanctioned violence in the precontact U.S. southwest. In Klaus, HD, Harvey, AR, and Cohen, MN (Eds.), Bones of Complexity: Bioarchaeological Case Studies of Social Organization and Skeletal Biology (pp. 363387). Gainesville, FL: University Press of Florida.10.2307/j.ctvx07bk4.20CrossRefGoogle Scholar
Hewitt, D, Westropp, CK and Acheson, RM (1955) Oxford child health survey: effect of childish ailments on skeletal development. British Journal of Preventive & Social Medicine 9(4), 179.Google ScholarPubMed
Honicky, RE, Osborne, JS 3rd and Akpom, CA (1985) Symptoms of respiratory illness in young children and the use of wood-burning stoves for indoor heating. Pediatrics 75(3), 587593.10.1542/peds.75.3.587CrossRefGoogle ScholarPubMed
Horos (2019) Horos. In (Version 4.0.0) [FOSS]. Nimble Co LLC d/b/a Purview. URL: https://horosproject.org/faqs/ Google Scholar
Iriart, C, Boursaw, B, Rodrigues, GP and Handal, AJ (2013) Obesity and malnutrition among Hispanic children in the United States: double burden on health inequities. Revista Panamericana de Salud Pública 34, 235243.Google ScholarPubMed
Iriart, C, Handal, AJ, Boursaw, B and Rodrigues, G (2011) Chronic malnutrition among overweight Hispanic children: understanding health disparities. Journal of Immigrant and Minority Health 13(6), 10691075.10.1007/s10903-011-9464-7CrossRefGoogle ScholarPubMed
Isaacs, SL and Schroeder, SA (2004) Class—the ignored determinant of the nation’s health. The New England Journal of Medicine 351(11), 1137–1142.10.1056/NEJMsb040329CrossRefGoogle ScholarPubMed
Jablonski, NG (2012) Living Color: The Biological and Social Meaning of Skin Color. Berkeley, CA: University of California Press.10.1525/9780520953772CrossRefGoogle Scholar
James, CV, Moonesinghe, R, Wilson-Frederick, SM, Hall, JE, Penman-Aguilar, A and Bouye, K (2017) Racial/ethnic health disparities among rural adults—United States, 2012–2015. MMWR Surveillance Summaries 66(23), 1.10.15585/mmwr.ss6623a1CrossRefGoogle ScholarPubMed
Jansen, W and Hazebroek-Kampschreur, AAJM (1997) Differences in height and weight between children living in neighbourhoods of different socioeconomic status. Acta Paediatrica 86(2), 224225.10.1111/j.1651-2227.1997.tb08875.xCrossRefGoogle ScholarPubMed
Juhn, YJ, Beebe, TJ, Finnie, DM, Sloan, J, Wheeler, PH, Yawn, B and Williams, AR (2011) Development and initial testing of a new socioeconomic status measure based on housing data. Journal of Urban Health 88(5), 933944.10.1007/s11524-011-9572-7CrossRefGoogle ScholarPubMed
Kamis, C, Stolte, A, West, JS, Fishman, SH, Brown, T, Brown, T and Farmer, HR (2021) Overcrowding and COVID-19 mortality across US counties: are disparities growing over time? SSM-Population Health, 15, 100845.10.1016/j.ssmph.2021.100845CrossRefGoogle ScholarPubMed
Kennedy, JW, Irwin, GJ and Huntley, JS (2014). Growth arrest lines and intra-epiphyseal silhouettes: a case series. BMC Research Notes 7(1), 15.10.1186/1756-0500-7-27CrossRefGoogle ScholarPubMed
Kirby, JB (2008) Poor people, poor places and access to health care in the United States. Social Forces 87(1), 325355.10.1353/sof.0.0062CrossRefGoogle Scholar
Klaus, HD, Harvey, AR and Cohen, MN (2017) Bones of Complexity: Bioarchaeological Case Studies of Social Organization and Skeletal Biology. Gainesville, FL: University Press of Florida.10.5744/florida/9780813062235.001.0001CrossRefGoogle Scholar
Krieger, N (1999) Embodying inequality: a review of concepts, measures, and methods for studying health consequences of discrimination. International Journal of Health Services 29(2), 295352.10.2190/M11W-VWXE-KQM9-G97QCrossRefGoogle ScholarPubMed
