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. 2022 Nov 30;49:89–93. doi: 10.1016/j.gerinurse.2022.11.005

Table 3.

Associations of demand and number of matches with staff to resident ratio.

Model 1
Model 2
Variable Lag Coeff. P value Variable Lag Coeff. P value
Number of needed staff 1 -0.0003 (-0.0016, 0.0010) 0.574 Number of matches 1 0.0021* (-0.0004, 0.0046) 0.083
2 0.0006 (-0.0007, 0.0018) 0.148 2 0.0021** (-0.0004, 0.0046) 0.022
3 0.0012*** (-0.0001, 0.0024) 0.004 3 0.0026** (0.0001, 0.0051) 0.034
4 0.0014** (0.0002, 0.0025) 0.02 4 0.0042 (0.0017, 0.0067) 0.165
Residents weekly COVID-19 deaths - 0.1019*** (0.0860, 0.118) <0.001 Residents weekly COVID-19 deaths - 0.1016*** (0.0857, 0.118) <0.001
Staff weekly COVID-19 cases - 0.0058 (0.0005, 0.0121) 0.239 Staff weekly COVID-19 cases - 0.0061 (-0.0002, 0.0123) 0.223
Residents weekly admissions COVID-19 - -0.0106** (-0.0157, -0.0055) 0.014 Residents weekly admissions COVID-19 - -0.0106** (-0.0156, -0.0055) 0.014

NOTES: Model 1 estimates the association between weekly staff to resident ratio and weekly number of staff that nursing homes in Massachusetts needed. Model 2 estimates the association between weekly staff to resident ratio and weekly number of staff that the central matching process provided for nursing homes in Massachusetts. Time lags are in weeks. We used nursing-home-week fixedeffects regression models. Coefficients show changes in daily staff to resident ratio by 1 unit change of each variable. *p<0.1, **p<0.05, ***p<0.01.