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The public health community has reached a consensus that where you live determines how long and how well you will live, with neighborhood wealth as one of the most important influences. In societies where everyone is supported to flourish socially and financially, people are healthier and so is the economy.

According to the World Health Organization, “(p)olicies that recognize that what makes societies prosper and flourish can also make people healthy have more impact. Fair access to education, good work, decent housing and income all support health. Health contributes to increased productivity, a more efficient workforce, healthier ageing and less expenditure on sickness and social benefits. The health and well-being of the population are best achieved if the whole of government works together to address the social and individual determinants of health.” As part of traditional public health practice, health departments collect data and implement programs based on individual health behaviors and outcomes—including indicators related to health and risk behaviors, infection, disease, injury, birth, and death. With most of these data, there are differences in outcomes and disparities in health between population groups classically defined by race, ethnicity, gender, disability status, and age. Public health interventions typically have been designed to reach and meet the needs of specified groups with higher rates of particular conditions—such as diabetes among Hispanic/Latinos or hypertension among African Americans/Blacks. Although there is an important role for culturally appropriate programs that build awareness and self-efficacy to make healthier individual choices (for example, in nutrition and exercise), this traditional, downstream view often also propagates a misunderstanding that individual behavior (i.e., “personal responsibility”) is the principle or only cause of preventable disease.

While this perspective has some merit, it ignores the influence of historically discriminatory public and economic polices that determine poverty, educational attainment, and neighborhood living conditions. These upstream social determinants promote, enable, and reinforce the unhealthy behaviors leading to preventable disease, disability, and death. Thus the use of the term ‘health inequities, defined by the World Health Organization as “the differences in health status and mortality rates across population groups that are systemic, avoidable, unfair, and unjust.”

The purpose of this guide is to show local health department (LHD) epidemiologists, data analysts, and other professionals how to collect, analyze, and display a prioritized list of social determinant of health living condition (SDOH-LC) indicators and frame these data in the context of neighborhood mortality, morbidity, and social conditions.

The recommendations in this guide are designed to help local health departments (LHDs) use SDOH-LC indicators to make measurable improvements in health and quality of life—particularly for neighborhoods and populations that emerge from the data as having the greatest SDOH needs.

By following the recommendations outlined in this guide, we expect the reader will be able to:

  • Understand the importance of SDOH-LC indicators and their role in local public health equity work.
  • Conduct a health equity analysis of death certificate files available to all LHDs.
  • Collect and analyze key SDOH-LC indicators for use in local public health activities and to monitor changes over time.
  • Respond to common questions and known limitations to SDOH indicators.
  • Connect SDOH-LC indicators to the ten essential public health services.
  • Show examples of successful partnerships from San Francisco Bay Area health departments with institutions traditionally outside of health and human services to address the SDOH.


The Bay Area Regional Health Inequities Initiative (BARHII) is a collaboration of public health staff and leadership from 11 of the San Francisco Bay Area LHDs whose mission is to “transform public health practice for the purpose of eliminating health inequities using a broad spectrum of approaches that create healthy communities.” This charge is carried out by an in-kind LHD staff committee structure, which includes a Data Committee (DC) composed of LHD epidemiologists and analysts. The DC addresses factors identified by research as underlying the health inequities seen between population groups, especially socioeconomic inequalities in living conditions, and helps build local capacity in epidemiology and evaluation to monitor these conditions and the strategies and actions to improve them.


This BARHII indicator project began in February 2009 to develop a set of indicators that best illustrate the effects of the SDOH on inequitable health outcomes for the purposes of: showing the connections between inequities and health; developing more effective public health interventions; creating data support for public health interventions that might fall outside of the traditional public health models for interventions; and to support and develop more effective approaches in health departments which address living conditions and other social determinants. This information can also be used for policy makers, program evaluation, data monitoring—including county-level tracking over time, input on statewide indicator projects, future grant funding, and as a source of potential ‘gaps’ in currently tracked indicators.

