4

Results of the Bottom-Up Procedure: Gaps in the Draft Objectives

This chapter describes the bottom-up process of comparing the 34 Leading Health Indicator (LHI) candidates produced by the committee’s top-down process (see Table 4-1) to the draft Healthy People 2030 (HP2030) core objectives (see Appendix E) reviewed by the committee. The committee also consulted the seventh report of the Secretary’s Advisory Committee on National Health Promotion and Disease Prevention Objectives for 2030 (SAC), containing that group’s recommendations regarding objectives (SAC, 2019). The draft objectives had also been released to the public for comment during the months of December 2018 and January 2019, and the committee reviewed a summary of public comments received by the Department of Health and Human Services (HHS) in response to the Federal Register notice (NORC, 2019).¹

LHIs DRAWN FROM EXISTING (DRAFT) HP2030 CORE OBJECTIVES

The committee found that 19 of its LHI candidates had draft HP2030 core objectives that matched the indicators committee members proposed (in Table 4-1, those are the items that include an acronym or abbreviation that represents the topic and the draft objective number, such as, SU-2030-03 Reduce drug overdose deaths, found under the topic heading “Substance Use” in the draft HP2030 objectives in Appendix E). The report

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¹ This information is available from the National Academies Public Records Office at PARO@nas.edu.

TABLE 4-1 Recommended Leading Health Indicators, Specific Objectives, and Healthy People 2030 Framework Concept with Which They Primarily Align

TABLE 4-1 Continued

TABLE 4-1 Continued

TABLE 4-1 Continued

NOTE: DTaP = diphtheria; Hep B = hepatitis B; Hib = haemophilus influenzae type B; MMR = measles, mumps, rubella.

^a Each candidate LHI may align with more than one of the themes in the HP2030 Framework graphic.

* Items that include an acronym or abbreviation that represents the topic and the draft objective number, such as “SU-2030-03: Reduce drug overdose deaths,” are draft HP2030 objectives, which are listed in Appendix E. All others are new objectives.

does not provide any discussion about those LHIs, except for several cases that merit a brief comment. In the case of the measure for reproductive health care services, the committee would have preferred a broad measure of access for people of all genders, but it was unable to identify such a measure, so the objective FP-2030-07 (“FP” referring to the topic heading Family Planning) was a good option, given that it may shed some light on the issue of access and applies to all adolescents. In the case of the tobacco LHI, objective TU-2030-13 (“TU” for Tobacco Use), the committee believes this is appropriate for use as an LHI if “tobacco product” is a category that includes electronic nicotine delivery systems in addition to traditional cigarettes and other forms of tobacco.² Finally, in the case of the education-related LHI, the committee would have preferred an earlier life measure to the solid but chronologically “later” fourth grade reading proficiency measure, and the committee provides an explanation at the end of the chapter.

LHIs REQUIRING NEW CORE OBJECTIVES

Next, the committee discusses the LHIs proposed that do not have a corresponding objective. This responds to the part of the charge to the committee “to identify new objectives that meet the core objective criteria” (see Box 2-1 for the core objective selection criteria developed by the SAC). The committee also discusses gaps in and proposes new objectives to fill gaps in the draft

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² The committee assumes that is the case because the data source listed for this objective is the Food and Drug Administration’s National Youth Tobacco Survey (NYTS), and multiple sources discussing NYTS research, including Cullen et al. (2018) refer to e-cigarettes as a tobacco product.

core objectives and provides a justification for each, including whether or not each meets the HP2030 objectives criteria (see Box 2-1). The draft core objectives for HP2030 do not include some topics or measures that the committee considers important for consideration as LHIs. In some cases, a related objective was available in the draft core objectives, but the committee did not find it adequate to the task of serving as an LHI, for reasons such as a narrowness of focus. The committee lists the missing topics or objectives as follows:

1. Life expectancy
2. Child well-being
3. Self-rated health
4. Well-being
5. Physical disability
6. Cognitive disability or impairment (a broad measure)
7. Mental health (a broad measure)
8. Immunization (a broad measure)
9. Environmental exposure (a broad measure)
10. Climate change-related threats to health
11. Discharges for ambulatory care-sensitive conditions
12. Civic engagement
13. Social environment/health equity: a measure of residential segregation
14. Social environment/equity: a second measure of residential segregation
15. Social environment/equity: neighborhood-level disinvestment
16. Public health systems
17. Health care spending
18. Education (a measure of early childhood care and education)

The committee offers the following recommended LHIs. For three of the areas above, the committee was unable to find sufficiently robust objectives that met the objective selection criteria, finding instead that they were better suited for consideration as developmental objectives. These areas are an early childhood care and education measure; a measure of public health spending; and two measures of health care spending. The committee has made a finding in each of these areas.

For each new LHI it recommends (e.g., an LHI not based on an already existing HP2030 draft objective), the committee discusses (1) the current draft core objective(s) (if any) available on that topic and why existing objective(s) may not make good LHIs (i.e., too narrow or specific); (2) a reference to the HP2020 measures if appropriate to the context; (3) a brief summary of the literature about that measure (where else it is used, its research and validation); (4) how it meets objective selection criteria; and (5) how it meets LHI criteria.

Life Expectancy

The recommended objective statement for this LHI is: “Increase life expectancy at birth.” The draft core objectives for HP2030 do not contain a life expectancy indicator. The committee recommends the LHI “increase life expectancy at birth.” Life expectancy was a foundation measure of HP2020, it is a widely used and available measure at every level of government, and it is collected by the National Center for Health Statistics. Life expectancy is also important to track as an overall measure of the nation’s health performance and as a means of comparing U.S. health to that of similar nations (OECD, 2017b)—as was acknowledged in HP2020 (HealthyPeople.gov, n.d.).

A life expectancy objective meets each of the objective selection criteria. It is a widely available and used measure, with ample current baseline data, it is of obvious national importance, and it is useful for assessing health equity and health disparities. The life expectancy objective has a known evidence base because there is deep knowledge about the factors that shape life expectancy. U.S. researchers and public health authorities have, for example, identified the key factors that contributed to an increase in life expectancy over the twentieth century, and they have similarly identified factors associated with an unprecedented decrease in life expectancy for some groups (particularly white, middle-aged men) in the second decade of the twenty-first century (see, for example, Case and Deaton, 2017).

Life expectancy may also be used to construct the Human Development Index (HDI) in combination with measures of educational attainment and a measure of per capita income (Measure of America, 2018; UNDP: Human Development Reports, n.d.). The HDI can be used for international comparisons, but as demonstrated by Measure of America, it may also be used at the state or local level for planning and tracking purposes.³

Life expectancy meets all LHI selection criteria: public health burden; magnitude of disparity (see, for example, Woolf et al., 2018); sentinel or bellwether (as noted above); and actionability—in the sense that life expectancy is a key indicator of society’s ability to implement the range of evidence-based interventions needed to improve life expectancy.