Krieger, N (2005) Embodiment: a conceptual glossary for epidemiology. Journal of Epidemiology & Community Health 59(5), 350355.10.1136/jech.2004.024562CrossRefGoogle ScholarPubMed
Krieger, N (2010) The science and epidemiology of racism and health: racial/ethnic categories, biological expressions of racism, and the embodiment of inequality—an ecosocial perspective. In Whitmarsh, I and Jones, DS (Eds.), What’s the Use of Race? Modern Governance and the Biology of Difference (pp. 225258). Cambridge, MA: MIT Press.10.7551/mitpress/8360.003.0015CrossRefGoogle Scholar
Krieger, N (2014) Discrimination and health inequities. In Berkman, LF, Kawachi, I and Glymour, MM (Eds.), Social Epidemiology (Vol. 2, pp. 63126). New York, NY: Oxford University Press.Google Scholar
Krieger, N and Davey Smith, G (2004) “Bodies count,” and body counts: social epidemiology and embodying inequality. Epidemiologic Reviews 26(1), 92103.10.1093/epirev/mxh009CrossRefGoogle ScholarPubMed
Krieger, N, Waterman, PD and Chen, JT (2020) COVID-19 and overall mortality inequities in the surge in death rates by zip code characteristics: Massachusetts, January 1 to May 19, 2020. Am J Public Health 110(12), 18501852. https://doi.org/10.2105/AJPH.2020.305913 CrossRefGoogle ScholarPubMed
Latimer, M and Woldoff, RA (2010) Good country living? Exploring four housing outcomes among poor Appalachians. Sociological Forum 25(2), 315–333.10.1111/j.1573-7861.2010.01178.xCrossRefGoogle Scholar
Lieberson, S and Waters, MC (1993) The ethnic responses of Whites: what causes their instability, simplification, and inconsistency? Social Forces 72(2), 421450.10.2307/2579855CrossRefGoogle Scholar
Liu, S, Lim, Y-H, Chen, J, Strak, M, Wolf, K, Weinmayr, G, Rodopolou, S, de Hoogh, K, Bellander, T and Brandt, J (2022) Long-term air pollution exposure and pneumonia-related mortality in a large pooled European cohort. American Journal of Respiratory and Critical Care Medicine 205(12), 14291439.10.1164/rccm.202106-1484OCCrossRefGoogle Scholar
Lu, C, Yang, W, Liu, Z, Liao, H, Li, Q and Liu, Q (2022) Effect of preconceptional, prenatal and postnatal exposure to home environmental factors on childhood pneumonia: a key role in early life exposure. Environmental Research 214(Pt 3), 114098. https://doi.org/10.1016/j.envres.2022.114098 CrossRefGoogle ScholarPubMed
Malik, F, Booker, JM, Brown, S, McClain, C and McGrath, J (2014) Improving developmental screening among pediatricians in New Mexico: findings from the developmental screening initiative. Clinical Pediatrics 53(6), 531538.10.1177/0009922814527499CrossRefGoogle ScholarPubMed
Marks, J (1996) Science and race. American Behavioral Scientist 40(2), 123133.10.1177/0002764296040002003CrossRefGoogle Scholar
Marmot, M (2005) Social determinants of health inequalities. The Lancet 365(9464), 10991104.10.1016/S0140-6736(05)71146-6CrossRefGoogle ScholarPubMed
Marmot, M, Friel, S, Bell, R, Houweling, TA, Taylor, S and Health, CoSDo (2008) Closing the gap in a generation: health equity through action on the social determinants of health. The Lancet 372(9650), 16611669.10.1016/S0140-6736(08)61690-6CrossRefGoogle Scholar
Morone, J (2017) An integrative review of social determinants of health assessment and screening tools used in pediatrics. Journal of Pediatric Nursing 37, 2228.10.1016/j.pedn.2017.08.022CrossRefGoogle ScholarPubMed
Morris, K, Morgenlander, M, Coulehan, JL, Gahagen, S and Arena, VC (1990) Wood-burning stoves and lower respiratory tract infection in American Indian children. American Journal of Diseases of Children 144(1), 105108. https://doi.org/10.1001/archpedi.1990.02150250117047 Google ScholarPubMed
