Appendix
More on public sector spending data
Using public finance data collected by the Census Bureau, we have shown a positive relationship between health spending and health outcomes, after accounting for other types of spending and socioeconomic factors.24 In our view, these data are best positioned for inclusion in a national rankings system, though some limitations do exist. These are discussed at length in the body of the manuscript.
Researchers and analysts have turned to local level analyses to assess the questions of cost-benefit.11,12,17 Several financial data sources have been compiled by the National Association of County City Health Officials, the Institute for Health Metrics and Evaluation, and our group.24-26
Lagging Spending Data for Analyses
The effects of local government spending for public health and social services would be expected to accrue over a period of years. Previous research studies have employed varying time lags of one to five years.12,17,18,24 One longitudinal study examined the cumulative impact of public health spending and found that effects were observable for approximately ten years after the initial investment, although some 80% of total impacts occurred in the first four years.11 Thus from an empirical perspective, a lag of anywhere from 1-10 years may be justifiable, though use of a 4-year lag between when an expenditure is made and when impacts are expected to be felt appears to have strong empirical basis.
It is also important to consider the policy implications of lag selection for models and ranking efforts.
Specifically, if elected officials are held accountable for demonstrating the value of the investments and initiatives they undertake during their elected terms, use of a lag of ten or more years may make demonstrating value impossible for officials who are elected to two- or four-year terms. Therefore, it may also be important to consider the use of a very-short lag for models, say 1-year. A 1-year lag specification is also especially relevant to consider for inclusion in any rankings undertaking since the goal of the health factors ranking is to measure (and rank) current characteristics, capabilities, and resources in a county that relate to health behaviors, clinical care, social and economic factors, and the physical environment. A county’s health factors ranking for 2018, for example, uses adult smoking rates as of 2016 even though the full population health impact of tobacco use may not manifest until years or decades in the future. Likewise, the impacts of public health and social services spending may not manifest until years or decades in the future, but the goal of a ranking of counties in terms of their health factors seeks to shine light on (relatively) current conditions. Therefore, a 1-year lag has strong conceptual and policy basis for consideration. This analysis used a 1-year lag as the default base case for analyses though selected models using 4-year lags are also shown.
Selecting Spending Types for Inclusion in Analyses
How spending is measured can also be important to determine. Many studies have measured spending on a per person or per capita basis. This has advantages of being relatively simple to calculate (all that is needed is total spending and total population) and easily communicable. Less commonly used but still highly relevant are measures of spending relative to total spending, so the percentage of total spending that goes towards a given service. This has advantages of being more readily comparable across
jurisdictions with differing total expenditures (e.g., high tax versus low tax areas) and may help to alleviate concerns about economies of scale for large versus small jurisdictions. At the same time, this
concept requires data on total government expenditures, which is not always readily available, and may or may not be easily communicable to all audiences.
For the purposes of our analysis, “Total” refers to the aggregate of public health, Social Services, and Education expenditures—not the grand total of all government spending. Social Services were further subcategorized into the following: parks and recreation, natural resources, solid waste management, sewerage, fire and ambulance, protective inspections, public welfare, libraries, transportation, housing, and development expenditures. Education was subcategorized into K-12 and higher education spending.
Selection of the most relevant type(s) of spending is important both conceptually and statistically. We calculated Pearson correlation coefficients as shown in the appendix (Table A1) and found that potentially-relevant spending categories are heterogeneously correlated, sometimes in opposite directions. For some variables (e.g., social services and education) the per capita version of the variable is not highly correlated with the percentage of total expenditures version (|r| < 0.35 for each), whereas for public health it is (r > 0.9). Education is also such a large proportion of most local governments’
budgets that it is very highly correlated with the total spending examined in this analysis. In short, selection of which spending categories to consider and how to measure that spending does matter for any effort that seeks to rank areas based on spending.
Individual spending varies and was driven in large part by hypothesized conceptual relevance to the county health rankings framework. Given the County Health Ranking’s existing measures of education- related concepts such as high school graduation rate, we did not elect to include education spending in this analysis. Instead, we opted to examine a county’s spending for public health and social services.