Adverse Childhood Experiences

The recommended objective statement for this LHI is: “Reduce the prevalence of one or more adverse childhood experiences (ACEs) from birth to age 17.”

Since 1998, when Felitti and his Kaiser Permanente colleagues published their research on ACEs (Felitti et al., 1998), the evidence has been accumulating about the lifelong effects on health and well-being of

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³ Lewis presentation, May 28, 2019.

adverse exposures (e.g., to abuse, severe deprivation) in childhood. National Academies reports A Roadmap to Reducing Child Poverty (NASEM, 2019a) and Vibrant Healthy Kids: Aligning Science, Practice, and Policy to Advance Health Equity (NASEM, 2019b) provide extensive discussion of the ways in which ACEs affect health and well-being later in life, and the latter also discusses the disparities along socioeconomic lines (e.g., children living in poverty are at greater risk of ACEs). Merrick et al. (2019) examined Behavioral Risk Factor Surveillance System (BRFSS) data from 25 states that collect state-added ACEs items and found that one in six adults reported having experienced four or more ACEs, and they found that ACEs were associated with multiple poor health outcomes. They concluded that interventions that prevent ACEs in childhood could reduce the prevalence of chronic conditions, risk behaviors, and other health outcomes in adulthood. Merrick et al. (2019) also noted that the Centers for Disease Control and Prevention’s (CDC’s) resource on ACEs provides evidence-based information about ways to prevent and mitigate exposure to ACEs (CDC, 2019). ACEs are also associated with increased likelihood of experiencing mental distress, substance use, and other health problems (see, for example, Bellis et al., 2019; Hughes et al., 2017).

Data on ACEs are available from multiple sources, and the measure available from the Health Resources and Services Administration’s (HRSA’s) National Survey of Children’s Health (NSCH) is “prevalence of one or more Adverse Childhood Experiences from birth to age 17.” The NSCH is funded and directed by HRSA and fielded by the U.S. Census Bureau.⁴ Evidence of effective policy and programmatic interventions is being gathered by the National Child Traumatic Stress Network (NCTSN, n.d.). There is also a lot of work related to broader definition of ACEs and variation by income and race (see, for example, Mersky and Janczewski, 2018). CDC published Preventing Adverse Childhood Experiences (ACEs): Leveraging the Best Available Evidence (CDC, 2019), which offers a series of strategies and approaches for preventing ACEs, the latter ranging from implementing early childhood home visitation programming to strengthening household financial security.

Self-Rated Health

The recommended objective statement for this LHI is: “Increase the mean healthy days (CDC HRQOL–14 Healthy Days).” Although wellbeing is a core aspect of the HP2030 Framework and is paired with health (i.e., health and well-being) in each of the foundational principles in the

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⁴ This text has been revised since prepublication release.

framework, the draft core objectives for HP2030 do not include measures of well-being or a closely related construct used in HP2020—health-related quality of life (HRQOL). The committee recommends the CDC HRQOL–14 Healthy Days measure for use as an LHI. Such measures as the HRQOL–14, SF-36 (36-Item Short Form Survey), and SF-1 have been well-validated, with multiple studies showing “a statistically significant association between worse [general self-rated] health and a higher relative risk of mortality” (DeSalvo et al., 2006; Kaplan et al., 2017). A measure of self-rated health may be interpreted negatively or positively, and the committee suggests reporting on the full spectrum of responses, with positive and negative sides to allow framing that is appropriate to the use of the measure.

CDC HRQOL-14 Healthy Days meets all objective selection criteria. It is regularly collected as part of the BRFSS, and it could also be oversampled for some demographic groups. This measure also meets the following LHI Phase 1 criteria: public health burden and significance to health and well-being (as discussed earlier), magnitude of disparity (see Beck et al., 2014; CDC, 2008; Jones et al., 2008), and actionability.

Well-Being: Cantril Self-Anchoring Striving Scale

The recommended objective statement for this LHI is: “Increase the proportion of the population ‘thriving’ on Cantril’s Self-Anchored Striving Scale.” HP2020 described HRQOL and well-being as among the initiative’s four overarching goals, as well as foundational measures. The concept of well-being is discussed as assessing “the positive aspects of a person’s life, such as positive emotions and life satisfaction” and as “a relative state where one maximizes his or her physical, mental, and social functioning in the context of supportive environments to live a full, satisfying, and productive life.”

Although self-rated physical and mental health were described as potential surrogates for HRQL, proposals for measuring well-being were described as “being explored.” Given the centrality of the concept of well-being to the HP2030 Framework, the committee reviewed several self-reported approaches to capturing well-being. Although there has been wariness of using subjective measures based on a potential for relativism related to the expectations people attach to their circumstances, self-rated health has been found to be a better predictor of longevity than information about cardiovascular risk factors, including tobacco use, age, race, and years of education. Cantril’s Self-Anchored Striving Scale, or the Cantril Scale, is a self-anchoring measure of well-being that has been used both nationally and internationally (OECD, 2011, 2017a). The measure asks people to rank well-being on a continuum from 0 (worst possible

life) to 10 (best possible life) (Cantril, 1965). Scores of 7 and above are viewed as “thriving,” those of 4 and below are described as “suffering,” and intermediate scores where individuals have inconsistent well-being and double the sick days of the thriving are considered scores that denote “struggling.” The scale is used for both the present and the future (5 years from now), and the two scales can be combined into an index.⁵ The scale has been validated in multiple populations (e.g., children, different cultures) (Singh-Manoux et al., 2003), and as a quality-of-life indicator (OECD, 2013, 2017a; Singh-Manoux et al., 2003, 2005).

In a study of the impact of subjective social status, social determinants, and their association with ill health on working-age civil servants (n = 10,308), Singh-Manoux et al. (2003) found that subjective well-being, as captured by the Cantril Scale, was a predictor of ill health (i.e., angina, diabetes, respiratory illness, depression) separate from the conventional measures of socioeconomic position (education, income, and occupation). They suggest that a self-anchoring scale captures less well-defined sociocultural aspects of people’s life circumstances that are critical to good health.

Helliwell et al. (2019) explored explanatory variables linked to national outcomes as measured by the Cantril Scale and found that gross domestic product (GDP) per capita, social support, healthy life expectancy, freedom to make life choices, generosity, and perceptions of corruption were predictors of a nation’s ranking. The United States, with a score of 6.892 currently ranks 19th among developed nations (Helliwell et al., 2019). Data for the Cantril Scale are collected annually for the Gallup World Poll and the Gallup-Sharecare Well-Being Index (Gallup, n.d.) and capture wellbeing for OECD members.