The BARHII Data Committee started out by compiling a comprehensive set of over 300 indicators from the literature, including several well-documented pioneering SDOH indicator lists such as the Centers for Disease Control and Prevention (http://www.cdc.gov/dhdsp/docs/data_set_directory.pdf ), the San Francisco Healthy Development Measurement Tool, and the World Health Organization—The Solid Facts. Additional sources included newly published reports such as Galea’s Estimated Deaths Attributable to Social Factors in the US, Healthy People 2020 SDOH

indicators, and an extensive literature review showing the effects of living conditions on health outcomes. Then, utilizing local knowledge and expertise, the DC followed a process of narrowing the list to a core set of 72 health equity measures (Appendix F). Criteria for inclusion in the list included the strength of each indicator in the literature reviewed and the degree to which each measure would impact health inequities. Data availability was not included in the selection criteria at this stage because the DC wanted to identify a ‘wish list’ of priority indicators to advocate for future tracking by the State of California. The 72 indicators were categorized along the same organization as the living conditions associated with health inequities from the BARHII Framework: economic environment, social environment, physical environment, and service environment.

In 2012, the data committee took the list of 72 core, prioritized indicators and, now also considering data availability, voted on which 15 SDOH indicators to use as examples in this ‘how to’ guide.


In the 2008 BARHII report, Health Inequities in the Bay Area, an analysis of mortality, neighborhood poverty, race, and ethnicity among BARHII member counties from 1999 to 2001 showed a strong, inverse relationship between Census tract poverty and life expectancy. Figure 1 is the updated version based on deaths in the Bay Area from 2009 to 2011 and the 2010 Census. While improvements in life expectancy have occurred since 2000, differences in life expectancy by race, ethnicity, and neighborhood poverty continue to exist.

Figure 1: Neighborhood Poverty versus Life Expectancy at Birth, BARHII REGION, 2009-2011

Figure 1: Neighborhood Poverty versus Life Expectancy at Birth, BARHII REGION, 2009-2011

In an attempt to explain and ultimately eliminate these differences, BARHII developed a theoretical framework (Figure 2) showing how upstream factors produce and reproduce health inequities across populations.

Figure 2: The BARHII Framework

Figure 2: The BARHII Framework
The BARHII framework argues that living conditions, institutional power, and social inequalities are factors “upstream” to the individual and mostly out of his or her control, but they directly determine his or her health behavior, morbidity, and mortality. The collection of these upstream factors (the social inequality, institutional power, and living conditions boxes in the framework), are defined as the social determinants of health (SDOH). Many of the inequities in the SDOH are associated with each other, and many groups suffering from the worst health profiles also struggle in many of these social and economic indicators.

This guide focuses on SDOH indicators in the living conditions column where concrete measurements of built environment and social factors can be examined. As explained in Health Inequities in the Bay Area, “Neighborhoods with high rates of poverty, often disproportionately communities of color, are more likely to have high concentrations of retail outlets that specialize in alcohol, tobacco, and fast foods, a relative absence of stores that sell fresh produce at reasonable prices, a lack of open space, limited public transportation, housing adjacent to ports, rail yards, freeways and/ or other sources of toxic exposures and socially segregated housing that contributes to high rates of community violence. These conditions constitute risk factors for heart disease, cancer, stroke, diabetes, asthma, alcohol and drug abuse, and homicide, among others.”

While the broad relationship between wealth, place, and health is known, LHDs are confronted with three questions: (1) What is different about the social, environmental, and living conditions of wealthier places versus poorer places that could explain this life expectancy gap?; (2) Once these differences are identified, how can communities best invest resources to improve disparate neighborhood conditions, considering the multitude of factors and the large economic and political capital required to change them?; and (3) What is a local public health department’s role in facilitating this change? Beginning with an equity analysis of birth and death certificates, a well-designed, locally focused SDOH indicator project can begin to answer these questions.

This guide will focus on 15 SDOH living condition (SDOH-LC) indicators that BARHII has identified as significant influences on health, which can be collected, analyzed, and monitored by LHDs. Taken together with health data (e.g., morbidity, mortality, and risk behaviors), data from SDOH-LC indicators can help show (a) the complex and multifaceted nature of social inequities leading to health inequities; (b) outcomes of the discriminatory, inequitable, and unethical exercise of institutional power; (c) the cross-domain and cumulative burdens of those suffering from the worst inequities; (d) the many pathways to policies, programs and practices that can reduce these inequities; and (e) the need for those concerned with local health inequities to work with other partners beyond the healthcare and public sector to address SDOH inequities.