Table A1: Pairwise correlation coefficients between local government health, social service, and education spending variables
Public Health ($)
Public Health (%)
Social Services ($)
Social Services (%)
Education ($)
Education (%)
Health and Social Services ($)
Health and Social Services (%)
Total ($)
Total (%)
Public Health ($)
1
Public Health (%)
0.9007* 1
Social Services ($)
0.2278* 0.0350* 1
Social Services (%)
-0.1484* 0.0436* -0.1721* 1
Education ($) 0.0932* -0.0633* 0.2006* 0.1156* 1
Education (%) -0.1785* -0.0007 -0.4716* 0.8370* 0.3272* 1 Health and
Social Services ($)
0.4591* 0.2666* 0.9528* -0.1972* 0.2051* -0.4703* 1
Health and Social Services (%)
-0.0092 0.1929* -0.1673* 0.9845* 0.1074* 0.8205* -0.1464* 1
Total ($) 0.3115* 0.0952* 0.6516* -0.0297* 0.8394* -0.0174 0.6814* -0.0065 1
Total (%) -0.0949* 0.1052* -0.3272* 0.9575* 0.2209* 0.9488* -0.3153* 0.9575* -0.0132 1
Note: In column and row headers, “($)” signifies that spending was measured via per capita expenditures whereas “(%)” signifies that spending was measured as a percent of total local government spending.
Relationships Between Spending and Community Health Factors
County-area spending and individual components of a county’s health factors ranking were found to have relatively low correlations. Both positive and negative correlations between spending and health factors were found. See Table A2 for full correlation matrix.
Ordinary least square regression models were also run to assess multicollinearity issues and, consistent with the results below, no health factors or spending variables were found to be problematically colinear.
When entered into models individually (e.g., Per Capita Spending on public health only or Percentage of Total Spending That is for Social Services only), all variance inflation factors were less than 1.2. The only model for which multicollinearity was an issue was a model in which all four spending variables were entered simultaneously; in that case per capita spending on public health and percentage of total spending that is for public health had variance inflation factors greater than 8, suggesting that models should either measure spending using per capita or percentage of total but not both.
Table A2: Pairwise correlation coefficients between spending variables and individual components of county health factors rankings
Health Factors Component Public
Health ($)
Public Health (%)
Social Services ($)
Social Services (%)
Education ($)
Education (%)
Health and Social Services ($)
Health and Social Services (%)
Total ($) Total (%)
Percentage of adults who are current smokers -0.0068 0.0191 -0.1230* 0.0699* 0.028 0.1235* -0.1084* 0.0717* -0.0407* 0.0999*
Percentage of adults that report a BMI of 30 or more
-0.0108 0.0137 -0.1317* 0.0643* 0.0883* 0.1352* -0.1190* 0.0655* 0.001 0.1030*
Index of factors that contribute to a healthy food environment, 0 (worst) to 10 (best)
-0.0159 0.0006 -0.0414* 0.026 -0.0299 0.0241 -0.0438* 0.0245 -0.0507* 0.0252 Percentage of adults aged 20 and over
reporting no leisure-time physical activity
0.0064 0.0129 -0.0672* 0.0465* 0.0602* 0.0767* -0.0559* 0.0472* 0.0134 0.0632*
Percentage of population with adequate access to locations for physical activity
0.013 -0.007 0.0687* -0.0801* -0.0072 -0.0850* 0.0627* -0.0806* 0.0327* -0.0869*
Percentage of adults reporting binge or heavy drinking
0.0089 -0.0209 0.0977* -0.0115 0.0111 -0.0457* 0.0878* -0.0158 0.0559* -0.0305 Percentage of driving deaths with alcohol
