Abstract
This chapter reports contextual, organizational, and ecological factors influencing the variations in risk-adjusted hospitalization rates for heart failure (HF) of Medicare patients served by rural health clinics (RHCs) in the eight Southeastern states in the United States. We conducted a longitudinal analysis to show trends and patterns of RHC variations in the race-specific, risk-adjusted rates. There was a steady decline in HF hospital admission rates. A net-period effect of the Affordable Care Act on HF hospital admissions was also observed in both White and African-American groups. The results affirm the importance of considering county characteristics and organizational factors for African-American patients in accounting for the variability in HF hospital admissions. However, for the White patients, the variables measured at the organizational level (such as the dual-eligibility status of Medicare patients and the total FTEs employed by RHCs) were influential and could be considered in the formulation of hospital incentive payment formula in the future.
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Notes
- 1.
The RUCA is a classification scheme that uses the Bureau of Census Urbanized Area and Urban Cluster definitions in combination with work commuting information to characterize US Census tracts regarding their rural and urban status.
- 2.
Generalized estimating equation method provides a semi-parametric approach to longitudinal analysis of categorical or continuous (repeated) measurements. GEEs were introduced by Liang and Zeger (1986) and expanded in a book by Diggle et al. (1994). The covariance structure does not need to be specified correctly to estimate regression coefficients and standard errors. The statistical assumptions are as follows: (1) the repeated measures or responses to be correlated or clustered, (2) covariates with a mixture of predictor variables and their interaction terms, (3) no requirement for equal variance or homogeneity of variance, (4) correlated errors assumed independent, (5) not required for multinormal distribution, and (6) a quasi-likelihood estimation rather than maximum likelihood estimation or ordinary least squares to estimate the parameters (Hardin and Hilbe 2012). The robustness of a GEE model is not determined by conventional goodness of fit statistics. However, an analog to Akaike’s Information Criterion (AIC) such as QIC (quasi-likelihood under the independence model criterion) is used to assess the competing models for varying correlation structures. A marginal R-squared value can be computed to be used as a reference to the magnitude of the total variance explained by predictor variables in the equation (Hardin and Hilbe 2012; Zheng 2000).
In this report, the GEE model was performed by using SAS with the PROC GENMOD procedure. The model fitting and link function were based on the link function of identity (change nothing in a dependent variable) with an assumption of a normal distribution. The assumption on correlated errors between seven levels of time points or waves on a dependent variable was set to AR(1), which means the following:
We performed hierarchical regression of a continuous response variable on the contextual, organizational, and aggregate personal predictors separately and kept statistically significant variables for the final equation. When we included them together in the final model, we added additional fixed variables such as year (1–6), dummy variables for seven states (using Mississippi as a reference group), and rurality code (three dummy variables using RHC located in urbanized areas as a reference group). The backward selection criterion was used to enter the statistically significant predictors one by one at the alpha of 0.1.
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Appendix 1: The Study Variables and Their Operational Definitions
Appendix 1: The Study Variables and Their Operational Definitions
Variable | Codes | Operational definition |
|---|---|---|
Contextual factors | ||
Older | Number of county population that is Medicare eligible (and age 65 and over) | |
Female | Number of county population that is female | |
Percent poverty population | Number of county population that is at 200% of poverty level | |
African-American | Number of county population that is African-American | |
Hispanic | Number of county population that is Hispanic | |
Native American | Number of county population that is Native American | |
White | Number of county population that is White | |
Rurality level | 1: urban 2: large rural 3: small rural 4: isolated | Four categories based on RUCA code: urban, large rural, small rural, isolated Urban, 1.0, 1.1, 2.0, 2.1, 3.0, 4.1, 5.1, 7.1, 8.1, 10.1; large rural, 4.0, 4.2, 5.0, 5.2, 6.0, 6.1; small rural, 7.0, 7.2, 7.3, 7.4, 8.0, 8.2, 8.3, 8.4, 9.0, 9.1, 9.2; isolated, 10.0, 10.2, 10.3, 10.4, 10.5, 10.6 |
ACA period effect | 0: before 2010 (2007 through 2009) 1: after 2010 (2010 through 2012) | The potential period effect of ACA on RHC performance |
State | Region 4: seven dummy variables were created, using MS as a reference group; AL, FL, GA, KY, MS, NC, SC, TN | |
Organizational factors | ||
The years of RHC operation | Number of years Medicare certified for participation in RHC program | |
Staff mix and size | Number of physicians + PA + NP FTEs | |
Provider-based practice | 1 = Provider-based RHC 0 = Independent RHC | RHC type |
Ownership | Type of control of RHC according to 1 of 9 classifications (for provider type “12”—RHCs) | |
Personal factors | ||
Size of Medicare beneficiary population served | Total patients of RHC | |
Percent of female patients served | Number of patients aged 65 and older who are female (expressed as a percentage of total patients) | |
Percent of African-American patients served | Number of patients aged 65 and older who are African-American (expressed as a percentage of total patients) | |
Percent of Hispanic patients served | Number of patients aged 65 and older who are Hispanic (expressed as a percentage of total patients) | |
Percent of Native American patients served | Number of patients aged 65 and older who are Native American (expressed as a percentage of total patients) | |
Percent of White patients served | Number of patients aged 65 and older who are White (expressed as a percentage of total patients | |
Percent of patients dually eligible | Number of Medicare program beneficiaries with at least 3 dual eligible months within 1 year | |
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Wan, T.T.H. (2018). Contextual, Organizational, and Ecological Factors Influencing the Variations in Heart Failure Hospitalization in Rural Medicare Beneficiaries in Eight Southeastern States. In: Population Health Management for Poly Chronic Conditions. Springer, Cham. https://doi.org/10.1007/978-3-319-68056-9_7
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