An important first step in transforming local public health practice to address the upstream health inequity factors is the collection and monitoring of SDOH-LC indicators. BARHII has drafted this guide to support health departments in doing so, especially those with limited resources. BARHII has developed eight general recommendations for LHD epidemiologists on how to collect and analyze SDOH-LC indicators. In addition to basic technical steps, BARHII also urges health departments to apply these indicators to program work and advises on where to begin in accomplishing this with examples from LHDs. In addition, BARHII has a report, Healthy Planning Guide, available online at http://barhii.org/resources/healthy-planning-guide/, to assist health departments in defining local policy recommendations, action steps and community partners with whom to build partnerships for healthy planning.


Recommendation 1. Analyze mortality and morbidity data to show health disparities, identify causes of death attributable to social and economic factors, and prioritize places and populations for further public health surveillance, intervention, and evaluation.

BARHII recommends that health departments analyze death certificate data to produce the charts and tables in this section. This analysis will identify priority places and populations for health equity work and track progress in building health equity over time. Stratification of life expectancy at birth and mortality by educational attainment and neighborhood poverty is essential because these two SDOH-LC indicators are: (1) among the strongest predictors of life expectancy and premature mortality; (2) factors on which public policy makers at all levels have significant influence; (3) factors recommended by the World Health Organization to be monitored as part of a health equity surveillance system; and (4) are readily available to most health departments. By identifying causes of death with a strong, statistical relationship with poverty or low educational attainment, LHDs can better tailor programs to improve the health of socially disadvantaged populations. While these are recommendations to analyze causes of death, they are based on the International Classification of Diseases version 10 (ICD-10) codes, the same codes that are found in electronic medical records (EMR); therefore, health departments can apply the methods here to monitor patient morbidity from EMRs as data become available. Further, this analysis can be considered a health equity analysis and can meet many of the data analysis and monitoring requirements for community health benefit reports or applications to the Public Health Accreditation Board.

Figure 3 shows neighborhood poverty versus life expectancy at birth (LEB) stratified by race and ethnicity in the Bay Area. LEB is a good overall measure of population health. Every LHD’s equity goal is to increase life expectancy in places and populations where it is lowest and reduce the disparities in this measure by race and ethnicity. Figure 3 shows that as poverty increases, LEB decreases for the total population and White and African American/Black races in the Bay Area. This gradient does not hold up as well for Asians and Hispanic/Latinos.

Figure 3: Neighborhood Poverty versus Life Expectancy at Birth, BARHII REGION, 2009-2011

Figure 3: Neighborhood Poverty versus Life Expectancy at Birth, BARHII REGION, 2009-2011

Another strong predictor of health determined by upstream policy is educational attainment, which is typically measured as the prevalence of adults 25 years and older with a high school education or its equivalent. As Figure 4 shows, neighborhoods with the highest rate of high school graduation also have the highest LEB in the Bay Area. However, the data suggest that educational attainment is not as strong a predictor of life expectancy than neighborhood poverty especially when broken out by race/ethnicity. For example, there is little change in LEB in the tracts with a 70-79% and 80-89% high school graduation rate, except for African Americans/Blacks and Whites. Conversely, as Figure 3 shows, there is at least some incremental change in LEB across all races as neighborhood poverty increases.

Figure 4: Neighborhood High School Graduation Rate Versus Life Expectancy at Birth, BarHII Region, 2009-2011

Figure 4: Neighborhood High School Graduation Rate Versus
Life Expectancy at Birth, BarHII Region, 2009-2011

Figure 5 shows that rates of mortality increase substantially with neighborhood poverty. Mortality rates among White and African American/Black populations living in poverty are most affected, while rates of mortality in Asian and Hispanic/Latino populations are less affected by neighborhood poverty.

Figure 5: Neighborhood Poverty versus all-cause, age-adjusted mortality rate, BARHII Region, 2009-2011

Figure 5: Neighborhood Poverty versus all-cause, age-adjusted mortality rate, BarHII Region, 2009-2011

Overall, rates of mortality decrease in neighborhoods as the proportion of adults living in that neighborhood with a high school education increases (Figure 6). However, this relationship is not as strong as neighborhood poverty versus age-adjusted mortality when stratified by race and ethnicity. This suggests that other factors—such as neighborhood poverty—confound the relationship between educational attainment and mortality rates and more robust epidemiologic analysis is needed to control for these other factors. The technical appendix discusses in greater detail the issues of colinearity and confounding.