involvement
0.0074 0.0088 -0.0004 0.0191 0.0063 0.0167 0.0013 0.0198 0.0053 0.019 Number of newly diagnosed chlamydia cases
per 100,000 population
0.0605* 0.0006 0.1263* -0.1522* 0.0965* -0.1082* 0.1270* -0.1491* 0.1446* -0.1327*
Number of births per 1,000 female population ages 15-19
0.0284 0.0004 -0.0372* -0.0154 0.1652* 0.0607* -0.0234 -0.0145 0.1079* 0.0221 Percentage of population under age 65 without
health insurance
0.0272 0.0165 -0.0004 0.0089 0.0212 0.0078 0.0085 0.0136 0.0241 0.011 Ratio of population to primary care physicians 0.0574* -0.0205 0.2536* -0.2329* 0.03 -0.2628* 0.2432* -0.2297* 0.1564* -0.2565*
Ratio of population to dentists 0.0446* -0.0008 0.1643* -0.1358* 0.0078 -0.1564* 0.1594* -0.1327* 0.0906* -0.1497*
Ratio of population to mental health providers 0.0827* 0.0451
*
0.1750* -0.1244* -0.0124 -0.1542* 0.1776* -0.1148* 0.0878* -0.1391*
Number of hospital stays for ambulatory-care sensitive conditions per 1,000 Medicare enrollees
-0.0062 -0.0074 -0.0738* 0.0347* 0.0838* 0.0711* -0.0660* 0.0329* 0.025 0.0527*
Percentage of diabetic Medicare enrollees ages 65-75 that receive HbA1c monitoring
-0.0123 0.0063 -0.0101 0.0232 -0.0755* 0.001 -0.0117 0.0238 -0.0626* 0.0121 Percentage of female Medicare enrollees ages -0.0015 0.0011 0.0731* -0.0539* -0.0853* -0.0884* 0.0665* -0.0524* -0.0238 -0.0725*
67-69 that receive mammography screening Percentage of ninth-grade cohort that graduates in four years
-0.0328* -0.0157 -0.007 0.0566* -0.013 0.0169 -0.0148 0.0537* -0.0215 0.0374*
Percentage of adults ages 25-44 with some post-secondary education
0.0154 -0.0201 0.1515* -0.1179* -0.0347* -0.1510* 0.1371* -0.1192* 0.0489* -0.1398*
Percentage of population ages 16 and older unemployed but seeking work
-0.0467* -0.0205 -0.1109* 0.0773* 0.0071 0.1127* -0.1106* 0.0712* -0.0476* 0.0952*
Percentage of children under age 18 in poverty 0.0411* 0.0369
*
-0.0188 0.0114 0.0541* 0.0416* -0.002 0.0183 0.0449* 0.0301 Percentage of children that live in a household
headed by single parent
0.0745* 0.0364
*
0.0730* -0.0784* 0.0646* -0.0497* 0.0869* -0.0719* 0.0990* -0.0634*
Number of reported violent crime offenses per 100,000 population
0.0610* -0.0022 0.1479* -0.1824* 0.0437* -0.1570* 0.1463* -0.1809* 0.1178* -0.1764*
Number of deaths due to injury per 100,000 population
0.0126 0.0159 -0.0045 0.0348* 0.0176 0.0303 -0.0005 0.0362* 0.0142 0.0346*
Average daily density of fine particulate matter in micrograms per cubic meter (PM2.5)
-0.0128 -0.0036 -0.0288 -0.0092 -0.0061 0.0061 -0.0296 -0.0079 -0.0235 -0.0012 Percentage of population potentially exposed
to water exceeding a violation limit during the pas
0.013 0.0143 -0.0151 0.0121 0.0134 0.0164 -0.011 0.013 0.0046 0.0157
Percentage of households with at least 1 of 4 housing problems: overcrowding, high housing costs
0.0098 -0.0058 0.0330* -0.0439* 0.0031 -0.0353* 0.0328* -0.0438* 0.025 -0.0413*
Percentage of the workforce that drives alone to work
-0.0657* -0.0249 -0.0882* 0.0418* 0.0193 0.0807* -0.0915* 0.0366* -0.0447* 0.0615*
Among workers who commute in their car alone, the percentage that commute more than 30 minutes
-0.0342* -0.0025 -0.1172* 0.1161* -0.0305 0.1227* -0.1158* 0.1134* -0.0920* 0.1236*
Note: In column and row headers, “($)” signifies that spending was measured via per capita expenditures whereas “(%)” signifies that spending was measured as a percent of total local government spending.
