Epidemiologists have found associations between where people live and many health outcomes, including cancer (Akinyemiju et al., 2015; Gomez et al., 2015; Hossain et al., 2019; Smith & Madak-Erdogan, 2018; Wray & Minaker, 2019), cardiovascular disease (Diez Roux et al., 2016), obesity (Feng et al., 2010), asthma (DePriest et al., 2019; Williams et al., 2009), adverse birth outcomes (Mutambudzi et al., 2017; Ncube et al., 2016), and adverse mental health outcomes (Blair et al., 2014; Cutrona et al., 2006), among many others (Duncan & Kawachi, 2018). Three well-documented examples are: (1) cardiovascular disease, (2) adverse birth outcomes, (3) COVID-19.
Cardiovascular disease
Cardiovascular disease (CVD) has been the leading cause of death in the United States since the early 1920s (Jones et al., 2012), and is a major contributor of racial/ethnic health inequities (Benjamin et al., 2019).
A robust body of literature has documented the influence of the social and built environments on CVD (Diez Roux et al., 2016; Malambo et al., 2016; Xiao & Graham, 2019). The built environment shapes the kinds of behaviors people can enact to protect their cardiovascular health. For example, people are more active in neighborhoods with good walkability, recreational facilities, streets that connect, residential density, and low traffic volume; more physical activity and walking can prevent CVD. Furthermore, the local food environment, characterized by availability, accessibility, and affordability of healthy food options, greatly influences the quality of what people eat and drink (Kelli et al., 2017). Living in a neighborhood that lacks these important health resources has been associated with higher BMI, higher blood pressure, as well as diabetes mellitus and metabolic syndrome, which influence residents’ risk of CVD (Sallis et al., 2012; Xiao & Graham, 2019)
The neighborhood social environment also impacts CVD (Diez Roux et al., 2016; Tamura et al., 2019; Xiao & Graham, 2018). For example, levels of safety or violence can create toxic stress, whereas social cohesion and social support can buffer the effects of that stress and improve cardiovascular health. While opportunities for physical activity and a healthy diet are directly shaped by the built environment, social norms also can influence these kinds of behaviors, which can in turn shape cardiovascular health.
- Benjamin, E. J., Muntner, P., Alonso, A., Bittencourt, M. S., Callaway, C. W., Carson, A. P., Chamberlain, A. M., Chang, A. R., Cheng, S., Das, S. R., Delling, F. N., Djousse, L., Elkind, M. S. V., Ferguson, J. F., Fornage, M., Jordan, L. C., Khan, S. S., Kissela, B. M., Knutson, K. L., … On behalf of the American Heart Association Council on Epidemiology and Prevention Statistics Committee and Stroke Statistics Subcommittee. (2019). Heart Disease and Stroke Statistics—2019 Update: A Report From the American Heart Association. Circulation, 139(10). https://doi.org/10.1161/CIR.0000000000000659
- Diez Roux, A. V., Mujahid, M. S., Hirsch, J. A., Moore, K., & Moore, L. V. (2016). The Impact of Neighborhoods on CV Risk. Global Heart, 11(3), 353–363. https://doi.org/10.1016/j.gheart.2016.08.002
- Xiao, Y. (Karen), & Graham, G. (2018). Where we live: The impact of neighborhoods and community factors on cardiovascular health in the United States. Clinical Cardiology, 42(1), 184–189. https://doi.org/10.1002/clc.23107
- Sallis, J. F., Floyd, M. F., Rodríguez, D. A., & Saelens, B. E. (2012). Role of built environments in physical activity, obesity, and cardiovascular disease. Circulation, 125(5), 729–737.
- Malambo, P., Kengne, A. P., De Villiers, A., Lambert, E. V., & Puoane, T. (2016). Built Environment, Selected Risk Factors and Major Cardiovascular Disease Outcomes: A Systematic Review. PLOS ONE, 11(11), e0166846. https://doi.org/10.1371/journal.pone.0166846
- Tamura, K., Langerman, S. D., Ceasar, J. N., Andrews, M. R., Agrawal, M., & Powell-Wiley, T. M. (2019). Neighborhood Social Environment and Cardiovascular Disease Risk. Current Cardiovascular Risk Reports, 13(4). https://doi.org/10.1007/s12170-019-0601-5
- Jones, D. S., Podolsky, S. H., & Greene, J. A. (2012). The burden of disease and the changing task of medicine. New England Journal of Medicine, 366(25), 2333-2338.
