Table 3 |.
Non-work activities outside the home: percentage point change in visits by category
| Neighbourhood income quintile of typical visitor | Percentage point change in visits (95% CI) t statistic residual d.f. | ||||||
|---|---|---|---|---|---|---|---|
| Beer, wine and liquor stores | Carryout restaurants | Convenience stores | Hospitals | Parks and playgrounds | Places of worship | Supermarkets | |
| Q1 (lowest) | −40.8 (−44.1, −37.6) −2,463.6 46,138 |
−48.5 (−49.9, −47.2) −6,917.5 186,094 |
−37.5 (−39.4, −35.5) −3,751.1 42,550 |
−58.6 (−65.0, −52.2) −1,793.6 8,206 |
−57.1 (−59.4, −54.8) −4,863 139,666 |
−60.1 (−61.2, −58.9) −10,320.6 297,874 |
−32.3 (−33.8, −30.7) −4,100.7 164,962 |
| Q2 | −35.1 (−37.1, −33.0) −3,352.7 52,078 |
−31.6 (−32.4, −30.9) −8,675.9 369,598 |
−33.3 (−34.7, −32.0) −4,726.5 73,054 |
−57.7 (−61.3, −54.1) −3,129.7 20,674 |
−50.9 (−52.7, −49.1) −5,557.6 164,218 |
−65.7 (−66.5, −64.8) −14,593.7 282,862 |
−24.8 (−26.0, −23.5) −3,831.4 195,886 |
| Q3 | −37.2 (−39.4, −35.1) −3,448.9 51,826 |
−35.9 (−36.6, −35.2) −10,406.6 399,406 |
−35.6 (−37.0, −34.3) −5,135.6 74,530 |
−60.9 (−64.7, −57.2) −3,181.9 18,814 |
−52.9 (−54.9, −50.8) −4,973.3 182,098 |
−73.6 (−75.3, −71.8) −8,153.6 253,450 |
−26.2 (−27.6, −24.8) −3,664.4 167,482 |
| Q4 | −42.5 (−45.8, −39.2) −2,531.5 49,594 |
−43.5 (−44.4, −42.6) −9,443.5 35,4478 |
−40.6 (−42.2, −39.0) −4,894.7 56,374 |
−63.9 (−68.9, −58.8) −2,474.5 11,962 |
−59.2 (−61.4, −57.0) −5,211.6 193,654 |
−81.6 (−82.9, −80.2) −11,467.1 197,098 |
−29.4 (−30.9, −28.0) −4,013.6 131,266 |
| Q5 (highest) | −46.9 (−49.6, −44.2) −3,376.4 41,782 |
−54.0 (−54.9, −53.1) −12,083 268,582 |
−48.7 (−50.8, −46.6) −4,560.5 35,122 |
−64.3 (−70.2, −58.4) −2,132.4 7,222 |
−62.9 (−64.7, −61.1) −6,752.7 207,574 |
−87.5 (−89.2, −85.9) −10,195.1 137,254 |
−34.0 (−35.6, −32.5) −4,308.9 95,854 |
All P < 0.001. To calculate differences from pre (6 January–1 March 2020) to post (6 April–3 May 2020) COVID-19 related changes, we used OLS regressions estimating post effects, stratified within each location category and visitor income quintile. Values were normalized against the pre period mean within each category and income quintile before modelling, to estimate proportional changes in visits from pre to post. These are reported here as percentages. The data source was SafeGraph Weekly Patterns (v.2). To identify non-work visits, we subtracted visits of >4 h from weekly visit totals. For sample size per category, see Table 1.