Table A3: Summary statistics for absolute changes in county health factors rankings following incorporation of local government spending into rankings formula
County Absolute Change in Ranking
Spending Weighted at 1%
Spending Weighted at 2.5%
Spending Weighted at 5%
Mean 1.093 2.379 4.423
Standard Deviation 1.997 3.560 6.050
25th percentile 0 0 1
Median 1 1 3
75th percentile 1 3 6
Figure A1: Mean absolute change in county health rankings following incorporation of local government spending into rankings formula, by year
20100 2011 2012 2013 2014
1 2 3 4 5 6
Weighted at 1% Weighted at 2.5% Weighted at 5%
Year
Mean Absolute Change in Ranking
Table A4: Mean values for other health factors rankings components, based on quartile of changes resulting from addition of spending data to rankings
Health Factors Component p-value for difference
Quartile 1 (Largest Declines)
Quartile 2
Quartile 3
Quartile 4 (Largest
Gains) Percentage of adults who are current
smokers
0.042 0.036 0.023 0.003 -0.017
Percentage of adults that report a BMI of 30 or more
0.001 0.061 0.037 -0.006 -0.024
Index of factors that contribute to a healthy food environment, 0 (worst) to 10 (best)
0.023 0.018 0.017 -0.004 -0.008
Percentage of adults aged 20 and over reporting no leisure-time physical activity
0.000 0.085 0.018 -0.013 -0.024
Percentage of population with adequate access to locations for physical activity
0.000 -0.045 -0.031 0.009 0.017
Percentage of adults reporting binge or heavy drinking
0.108 -0.031 -0.01 0.02 0.004
Percentage of driving deaths with alcohol involvement
0.306 0.014 0.005 -0.008 -0.002
Number of newly diagnosed chlamydia cases per 100,000 population
0.000 -0.168 -0.125 0.06 0.056
Number of births per 1,000 female population ages 15-19
0.142 -0.005 -0.047 0.007 0.01
Percentage of population under age 65 without health insurance
0.000 0.051 0.018 -0.043 -0.003
Ratio of population to primary care physicians
0.000 -0.225 -0.138 0.014 0.091
Ratio of population to dentists 0.000 -0.119 -0.064 0.022 0.042
Ratio of population to mental health providers
0.000 -0.121 -0.074 0.028 0.043
Number of hospital stays for ambulatory-care sensitive conditions per 1,000 Medicare enrollees
0.897 0.011 0.003 0.003 -0.005
Percentage of diabetic Medicare enrollees ages 65-75 that receive HbA1c monitoring
0.021 0.028 0.049 -0.029 -0.009
Percentage of female Medicare enrollees ages 67-69 that receive mammography screening
0.000 -0.067 -0.024 0.007 0.022
Percentage of ninth-grade cohort that graduates in four years
0.000 0.056 0.046 -0.003 -0.026
Percentage of adults ages 25-44 with 0.000 -0.139 -0.071 0.039 0.043
some post-secondary education Percentage of population ages 16 and
older unemployed but seeking work
0.000 -0.007 0.04 0.081 -0.038
Percentage of children under age 18 in poverty
0.039 -0.005 -0.042 0.039 -0.003
Percentage of children that live in a household headed by single parent
0.000 -0.078 -0.051 0.03 0.024
Number of reported violent crime offenses per 100,000 population
0.000 -0.163 -0.126 0.057 0.056
Number of deaths due to injury per 100,000 population
0.029 0.023 0.006 -0.007 -0.006
Average daily density of fine particulate matter in micrograms per cubic meter (PM2.5)
0.031 -0.022 -0.023 -0.002 0.013
Percentage of population potentially exposed to water exceeding a violation limit during the pas
0.975 0.005 0.001 0 -0.002
Percentage of households with at least 1 of 4 housing problems:
overcrowding, high housing costs
0.000 -0.033 -0.012 0 0.013
Percentage of the workforce that drives alone to work
0.246 -0.018 0.033 0.007 -0.005
Among workers who commute in their car alone, the percentage that commute more than 30 minutes
0.000 0.044 0.036 -0.005 -0.02
Note: Not all factors are included in each year of the County Health Rankings. Values shown in table are average z-score for each component for the years in which it was included in the rankings.