Figure 6: Neighborhood High School Graduation Rate Versus all-cause, age-adjusted mortality rate, BARHII Region, 2009-2011

Figure 6: Neighborhood High School Graduation Rate Versus all-cause, age-adjusted mortality rate, BARHII Region, 2009-2011

Table 1 shows how much having no high school diploma affects the population attributable risk for specific causes of death among adults (25 to 64 years). The population attributable risk column estimates—in order of highest risk—the excess burden of mortality among adults with low educa-tional attainment. The analysis was limited to adults of working age because those deaths have the most significant economic and political impact on a community. For example, the rate of death by pedestrian collisions is 27.3% higher in adults 25 to 64 years with no high school diploma compared to adults who graduated high school. This analysis suggests that in the Bay Area, adults with low educational attainment share a higher burden of external causes of death (accidents, violence, and substance abuse). For detailed notes on how to calculate the population attributable fraction, see Appendix A.

Table 1: Top 15 causes of deaths of adults (25 to 64 years) without a high school education by population attributable risk, barhii Region, 2009-2011

Table 1: Top 15 Causes of Deaths of Adults (25 to 64 years) without a High School Education by Population Attributable Risk, BARHII Region, 2009-2011

An advanced method to measure the relationship between neighborhood poverty and mortality is the slope index of inequality (SII). This method calculates a log-linear regression coefficient of Census tract poverty versus cause-specific death rates in those Census tract poverty groups. Causes of death with a more negative slope index (e.g., assault by firearm) suggest a stronger association with neighborhood poverty (i.e., as neighborhood poverty decreases so do the death rates of that cause of death). Slopes indices closer to zero (e.g., trachea, bronchus, and lung cancer) indicate that the effect of neighborhood poverty on that cause of death is weaker compared to other causes. BARHII calculated the slope index of inequality for all group causes of death of adults 18 to 64 years living in BARHII counties, 2009-2011. Those shown in the table are statistically significant (p < .05) and had the steepest and most negative slope index score compared to other causes. For example, Figure 7 illustrates the slope index of inequality for “Other COPD” (ICD-10 group cause of death 205). The observed values fit the predicted model well.

Figure 7: Slope Index of Inequality Rates of Mortality for Other Chronic Obstructive Pulmonary Disease, BARHII Region, 2009-2011

Figure 7: Slope Index of Inequality Rates of Mortality For Other Chronic Obstructive Pulmonary Disease, Barhii Region, 2009-2011

The charts of the SIIs for the other causes of death in Table 2 look very similar, which are available on request. While this method is complex and requires geocoded mortality data and statistical software (BARHII used SAS version 9.3), it is an additional, useful method to suggest relationships with specific causes of death and neighborhood poverty.

Table 2: Statistically Significant Slope Indices of Inequality (Census Tract Poverty) or Cause of Death of Adults (18 to 64 years), BARHII Region, 2009-2011

Table 2: Statistically Significant Slope Indices of Inequality (Census Tract Poverty) or Cause of Death of Adults (18 to 64 years), BARHII Region, 2009-2011
Recommendation 2. Track morbidity and mortality data in priority places and populations over time to measure progress in affecting the SDOH indicators attributable to these health disparities.

BARHII recommends that health departments monitor changes in mortality over time and prioritize those places or populations with an increase in adverse mortality measures or little improvement in mortality outcomes for further intervention and assessment. One important limitation to this analysis is that some communities may experience displacement where the age, gender, race, or ethnic composition of a community in 2000 may have changed significantly in 2010 because of changes in the local economy. In other words, decreases in neighborhood morbidity and mortality could be explained by one population displacing another due to gentrification. Gentrification occurs when rent and other costs of living became too high for the original population, forcing them to leave.

When reviewing trends in LEB, it is expected that they will improve naturally:

The trend in the life expectancy of humans during the past thousand years has been characterized by a slow, steady increase—a pattern frequently punctuated by a volatility in death rates caused by epidemics and pandemic infectious diseases, famines, and war. 

Olshansky et al, 2005

However, Olshansky and colleagues (2012) argue that LEB for different populations based on race, ethnicity, education, or social status will change at different rates, leaving some population groups behind others in gains in LEB. Analysis of local data will help identify those populations specific to individual health departments.

