Truche Paul, Campos Letícia Nunes, Marrazzo Enzzo Barrozo, Rangel Ayla Gerk, Bernardino Ramon, Bowder Alexis N, Buda Alexandra M, Faria Isabella, Pompermaier Laura, Rice Henry E, Watters David, Dantas Fernanda Lage Lima, Mooney David P, Botelho Fabio, Ferreira Rodrigo Vaz, Alonso Nivaldo
Program in Global Surgery and Social Change, Harvard Medical School, Boston, MA, United States.
Faculty of Medical Sciences, Universidade de Pernambuco, Recife, PE, Brazil.
Lancet Reg Health Am. 2021 Nov;3:100056. doi: 10.1016/j.lana.2021.100056. Epub 2021 Aug 24.
The impact of public health policy to reduce the spread of COVID-19 on access to surgical care is poorly defined. We aim to quantify the surgical backlog during the COVID-19 pandemic in the Brazilian public health system and determine the relationship between state-level policy response and the degree of state-level delays in public surgical care.
Monthly estimates of surgical procedures performed per state from January 2016 to December 2020 were obtained from Brazil's Unified Health System Informatics Department. Forecasting models using historical surgical volume data before March 2020 (first reported COVID-19 case) were constructed to predict expected monthly operations from March through December 2020. Total, emergency, and elective surgical monthly backlogs were calculated by comparing reported volume to forecasted volume. Linear mixed effects models were used to model the relationship between public surgical delivery and two measures of health policy response: the COVID-19 Stringency Index (SI) and the Containment & Health Index (CHI) by state.
Between March and December 2020, the total surgical backlog included 1,119,433 (95% Confidence Interval 762,663-1,523,995) total operations, 161,321 (95%CI 37,468-395,478) emergent operations, and 928,758 (95%CI 675,202-1,208,769) elective operations. Increased SI and CHI scores were associated with reductions in emergent surgical delays but increases in elective surgical backlogs. The maximum government stringency (score = 100) reduced emergency delays to nearly zero but tripled the elective surgical backlog.
Strong health policy efforts to contain COVID-19 ensure minimal reductions in delivery of emergent surgery, but dramatically increase elective backlogs. Additional coordinated government efforts will be necessary to specifically address the increased elective backlogs that accompany stringent responses.
公共卫生政策在减少新冠病毒传播方面的举措对手术治疗可及性的影响尚不明确。我们旨在量化巴西公共卫生系统在新冠疫情期间的手术积压情况,并确定州级政策应对措施与公共手术治疗的州级延误程度之间的关系。
从巴西统一卫生系统信息部门获取2016年1月至2020年12月各州每月手术量的估计数据。利用2020年3月(首例新冠病毒报告病例)之前的历史手术量数据构建预测模型,以预测2020年3月至12月的预期月手术量。通过将报告手术量与预测手术量进行比较,计算出每月手术积压总量、急诊手术积压量和择期手术积压量。采用线性混合效应模型对公共手术治疗与两项卫生政策应对措施之间的关系进行建模:按州划分的新冠病毒严格指数(SI)和遏制与健康指数(CHI)。
2020年3月至12月期间,手术积压总量包括1,119,433例(95%置信区间762,663 - 1,523,995)手术,161,321例(95%置信区间37,468 - 395,478)急诊手术,以及928,758例(95%置信区间675,202 - 1,208,769)择期手术。SI和CHI得分的增加与急诊手术延误的减少相关,但与择期手术积压的增加相关。政府最高严格度(得分 = 100)将急诊延误降至几乎为零,但使择期手术积压增加了两倍。
为遏制新冠病毒而采取的强有力卫生政策措施确保了急诊手术的交付量减少到最低限度,但显著增加了择期手术积压。政府需要进一步协调努力,以专门解决严格应对措施带来的择期手术积压增加问题。