Ambade Preshit Nemdas, Thavorn Kednapa, Pakhale Smita
Department of Population Health Sciences, Medical College of Georgia, Augusta University, Augusta, GA 30912, USA.
Faculty of Medicine, School of Epidemiology, Public Health and Preventive Medicine, University of Ottawa, Ottawa, ON K1G 5Z3, Canada.
Healthcare (Basel). 2023 Jul 24;11(14):2112. doi: 10.3390/healthcare11142112.
Maharashtra, India, remained a hotspot during the COVID-19 pandemic. After the initial complete lockdown, the state slowly relaxed restrictions. We aim to estimate the lockdown's impact on COVID-19 cases and associated healthcare costs.
Using daily case data for 84 days (9 March-31 May 2020), we modeled the epidemic's trajectory and predicted new cases for different phases of lockdown. We fitted log-linear models to estimate the growth rate, basic (R), daily reproduction number (R), and case doubling time. Based on pre-restriction and Phase 1 R, we predicted new cases for the rest of the restriction phases, and we compared them with the actual number of cases during each phase. Furthermore, using the published and gray literature, we estimated the costs and savings of implementing these restrictions for the projected period, and we performed a sensitivity analysis.
The estimated median R during the different phases was 1.14 (95% CI: 0.85, 1.45) for pre-lockdown, 1.67 (95% CI: 1.50, 1.82) for phase 1 (strict mobility restrictions), 1.24 (95% CI: 1.12, 1.35) for phase 2 (extension of phase 1 with no restrictions on agricultural and essential services), 1.12 (95% CI: 1.01, 1.23) for phase 3 (extension of phase 2 with mobility relaxations in areas with few infections), and 1.05 (95% CI: 0.99, 1.123) for phase 4 (implementation of localized lockdowns in high-case-load areas with fewer restrictions on other areas), respectively. The corresponding doubling time rate for cases (in days) was 17.78 (95% CI: 5.61, -15.19), 3.87 (95% CI: 3.15, 5.00), 10.37 (95% CI: 7.10, 19.30), 20.31 (95% CI: 10.70, 212.50), and 45.56 (95% CI: 20.50, -204.52). For the projected period, the cases could have reached 631,819 without the lockdown, as the actual reported number of cases was 64,975. From a healthcare perspective, the estimated total value of averted cases was INR 194.73 billion (USD 2.60 billion), resulting in net cost savings of 84.05%. The Incremental Cost-Effectiveness Ratio (ICER) per Quality Adjusted Life Year (QALY) for implementing the lockdown, rather than observing the natural course of the pandemic, was INR 33,812.15 (USD 450.83).
Maharashtra's early public health response delayed the pandemic and averted new cases and deaths during the first wave of the pandemic. However, we recommend that such restrictions be carefully used while considering the local socio-economic realities in countries like India.
在新冠疫情期间,印度马哈拉施特拉邦一直是热点地区。在最初的全面封锁之后,该邦逐渐放宽了限制措施。我们旨在评估封锁措施对新冠病例及相关医疗费用的影响。
利用84天(2020年3月9日至5月31日)的每日病例数据,我们对疫情轨迹进行建模,并预测了不同封锁阶段的新增病例数。我们拟合对数线性模型以估计增长率、基本再生数(R)、每日再生数(Rt)和病例倍增时间。基于限制措施实施前及第一阶段的Rt,我们预测了其余限制阶段的新增病例数,并将其与各阶段的实际病例数进行比较。此外,利用已发表文献和灰色文献,我们估计了预计期间实施这些限制措施的成本和节省情况,并进行了敏感性分析。
不同阶段估计的Rt中位数在封锁前为1.14(95%置信区间:0.85,1.45),第一阶段(严格的出行限制)为1.67(95%置信区间:1.50,1.82),第二阶段(第一阶段的延长,对农业和基本服务无限制)为1.24(95%置信区间:1.12,1.35),第三阶段(第二阶段的延长,在感染较少地区放宽出行限制)为1.12(95%置信区间:1.01,1.23),第四阶段(在高病例负荷地区实施局部封锁,对其他地区限制较少)为1.05(95%置信区间:0.99,1.123)。病例相应的倍增时间率(以天计)分别为17.78(95%置信区间:5.61, -15.19)、3.87(95%置信区间:3.15,5.00)、10.37(95%置信区间:7.10,19.30)、20.31(95%置信区间:10.70,212.50)和45.56(95%置信区间:20.50, -204.52)。在预计期间,如果没有封锁措施,病例数可能会达到631,819例,而实际报告的病例数为64,975例。从医疗保健角度来看,估计避免的病例总价值为1947.3亿印度卢比(26亿美元),净成本节省84.05%。实施封锁措施而非任由疫情自然发展,每质量调整生命年(QALY)的增量成本效益比(ICER)为33,812.15印度卢比(450.83美元)。
马哈拉施特拉邦早期的公共卫生应对措施延缓了疫情,并在疫情第一波期间避免了新增病例和死亡。然而,我们建议在像印度这样的国家,在考虑当地社会经济现实的同时谨慎使用此类限制措施。