The Cantril Scale is one of the Well-Being in the Nation measures proposed by the 100 Million Healthier Lives public–private partnership with the National Committee on Vital and Health Statistics, which advises the National Center on Health Statistics (100 Million Healthier Lives and the National Committee on Vital and Health Statistics, 2019).

One option to be considered in ensuring that data for this indicator, the Cantril Scale, are collected and accessible, is to add the question into a national survey, such as the BRFSS. This would help decision makers track well-being for U.S. subpopulations, with a focus on gaps to overcome to achieve equity.

This proposed well-being measure meets the following Phase 1 criteria for LHIs: public health burden, magnitude of disparity, bellwether.

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⁵ See https://news.gallup.com/poll/122453/understanding-gallup-uses-cantril-scale.aspx (accessed December 19, 2019).

Disability: Limitations in Daily Activities in Adults Aged 65 and Older

The recommended objective statement for this LHI is: “Reduce the percentage of adults aged 65 years and over with limitations in daily activities.”

The HP2030 draft objectives contain six core objectives under the topic Disability and Health, but none is broad enough to serve as an LHI. The committee therefore recommends for use as an LHI: “Limitations in activities of daily living in adults aged 65 and older.”

This measure meets the core objective selection criteria. It is measurable; a baseline is available along with additional data points (data regularly collected in the American Community Survey). This measure is of national importance, especially given the aging population and the need to understand future needs and implement programming to improve future outcomes, and there is a well-developed evidence base about the protective factors, such as being physically active (see van der Vorst, 2016). Regarding the fifth criterion for selecting core objectives (see Box 2-1), this measure also addresses health disparities and can inform efforts to support achieving health equity. Research from Fuller-Thomson et al. (2009) indicates that disparities in physical disability between black and white Americans are associated with differences in socioeconomic status (poverty and high school graduation); yet, even after adjusting for education level,

Geronimus et al. (2006) have hypothesized that the repeated exposure to chronic stressors attributable to racism and discrimination causes more rapid deterioration in the health of African Americans. Geronimus et al. (2006) called this rapid aging “weathering” and stated the following:

The measure Limitations in Activities of Daily Living in Adults 65 and Older meets the following LHI Phase 1 criteria: public health burden; relevance to addressing health disparities; and actionability.

Mental Disability (Cognitive Functioning)

The recommended objective statement for this LHI is: “Reduce the rate of mental disability.” This measure from the U.S. Census (“Because of a physical, mental, or emotional condition lasting 6 months or more, the person has difficulty learning, remembering or concentrating”) may be used as a general measure of cognitive disability in the population. Mental disability captures cognitive difficulties based on a wide range of conditions (e.g., genetic, traumatic, disease-related, psychiatric, dementia). Disaggregating its prevalence by different age cohorts allows the public and policy makers to understand the burden of disease and consider interventions to support individuals.

Cognitive impairment is a concern particularly as the population ages, and the committee did not find the three dementia-related core objectives adequate to consideration as an LHI.⁶ Specifically, one measure, from the Health and Retirement Survey (DIA-2030-02) is too clinically oriented, and the other two measures are too indirect.

Although it is a somewhat heterogeneous measure in that it combines having “physical, mental, or emotional condition lasting 6 months or more” that produces “difficulty in learning, remembering or concentrating” this measure has the advantage of having easily accessible data for the entire country. The mental disability measure is not a measure of mental health in the population; rather it is a measure of cognitive disability in the population (which would include dementia, a condition that has a draft core objective associated with it).

The mental disability measure meets all objective selection criteria. The measure is available from the U.S. Census, includes both baseline and regularly updated data points, and can be disaggregated by race and ethnicity and other demographic groups. The measure also meets LHI Phase 0 criteria—specifically, it is relevant to the Health and Well-Being Across the Lifespan top-level concept of the HP2030 Framework (and as noted, especially for shedding light on an important dimension of the well-being of older adults), and it also meets Phase 1 selection criteria (public health burden; sentinel; actionable).

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⁶ DIA-2030-01 Increase the proportion of adults aged 65 years and older with diagnosed Alzheimer’s disease and other dementias, or their caregiver, who are aware of the diagnosis; DIA-2030-02 Reduce the proportion of preventable hospitalizations in adults aged 65 years and older with diagnosed Alzheimer’s disease and other dementias; DIA-2030-03 Increase the proportion of adults aged 65 years and older with Subjective Cognitive Decline (SCD) who have discussed their confusion or memory loss with a health care professional.

Mental Health: Frequent Mental Distress

The recommended objective statement for this LHI is: “Reduce percentage of adults who reported their mental health was not good in 14 or more days in the past 30 days (i.e., frequent mental distress).” The committee recommends this measure from the BRFSS, which assesses answers to the question “Now, thinking about your mental health, which includes stress, depression, and problems with emotions, for how many days during the past 30 days was your mental health not good?” This measure attempts to capture a broad range of mental health issues and is used in multiple well-known measurement sets, including the County Health Rankings.

The eight draft HP2030 core objectives do not include any general measures of mental health; the frequent mental distress measure could offer a measure that provides a broad sense of mental health in the population, at each level of measurement. This LHI also meets all objective selection criteria: it is measurable and both baseline data and additional data points are available from the BRFSS; it is of national importance; a robust evidence base exists for addressing mental distress (e.g., effectiveness of legislation promoting mental health benefits and integration of behavioral and primary care) (see Berry et al., 2017; Friedman et al., 2017); and the objective can help address equity and disparities if the data are disaggregated by race and ethnicity, among other demographic categories. This measure also meets Phase 1 LHI criteria: public health burden, serving as a sentinel or bellwether (given worsening mental health in some populations), and actionability.

Immunization

The recommended objective statement for this LHI is: “Increase the proportion of 19–35-month-old children who are up to date on DTaP, MMR, polio, Hib, HepB; varicella, and pneumococcal conjugate vaccines.”

The draft core objectives for HP2030 include eight items that refer to vaccines and immunization, but none of those are sufficiently broad to warrant consideration for the LHIs. The committee recommends use of the measure “Children 19–35 months old who received recommended doses of diphtheria, tetanus and acellular pertussis (DTaP); measles, mumps and rubella (MMR); polio; Haemophilus influenzae type b (Hib); hepatitis B; varicella; and pneumococcal conjugate vaccines.” This measure was an HP2020 LHI and one of the measures tracked annually by America’s Health Rankings. Given its status as an HP2020 objective and LHI, it meets all relevant objective and LHI selection criteria, including being a potential bellwether for the status of acceptance of immunization

as a safe and effective public health intervention and a sentinel for an upward trend in the prevalence of vaccine-preventable diseases.

