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. Author manuscript; available in PMC: 2024 Jun 1.
Published in final edited form as: Soc Sci Med. 2023 Apr 28;327:115927. doi: 10.1016/j.socscimed.2023.115927

Table 3:

Peer-Agency Associations of Log-Peer Home Health Billing with Log Ego Billing

Model (1) Model (2) Model (3) Model (4)
Average Linked Home Health Billing −0.004
(.002)		 0.003
(.003)		 0.036***
(.003)		 0.021**
(.004)
Number of Peers 0.327***
(.002)		 0.029***
(.003)		 0.332***
(.002)		 0.018***
(.004)
Average Linked Home * Number of Peers 0.036***
(.002)		 0.009***
(.002)
Lagged HRR Isolate Home Health Billing 0.233***
(.004)		 0.160***
(.003)		 0.210***
(.004)		 0.154***
(.004)
Lagged Ego Home Health Billing 0.608***
(.003)		 0.607***
(.003)
R2 0.536 0.738 0.538 0.738
Bayesian Information Criterion (BIC) 159,540 123,133 159,193 123,114

All models include HRR fixed-effects and home health care agency (HHA) random-effects and are estimated on N = 126,749 agency-by-year observations involving 14,326 distinct agencies across the 306 HRRs. The R2 measure is computed with the random-effects for agency being part of the error-term; this quantity is often referred to as marginal R2 for a mixed-effect model. We also use the Bayesian Information Criterion (BIC) to compare the fitted models. Smaller values of the BIC represent superior model fit. However, because the BIC increases with the sample-size, it only makes sense to make comparisons within models (1) and (3) and within models (2) and (4).