Inequalities in multiple health outcomes by education, sex, and race in 93 US counties: Why we should measure them all

Asada, Yukiko; Whipp, Alyce; Kindig, David; Billard, Beverly; Rudolph, Barbara

doi:10.1186/1475-9276-13-47

International Journal for Equity in Health

Table 1 Three counties with the smallest and largest summary inequality in total morbidity

From: Inequalities in multiple health outcomes by education, sex, and race in 93 US counties: Why we should measure them all

	Counties with the smallest summary inequality			Counties with the largest summary inequality
	Minnehaha, SD	Douglas, CO	Douglas, NE	Aiken, SC	Berkeley, SC	Philadelphia, PA
Summary inequality in total morbidity	0.004	0.007	0.009	0.030	0.030	0.034
Overall inequality
Poor or fair health	0.020	0.013	0.026	0.060	0.052	0.068
Attribute-specific inequality (contribution) Education	0.044 (71.2%)	0.018 (45.7%)	0.056 (71.1%)	0.104 (57.2%)	0.114 (72.7%)	0.136 (66.5%)
Sex	0.014 (22.5%)	0.010 (24.9%)	0.005 (6.01%)	0.040 (22.1%)	0.015 (9.3%)	0.024 (11.7%)
Race	0.004 (6.3%)	0.011 (29.5%)	0.018 (22.8%)	0.038 (20.7%)	0.028 (18.1%)	0.044 (21.8%)
Poor physical health days	0.008	0.005	0.011	0.031	0.039	0.028
Attribute-specific inequality (contribution) Education	0.021 (92.1%)	0.006 (45.2%)	0.032 (94.8%)	0.058 (62.7%)	0.083 (71.2%)	0.052 (61.5%)
Sex	0.001 (2.7%)	0.007 (50.0%)	0.000 (0.0%)	0.028 (29.6%)	0.014 (12.2%)	0.026 (30.8%)
Race	0.001 (5.2%)	0.001 (4.8%)	0.002 (5.2%)	0.007 (7.7%)	0.019 (16.6%)	0.007 (7.8%)
Poor mental health days	0.014	0.010	0.016	0.026	0.035	0.032
Attribute-specific inequality (contribution) Education	0.027 (63.1%)	0.013 (29.2%)	0.022 (46.1%)	0.035 (45.2%)	0.052 (49.7%)	0.051 (53.7%)
Sex	0.015 (34.8%)	0.017 (37.6%)	0.000 (0.0%)	0.040 (51.1%)	0.037 (35.1%)	0.034 (35.4%)
Race	0.001 (2.1%)	0.015 (33.2%)	0.026 (53.9%)	0.003 (3.7%)	0.016 (15.3%)	0.010 (10.9%)
Low birthweigtht	0.006	0.003	0.009	0.016	0.013	0.022
Attribute-specific inequality (contribution) Education	0.011 (64.7%)	0.003 (25.9%)	0.014 (47.5%)	0.021 (43.9%)	0.023 (61.7%)	0.028 (42.2%)
Sex	0.003 (15.4%)	0.006 (55.9%)	0.004 (13.6%)	0.013 (26.3%)	0.000 (0.5%)	0.008 (11.5%)
Race	0.003 (19.9%)	0.002 (18.2%)	0.011 (38.9%)	0.014 (29.8%)	0.014 (37.9%)	0.031 (46.3%)

Data sources: A pooled 2008, 2009, and 2010 Behavioral Risk Factor Surveillance System (BRFSS) Selected Metropolitan/Micropolitan Area Risk Trends (SMART) and a pooled 2008, 2009, and 2010 United States Birth Records from the National Vital Statistics System (NVSS).
Attribute (group characteristic)-specific inequality is education-, sex-, or race-specific inequality in each of the four health outcomes (poor or fair health, poor physical health days, poor mental health days, and low birthweight). Overall inequality is the average of these three attribute-specific inequalities for each health outcome. Summary inequality in total morbidity is the weighted average of the overall inequalities across the four health outcomes: 20% each for poor or fair health, poor physical health days, poor mental health days, and 40% for low birthweight. Values of attribute-specific inequality range between zero and one (0 ≤ and <1). Zero means all groups have the same health outcomes, thus, no inequality, while a value close to one suggests a greater gap between groups, hence, greater inequality. The value 0.034 of attribute-specific inequality suggests that, to eliminate inequality, an additional 3.4% of the population from the less healthy groups need to improve their health to the level of the healthiest group. Interpretations of values of overall inequality and summary inequality are similar. For overall inequality, one should interpret the value as an average across the three attributes considered, and for summary inequality, as an average across the three attributes and the four outcomes considered.

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ISSN: 1475-9276

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