Physical Environment: EQI

The recommended objective statement for this LHI is: “Improve the Environmental Quality Index (EQI).”

The HP2030 draft objectives contain 15 core objectives under the Environmental Health (EH) topic, ranging from “Reduce the number of days people are exposed to unhealthy air” (EH-2030-01) to “Increase the number of states, territories, tribes, and the District of Columbia that monitor diseases or conditions that can be caused by exposure to arsenic poisoning” (EH-2030-15). The core objectives available are largely related to broad environmental exposure (e.g., water or air, or to specific chemical exposure [e.g., lead, bisphenol A, perchlorate]). Only one objective is broader—EH-2030-07 Reduce the amount of toxic pollutants released into the environment, and would be a reasonable secondary candidate for LHI should the following recommendation not succeed.

For HP2030, the committee sought to find a comprehensive measure of environmental quality across the nation that is also available at the local level. The committee recommends the EQI as such a measure, which has been developed by the Environmental Protection Agency (EPA) and integrates data for five domains: air, water, land, built, and sociodemographic environments (Messer et al., 2014). A summary of its domains and the measures used to produce the index and the data sources are provided in Box 4-1.

Currently the EPA Office of Research and Development, National Health and Environmental Effects Research Laboratory (NHEERL), hosts publicly available data for the EQI for years 2000–2005. The data are available at the county level through EPA’s Environmental Dataset Gateway.⁷ A dataset for years 2006–2010 is expected to be publicly available in 2020, and a census track level EQI is under development.

Local, state, and national public health agencies as well as public health and environmental researchers at the county level can reproduce the EQI by using publicly available data. The methods for constructing and reproducing the EQI are available in the EQI Technical Report, and additional context is provided in peer-reviewed literature (Lobdell et al., 2011). A deep and growing evidence base assessing the relationship between environmental exposures captured by the EQI and their effects on health—mortality (Jian et al., 2017a,b) and morbidity (Gray et al., 2018a,b;

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⁷ See https://edg.epa.gov/data/Public/ORD/NHEERL/EQI (accessed November 21, 2019).

Jagai et al., 2017; Rappazzo et al., 2015)—substantiates the index’s utility as a sentinel for population health.

The EQI meets core objective selection criteria because environmental exposures are an established public health risk with the potential for mass exposures. Additionally there are well-documented health disparities in environmental risk exposures exerting an undue burden on marginalized populations (Lobdell et al., 2016) that merit equity considerations (Brulle and Pellow, 2006). The EQI’s documented association with morbidity and mortality substantiates the index’s utility as a sentinel or bellwether measure that is actionable at the county, state, and national levels.

Physical Environment: Heat Vulnerability Index

The recommended objective statement for this LHI is: “Lower the Heat Vulnerability Index.”

The committee did not find any HP2030 draft core objectives that could be used to serve as a sentinel or bellwether related to climate change effects on health, but it was pleased to see the developmental objective “PREP-2030-D03 Increase the proportion of adults who are aware of their transportation support needs to evacuate in preparation of a hurricane, flood, or wildfire” under the “Preparedness” topic.

Because an LHI relevant to the health effects of climate change would be timely and important, the committee recommends the Heat Vulnerability Index (HVI) described by the Lancet Countdown on Health and Climate Change. The HVI can be derived from data in the Institute for Health Metrics and Evaluation Global Burden of Disease and is a measure of heat-related morbidity and mortality, including heat stress, cardiovascular disease, and renal stress. The committee first considered heat-related deaths, but ultimately concluded that the HVI may be more robust given its breadth.⁸ The index ranges from 0 to 100 and is calculated using the

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⁸ EPA’s technical documentation on heat-related deaths notes that “many deaths associated with extreme heat are not identified as such by the medical examiner and might not be correctly coded on the death certificate,” leading to underestimating the heat-related mortality (EPA, 2017).

⁹ See https://www.thelancet.com/cms/10.1016/S0140-6736(19)32596-6/attachment/7ec6fc0f-7b2f-4c9d-9c92-7cb88c875b59/mmc1.pdf (accessed November 25, 2019).

Broad overviews of health indicators related to climate change may be found in the federal government’s Global Change Research Program Climate Change Assessment and in Ebi et al. (2018).

The effects of climate change on human health have been documented extensively; interventions to mitigate them are evolving, and many are well supported. Examples include emerging research on urban heat islands and ways to “cool” them through urban forestry; novel road, parking lot, and roofing materials; and other strategies. Several states and localities—San Francisco, New York State, Wisconsin, New York City, Philadelphia, to name a few—have developed or adapted an HVI to measure and improve their response to heat emergencies (see, for example, Madrigano et al., 2015; Wisconsin DHS, 2017). The committee notes that the HVI can be used for preparedness. Areas with a high HVI could use that information to prepare for and to prevent some of the mortality and morbidity (by deploying cooling centers, checking on isolated elders, etc.). The HVI’s geography (urban) and demographics (aging populations) dimensions are actionable in the sense that they inform planning and preparation

The Lancet HVI meets objective selection criteria—it is a composite of several measurable items, and both baseline data and additional data points are available at the national level, and in many cases at the state, county, or local level. The measure also meets Phase 1 LHI selection criteria—the public health burden of climate change-associated health conditions has been growing, disparities are aligned with typical vulnerabilities (e.g., demographics, especially older age and along lines of race and ethnicity), and they typically layer over other disadvantages, such as economic and housing instability and poverty.

Health Care Quality: Discharges for Ambulatory Care-Sensitive Conditions

The recommended objective statement for this LHI is: “Reduce the discharges for ambulatory care-sensitive conditions per 1,000 Medicare enrollees (CMS-2).”

Over the past 25 years, ambulatory care-sensitive conditions (ACSCs) have been used at state, national, and international levels to track hospital discharges for illnesses that can be prevented through access to high-quality primary care. The committee recommends ACSCs as an LHI because of their ability to provide a picture of both access to and quality of ambulatory care delivery across the nation and regionally.¹⁰ Hospitalizations

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¹⁰ See, for example, the Dartmouth Atlas, which allows users to view Medicare data on each Prevention Quality Indicator (PQI; see AHRQ, 2001) by state and different demographic categories.

for chronic conditions that could have been treated in outpatient settings mean that people experience unnecessary severity of illness, and health systems generate unnecessary cost. A study using data from the Medical Expenditure Panel Survey (2005–2010) reported that charges for ACSCs were four times higher when treated in inpatient rather than outpatient settings (Galarraga et al., 2015).

