Public Health Research Center, Comprehensive Health Research Center, NOVA National School of Public Health, NOVA University Lisbon, CHRC, Lisbon, Portugal.
Centre for Vectors and Infectious Diseases Research, National Institute of Health Doutor Ricardo Jorge, Águas de Moura, Portugal.
BMC Public Health. 2024 Apr 4;24(1):956. doi: 10.1186/s12889-024-18369-1.
In November 2020, similar to other European countries, Portugal implemented a tiered restrictions system to control the COVID-19 pandemic. We aimed to compare the COVID-19 growth rate across tiers to assess the effect of a tiered restrictions system in Portugal, using models with different times between tiers assessment. Our hypothesis was that being in a higher tier brings a faster deceleration in the growth rate than being in a lower tier.
The national database of notified COVID-19 cases and publicly available data were used to analyse the effect of the tiered restrictions system on the COVID-19 incidence growth rate. The tiers were based on the European Centre for Disease Control risk classification: moderate, high, very and extremely high. We used a generalised mixed-effects regression model to estimate the growth rate ratio (GRR) for each tier, comparing the growth rates of higher tiers using moderate tier as reference. Three models were fitted using different times between tiers assessment, separated by 14 days.
We included 156 034 cases. Very high tier was the most frequent combination in all the three moments assessed (21.2%), and almost 50% of the municipalities never changed tier during the study period. Immediately after the tiers implementation, a reduction was identified in the municipalities in high tier (GRR high tier: 0.90 [95%CI: 0.79; 1.02]) and very high tier (GRR very high tier: 0.68 [95%CI: 0.61; 0.77]), however with some imprecision in the 95% confidence interval for the high tier. A reduction in very high tier growth rate was identified two weeks (GRR: 0.79 [95%CI: 0.71; 0.88]) and four weeks (GRR: 0.77 [95%CI: 0.74; 0.82]) after the implementation, compared to moderate tier. In high tier, a reduction was also identified in both times, although smaller.
We observed a reduction in the growth rate in very high tier after the tiered restriction system was implemented, but we also observed a lag between tiered restriction system implementation and the onset of consequent effects. This could suggest the importance of early implementation of stricter measures for pandemic control. Thus, studies analysing a broader period of time are needed.
2020 年 11 月,与其他欧洲国家类似,葡萄牙实施了分级限制制度以控制 COVID-19 大流行。我们旨在通过使用不同分级评估时间间隔的模型,比较各分级之间的 COVID-19 增长率,以此评估葡萄牙分级限制制度的效果。我们的假设是,处于较高分级会比处于较低分级带来更快的增长率下降。
使用国家通报的 COVID-19 病例数据库和公开数据来分析分级限制系统对 COVID-19 发病率增长率的影响。这些分级基于欧洲疾病预防控制中心的风险分类:中等、高、非常高和极高。我们使用广义混合效应回归模型来估计每个分级的增长率比值(GRR),将较高分级的增长率与中等分级作为参考进行比较。使用不同的分级评估时间间隔拟合了三个模型,间隔为 14 天。
我们纳入了 156034 例病例。极高分级在所有三个评估时刻都是最常见的组合(21.2%),并且在研究期间,近 50%的直辖市从未改变过分级。在分级实施后立即,高分级的直辖市(高分级的 GRR:0.90[95%CI:0.79;1.02])和极高分级的直辖市(极高分级的 GRR:0.68[95%CI:0.61;0.77])的增长率有所下降,但高分级的 95%置信区间的精度有些不足。在分级实施两周后(GRR:0.79[95%CI:0.71;0.88])和四周后(GRR:0.77[95%CI:0.74;0.82]),极高分级的增长率与中等分级相比有所下降。在高分级中,虽然幅度较小,但在两个时间点都观察到增长率下降。
我们观察到在实施分级限制制度后,极高分级的增长率有所下降,但我们也观察到分级限制制度实施与随后效果出现之间存在滞后。这可能表明在大流行控制中尽早实施更严格的措施的重要性。因此,需要进行分析更广泛时间段的研究。