Social Determinants of Health

V. Key Issues: Population Health >> B. Public Health >> Determinants of Health >> Social Determinants of Health (last updated 10.16.17)

General Resources

Socioeconomic Status

Poverty, Income and Health

Measuring Poverty

Official Poverty Rate
Alternative Poverty Measures
  • Edsall, Thomas. How Poor Are the Poor? New York Times 3.25.15. Explains that U.S. poverty ranges from 4.8% (according to Christopher Jencks) to nearly five times that level (according to Center for American Progress), depending on how poverty is defined.
  • Matthews, Dylan. The official poverty measure is garbage. The census has found a better way. Vox 9.16.15. Explains how poverty currently is defined and contrasts this to how the Supplemental Poverty Measure (SPM) does so. It shows that according to SPM, pre-tax/pre-transfer poverty has stayed remarkably steady since 1967–hovering around 25%, but that after accounting for government cash and non-cash transfers, poverty has declined from 25% in 1967 to 16% in 2012.

Poverty Dynamics

Data from the Panel Study of Income Dynamics (PSID) starting in 1968, the first year the data was collected, through 2003, show:

  • The average spell of poverty lasts 2.8 years. The longest were among households headed by single women (3.1 years), African American men (2.7 years) and those with less than a high school diploma (2.6 years).
  • Many individuals experience multiple spells of poverty, so that these spell lengths substantially understate the total time spent in poverty.  Thirty-six percent of individuals return to poverty within four years of ending a spell.
  • Increased time spent in poverty is associated with lower chances an individual will exit poverty, which ranges from 56% after one year poor to 13% for those in poverty for 7 or more years.

Research and Analysis

  • Subramanian SV, Belli P, Kawachi I. The macroeconomic determinants of health. Annual Review of Public Health (2002) 23:287–302. [Abstract]
  • Marmot M. The influence of income on health: views of an epidemiologist. Health Affairs (2002) 21(2):31–46. [Full Text]
  • Rehkopf DH, Berkman LF, Coull B, et al. The non-linear risk of mortality by income level in a healthy population: US National Health and Nutrition Examination Survey mortality follow-up cohort, 1988–2001. BMC Public Health (2008) 8:383. [Abstract]
  • Muennig P, Franks P, Jia H, et al. The income-associated burden of disease in the United States. Social Science and Medicine 2005; 61:2018–26. [Abstract]
  • Cristia, Julian P. The Empirical Relationship Between Lifetime Earnings and Mortality. Congressional Budget Office. August 2007.
  • Bosworth, Barry, Gary Burtless, and Kan Zhang (2016). Later retirement, inequality in old age, and the growing gap in longevity between rich and poor. Brookings Institution. Female life expectancy at age 50 for those with incomes in bottom 10% was identical for those born in 1920 and 1940 (80.4 years), but for those in top 10%, LE@50 rose from 84.1 to 90.5. For men, LE@50 rose slightly for bottom 10% from 74.3 years to 76 years while for top 10% it rose from 79.3 to 88 years.
  • Rector, Robert and Rachel Sheffield, ‘‘Air Conditioning, Cable TV, and an Xbox: What is Poverty in the United States Today,’’ The Heritage Foundation (July 19, 2011).
  • Eli, Shari and Nicholas Li. Caloric Requirements and Food Consumption Patterns of the Poor.  NBER Working Paper No. 21697. Issued in November 2015. How much do calorie requirements vary across households and how do they affect food consumption patterns? Since caloric intake is a widely-used indicator of poverty and welfare, investigating changes in caloric requirements and food consumption patterns is important, especially for the poor. Combining anthropometric and time-use data for India, we construct a quantitative measure of individual and household caloric requirements. We then link our estimates of caloric requirements with consumption data to examine how caloric requirements coupled with household expenditures shape food demand. Our applications include the measurement of hunger and the role of caloric requirements in explaining food consumption puzzles related to household-scale and changes in caloric intake over time.

Data

  • Household Percentile Ranking (New York Times).  Enter your household income and see how you rank in 344 zones across the country; see summary table for selected household incomes ranging from $5,000 (bottom 2% to 383,001 (top 1%).

Wealth and Health

Research and Analysis

  • Piketty, Thomas. Capital in the 21st Century. 2014. Argues that since growth in capital (5% annually) invariably exceeds growth in the economy (~3% annually), wealth inequality inevitably will grow. He uses data from 1810-2010 to make this case.
  • Edward N. Wolff, Top Heavy (2nd ed. 2001). In 1929, before the stock market crash, the top 1 percent controlled 44.2 percent of the nation’s wealth.
  • Louisa Kroll and Kerry A. Dolan, ‘‘Inside the List: Facts and Figures,’’ Forbes, Sept. 21, 2011, available at http://www.forbes.com/sites/luisakroll/2011/09/21/inside-the-list-facts-andfigures/.The Forbes 400 own approximately $1.53 trillion worth of assets, or almost $4 billion average.
  • Wolff, Edward N. ‘‘Recent Trends in Household Wealth in the United States: Rising Debt and the Middle-Class Squeeze — an Update to 2007,’’ Levy Economics Institute of Bard College, Working Paper 589 (June 2007). The top 10 percent own approximately 75 percent of the nation’s wealth, and the top 1 percent own nearly half of that 75 percent.

