key: cord-0324194-wyplmd4c authors: Mueed, A.; Ahmad, T.; Abdullah, M.; Sultan, F.; Khan, A. title: Impact of school closures and reopening on COVID-19 caseload in 6 cities of Pakistan: An Interrupted Time Series Analysis date: 2022-05-26 journal: nan DOI: 10.1101/2022.05.25.22275590 sha: 6bcd23337018de36e570c2d42f43e29518dce95a doc_id: 324194 cord_uid: wyplmd4c Schools were closed all over Pakistan on November 26, 2020 to reduce community transmission of COVID-19 and reopened between January 18 and February 1, 2021. However, these closures were associated with significant economic and social costs, prompting a review of effectiveness of school closures to reduce the spread of COVID-19 infections in a developing country like Pakistan. An interrupted time series analysis (ITSA) was used to measure the impact of school closures, as well as reopening schools on daily new COVID-19 cases in 6 major cities across Pakistan: Lahore, Karachi, Islamabad, Quetta, Peshawar, and Muzaffarabad. School closures are associated with a clear and statistically significant reduction in COVID-19 cases by 0.07 to 0.63 cases per 100,000 population, while reopening schools is associated with a statistically significant increase. Lahore is an exception to the effect of school closures, but it too saw an increase in COVID-19 cases after schools reopened in early 2021. We found that closing schools reduced COVID-19 incidence in the community by approximately a third of all cases nationwide. However, any benefits were contingent on continued closure of schools, as cases bounced back once schools reopened. We show that closing schools was a viable policy option, especially before vaccines became available. However, its social and economic costs must also be considered. Since the beginning of the global spread of COVID-19 and before effective vaccines 46 became available, non-pharmaceutical interventions (NPIs), either barriers or means to limit contact between individuals, were the mainstay to control the spread of COVID-19. This paper is a continuation of our earlier work, which examined the effects of school 70 closures on the daily cases of COVID-19 in Islamabad vs. Peshawar, during the same 71 period as in this study [25] . However, this study attempts to examine the effect of school 72 closures with a different methodology, and also with a larger sample of cities. In this paper we conduct a pre-and post-school closures and reopening analysis of 75 changes in the daily incidence of COVID-19 cases in 6 cities of Pakistan: Lahore, Karachi, 76 Islamabad, Quetta, Peshawar, and Muzaffarabad using a single-group ITSA. We use a 77 single-group ITSA because it is a quasi-experimental tool that is particularly useful when 78 data cannot be fully randomized, there is no comparison group, and there is a need to 79 consider the effect of only one intervention. 80 This suits our study as, in Pakistan, all non-school NPIs in were enacted in groups -81 except for the closure of schools. For example, marriage hall restrictions and ban on large 82 scale gatherings were notified at the same time, as were mask-wearing, broader "smart" 83 lockdowns (lockdowns in parts of cities), and reduced market timings. School closures, 84 on the other hand, were universally enforced and applied to all schools -whether day 85 schools or boarding schools -and to students of all grades across Pakistan [26] . These 86 were the only NPI that changed (i.e. were applied and then lifted) during the period of 87 examination in this study. 88 Data for this analysis were sourced from the daily National Situation Reports (Sitreps) 89 published by the National Emergency Operations Centre (NEOC) in Islamabad, Pakistan. 90 This data is anonymized and aggregated by city, with no disaggregation by age, gender, 91 ethnicity, or any other potentially identifying characteristic. We use this data for an 92 inferential analysis of the change in daily COVID-19 incidence in the overall populations 93 of the 6 aforementioned cities, regardless of demographic characteristics, due to the 94 change in one particular NPI. It is because this NPI is the only policy intervention that 95 could be isolated in our chosen time period of observation. 96 We estimated 2 sets of ordinary least square (OLS) regressions for each city using a 10-97 or 20-day delay since COVID-19 incidence changes from school-related NPIs take effect 98 10 [27] or more days [17, 28, 29] after closures or reopening. Daily new COVID-19 cases 99 were taken for equally spaced time frames with 10-and 20-day delay after the actual 100 school closures and reopening dates. In order to analyze the effect of school closures 101 and reopening, we took a total of 60 days for pre-and post-intervention periods. Means of daily COVID-19 cases over each period are reported. Standard deviations are in parentheses. After adding a 10-day delay after the actual date of school closures (Table 3) The opposite trend was seen following schools reopening in early 2021. Before schools is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review) larger, denser cities, and had the most cases; while the association was modest for 188 sparse and smaller cities of Quetta and Muzaffarabad, that also had fewer overall cases. Since average daily cases in Pakistan in that period were around 3000 cases per day, [19, 21, 24, [40] [41] [42] . However, more recent studies 217 using empirical community transmission data have generally shown a more robust . CC-BY 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. The copyright holder for this preprint this version posted May 26, 2022. ; https://doi.org/10.1101 https://doi.org/10. /2022 association between school closures and reductions in community cases of Limitations 221 There are limitations of this analysis. The daily COVID-19 data are aggregated nationally, 222 regionally, and by certain major cities, with no disaggregation by age or gender. Additionally, we acknowledge that a pre-and post-intervention analysis itself has 224 limitations. For example, it may not effectively discern the effects of an intervention from 225 that of a long-term trend on an outcome variable. This is referred to as the "maturation" It is difficult to explain why Lahore did not show any reduction in cases after school 232 closures. It is possible that the epidemic affected cities at different points in time and that 233 it was at a relatively lower level in Lahore during the study period. We also acknowledge 234 that there could be potential cross-contamination of COVID-19 cases between Islamabad 235 and Peshawar, which are separated by a 2-hours commute by road, and between 236 Islamabad and Lahore, which are 4-hours apart by road. However, there are no data on 237 the magnitude of any potential contamination due to bilateral intra-city travel. . CC-BY 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. The copyright holder for this preprint this version posted May 26, 2022. . CC-BY 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. The copyright holder for this preprint this version posted May 26, 2022. ; https://doi.org/10.1101 https://doi.org/10. /2022 . CC-BY 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review) The copyright holder for this preprint this version posted May 26, 2022. ; https://doi.org/10.1101 https://doi.org/10. /2022 CC-BY 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review) The copyright holder for this preprint this version posted May 26, 2022. ; https://doi.org/10.1101 https://doi.org/10. /2022 The economic impacts of learning losses Estimating the costs of school closure for 256 mitigating an influenza pandemic Closures During COVID-19: Opportunities for Innovation in Meal Service Was school closure effective in mitigating 309 coronavirus disease 2019 (COVID-19)? 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