In 2011 ACSCs accounted for 10 percent of all hospitalizations and 5.8 percent of Medicaid inpatient stays (Stranges and Stocks, 2008). A large body of research has demonstrated that ACSC hospitalizations vary by income and by minority status, with higher rates of hospitalizations seen in African American and low-income populations (Billings et al., 1996; O’Neil et al., 2010). This disparity has raised questions about access to care (affordability and physical access) and differentials in quality of care provided. In reviewing the impact of Medicaid expansions under the Patient Protection and Affordable Care Act, Wen et al. (2019) report meaningful reductions in these hospitalizations.

The Agency for Healthcare Research and Quality (AHRQ) has selected 16 prevention quality indicators (PQIs) of ACSCs (AHRQ, 2001) culled from larger sets that are reliable and valid indicators that can be tracked through administrative data and are available at regional levels. These include conditions such as bacterial pneumonia, hypertension, dehydration, adult and pediatric asthma, urinary tract infections, chronic obstructive pulmonary disease, perforated appendix, and short and long-term complications of diabetes. The Centers for Medicare & Medicaid Services (CMS) has developed two composite measures based on Medicare data: an acute conditions composite (CMS-1) and a chronic conditions composite (CMS-2) (CMS, 2015). CMS-2 could be used as the LHI for ACSCs, revealing crucial insights about the functioning of the health care delivery system and about unnecessary costs that compound the overarching problem of out-of-control health care costs. Medicaid data could also be mined for ACSCs, as those data are likely available at state and national levels for younger (non-Medicare) populations, where avoidable hospitalizations are particularly problematic.

The composite ACSC measure CMS-2 meets the objective and LHI (Phase 1) selection criteria.

Social Environment: Civic Engagement—Voter Participation

The recommended objective statement for this LHI is: “Increase the proportion of voting-eligible population who votes.”

The committee believes that because dimensions of social capital (i.e., civic engagement and social cohesion) have implications for health, it is important to consider them in composing the set of LHIs. There is a large

literature on social capital, some of which has been previously reviewed as part of the Healthy People work.¹¹ Committee members considered several possible measures of social capital and came to agree with those who have incorporated a measure of civic engagement in indicator sets—that people’s interest in creating and sustaining a vibrant polity and social fabric appears to be associated with other measures of well-being. Although some measures of social capital may not be sufficiently robust, whether due to a not yet adequate evidence base or other issues, such as difficulties measuring in subpopulation or other data challenges, voting participation appears to be a reasonably strong measure (see, for example, NRC, 2014).

The Well-being in the Nation measure set lists “total votes cast in the most recent midterm or presidential election as share of total voting-age citizens.” The Robert Wood Johnson Foundation (RWJF) Culture of Health measures also include voting participation, as does America’s Health Rankings, and the Department of Housing and Urban Development (HUD) Healthy Communities Index, which uses “proportion of voting-eligible population who voted in last election.”

Voting is a way to operationalize social capital. Murayama et al. (2012) conducted a review that found “both individual social capital and area/workplace social capital had positive effects on health outcomes,” but they noted that more research was needed to show how building social capital can improve health (Murayama et al., 2012). Evidence of the relationship between voting and health indicates it is bi-directional, and historically, there has been more research showing health status affects voting behavior (Blakely et al., 2001; Denny and Doyle, 2007; Reichel, 2018). Some evidence of the positive effects that voting participation has on health has been emerging in recent years. Research from Klar and Kasser (2009)—cited in support of the RWJF voting participation measure—had shown that “several indicators of activism were positively associated with measures of hedonic, eudaimonic, and social well-being” (Klar and Kasser, 2009). A study by Ballard et al. (2019) shows a positive association between voting and volunteerism behaviors in adolescence and positive health effects in later life, including better mental health and lower likelihood to engage in unhealthy behavior. Panel studies using Canada’s General Social Survey to assess the effect of three dimensions of social capital (social networks and social support, civic participation, and social participation) on self-rated health have shown causation in both directions (Habibov and Weaver, 2014). Two additional studies that examined the relationship between

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¹¹ See https://www.healthypeople.gov/2020/topics-objectives/topic/social-determinantshealth/interventions-resources/social-cohesion (accessed December 9, 2019) and also https://www.healthypeople.gov/2020/topics-objectives/topic/social-determinants-health/interventions-resources/civic-participation (accessed December 9, 2019).

Medicaid expansion and voter participation have shown at least a temporary positive relationship (Clinton and Sances, 2018; Haselswerdt, 2017).

The proposed voter participation LHI meets all objective selection criteria: it is measurable and has both baseline and additional data points (U.S. Census Current Population Survey; see also NRC, 2014), the evidence base for it is fairly strong and growing, and it has considerable bearing on health equity and disparities given the robust understanding of what shapes structural inequities (NASEM, 2017). Voter participation is a measure of national importance for several reasons.¹² Voting is a reflection of social capital, and the relationship between social capital and health is well established (Ehsan, 2019; Murayama et al., 2012). This is noteworthy, along with the growing evidence linking voting with health. It is, however, important to view voting participation in its broader context, which includes an understanding of voting administrative burdens and potential voter suppression, along with disenfranchisement experienced by people with involvement in the criminal justice system, and individuals experiencing housing instability. These factors demonstrate a key weakness of voting participation—the variation in the denominator from one state to another, depending on the array of laws, policies, and practices that shape voting behavior and voting access.

Equity: Neighborhood Disinvestment Index

The recommended objective statement for this LHI is: “Lower the Neighborhood Disinvestment Index.”

The index is a measure pertinent to structural inequity. It is a measure included in the Prevention Institute health equity measures, and it includes such standardized data collection of the following:

1. Percent of residents in poverty
2. Percent of (male) unemployed residents
3. Percent home ownership (or some other measure of residential stability such as average length of current residence)
4. Percent single parent/single income households
5. Percent of residents with low educational attainment (and/or the reverse, percent residents with college degrees)
6. Percent of residents in management/professional occupations (sometimes the age structure and/or the racial/ethnic composition of the neighborhood is also included)

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¹² Described in the Objective Selection Criteria for Core Objectives as having “a direct impact or influence on health, broad and comprehensive applicability, a substantial burden,” and “address a national health priority.”

Poverty, low educational attainment, and the lack of opportunity are powerful influences on health and well-being (see, for example, Chetty and Hendren, 2017; Chetty et al., 2016; NASEM, 2017, 2019a,b), and the committee underscores the importance of monitoring poverty as part of its efforts to improve population health by addressing the social and structural factors that shape health and equity.