New Mexico Department of Health, Division of Policy and Performance (2012) Racial and Ethnic Health Disparities in New Mexico Report Card. 7th. Produced by the Office of Health Equity Office of Policy and Accountability. www.nmhealth.org/opa/ Google Scholar
NMVoices (2010) 2010 New Mexico Kids Count Data Book. URL: https://www.nmvoices.org/attachments/nm-kids-count-rpt-2010.pdf Google Scholar
NMVoices (2020) 2020 New Mexico Kids Count Data Book: Building on Resilience. URL: https://www.nmvoices.org/wp-content/uploads/2021/02/KidsCount-DataBook2020-web.pdf Google Scholar
NMVoices (2023) 2023 New Mexico Kids Count Data Book: Choosing Equity in All Policies. https://www.nmvoices.org/wp-content/uploads/2024/01/2023-NM-KCDB.pdf Google Scholar
Nowak, O and Piontek, J (2002) The frequency of appearance of transverse (Harris) lines in the tibia in relationship to age at death. Annals of Human Biology 29(3), 314325.10.1080/03014460110086105CrossRefGoogle ScholarPubMed
Nwosu, BU and Lee, MM (2008) Evaluation of short and tall stature in children. American Family Physician 78(5), 597604.Google Scholar
O’Donnell, L (2019) Indicators of stress and their association with frailty in the precontact Southwestern United States. American Journal of Physical Anthropology 170(3), 404417. https://doi.org/10.1002/ajpa.23902 CrossRefGoogle ScholarPubMed
O’Donnell, L, Buikstra, JE, Hill, EC, Anderson, AS and O’Donnell, MJ Jr (2023) Skeletal manifestations of disease experience: length of illness and porous cranial lesion formation in a contemporary juvenile mortality sample. American Journal of Human Biology, e23896. https://doi.org/10.1002/ajhb.23896 CrossRefGoogle Scholar
O’Donnell, L and Edgar, HJH (2020) Sociocultural determinants of health and wealth in historic African Americans. Biodemography and Social Biology 66(1), 6989. https://doi.org/10.1080/19485565.2020.1833705 CrossRefGoogle ScholarPubMed
O’Donnell, L, Hill, EC, Anderson, AS and Edgar, HJH (2020) Cribra orbitalia and porotic hyperostosis are associated with respiratory infections in a contemporary mortality sample from New Mexico. American Journal of Physical Anthropology 173(4), 721733.10.1002/ajpa.24131CrossRefGoogle Scholar
O’Donnell, L, Hill, EC, Anderson, AS and Edgar, HJH (2022) A biological approach to adult sex differences in skeletal indicators of childhood stress. American Journal of Biological Anthropology 177(3), 381401. https://doi.org/10.1002/ajpa.24424 CrossRefGoogle ScholarPubMed
Pierce, G and Jimenez, S (2015) Unreliable water access in US mobile homes: evidence from the American housing survey. Housing Policy Debate 25(4), 739753.10.1080/10511482.2014.999815CrossRefGoogle Scholar
Primeau, C, Jakobsen, LS and Lynnerup, N (2016) CT imaging vs. traditional radiographic imaging for evaluating Harris Lines in tibiae. Anthropologischer Anzeiger 73(2), 99108.10.1127/anthranz/2016/0587CrossRefGoogle ScholarPubMed
Robin, LF. Lees, PS, Winget, M, Steinhoff, M, Moulton, LH, Santosham, M and Correa, A (1996) Wood-burning stoves and lower respiratory illnesses in Navajo children. The Pediatric Infectious Disease Journal 15(10), 859865.10.1097/00006454-199610000-00006CrossRefGoogle ScholarPubMed
Salo, PM, Wilkerson, J, Rose, KM, Cohn, RD, Calatroni, A, Mitchell, HE, Sever, ML, Gergen, PJ, Thorne, PS and Zeldin, DC (2018) Bedroom allergen exposures in US households. Journal of Allergy and Clinical Immunology 141(5), 18701879. e1814.10.1016/j.jaci.2017.08.033CrossRefGoogle ScholarPubMed
Schell, LM (1997) Culture as a stressor: a revised model of biocultural interaction. American Journal of Physical Anthropology 102, 6777.10.1002/(SICI)1096-8644(199701)102:1<67::AID-AJPA6>3.0.CO;2-A3.0.CO;2-A>CrossRefGoogle ScholarPubMed
Seltenrich, N (2012) Healthier Tribal Housing: Combining the Best of Old and New. Bethesda, MD: National Institute of Environmental Health Sciences.Google ScholarPubMed
