Supplementary Table 1.

Regression Results of Main and Sensitivity Analyses (N = 134)

	Model 1			Model 2			Model 3
Time
Baseline	86.84	79.31	94.36	86.84	78.31	95.36	86.98	79.21	94.75
Pre CA	−0.02	−0.20	0.16	−0.02	−0.22	0.18	−0.03	−0.21	0.16
(Time at CA)	85.68
Post CA short term	−10.87 (=12.7% of 86.68)	−17.21	−4.52	−10.87	−17.48	−4.26	−10.47	−17.44	−3.51
Post CA long term	−0.08	−0.33	0.18	−0.08	−0.34	0.18	−0.08	−0.35	0.20
(Time at SE)	82.66
Post SE short term	−6.46 (=7.8% of 82.66)	−11.13	−1.80	−6.46	−11.28	−1.65	−6.31	−11.30	−1.31
Post SE long term	0.27	0.06	0.48	0.27	0.06	0.48	0.28	0.05	0.51
Trend: CA	−0.10	−0.28	0.09	−0.10	−0.28	0.09	−0.10	−0.31	0.10
Trend: SE^∗	0.18	0.07	0.28	0.18	0.06	0.28	0.17	0.06	0.29
	F (11,134) = 27.3, P < .000			F (11,134) = 27.4, P < .000			F (11,134) = 24.0, P < .000 R² = 0.62, d statistic^† = 1.95
Walking distance
Baseline	1097.23	1009.39	1185.06	1097.23	1000.11	1194.34	1096.31	1001.97	1190.66
Pre CA	0.64	−1.38	2.65	0.64	−1.58	2.86	0.65	−1.59	2.89
Distance at CA	1133.60
Post CA short term	−37.70 (=3.3% of 1133.60)	−138.21	62.80	−37.70	−140.87	65.46	−34.00	−146.41	78.41
Post CA long term	−5.37	−10.39	−0.36	−5.37	−10.85	0.10	−5.51	−11.02	−0.01
Distance at SE	918.63
Post SE short term	−186.79 (=20.3% of 918.63)	−333.01	−40.57	−186.79	−348.23	−25.35	−183.69	−341.39	−25.99
Post SE long term	5.61	0.40	10.82	5.61	0.05	11.18	5.70	−0.08	11.48
Trend: CA	−4.73	−9.34	−0.13	−4.74	−9.78	0.31	−4.86	−9.90	0.17
Trend: SE^∗	0.87	−1.68	3.43	0.88	−1.61	3.36	0.84	−2.03	3.70
	F (11,134) = 38.3, P < .000			F (11,134) = 46.8, P < .000			F (11,134) = 29.6, P < .000 R² = 0.64, d statistic = 1.97

CA, CCRC announcement; SE, state of emergency.

Confidence intervals in brackets.

Model 1: Regression with Newey-West standard errors without lag indicated (main analyses).

Model 2: Model 1 with 1 day lag indicated.

Model 3: Prais-Winsten autoregressive model. Dummy variables for week are not shown in the tables.

Postintervention linear trend = _b [pre CA] + _b [post CA long term].

^∗

Postintervention linear trend = _b [pre CA] +_b [post CA long term] + _b [Post SE long term].

^†

Durbin–Watson d statistic is an indicator of how well the model corrects for first-order autocorrelation; d can take on values between 0 and 4, and under the null hypothesis, d is equal to 2.