The Neighborhood Disinvestment Index meets the criteria for core objective selection. It is measurable, constructed from existing U.S. Census data for which both baseline and additional data points are available; it is of national importance (health impact, broad applicability, substantial burden, and a national public health priority); it is based on rigorous evidence for each component of the index (see, for example, Chetty and Hendren, 2017; Izenberg et al., 2018; Pickett and Pearl, 2001; Sampson et al., 2002); and it is highly relevant to health equity and disparities.

Equity: Residential Segregation (Two Measures)

The recommended objective statements for this LHI are: “Reduce the level of residential segregation captured by the Index of Dissimilarity; and Reduce the level of residential segregation captured by the Isolation Index.”

As noted earlier, the draft core objectives lack measures of equity that shed light on the causes of health disparities (i.e., structural inequities). The committee recommends the LHI racial and ethnic residential segregation, drawing two measures that can be easily calculated with data collected by the U.S. Census, the Index of Dissimilarity, and the Isolation Index (Iceland et al., 2002; Massey and Denton, 1993; Weinberg et al., 2002). Segregation has been considered a key determinant of racial/ethnic inequities in health (Gee and Payne-Sturges, 2004; Mehra et al., 2017; Williams and Collins, 2001).

The committee recommends two LHIs that capture racial and ethnic residential segregation, the Index of Dissimilarity and the Isolation Index. Both are commonly used measures in the literature, they capture two different dimensions of residential segregation (Massey and Denton, 1993), and some studies show that they do not have equivalent associations with health outcomes or health risk factors (Goodman et al., 2018; Kramer and Hogue, 2009; Nobles et al., 2017).

The Index of Dissimilarity and the Isolation Index can be calculated using the data from the U.S. Census. The former measures how evenly racial and ethnic groups are distributed across geographic areas, whereas the latter captures the degree to which one racial/ethnic group might have contact with other racial or ethnic groups. Dissimilarity has been linked to the unequal distribution of local and municipal resources, as well as exposure to environmental toxins (LaVeist et al., 2011; Morello-Frosh and Jesdale, 2006;

The Annie E. Casey Foundation 2019). Isolation has been linked to problems such as restricted access to health care facilities and to transmission of infectious diseases (Acevedo-Garcia, 2000; Dai, 2010). For example, Hayanga et al. (2009) found that in counties with high rates of isolation segregation were related to lower access to surgical services among minority populations, compared to counties with less segregation. The 2019 County Health Rankings report shows that higher levels of residential segregation are associated with higher rates of severe housing cost burden for households headed both by whites and by blacks, demonstrating that inequity can have widespread effects (County Health Rankings and Roadmaps, 2019).

It is increasingly recognized by multiple sectors that racial residential segregation is an important determinant of social, economic, and health-related well-being (County Health Rankings & Roadmaps Program, 2019; HUD, 2015; Landrine et al., 2017). Chetty et al. (2019) have found that neighborhood-level segregation affects economic opportunity. Segregation has been considered a key determinant of racial and ethnic inequities in health (Gee and Payne-Sturges, 2007; Mehra et al., 2017; Williams and Collins, 1999). For example, data from the Coronary Artery Risk Development in Young Adults (CARDIA) study showed that changes in segregation were associated with changes in systolic blood pressure over 25 years (Kershaw et al., 2017).

The committee discussed both community or structural and individual-level measures of discrimination, like the Everyday Discrimination Scale, or the BRFSS Reactions to Race measure that is included in the Well-being in the Nation measure set (100 Million Healthier Lives and NCVHS, 2019), but ultimately concluded that a structural and/or community-level measure would be more useful as an LHI.

The measures of residential segregation meet core objective selection criteria: measurable, with baseline data and two additional data points; of national importance, with robust evidence on the relationship between segregation and well-being and health; and of great relevance to addressing disparities and improving health equity. The measures also meet LHI selection criteria (measurable; both baseline and other data points are available from the U.S. Census; there is robust evidence that mitigating residential segregation can lead to improved well-being and thus health [Chetty and Hendren, 2017]; and it is actionable).

DISCUSSION OF LHI OBJECTIVES PARTLY FOUND AMONG THE HP2030 DRAFT CORE OBJECTIVES

The following three topics and corresponding LHI contenders were found among the HP2030 draft core objectives but deserve additional discussion. These are the objectives on tobacco, poverty, and education.

Adolescent Use of Tobacco Products

The recommended objective statement for this LHI is: “Reduce current use of any tobacco products among adolescents (TU-2030-03).” The committee would clarify the meaning of the term “tobacco products.” If the objective is intended to include electronic nicotine delivery systems, the committee agrees that it is adequate to serve as a Leading Health Indicator. Both CDC (in BRFSS) and the Food and Drug Administration (in the National Youth Tobacco Survey) use the term “tobacco products” to refer to all types of cigarettes, including electronic, along with other products (such as chewing tobacco).

Poverty

The recommended objective statement for this LHI is: “Reduce the proportion of persons living in poverty (SDOH-2030-03).”

The committee recommends disaggregating this by age group, with particular attention to child poverty. The committee notes, as did the SAC, that child poverty is essential to track because of the implications it has for health and well-being in later life (see, for example, Chetty et al., 2016). The core objective SDOH-2030-03 “Reduce the proportion of persons living in poverty” meets core objective selection criteria, and so does child poverty, but the committee would like to underscore the robust evidence base that links child poverty with a range of poor health outcomes, extensively discussed and documented in two recent National Academies reports on child health and well-being (NASEM, 2019b) and on child poverty (NASEM, 2019a). The 2019 report A Roadmap to Reducing Child Poverty found that “many programs that alleviate poverty—either directly, by providing income transfers, or indirectly, by providing food, housing, or medical care—have been shown to improve child well-being” (NASEM, 2019a). The same report listed the Earned Income Tax Credit Program and improvements in child educational and health outcomes; the Supplemental Nutrition Assistance Program (SNAP) and improved “birth outcomes as well as many important child and adult health outcomes”; and “expansions of public health insurance for pregnant women, infants, and children” that “have led to substantial improvements in child and adult health, educational attainment, employment, and earnings” (NASEM, 2019a).

Social Environment: Education

The recommended objective statement for this LHI is: “Improve fourth grade reading level (AH-2030-04).” The committee believes that including an LHI on education is crucial given the extensive evidence on

the relationship between educational attainment and health status in later life (NASEM, 2019b; NRC and IOM, 2013; Woolf et al., 2007). Although educational attainment is a protective factor and has a positive relationship with health, the magnitude of the benefit reaped by different sociodemographic groups is disparate/unequal. The committee recommends as an LHI related to education “Increase the proportion of fourth grade students whose reading skills are at or above the proficient achievement level for their grade” (AH-2030-04 in the draft HP2030 core objectives). This measure is supported by evidence indicating that children who are not proficient in reading at the start of fourth grade face a greater likelihood of high school dropout and associated lower earning potential (Hernandez, 2012), and school dropout is associated with poorer social and health outcomes (Lansford et al., 2016).¹³ The committee would have preferred an early childhood education measure, but as discussed below, the measure it sought is not available at this time.