Shaw, M (2004) Housing and public health. Annual Review of Public Health 25, 397418.10.1146/annurev.publhealth.25.101802.123036CrossRefGoogle ScholarPubMed
Simpson, SQ, Jones, PW, Davies, PD and Cushing, A (1995) Social impact of respiratory infections. Chest 108(2), 63S69S.10.1378/chest.108.2_Supplement.63SCrossRefGoogle ScholarPubMed
Smit, LA (2022) The air we breathe: understanding the impact of the environment on pneumonia. Pneumonia 14(1), 2.10.1186/s41479-022-00094-1CrossRefGoogle ScholarPubMed
Steckel, RH (1995) Stature and the standard of living. Journal of Economic Literature 33(4), 19031940.Google Scholar
Stuart-Macadam, P (1985) Porotic hyperostosis: representative of a childhood condition. American Journal of Physical Anthropology 66(4), 391398.10.1002/ajpa.1330660407CrossRefGoogle ScholarPubMed
Sutcliffe, CG, Grant, LR, Reid, A, Douglass, GK, Weatherholtz, RC, Hubler, R, Quintana, A, Reid, R, Yazzie, D and Santosham, M (2019) The burden of Staphylococcus aureus among native Americans on the Navajo Nation. PLoS One 14(3), e0213207.10.1371/journal.pone.0213207CrossRefGoogle ScholarPubMed
Thakur, N, McGarry, ME, Oh, S, Galanter, MJ, Finn, PW, Burchard, EG and Committee, AHE (2014) The lung corps’ approach to reducing health disparities in respiratory disease. Annals of the American Thoracic Society 11(4), 655660.10.1513/AnnalsATS.201402-061ARCrossRefGoogle ScholarPubMed
Thayer, ZM and Kuzawa, CW (2011) Biological memories of past environments: epigenetic pathways to health disparities. Epigenetics 6(7), 798803.10.4161/epi.6.7.16222CrossRefGoogle ScholarPubMed
Trent, M, Dooley, DG, Dougé, J, Cavanaugh, RM, Lacroix, AE, Fanburg, J, Rahmandar, MH, Hornberger, LL, Schneider, MB and Yen, S (2019) The impact of racism on child and adolescent health. Pediatrics 144(2), 1–14.10.1542/peds.2019-1765CrossRefGoogle ScholarPubMed
Twiss, PC and Mueller, TR (2004) Housing Appalachians: recent trends. Journal of Appalachian Studies 10(3), 389406.Google Scholar
Vaupel, JW (1988) Inherited frailty and longevity. Demography 25(2), 277287.10.2307/2061294CrossRefGoogle ScholarPubMed
Vaupel, JW, Manton, KG and Stallard, E (1979) The impact of heterogeneity in individual frailty on the dynamics of mortality. Demography 16(3), 439454.10.2307/2061224CrossRefGoogle ScholarPubMed
Viner, RM, Ozer, EM, Denny, S, Marmot, M, Resnick, M, Fatusi, A and Currie, C (2012) Adolescence and the social determinants of health. The Lancet 379(9826), 16411652.10.1016/S0140-6736(12)60149-4CrossRefGoogle ScholarPubMed
Waitzkin, H (1981) The social origins of illness: a neglected history. International Journal of Health Services 2(1), 77103. URL: http://journals.sagepub.com/doi/abs/10.2190/5CDV-P4FE-Y6HN-JACD?url_ver=Z39.88-2003&rfr_id=ori:rid:crossref.org&rfr_dat=cr_pub%3dpubmed CrossRefGoogle Scholar
Walker, ES, Noonan, CW, Semmens, EO, Ware, D, Smith, P, Boyer, BB, Erdei, E, Hopkins, SE, Lewis, J and Belcourt, A (2021) Indoor fine particulate matter and demographic, household, and wood stove characteristics among rural US homes heated with wood fuel. Indoor Air 31(4), 11091124.10.1111/ina.12808CrossRefGoogle ScholarPubMed
Watts, R (2013) Childhood development and adult longevity in an archaeological population from Barton-upon-Humber, Lincolnshire, England. International Journal of Paleopathology 3(2), 95104.10.1016/j.ijpp.2013.05.001CrossRefGoogle Scholar
Wood, JW, Milner, GR, Harpending, HC and Weiss, KM (1992) The Osteological paradox: problems of inferring prehistoric health from skeletal samples. Current Anthropology 33(4), 343370. https://doi.org/10.1086/204084 CrossRefGoogle Scholar
Figure 0