Figure 8 illustrates that residents of all neighborhood poverty groups in the Bay Area experienced gains in life expectancy at birth from 2000 to 2010, with the sharpest increase in the highest poverty neighborhood (30% or more poverty). However, overall gaps in LEB between neighbor-hood poverty groups have not closed significantly except the gap between the 20.0-29.9% poverty groups and 30%+ poverty groups. While the population has migrated to and from and within all these areas—the poverty groups are not cohorts—there is significance in neighborhood poverty rate as a place-based unit, as concentrated poverty affects individuals as well as neighborhood conditions. Further assessment is needed to examine cohorts of population and to look at migration, especially in and out of high-poverty neighborhoods.

Figure 8: Trends in Life Expectancy at birth by Neighborhood Poverty Group,
BARHII Region, 2000 To 2010
Figure 8: Trends in Life Expectancy at birth by Neighborhood Poverty Group, BARHII Region, 2000 To 2010

Figure 9 illustrates trends in LEB in the highest poverty group in the Bay Area, stratified by race and ethnicity. From 2000 to 2010, LEB improved for each population group in high-poverty neighborhoods, but racial and ethnic inequities persist. Figure 10 has a pattern similar to Figure 9, except it is expressing mortality rates. Mortality declined from 2000 to 2010 for all racial and ethnic groups. However, differences by race and ethnicity continue to exist.

Figure 9: Trends in Life Expectancy at Birth, 30%+ Neighborhood Poverty Group,
BARHII Region, 2000 to 2010
Figure 9: Trends in Life Expectancy at Birth, 30%+ Neighborhood Poverty Group, BARHII Region, 2000 to 2010

Figure 10: Trends in All-Cause, Age-Adjusted Mortality Rates, 30%+ Neighborhood Poverty Group, BARHII Region, 2000 to 2010

Figure 10: Trends in All-Cause, Age-Adjusted Mortality Rates, 30%+ Neighborhood Poverty Group, BARHII Region, 2000 to 2010
Recommendation 3. Identify the Census tracts in your jurisdiction with a high prevalence of people living below 100% or 200% FPL.

Poverty is an outcome of social, public, and economic policies, and poverty contributes to high morbidity, high mortality, and low quality of life. In the technical appendix, BARHII specifically recommends creating a geographic information systems (GIS) layer showing high poverty at the Census tract level and using this layer to identify Census tracts, their respective cities, and the populations living in them to build health equity. Areas identified with the highest proportion of people living in poverty should be designated as priority areas for equity work. Census tracts in red in Figure 11 meet these criteria. These data are freely available from the American Community Survey. See Appendix B for steps on how to download and display the data.

Figure 11: Neighborhood Poverty, BARHII Region, 2008-2012

Figure 11: Neighborhood Poverty, BARHII Region, 2008-2012
Recommendation 4. Collect, analyze, and interpret 15 SDOH-LC indicators recommended in this guide.

By collecting SDOH data in the neighborhoods and populations identified by mortality and morbidity analysis, comprehensive and need-based prioritization can occur. If certain neighborhoods and communities have high need in several SDOH indicators, then the data exist to justify and prioritize these neighborhoods for programming and policy change.

These 15 indicators were narrowed from an initial list of several hundred selected by members of the BARHII data committee. The criteria included relevance and availability. Members drew on a review of the literature and years of experience in LHD epidemiology. Each of the 15 indicators has its own chapter that outlines how to locate, analyze, and tailor indicators to local health equity work. Furthermore, examples of how BARHII-member health departments have used these indicators (or related data) in public health practice are included at the end of each chapter.

Recommendation 5. Track SDOH-LC indicators over time to show improvement, decline, or stagnation in the totality of policies, programs, and procedures related to that indicator for a geography and population over time.

To determine if public health activities and other equity work are improving the living conditions that influence life expectancy and mortality, SDOH-LC indicators are needed to identify what conditions are present before an intervention, or a baseline measure, and if any change in SDOH-LC has occurred along with the health outcomes after the intervention’s implementation. From this, decision-makers can see whether programs or policies can continue as implemented or if they need modification. Typically, an indicator trend chart will look like Figure 12 showing trends in educational attainment in San Pablo versus the San Francisco Bay Area.