CONSIDERATIONS FOR POTENTIAL DEVELOPMENTAL OBJECTIVES

A Developmental Objective for Early Childhood Care and Education

The HP2030 draft core objectives contain several good options: fourth grade reading proficiency, chronic school absence, and on-time high school graduation. (It is important to note that these measures reflect system-level failure or success and should not be interpreted as simply measures of individual effort or capability.) Although the committee selected fourth grade reading proficiency as an LHI because of its importance to academic achievement later in life (The Annie E. Casey Foundation, 2013), the committee would have preferred a measure for tracking earlier-life educational experiences, such as access to quality early childhood care and education. Quality early childhood care and education are key to school success (NASEM, 2019b), but the committee was unable to find an adequate measure, apart from the measure “3- and 4-year olds in school” as an LHI as the next best thing.

The Administration for Children and Families (ACF) in HHS has several relevant resources. Its report Defining and Measuring Access to High-Quality Early Care and Education: A Guidebook for Policymakers and Researchers discusses measurement, but it does not provide a national-level measure of early childhood care and education quality. The Quality Rating and Improvement System (QRIS) approach developed by ACF offers states

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¹³ Note that the literature includes measures of third as well as fourth grade reading proficiency.

guidance, resources, and some funding to implement QRISs for early- and school-age care and education programs. Although many states have QRISs, there is no national system of measurement, and it is up to the states how they implement the standards (NASEM, 2019b). Validation of the QRIS approach is mixed and it is largely a point of entry to promote high quality programming and is not broadly or uniformly adopted by states (NASEM, 2019b). Moreover, “[t]he difference in [QRIS] system designs across states make it difficult to draw general conclusions from these validation studies about their links to various domains of children’s development, especially health. The voluntary nature of QRISs in most states and the varying standards also make it difficult to establish a causal link between them and child outcomes” (p. 392). From the standpoint of equity, although states could and many do rate early childhood education programs (public or private), there is also a concern about ratings potentially victim-blaming under-resourced school systems or communities.

The Annie E. Casey Foundation, which produces the annual Kids Count Data Book, includes the following four education measures: young children ages 3 and 4 not in school, fourth graders not proficient in reading, eighth graders not proficient in math, and high school students not graduating on time. Although the discussion of the first measure (enrollment in early childhood education) acknowledges the importance of high quality programs, the measure itself is merely a measure of access, not access to quality services (The Annie E. Casey Foundation, 2019).

The Kids Count Data Book states the following:

The Data Book reports that during 2015–2017, 52 percent of U.S. children ages 3 and 4 (4.2M) were not in school (The Annie E. Casey Foundation, 2019). The committee found that although (1) there are measures of access to preschool (or pre-kindergarten, or early childhood care and education program), (2) there is evidence that quality early childhood care and education are crucial, and (3) there are no measures of access to quality early childhood care and education. In the absence of a measure of access to quality early childhood care and education—an important predictor of educational and other outcomes, the committee made the following finding:

Costs and Investments in Public Health and Health Care

The Cost of Health Care

The committee was in full agreement that the high cost together with the poor distribution of medical care—where some families and individuals lack access to needed care—is a significant impediment on the economic stability of Americans and the national economy (Auerbach and Kellermann, 2011; Baicker, 2001; Orszag and Kane, 2003). Research indicates that states offset the Medicaid increases with less support for other programs targeting lower-income residents, and health care cost growth harms the economy with repercussions for American families. There is an opportunity cost of foregoing social goods that have been shown to be greater correlates of population health (higher education), as those social goods have become increasingly unaffordable because of health care budgets—Orszag and Kane (2003) have shown that rising Medicaid costs negatively affect state university system funding.

At the same time that health care costs continue to rise, many members of the public continue to be uninsured or underinsured, thereby making needed medical care inaccessible. Per capita health care costs in the United States are far greater than those of peer nations, and yet U.S. health outcomes remain disappointing when compared internationally (NRC and IOM, 2013; OECD, 2017). Accordingly, the committee initially considered setting targets for reductions in per capita health care spending as an LHI. However, since aggregate spending measures are minimally informative about the extent of overtreatment (spending too much without health benefits), undertreatment (spending too little for effective care), or excess prices relative to other high-income countries, the committee concluded that tracking per capita expenditures alone would not be sufficient.

Instead of recommending an LHI of per capita or percent GDP health care expenditures, the committee discussed tracking the cost of a market basket of widely used pharmaceuticals in comparison to their cost in other affluent nations as a next best measure relevant to health care spending. There is evidence that the high price of U.S. pharmaceuticals has led many patients to reduce their use of potentially life-saving drugs (Borrescio-Higa, 2015); this in turn is likely to adversely affect health. Thus, the committee viewed as desirable the objective of tracking the cost of U.S. pharmaceuticals in comparison to other high-income nations,

where greater alignment with these countries is viewed as a positive development. The high price of U.S. pharmaceuticals has rendered them unaffordable to many individuals with chronic and life-threating diseases (Cohen et al., 2019) and continues to take a toll on people’s health. Tracking the cost of U.S. pharmaceuticals in an international perspective is aligned with the HHS objective to make U.S. pharmaceuticals comparably priced to other nations, so

The 2018 HHS study Comparison of U.S. and International Prices for Top Medicare Part B Drugs by Total Expenditures highlights current thinking in the department about the rationale for addressing the fact that costs for top drugs under Medicare Part B are 1.8 times higher than those of other countries (ASPE, 2018).¹⁴

Administrative Costs

This is another subset of health care spending that warrants attention. Considerable research has repeatedly indicated that a large proportion of health care spending, perhaps as much as 30 percent, is wasted (IOM, 2006), and administrative costs are one of the drivers (Anderson et al., 2019). A Commonwealth Fund study found that in the United States, the 2015 cost of health insurance administration was $787, compared to just under the $100 OECD median, and $89 for Sweden, $90 for the United Kingdom, and $141 for Canada (Tikkanen, 2018). The United States does not measure this routinely, possibly because there is no consensus on a measure that would be adequate to the task. Given the evidence of a stunning difference in administrative (and overall) spending between the United States and peer nations, and juxtaposed with the nation’s lackluster health performance, the committee believes that an actionable administrative cost measure that is not linked with any political agenda could be a useful and compelling indicator. Therefore the committee made the following finding:

The committee finds that a developmental objective on health care system administrative cost could provide a concrete proxy for health

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¹⁴ See https://aspe.hhs.gov/pdf-report/comparison-us-and-international-prices-top-medicare-part-b-drugs-total-expenditures (accessed December 20, 2019).

care spending more broadly, and could offer compelling and easier to communicate information to a range of audiences.

