Contractual Arrangements between

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1 Spatial Model Specification for Contractual Arrangements between Rural Hospitals and Physicians J. Matthew Fannin James N. Barnes Louisiana State University Rural Health SERA 19 Meetings Tuesday, August 5, 2008 Houston, TX

2 Outline Defining Rural for Healthcare Services Research Comparison of Spatial Modeling in Rural Health Research vs Regional Science Presentation of Spatial Model of Contractual arrangements between Rural Hospitals and Physicians Results and Next Steps

3 Defining Rural for Healthcare Services Research Healthcare delivery to rural areas important in federal and state healthcare policy Defining rural or remoteness becomes essential to the policy Major example: Critical Access Hospital (CAH) 35 miles from nearest hospital 25 acute care beds 96 hour maximum inpatient stay

4 Strategies to Model Space in Rural Healthcare Research Spatial right-hand hand side variables Closest hospital to patient (Tai et al 2004) Hospitals within X miles (Gresenz et al 2004) Heterogeneity in parameter estimates Full dataset, urban vs rural, rural urban continuum codes, etc.

5 Popular Estimation Strategies for Spatial in Regional Science Research Global parameter techniques Spatial Autoregressive Models, Spatial Error Models Varying parameter techniques Geographically Weighted Regression

6 Objective Goal Wed modern spatial econometric techniques from regional science research to research questions in rural healthcare services research At the same time maintain a theoretical or policy framework that justifies the spatial model chosen

7 Research Question Today s Rural Healthcare Research Question to Address How does space impact the contractual arrangement chosen by hospitals for physician i services? Hypothesis to be tested As the distance between a hospital with a specific contractual arrangement structure with physicians i and a reference hospital increase, the less likely the reference hospital chooses the same contractual arrangement

8 How does space impact the contractual ldecision i A less discussed part of Coase s s transaction cost arguments is the similarity hypothesis An organization with a contractual arrangement to procure a good or service is more likely to use the same contractual arrangement in the future

9 How does space impact the contractual ldecision i Physicians that maintain an autonomous clinic from the hospital and have a specific contractual arrangement with one hospital may desire to have a similar arrangement with another hospital when admitting patients Physicians in a region may also discuss the nature of their arrangements possibly resulting in certain physicians requesting to renegotiate contracts in order to obtain most favorable structure thereby creating a region with homogeneous contractual arrangements

10 Empirical Model Dependent Variable: Dummy Variable 1 if hospital uses only arm s length contractual arrangements, 0 otherwise Explanatory variables Spatial variables no_arrange_15, no_arrange_15_35 Theoretical variables hmo and cmi Supply and Demand controls

11 Data and Model Data from American Hospital Association, Area Resource File, and Center for Medicare and Medicaid Service Data for calendar year 2004 Logit model with robust standard errors g applied in STATA

12 Table 1. Descriptive Statistics Variable Mean Std. Dev. Min Max npho nphop nphop15_ nphop35_ nphop50_ netwrk hmo cmi beds pct_medicare pct_medicaid mhi 43, ,090 94,658 pop_sq_mi

13 Table 2. Spatial Regressions with Rural/Urban Comparison Dep. Variable: npho Full Model (1) Urban (2) Rural (3) Note: p-values nphop (0.000) [1.4880] (0.000) [1.3701] in parenthesis (0.000) [1.5990] nphop15_ (0.901) [0.0072] p p _ (0.167) [ ] nphop50_ (0.000) [ ] netwrk (0.426) [ ] hmo (0.118) [ ] (0.564) [0.0409] (0.4999) [ ] nphop35_ (0.301) [ ] (0.770) [0.0370] (0.004) (0.565) [ ] [ ] (0.562) (0.252) [ ] [ ] [ ] (0.210) (0.073) [ ] [ ] cmi (0.092) (0.026) (0.348) [ ] [ 0.125] [ 0.22`6]

14 Table 2 (Cont). Spatial Regressions with Rural/Urban Comparison. Dep. Variable: npho Full Sample (4) Urban (5) Rural (6) beds (0.000) [ ] (0.000) [ ] (0.368) [0.0006] pct_medicare (0.012) [0.2184] (0.016) [0.2428] (0.482) [0.1367] pct_medicaid (0.643) (0.990) (0.523) [0.0492] [0.0016] mhi 1.12E 05 (0.052) [2.41E 06] 4.67E 06 (0.434) [1.08E 06] 06] 4.85E 05 (0.132) [7.84E 06] pop_sq_mi 2.93E 05 (0.016) [6.34E 06] constant (0.001) 2.75E 05 (0.021) [0.0016] (0.08) (0.215) [7.84E 06] (0.007) constant R square observations

15 Table 4. Geographically Weighted Regression Results. Variable Min L.Quartile Median U.Quartile Max Intercept t nphop nphop15_ nphop35_ nphop50_ netwrk hmo cmi beds pct_medicare pct_medicaid mhi 4.00e e e e e 05 pop_sq_mi 2.30e e e

16 Parameter estimates npho15

17 Parameter estimate contours npho

18 Parameter estimates HMO

19 Parameter estimates CMI

20 Results Spatial right hand side variables in Tables 2 and 3 confirm hypothesis: space matters in rural hospital physician contractual arrangements! Market bargaining power hypothesis generally supported in most equations Complexity attribute from transaction cost also consistent with theory

21 Results West and Northwest highest probability of choosing similar arrangement when neighboring hospitals have that arrangement; least likely for Atlantic seabord hospitals HMO has greatest impact on hospitals in Central US CMI has least impact on Mississippi i i i Valley and Southeastern US

22 Next Steps Analyze tighter arrangements structures with GWR to see if spatial structure is consistent Evaluate additional years of data

23 Questions Contact Information J. Matthew Fannin James Barnes

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