Figure 12: Educational Attainment, BARHII Region and San Pablo, 2000 To 2008–2010

Figure 12: Educational Attainment, BARHII Region and San Pablo, 2000 To 2008–2010
Following trends and changes in indicators over time are part of the health impact assessment (HIA) framework (Figure 13), which is frequently used to identify the effects of transportation and land use planning on health. For example, the rate of accidents and at a busy intersection could be used to evaluate the effectiveness of investment in traffic-calming devices.

Health Impact Assessment (HIA) is a means of assessing the health impacts of policies, plans and projects in diverse economic (and social) sectors using quantitative, qualitative and participatory techniques. HIA is a practical approach used to judge the potential health effects of a policy, program or project on a population, particularly on vulnerable or disadvantaged groups. Recommendations are produced for decision-makers and stakeholders, with the aim of maximizing the proposal’s positive health effects and minimizing its negative health effects.

World Health Organization, 2008

Figure 13: The HIA Procedure

Figure 13: The HIA Procedure
Recommendation 6. Use SDOH-LC analysis to write competitive funding applications.

Describing communities through SDOH-LC indicators can help local agencies and health departments craft funding proposals that are more likely to be successful for two reasons. First, initial analysis of SDOH-LC indicators can determine if the funding opportunity actually aligns with the identified needs of a community. Second, philanthropic and government funders favor applications from data-literate agencies that can articulate needs through data, collaborate across sectors, and show measurable progress on program or funding objectives.

Recommendation 7. Use SDOH-LC indicators to mobilize community partnerships with organizations traditionally outside health and human services.

One of the ten essential public health services is to mobilize community partnerships. Because health departments are not experts in most of the SDOH-LC indicators discussed in this guide, progress in these domains will only come from constructive partnerships from the relevant institutions and organizations. Collectiing and analyzing SDOH-LC indicators is an important contribution that health departments can make to help establish external partnerships where they do not already exist.

A health department’s work connecting SDOH-LC data to neighborhood health outcomes show where to allocate resources under its control and where to build cross-sector partnerships for increasing health equity. After LHDs have analyzed basic health and SDOH-LC data, partnerships with other institutions can be developed where more granular data can be shared. Collaborative evaluation and analysis of granular data leads to progressive policies and programming across public and private sectors advancing health in all policies. Further, SDOH-LC indicators will help health departments and community agencies identify opportunities for effective collaborations and grass-roots organization for equitable, local policy change.

Once the priority places and populations are identified through analysis of mortality and SDOH-LC data, public health can collaborate with other sectors to integrate strategies that affect social determinants. For example, a youth tobacco education program may work with schools on high school graduation goals in addition to health messages regarding smoking, as higher educational attainment is linked to lower rates of smoking. Public health departments may also find ways to leverage their current contracts and cross-sector agreements to influence progressive policies. For example, staff inspecting restaurants for health and safety code violations may also inquire about worker pay and labor law violations before granting licenses, with the understanding that a liveable wage and humane working conditions are public health issues that affect health and well-being. For additional examples, see the indicator chapters.

One approach to working across sectors for improved health outcomes is modeled by the California Department of Public Health (CDPH)’s Health in All Policies (HiAP) program within the Office of Health Equity. According to the CDPH definition, “Health in All Policies is a collaborative approach to improving the health of all people by incorporating health considerations into decision-making across sectors and policy areas.” The HiAP program produced a guide for local and state governments on how to work collaboratively across disciplines to incorporate health into all policy sectors.

Another highly effective, cross-sectoral, collaborative approach in the research in recent years is the concept of collective impact. Initiatives that include the following five key conditions distinguish collective impact from other forms of collaborative efforts.


Due to the complex nature of most social programs, this collective impact approach of using shared data and collective action increases the breadth of impact and sustainability of efforts.

Recommendation 8. Use SDOH-LC and mortality indicators in the design, implementation, and evaluation of the other ten essential public health services to build health equity. 

The ten essential services of public health (Figure 14) provide a guiding framework for the responsibilities of public health systems. The following describes how each essential service can more intentionally and explicitly address health inequities experienced by residents of your community.

