A bootstrapping approach for generating an inverse distance weight matrix when multiple observations have an identical location in large health surveys

Table 4 Sensitivity analysis—OLS (health service use)

	Coef	SE	CI (lower)	CI (higher)
Women
Original data^a
Spatial lag	1.645	0.159	1.333	1.956
Wealth	− 0.012	0.004	− 0.020	− 0.003
Education	0.029	0.009	0.011	0.048
Constant	− 0.382	0.094	− 0.566	− 0.198

	Coef	SE	Boot CI (lower)	Boot CI (higher)	Coverage probability (%)	MSE
5000 simulation
Wealth	− 0.007	0.012	− 0.030	0.017	95.4	0.000169
Education	0.024	0.025	− 0.025	0.074	96.5	0.000659
10,000 simulation
Wealth	− 0.007	0.012	− 0.030	0.016	95.4	0.000166
Education	0.025	0.025	− 0.024	0.075	96.6	0.000651

	Coef	SE	CI (lower)	CI (higher)
Men
Original data^a
Spatial lag	− 0.053	0.045	− 0.142	0.036
Wealth	− 0.012	0.003	− 0.018	− 0.006
Education	0.019	0.006	0.007	0.032
Constant	0.712	0.052	0.611	0.813

	Coef	SE	Boot CI (lower)	Boot CI (higher)	Coverage probability (%)	MSE
5000 simulation
Wealth	− 0.014	0.013	− 0.039	0.011	95.3	0.000165
Education	0.019	0.028	− 0.036	0.073	95.2	0.000773
10,000 simulation
Wealth	− 0.014	0.013	− 0.038	0.011	95.4	0.000161
Education	0.018	0.027	− 0.035	0.072	95.8	0.000752

ISSN: 1476-072X