Data

Education and Health

Occupation and Health

Health Disparities Data Collection

  • Beezley-Smith. Treatment has Minor Role in Health Outcomes. “Tufts University School of Medicine researchers studied a variety of SDoH (Social Determinants of Health) assessment tools and found 15 core domains: culture/religion, demographics, economic indicators, education, employment status, family/living arrangements, functional status, healthcare access, health-related behaviors, language, material hardship, mental health, social support, trauma/violence and veteran status. Health-related indices measure alcohol, caffeine and tobacco use, secondhand smoke, physical activity, sexual activity, diet, safety, use of bike helmets, seat belts, and smoke detectors, baby-proof environments, gun ownership,driving safety and screen time. Trauma/violence subdomains include intimate partner violence, trauma, and physical, sexual, mental and child abuse. While some psychologists may have concerns about sharing such personal details, that door was cracked open in 2015 when the country adopted the WHO’s detailed ICD-10-CM system. The Z codes in particular were intended to capture contact with health services and factors that influence health status, including Z55- Z65 codes that specifically identify potential socioeconomic and psychosocial health hazards. Z codes target nonclinical circumstances such as Hostility toward and scapegoating of child (Z62.3), High-risk bisexual behavior (Z72.53), Personal history of self-harm (Z91.5), Encounter for mental health services for victim of child sexual abuse by parent (Z69.010), and Discord with counselors (Z64.4), neighbors (Z59.2) or workmates (Z56.4).” National Psychologist, September, 2019.

Psychosocial Factors

Income Inequality and Health

Racial/ethnic inequality and health

Employment Status and Health

Relationship Between Unemployment and All-Cause Mortality

Inverse Relationship at Aggregate Level

  • Cutler/Huang/LLeras-Muney (2016). In their analysis of 100 birth-cohorts in 32 countries to determine how economic growth increases mortality, the authors found that small, but not large, booms increase contemporary mortality. Yet booms from birth to age 25, particularly those during adolescence, lower adult mortality. A simple model can rationalize these findings if economic conditions differentially affect the level and trajectory of both good and bad inputs into health. Indeed, air pollution and alcohol consumption increase in booms. In contrast, booms in adolescence raise adult incomes and improve social relations and mental health, suggesting these mechanisms dominate in the long run. Overall, two-thirds of the adverse effect on mortality effect can be attributed to air pollution alone.
  • Ruhm (2000). A U.S. study found that total mortality and eight of the ten sources of fatalities examined are shown to exhibit a procyclical fluctuation, with suicides representing an important exception. The variations are largest for those causes and age groups where behavioral responses are most plausible, and there is some evidence that the unfavorable health effects of temporary upturns are partially or fully offset if the economic growth is long-lasting. An accompanying analysis of micro data indicates that smoking and obesity increase when the economy strengthens, whereas physical activity is reduced and diet becomes less healthy.
  • Granados & Ionides (2017). A study of 27 European countries just before and during the Great Recession (2007-2009)  found that a one-percentage-point increase in the unemployment rate is associated with an 0.5 percent decline in the overall age-adjusted mortality rate. “we conclude that in the European experience of the past 20 years, recessions, on average, have beneficial short-term effects on mortality of the adult population.” The authors further found that a one-percentage-point increase in the unemployment rate is associated with a one percent lower mortality rate for respiratory illnesses, as well as reductions in mortality for cardiovascular disease and heart conditions, which are known to be sensitive to air pollution.
  • Gerdtham & Ruhm (2006). A study of 23 (OECD) countries over the 1960-1997 period showed that total mortality and deaths from several common causes rise when labor markets strengthen.
  • Frakt (2017) notes that “during recessions, people without jobs may have more time to sleep and exercise and may eat more healthfully. One study found that higher unemployment is associated with lower rates of obesity, increased physical activity and a better diet. On the other hand, suicides increase during economic downturns.”
  • Lam & Pierard (2015). We examine the relationship between total mortality, deaths due to motor vehicle accidents, cardiovascular disease and measures of business cycles for the USA, using a time-varying parameter model for the periods 1961–2010. We first present a theoretical model to outline the transmission mechanism from business cycles to health status, to motivate our empirical framework and to explain why the relationship between mortality and the economy may have changed over time. We find overwhelming evidence of structural breaks in the relationship between mortality and business cycles over the sample period. Overall, the relationship between total mortality, cardiovascular mortality and the economy has become less procyclical over time and even countercyclical in recent times for certain age groups. Deaths due to motor vehicle accidents have remained strongly procyclical. Using drugs and medical patent data and data on hours worked, we argue that important advances in medical technology and changes in the effects that working hours have on health are important reasons for this time-varying relationship.

Positive Relationship at Individual Level

  • Reduced Life Expectancy Among Unemployed. Rises in unemployment during large recessions can set in motion a domino effect of reduced income, additional stress and unhealthy lifestyles. Those setbacks in income and health often mean people die earlier, said Till von Wachter, a University of California Los Angeles professor who researches the impact of job loss. Von Wachter said his research of past surges in unemployment suggests displaced workers could lose, on average, a year and a half of lifespan.
  • Reduced Life Expectancy Among First-Time Job-Seekers. First-time job hunters seeking work during periods of high unemployment live shorter and unhealthier lives, research shows. An extended freeze of the economy could shorten the lifespan of Americans entering the job market by an average of about two years, said Hannes Schwandt, a health economics researcher at Northwestern University, who conducted the study with von Wachter.

Positive Relationship Between Unemployment and Suicides

Working Conditions and Health

Living Conditions and Health

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