Figure 14: The Ten Essential Services of Public Health

Figure 14: The Ten Essential Services of Public Health
Mobilize Community Partnerships: As discussed in recommendation 7, the formation of community partnerships outside of the public health system is essential to addressing the conditions that most influence health inequities. The selection of SDOH-LC indicators can help a health department prioritize with which community organizations and government agencies to form relationships. LHDs can help engage community members, bring together key players in local decision-making, and give these community partners the SDOH data to identify priority social determinants in their community in which to focus their advocacy that are beyond the capabilities of the health department.

Monitor Health: Through tracking SDOH-LC indicators in addition to vital records, public health departments can highlight the broader health issues and risk factors of its population. These data and skills are unique to public health professionals and will become more valuable as medical records become digitized and their analysis becomes mandated.

Diagnose and Investigate: SDOH-LC indicators are diagnostic tools to identify possible disease risk behaviors, as well as social and environmental risk factors, in populations not captured by classic infectious disease diagnosis techniques. Because most of the leading causes of morbidity and mortality today are not microorganisms, public health diagnosis and investigation must find causes other than bacteria and viruses. Unfavorable SDOH contribute substantially to disease outcomes.

Evaluate: Health departments have traditionally evaluated the effectiveness of health care and health promotion programs as part of quality improvement. Public health evaluation methods are backed by empirical research and have been shown to improve programs and ultimately health.

Many of the quantitative methods in public health evaluation can also be applied to evaluate the effectiveness of the social and economic policies that determine health.

Assure Competent Workforce: The more LHD staff that receive training on SDOH and are aware of and can discuss SDOH issues, the more likely they are to find ways to address them in their work. Despite the limitations of categorical programs and services in public health, LHD staff have some discretion in how these services are provided. Information on SDOH-LC indicators can help staff identify and apply that discretion to deliver more effective services and create more effective partnerships to advance health equity.

Inform, Educate, Empower: In some areas, the health department may be the only organization that can credibly speak to the relationship of social determinants and health. LHDs are often expected to advise other institutions as well as the public on health and disease. Using SDOH-LC indicators will improve the LHD’s ability to fulfill this role of informing, educating, and empowering both other institutions and individuals by relating health to larger social and environmental factors and encouraging action to improve these living conditions for all communities.

Develop Policies: Through monitoring SDOH-LC indicators, LHDs are better equipped to identify how local policies affect health. If a LHD can ensure that SDOH-LC and health outcomes are considered in the creation of its own policies, it will gain the experience and credibility to guide HiAP work with other institutions. In addition, as LHDs are increasingly being invited to inform policy-making, by developing local policy review criteria that prioritizes health equity, LHDs can provide consistent, equitable, public health responses to local policy and planning issues that are related to SDOH-LC.

Enforce Laws: By monitoring SDOH indicators, a health department can ensure that the laws it is responsible to enforce (e.g., food safety, sanitation, occupational health, and hygiene) are promoting better health outcomes for all populations and can also help identify unintended consequences leading to inequitable outcomes. In addition, LHDs can leverage their public health mandates (e.g., restaurant health and safety inspection certificates) to ensure other SDOH issues are also being addressed (e.g., fair labor practices for employees of inspected restaurants). Tracking SDOH indicators can also help monitor the enforcement of laws of other institutions that lead to disproportionately negative health impacts.

Research: SDOH-LC indicators provide a common framework for health departments to share their program and policy experiences addressing the social determinants, and to facilitate and expand the research process to address the underlying conditions that influence health outcomes.


What is a social determinant of health (SDOH-LC) indicator? Administrative data from agencies, governments, institutions, and programs about a SDOH summarized to a geographic level, which may not include data about specific individuals.

Who are the audiences for SDOH-LC indicators? SDOH-LC indicators are intended for LHDs and the citizens, community groups, and institutions they wish to partner with or influence. For example, in working with land use planning policy-makers, demonstrating the overall cost benefit of affordable housing to the health and well-being of the community at large would be helpful data to support progressive housing policies in high need areas. Whereas, in working with community members, SDOH-LC indicators will help these audiences identify the underlying causes of disease and community assets needed to address them. From these data, more encouraging, structural strategies to positively affect the highlighted needs can be designed.

Why not just use poverty as a proxy for all SDOH-LC indicators? BARHII considers neighborhood poverty (proportion of individuals living below the federal poverty level) the fundamental SDOH-LC indicator and recommends that every health department identify the Census tracts with the highest concentration of people living below the federal poverty level. (See recommendation 3 in this guide.) This recommendation is supported by the conclusions of the Harvard Health Disparities Geocoding project, which shows that poverty alone can serve as a proxy for many of the individual SDOHs.

