Healthy individuals form the foundation of strong and healthy families. Individual chronic health problems or other serious health concerns can constrain not only a person’s time, energy, and resources, but also those of the whole household. Good and stable health helps people surmount life’s challenges, excel in school and on the job, and fully participate in their communities. Conversely, poor health and debilitating health conditions rob individuals of a sense of control over their lives and potentially of the dignity that comes from being able to fully participate in society.
To see more information about these predictors, see our updated report, Boosting Upward Mobility: Metrics to Inform Local Action, Second Edition. For more insight into the decisions behind updates to the predictors and metrics, see that paper’s accompanying Technical Report.
Research shows that self-rated health provides an accurate measure of a person's overall health, and it is a strong predictor of later-life outcomes. Good health helps people surmount life’s challenges and excel in school and on the job. When people’s health is compromised, their overall well-being and their personal autonomy are compromised.
Metric: Share of adults who rate their own and their children’s health as good or excellent
This metric is measured through one question that asks “How would you rate your health?” and has respondents answer along a five-point Likert scale of “very poor,” “poor,” “fair,” “good,” and “excellent.” The share of people who respond “good” or “excellent” constitutes this metric.
Validity: Asking people to rate their own health provides one of the most reliable measures of food security and other health problems (including mortality) and remains a significant predictor even after controlling for other demonstrated health-related issues and socioeconomic status. Though the research focuses on mortality rather than mobility, we argue that good health is a condition that supports autonomy and promotes mobility.
Availability: This information is not available widely enough in existing data sources to provide coverage at the local level across many geographies. Data may be available for limited geographies through the Centers for Disease Control and Prevention’s Behavioral Risk Factor Surveillance System.
Frequency: The frequency of how often data for the metric would be collected would depend upon local data collection efforts, but we recommend regular follow-up data collection at least every two years.
Geography: The level of geography that the metric would represent (e.g., county, city, or zip code) would depend on the sampling frame, stratification, and the number of people ultimately surveyed to obtain sufficient power for the survey.
Consistency: Self-rated health can be consistently defined and determined over time. The degree of consistency in this metric across different places will vary with the extensiveness of the survey design and number of people surveyed in each place.
Subgroups: Like geography, the range of subgroups represented and the ability to compare subgroups (e.g., by age, race or ethnicity, and gender) would depend on the sampling frame, stratification, and the number of people surveyed.
Limitations: A primary limitation is that these data will need to be collected directly by communities. Communities will need to identify through which vehicles data can be gathered. Further, this metric can be sensitive to residential mobility if the same people cannot be followed over time. A community transitioning out residents of poorer health in favor of those who are healthier may appear to be improving the overall health of the community. Therefore, it is important to collect these data along with demographic characteristics to ensure improved health is felt by people of all races and socioeconomic backgrounds.
Access to Health Services
Access to and utilization of health services can help ensure children receive basic care through critical formative years and obtain needed prescriptions and that adults can obtain early screenings for diseases so as to enhance the likelihood of effective treatment. A lack of regular medical care can compromise one’s short- and long-term health and have negative effects on later life outcomes. Access to and utilization of health services leads to improved physical health, which promotes power and autonomy
Metric: Health professional shortage area (HPSA) ranking for primary care providers
This metric denotes whether a geography has a shortage of primary care providers based on four elements: weighted population-to-provider ratio, share of individuals with incomes below 100 percent of the federal poverty level, infant health index, and travel time or distance to the nearest source of nondesignated accessible care. The Division of Policy and Shortage Designation through the US Department of Health and Human Services calculates a score between 0 to 25 for primary care HPSAs; the higher the score, the greater the shortage.
Validity: This metric is defined and established by the US Department of Health and Human Services.
Availability: Data for this metric are nationally available, are identified at the facility level, and can be aggregated by state and county through the US Department of Health and Human Services.
Frequency: Data are updated daily. Geographies and facilities are evaluated for their shortage status at the facility’s request, but all have been evaluated at some time in the past four years.
Geography: HPSAs are designated as a geography, population, or facility. We focus on the geography for the purpose of getting a county-level HPSA flag. HPSAs are available by state, county, or service area. Although data are not available at the neighborhood level, facility addresses can be geocoded and therefore could be used to derive a value for the neighborhood. Data are available for urban and rural areas.
Consistency: HPSA status can be measured the same way over time and across geographies.
Subgroups: This metric can be disaggregated by low-income population and by low-income members of the following groups: migrant farm or seasonal workers, Medicaid-eligible individuals, people experiencing homelessness, and Native Americans.
Limitations: Even though HPSA status is a characteristic of the local area, some elements used to determine the designation are based on the characteristics of the local population. As such, changing residential mobility patterns may influence this metric. Because of data quality limitations at the geographic level, we present the result in the HPSA score (0 to 25) as either a yes (1) or no (0) value.
Strong evidence demonstrates that neonatal health has lasting effects throughout the life course. Poor childhood health has short-term effects on educational attainment and long-term negative effects on adult physical health and mental health, which in turn can affect employment opportunities and wages.
Metric: Share of low-weight births
A child born weighing less than 5 pounds 8 ounces (about 2,500 grams) is considered to have a low birth weight. Children with low birth weight are at elevated risk for health conditions and infant mortality. This metric looks at the share of low birth weight babies out of all births.
Validity: This metric is the standard currently used by the Centers for Disease Control and Prevention (CDC) as part of its national assessment on health among infants.
Availability: Data on the share of children born with low birth weights are nationally available through the CDC’s National Center for Health Statistics, Division of Vital Statistics.
Frequency: New data for the metric are available annually.
Geography: County-level estimates are available through public-use microdata files provided by the National Center for Health Statistics as well as through other data collection efforts, such as the Kids Count Data Center or the CDC WONDER system.
Consistency: Medical advances have improved the outcomes for low birth weight babies, so this metric may change in the future. However, it has been consistently used for decades as a metric for neonatal health.
Subgroups: The share of children born with low birth weights can be disaggregated by race or ethnicity and mother’s age.
Limitations: Data are not readily available at lower levels of geography, such as neighborhoods, where disparities by race and socioeconomic status within a city are most notable. Large numbers of women with risky pregnancies moving in or out of a jurisdiction could influence this metric. Counties with populations under 100,000 persons based on the decennial census are pooled into “Unidentified Counties” in the CDC WONDER data.