- Kelli, H. M., Hammadah, M., Ahmed, H., Ko, Y. A., Topel, M., Samman-Tahhan, A., … & Quyyumi, A. A. (2017). Association between living in food deserts and cardiovascular risk. Circulation: Cardiovascular Quality and Outcomes, 10(9), e003532.
Adverse birth outcomes
When babies die, it is usually because they are born too soon (preterm birth or birth before 37-weeks gestation) or don’t weigh enough (low birthweight is less than 2500g or about five and a half pounds). Preterm birth and low birthweight are the second leading causes of infant mortality (death before 1 year of age) for all babies born in the United States and the first leading causes of infant mortality for infants born to birthing people who are Non-Hispanic or Black (Ely & Driscoll, 2020). Both conditions are risk factors for health and socioeconomic disadvantage for the babies who survive, even into adulthood (Choi & Martinson, 2018; S Petrou et al., 2001; Stavros Petrou et al., 2019; Saigal & Doyle, 2008). Non-Hispanic Black birthing people continue to experience the highest rates of infant deaths, more than twice the rate of Non-Hispanic white birthing people (Ely & Driscoll, 2020), as well as disproportionately high rates of preterm births and low birthweight births (Martin et al., 2017; Martin et al., 2021). These inequities persist independent of birthing persons’ individual-level characteristics (such as socioeconomic status or health status), motivating researchers and practitioners to assess how neighborhood contexts influence birth outcomes generally and birth outcome inequities in particular.
Neighborhood environments impact adverse birth outcomes primarily through the influence on individual health behaviors and chronic stress (Culhane & Elo, 2005). For individual birthing people, risk factors include whether they use tobacco or other substances, eat a healthy diet, or have hypertension or diabetes (Goldenberg et al., 2008). The birthing person’s physical and social environments can increase the exposure to these risk factors if, for example, their neighborhood social norms are supportive of substance use, or it is not safe to walk around for exercise, there is limited access to healthy foods, and/or the built environment of sidewalks, parks, and greenspace is rundown or insufficient (Culhane & Elo, 2005; Vos et al., 2014). Relatedly, neighborhoods that are far from quality healthcare facilities and/or lack safe and efficient transportation options may make it more difficult for a pregnant person to support their health and well-being prior to or during pregnancy. Beyond this, living in neighborhoods with insufficient resources, few socioeconomic opportunities, fear of crime or police, and/or high pollution and toxic exposure can be a chronic stressor for residents including those who are pregnant. In addition to motivating potentially harmful coping mechanisms (e.g. substance abuse), high levels of stress over time can increase allostatic load, which has been associated with adverse birth outcomes such as preterm birth and low birthweight births (Culhane & Elo, 2005; Giurgescu et al., 2013; Goldenberg et al., 2008; Ribeiro et al., 2018).
The bottom line: neighborhoods that foster health, foster healthy babies. This has enormous implications not just for population health now but for the society we become in the future. We can create healthier neighborhoods and dismantle and rectify the policies that segregate us, so that everyone giving birth has babies who can thrive into a healthy adulthood.
- Choi, K. H., & Martinson, M. L. (2018). The relationship between low birthweight and childhood health: Disparities by race, ethnicity, and national origin. Annals of Epidemiology, 28(10), 704-709.e4. https://doi.org/10.1016/j.annepidem.2018.08.001
- Culhane, J. F., & Elo, I. T. (2005). Neighborhood context and reproductive health. American Journal of Obstetrics and Gynecology, 192(5), S22–S29. https://doi.org/10.1016/j.ajog.2005.01.071
- Ely, D. M., & Driscoll, A. K. (2020). Infant Mortality in the United States, 2018: Data From the Period Linked Birth/Infant Death File. National Vital Statistics Reports, 69(7).