While poverty is the fundamental SDOH‑LC indicator, analysis of it alone is not sufficient for a health department to develop robust interventions tailored to the specifics of a place and its inhabitants. For example, if a local data analysis reveals that high and disproportionate incarceration rates are one of its main concerns in one high-poverty neighborhood, the health department may choose to focus strategies on crime, violence prevention, or police profiling policies. It is possible that analysis of the same indicator in another high-poverty neighborhood may not identify incarceration rates as a priority.

Won’t SDOH-LC Indicators single out, blame, or disfavor communities and populations? There is a risk that some communities may take offense when they are shown SDOH-LC data, although a health department may have the best of intentions. The risk of offending communities can be avoided through carefully framing messages and building trust with communities so that open and honest dialogue about improving health and living conditions can take place. At a minimum, any messages or conclusions that are adverse must be delivered using language that is respectful, honest, understandable to the audience, and not inflammatory. There is a body of literature on how to do this. Other suggestions when discussing these issues include: 1) describing the positive attributes of a community (i.e., resilience factors and assets); 2) displaying data that compare communities with themselves over time; and 3) comparing SDOH-LC data with communities similar in demographic and economic composition. BARHII also recommends seeking the advice of a health educator on how to best frame messages about the SDOHs.

Haven’t communities already seen enough charts, maps, and graphs of problems they are already aware of? If the indicators continue to say the same thing with little change over time, something needs to change. To understand this, health departments must build relationships with community members and leaders to obtain data with a purpose of identifying and evaluating the specific policies, programs, and procedures within a priority area that drive improvement in living conditions.

How can a health department identify or track the specific policies, programs and procedures from these broad indicators? The SDOH-LC indicators in this guide are a starting point for the health department to address the SDOH in its own work. Because of the inherent limitations of the data, it is true that specific solutions to unfavorable SDOHs will not reveal themselves from these broad indicators, but they will show a LHD where to begin to look. Once the places and populations most affected by the SDOH are known and revealed by these indicators, the LHD can evaluate its own programs and build partnerships to identify and address causes.

Public health professionals are not experts in economic development, transportation, law enforcement, urban planning, or education. What gives public health the credibility to advise or influence these institutions? Why should local health departments spend its limited resources in areas where they have little expertise or control? Public health’s purpose is to promote health and prevent disease. Many of public health’s successful services used in the 20th century to prevent infectious disease are applicable to preventing chronic disease in the 21st. Because these services are numerous and complex, this guide recommends identifying which of the ten essential services health departments can offer to other institutions to advance health. It is through the improved delivery of the essential services, that the LHD will gain the trust and credibility it needs to advise and influence other institutions. The real-world program and policy examples in this guide show how LHDs in the Bay Area have integrated health into social and economic policies and applied SDOH data analyses and the ten essential public health services to local health equity work.

How does stress link to SDOH-LC indicators and health outcomes and how can it be measured? The indicators of both acute and chronic stress are not often captured directly in public health data collection and analysis. However, there are clear pathways that link the mental and physical effects of stress to poorer health outcomes as well as unhealthy behavioral decision-making, including alcohol and drug use as self-medication or a coping mechanism.

In addition, disadvantaged populations are often poorly affected by stressful living and working conditions (e.g., crowded housing, violence, toxic environments, unemployment and financial stress, occupational hazards, trauma leading to the inability to work or stay in school, lack of supportive personal relationships). Many of these risk factors that cause stress are not under the control of the individual to change, rather are affected by unhealthy social and political systems of inequality.

There are then physiological effects of stress on the body, such as raised blood pressure and cortisol levels, that increase the risks for harmful effects of pre-term labor and chronic disease (e.g., cancer, cardiovascular disease). Community empowerment and a sense of control over ones’ circumstances have been shown to be positively associated with decreased stress.

Due to these links between stress and health outcomes, BARHII recommends that public health departments include research-validated questions about perceived individual stress as well as questions that assess a wider sense of control and community empowerment in their community health assessments, and other data collection and analyses.

VII. SDOH-LC Indicators included in the Guide

Table 3: SDOH-LC Indicators Presented in this Guide

Table 3: SDOH-LC Indicators Presented in this Guide


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