Table 1. Descriptions of Each Variable used in Analysis

Figure 1

Table 2. Descriptive Statistics for the Sample.

Figure 2

Figure 1. PMCT images of porous cranial lesions of the orbit (cribra orbitalia) and vault (porotic hyperostosis) and Harris lines. (a) Orbital and vault lesions present with marrow expansion; (b) Orbital and vault lesions present without expansion; (c) absence of PCLs; (d) Harris lines (marked with arrows) as observed in a single individual. The far-left image is a radiograph, and the others are from PMCT. Harris lines are observable in both radiographs and PMCT.

Figure 3

Figure 2. Forest plots of linear regression results (a) and logistic regression results (b–f) for each variable. Included are coefficient estimates (a) and odds ratios (b–f) and 95% confidence intervals. The vertical line is at 0 (a) and 1 (b–d).

Figure 4

Table 3. Regression Results for Models 1–6.

Figure 5

Figure 3. Margins plots for interaction terms from Table 4 (for age at death). The left plot shows predictive margins for housing type and poverty, and the right shows predictive margins for housing type and race/ethnicity.

Figure 6

Figure 4. Margins plots for interaction terms from Table 4 for stunting [Model 2] (a, b) and Harris Lines [Model 3] (c, d). The left plots (a, c) show predictive margins for housing type and poverty, and the right plots (b, d) show predictive margins for housing type and race/ethnicity.

Figure 7

Figure 5. Margins plots for interaction terms from Table 4 for orbital lesions [Model 4] (a, b) and vault lesions [Model 5] (c, d). The left plots (a, c) show predictive margins for housing type and poverty, and the right plots (b, d) show predictive margins for housing type and race/ethnicity.

Figure 8

Figure 6. Margins plots for interaction terms from Table 4 for respiratory conditions [Model 6]. Left plot (a) shows predictive margins for housing type and poverty, and right (b) shows predictive margins for housing type and race/ethnicity.

Figure 9

Table 4. Estimated Margins at Means for Interactions by Regression Model. Delta-Method Standard Errors Are in Italics, P-Values are Underlined, Significant Results Are in Bold. Base Levels: Race/ethnicity – Native American; Manner of Death – Natural; Rural-Urban – Metropolitan; Per cent Families in Poverty 0–20%; home Type – House. Age at Death Is the Mean Age at Death, All Others Are Probabilities. Age at Death (Model 1), Stunting (Model 2), Harris Lines (Model 3), Cribra Orbitalia (Model 4), Porotic Hyperostosis (Model 5), and Respiratory Conditions (Model 6).