Public Health Spending

The HP2030 core draft objectives contain seven items under the topic Public Health Infrastructure (and an additional seven developmental objectives). The committee did not find any of the draft objectives well suited to serving as an LHI either because they are narrow or because—although accreditation and its effects on quality are important—insufficient evidence is available to link aspects of public health system quality with health outcomes. Measures of public health accreditation are important, but they may be solely indicative of public health system ability to perform at a higher level (Riley et al., 2012).

Public health expenditure as a proportion of total health spending has declined to levels not seen since the Great Depression (Himmelstein and Woolhandler, 2016). In 2017, the United States spent $88.9 billion on government public health activities out of $3.49 trillion (CMS.gov, 2018; Martin et al., 2019). Public health expenditure currently accounts for less than 5 percent of U.S. spending (Sensenig, 2007; TFAH, 2006), and it is projected to decline to 2.4 percent by 2023 (Himmelstein and Woolhandler, 2016). The decrease in funding seems unjustified as the evidence of positive returns on investments in public health continues to accumulate (Brown, 2016; Mays and Smith, 2011; McCullough, 2019). Internationally, a systematic review of the return on investment on public health interventions in high-income countries, which included 52 peer-reviewed studies, estimated that the overall median return on investment (ROI) for public health interventions is 14.3 to 1 (Masters et al., 2017). Stratifying the interventions by geographic location showed that for local public health interventions the median ROI was 4.1 to 1, and for nationwide public health interventions, the median ROI was 27.2 to 1. The conclusion was that local and national public health interventions are highly cost-saving and that therefore dis-investments in public health interventions are a “false economy” because these decrements induce additional costs to health care services and the economy in general. In terms of morbidity, research has shown that public health investments yield results for specific outcomes (Singh, 2014). Other research has found that public health staffing and the provision of maternal and child services have a positive effect on infant health (Schenck et al., 2015), and state-level public health spending is associated with lower rates of certain vaccine-preventable diseases in subsequent years (Verma et al., 2017). In terms of mortality, studies have documented a decrease in preventable deaths (infant mortality, cardiovascular disease deaths, diabetes, and cancer) (Mays and Smith, 2011) and in all-cause mortality (Leider

et al., 2018). The hypothesized/plausible mediating mechanism, that is, the link between improved funding and better population health outcomes, seems to be the improved organizational capacity of public health agencies to improve internal processes and performance, which ultimately influence outcomes (Handler et al., 2001; Meyer et al., 2012; Scutchfield et al., 2009). Mays and Mamaril (2017) also found that public health expenditures offset Medicare expenditures.

The committee believes that public health spending is a more promising measure than other indicators related to access to and quality of public health services, but the wide variation in how public health funding is allocated, distributed, and reported limits the reliability of the data somewhat. Public health funding data include administrative data available from the CMS Office of the Actuary in the U.S. Census Bureau’s Census of State Governments and survey data from the Association of State and Territorial Health Officials’ Profile of State and Territorial Public Health. The measure also meets LHI Phase 1 selection criteria: addresses a public health burden, may be a sentinel given association between drops in public health funding and health status, and is actionable (see, for example, IOM, 2012). Therefore the committee made the following finding:

Comparison to HP2020 LHIs

The committee compared the recommended set of HP2030 LHIs to HP2020 LHIs and found both a great deal of congruence and some differences that highlight the newly proposed set’s alignment with the components of the HP2030 Framework and in particular the attention to well-being, health equity, and the role of partners in non-health sectors (see Table 4-1). These differences have yielded an LHI set that is distributed across several more topics (general health/well-being/health-related quality of life; determinants of health equity; more social determinants, including a separate topic for social capital and specifically the civic engagement domain). The topics in the left column of Table 4-2 are provided in alphabetical order (per the HHS HP2020 website), and the topics on the right were slotted in partially alphabetical order, unless it was not possible to do so, such as in the case of topics closely related to HP2020 topics.

TABLE 4-2 Side-by-Side Comparison of Healthy People 2020 Leading Health Indicators and Recommended Healthy People 2030 Leading Health Indicators

TABLE 4-2 Continued

TABLE 4-2 Continued

NOTE: DTaP = diphtheria; Hep B = hepatitis B; Hib = haemophilus influenzae type B; MMR = measles, mumps, rubella.

SOURCE: HealthyPeople.gov, 2019.

APPLYING PHASE 2 OF THE LHI SELECTION CRITERIA TO THE PROPOSED LHI SET

The final question the committee must answer is how does the set of 34 LHIs compare to the Phase 2 criteria defined by the SAC:

• The LHIs represent a balanced portfolio or cohesive set of indicators of health and well-being across the lifespan.
• The LHIs are balanced between common, upstream root causes of poor health and well-being and measures of high-priority health states.
• The LHIs are amenable to policy, environmental, and systems interventions at the local, state, tribal, and national levels.
• The LHIs are understandable and will resonate with diverse stakeholders to drive action.

The 34 measures in the set include both measures of health and measures of well-being. For example, there are several measures of health outcomes (e.g., cancer death rate), measures of self-rated health and disability, and measures of mental distress, but there are also measures of child and adult well-being (prevalence of ACEs, the Cantril Scale). The set of LHIs includes measures that apply to different age groups, from infants and children, to older adults.

The proposed LHIs also include a combination of upstream root causes (e.g., poverty, residential segregation, educational attainment) and of high-priority health states (e.g., overdose deaths, rate of hypertension). The set of LHIs may be mapped to all categories of interventions and one or more geographic levels. The tobacco use LHI tracks the effect of tobacco policies implemented at the national and state levels (e.g., raising the minimum age for purchase to 21 years of age, implementing regulatory strategies to address youth vaping) (FDA, 2019; IOM, 2015a). The topic residential segregation has two LHIs, measures that may be tracked at the neighborhood, city, and county level (Logan and Parman, 2014). The HVI proposed to track climate change-associated health effects may be measured at the national, state, and local level, and is amenable to a variety of environmental interventions, including approaches for cooling urban heat islands (Harlan and Ruddell, 2011; Rotzer et al., 2019). Finally, a number of the LHIs may be used for international comparisons. These include life expectancy, the measures of self-rated health and well-being, and the HVI.

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