Results from Exploratory Factor Analysis May 18, 2011

2 Goal To explore –the associations between the variables under consideration in terms of latent “factors” around which the potential variables group –their relative contribution to those factors –the degree to which these variables describe a single unmeasured underlying parameter (ie. underservice)

3 County-level Measures Population to provider ratio (all provider types, age- adjusted) Average travel time to nearest primary care provider Population density Hispanic ethnicity Non-White race Non-White race or Hispanic ethnicity Limited English proficiency Linguistically isolated Standardized mortality ratio (SMR) Infant mortality rate (IMR) Low birthweight (LBW) Disability (age-adjusted) Diabetes (age-adjusted) Pap testing (age-adjusted) Social deprivation index (SDI) High school drop outs Poverty Single mother households Unemployed Uninsured Low income Medicaid ACSC hospitalizations Fair/poor health No usual provider (age- adjusted)

4 County-level measures included in final EFA Population to provider ratio (all provider types, age-adjusted) Average travel time to nearest primary care provider Population density Non-White race Limited English proficiency Standardized mortality ratio (SMR) Low birthweight (LBW) Diabetes (age-adjusted) Social deprivation index (SDI) o Comprised of high school dropouts, unemployment, single mother households, and poverty Uninsured ACSC hospitalizations

5 County-level measures also considered Hispanic ethnicity Highly correlated with LEP, so didn’t include LEP vs. linguistic isolation Highly correlated - can’t include both. Run both ways – did not appreciably alter results; only showing results from LEP for the purposes of this presentation. LBW vs. IMR Run both ways – did not appreciably alter results; only showing results from LBW for the purposes of this presentation. Pap testing and disability measures from the BRFSS Large number of counties with missing data Low income and Medicaid - Highly correlated with SDI Components of SDI vs. the SDI itself Components highly correlated with other factors (ie, poverty)

6 Descriptives of Variables for Counties Included in EFA (n=2856) NRangeMeanStd. Dev LBW2915( ) Diabetes (adj)3141( ) SMR3141( ) ACSC hospitalizations3069( ) Population density3143(0.0-71,505.7)260.01,762.3 Average travel time3140( ) Population-to-provider ratio3074( ,500.0)2,059.74,634.5 Non-White3137( )0.2 Uninsured3140( ) LEP3137( ) SDI3137( ) Single mother household3140( ) Poverty3140( ) High school dropouts3140( ) Unemployment3138( )9.03.2

7 Three Factors Identified (n=2856 counties) Total variance explained by three factors = 55% Factor 1Factor 2Factor 3 Diabetes (age-adjusted) SMR SDI LBW ACSC hospitalizations Population density Average population-weighted travel time Population-to-Provider ratio (all providers; age-adjusted) LEP Uninsured Non-White race Variance explained post-extraction & rotation28%14%13%

8 Rotated Factor Loadings of County-level Measures

9 Health status/SDI Factor Independent vs. Included with other variables IndependentWith other factors/ variables Diabetes (age-adjusted) SMR SDI LBW ACSC hospitalizations Variance explained post-extraction &/or rotation55%28% In general, similar factor loadings for the health status/SDI factor whether looked at independently or with barriers and population-related variables.

10 Barriers Factor Independent vs. Included with other variables IndependentWith other factors/ variables LEP Non-White Uninsured Variance explained post-extraction &/or rotation35%13% In general, similar factor loadings for the barriers factor whether looked at independently or with health status, SDI and population-related variables.

11 Population-related Factor Independent vs. Included with other variables IndependentWith other factors/ variables Population density-0.74 Average travel time (population weighted)0.71 Population-to-Provider ratio0.42 Variance explained post-extraction &/or rotation14% Population-related factor is unreliable when looked at independently.

12 Discussion Points/Next Steps How to handle population-related variables (pop-to-provider, pop density, travel time) Other variables to include in EFA (e.g., linguistic isolation vs. LEP) How to apply for weighting

13 Methods Exploratory Factor Analysis (EFA) –The number of latent factors was unknown and had to be determined from the data Assumptions –Variables should be correlated, but not highly correlated (rho>0.9) Determinant was greater than 0 (0.012) –Variables have a normal distribution Natural log transformed LEP, non-White, SMR, ACSC, average travel time, population density, and Pop-to-provider ratio –Factor analysis is appropriate to use with these data Kaiser-Meyer-Olkin Measure of Sampling Adequacy = 0.75 Bartlett’s Test of Sphericity, p<0.0001

14 Methods Specifications –Maximum likelihood method used –Number of factors retained was based on eigenvalues (i.e., Kaiser Criterion). Factors with eigenvalues <1 were dropped. This criterion was used because we had a sample size >250 and an average communality of >0.6 –Varimax rotation to aid in interpretation Resultant factors are not correlated with each other Maximizes loading of variable on one factor and minimizes its loading on all other factors (creates simple structure) –Variables with factor loadings >0.4 (level of correlation with factor) included Analyses conducted in PASW Statistics v.18

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16 Communalities Proportion of variation in variable explained by the 3 factors Extraction Average population-weighted travel time0.51 Population density0.56 Diabetes0.71 ACSC0.38 SMR0.65 Non-White0.60 LBW0.55 Population-to-Provider (all providers)0.20 Uninsured0.57 SDI0.68 LEP0.67

17 Regression coefficients (for scoring purposes) Factor Factor 1Factor 2Factor 3 Avg Pop weighted travel time Pop Density Diabetes ACSC SMR Non-White LBW Population to Provider ratio Uninsured SDI LEP