- Giurgescu, C., Engeland, C. G., Zenk, S. N., & Kavanaugh, K. (2013). Stress, Inflammation and Preterm Birth in African American Women. Newborn and Infant Nursing Reviews, 13(4), 171–177. https://doi.org/10.1053/j.nainr.2013.09.004
- Goldenberg, R. L., Culhane, J. F., Iams, J. D., & Romero, R. (2008). Epidemiology and causes of preterm birth. The Lancet, 371(9606), 75–84. Scopus. https://doi.org/10.1016/S0140-6736(08)60074-4
- Martin, J.A., Hamilton, B.E., Osterman, M.J.K., Discroll, A.K., & Mathews, T.J. (2017). Births: Final Data for 2015. National Vital Statistics Reports, 66(1). https://www.cdc.gov/nchs/data/nvsr/nvsr66/nvsr66_01.pdf
- Martin, J. A., Hamilton, B. E., Osterman, M. J. K., & Driscoll, A. K. (2021). Births: Final Data for 2019. National Vital Statistics Reports, 70(2). https://www.cdc.gov/nchs/data/nvsr/nvsr70/nvsr70-02-508.pdf
- Petrou, S, Sach, T., & Davidson, L. (2001). The long-term costs of preterm birth and low birth weight: Results of a systematic review. Child: Care, Health and Development, 27(2), 97–115. https://doi.org/10.1046/j.1365-2214.2001.00203.x
- Petrou, Stavros, Yiu, H. H., & Kwon, J. (2019). Economic consequences of preterm birth: A systematic review of the recent literature (2009–2017). Archives of Disease in Childhood, 104(5), 456–465. https://doi.org/10.1136/archdischild-2018-315778
- Ribeiro, A., Amaro, J., Lisi, C., & Fraga, S. (2018). Neighborhood Socioeconomic Deprivation and Allostatic Load: A Scoping Review. International Journal of Environmental Research and Public Health, 15(6), 1092. https://doi.org/10.3390/ijerph15061092
- Saigal, S., & Doyle, L. W. (2008). An overview of mortality and sequelae of preterm birth from infancy to adulthood. The Lancet, 371(9608), 261–269. https://doi.org/10.1016/S0140-6736(08)60136-1
- Vos, A. A., Posthumus, A. G., Bonsel, G. J., Steegers, E. A. P., & Denktaş, S. (2014). Deprived neighborhoods and adverse perinatal outcome: A systematic review and meta-analysis. Acta Obstetricia et Gynecologica Scandinavica, 93(8), 727–740. https://doi.org/10.1111/aogs.12430
COVID-19
The distribution of COVID-19 cases across the United States is not uniform, and certain states, counties, and cities have been harder-hit than others (Wissel et al., 2020). Epidemiologists have shown how features of neighborhoods may contribute to COVID-19 spread and harm the health among those infected. Research shows disproportionately high COVID-19 rates in areas with higher poverty levels, greater household crowding, and larger populations of Black and Brown residents (Chen & Krieger, 2020; Chin et al., 2020; Mahajan & Larkins-Pettigrew, 2020; Millett et al., 2020; Siegel et al., 2021).
These geographic patterns are unsurprising given how COVID-19 is spread. People living in crowded areas have less ability to physically distance, and communities with greater poverty and higher proportions of people of color have a greater number of residents in the essential workforce, more reliance on public transportation, and less access to medical care compared to more affluent communities. These structural neighborhood factors independently and jointly increase coronavirus transmission and contribute to racial and socioeconomic inequities in the number of COVID-19 cases and deaths (Berkowitz et al., 2020; Chen & Krieger, 2020; Chin et al., 2020; Egede & Walker, 2020).
- Berkowitz, R. L., Gao, X., Michaels, E. K., & Mujahid, M. S. (2020). Structurally vulnerable neighbourhood environments and racial/ethnic COVID-19 inequities. Cities & Health, 1-4. https://doi.org/10.1080/23748834.2020.1792069
- Chen, J., & Krieger, N. (2020). Revealing the unequal burden of COVID-19 by income, race/ethnicity, and household crowding: US county vs ZIP code analyses. Harvard Center for Population and Development Studies Working Paper Series, 19(1).
- Chin, T., Kahn, R., Li, R., Chen, J. T., Krieger, N., Buckee, C. O., Balsari, S., & Kiang, M. V. (2020). US county-level characteristics to inform equitable COVID-19 response. medRxiv.
- Egede, L. E., & Walker, R. J. (2020). Structural Racism, Social Risk Factors, and Covid-19—A Dangerous Convergence for Black Americans. New England journal of medicine.
- Mahajan, U. V., & Larkins-Pettigrew, M. (2020). Racial demographics and COVID-19 confirmed cases and deaths: a correlational analysis of 2886 US counties. Journal of Public Health.
- Millett, G. A., Jones, A. T., Benkeser, D., Baral, S., Mercer, L., Beyrer, C., Honermann, B., Lankiewicz, E., Mena, L., & Crowley, J. S. (2020). Assessing differential impacts of COVID-19 on Black communities. Annals of Epidemiology.
- Siegel, M., Critchfield-Jain, I., Boykin, M., & Owens, A. (2021). Actual Racial/Ethnic Disparities in COVID-19 Mortality for the Non-Hispanic Black Compared to Non-Hispanic White Population in 35 US States and Their Association with Structural Racism. Journal of Racial and Ethnic Health Disparities, 1-13.
