Department of Epidemiology, Brown University School of Public Health, 121 S Main St, Providence, RI, 02912, USA.
Department of Health Services, Policy and Practice, Brown University School of Public Health, 121 S Main St, Providence, RI, 02912, USA.
Popul Health Metr. 2024 Jun 15;22(1):12. doi: 10.1186/s12963-024-00333-1.
Heterogeneity in national SARS-CoV-2 infection surveillance capabilities may compromise global enumeration and tracking of COVID-19 cases and deaths and bias analyses of the pandemic's tolls. Taking account of heterogeneity in data completeness may thus help clarify analyses of the relationship between COVID-19 outcomes and standard preparedness measures.
We examined country-level associations of pandemic preparedness capacities inventories, from the Global Health Security (GHS) Index and Joint External Evaluation (JEE), on SARS-CoV-2 infection and COVID-19 death data completion rates adjusted for income. Analyses were stratified by 100, 100-300, 300-500, and 500-700 days after the first reported case in each country. We subsequently reevaluated the relationship of pandemic preparedness on SARS-CoV-2 infection and age-standardized COVID-19 death rates adjusted for cross-country differentials in data completeness during the pre-vaccine era.
Every 10% increase in the GHS Index was associated with a 14.9% (95% confidence interval 8.34-21.8%) increase in SARS-CoV-2 infection completion rate and a 10.6% (5.91-15.4%) increase in the death completion rate during the entire observation period. Disease prevention (infections: β = 1.08 [1.05-1.10], deaths: β = 1.05 [1.04-1.07]), detection (infections: β = 1.04 [1.01-1.06], deaths: β = 1.03 [1.01-1.05]), response (infections: β = 1.06 [1.00-1.13], deaths: β = 1.05 [1.00-1.10]), health system (infections: β = 1.06 [1.03-1.10], deaths: β = 1.05 [1.03-1.07]), and risk environment (infections: β = 1.27 [1.15-1.41], deaths: β = 1.15 [1.08-1.23]) were associated with both data completeness outcomes. Effect sizes of GHS Index on infection completion (Low income: β = 1.18 [1.04-1.34], Lower Middle income: β = 1.41 [1.16-1.71]) and death completion rates (Low income: β = 1.19 [1.09-1.31], Lower Middle income: β = 1.25 [1.10-1.43]) were largest in LMICs. After adjustment for cross-country differences in data completeness, each 10% increase in the GHS Index was associated with a 13.5% (4.80-21.4%) decrease in SARS-CoV-2 infection rate at 100 days and a 9.10 (1.07-16.5%) decrease at 300 days. For age-standardized COVID-19 death rates, each 10% increase in the GHS Index was with a 15.7% (5.19-25.0%) decrease at 100 days and a 10.3% (- 0.00-19.5%) decrease at 300 days.
Results support the pre-pandemic hypothesis that countries with greater pandemic preparedness capacities have larger SARS-CoV-2 infection and mortality data completeness rates and lower COVID-19 disease burdens. More high-quality data of COVID-19 impact based on direct measurement are needed.
国家 SARS-CoV-2 感染监测能力的异质性可能会影响对 COVID-19 病例和死亡的全球计数和跟踪，并对大流行的损失产生偏差分析。因此，考虑数据完整性的异质性可能有助于澄清 COVID-19 结果与标准防范措施之间的关系分析。
我们研究了大流行防范能力清单的国家层面关联，这些清单来自全球卫生安全指数（GHS）和联合外部评估（JEE），调整了收入因素后，对 SARS-CoV-2 感染和 COVID-19 死亡数据完成率进行了评估。分析分为各国首次报告病例后的 100、100-300、300-500 和 500-700 天。随后，我们重新评估了在疫苗接种前时代，根据国家间数据完整性差异，大流行防范对 SARS-CoV-2 感染和年龄标准化 COVID-19 死亡率的关系。
GHS 指数每增加 10%，SARS-CoV-2 感染完成率就会增加 14.9%（95%置信区间 8.34-21.8%），死亡完成率增加 10.6%（5.91-15.4%），整个观察期内都是如此。疾病预防（感染：β=1.08[1.05-1.10]，死亡：β=1.05[1.04-1.07]）、检测（感染：β=1.04[1.01-1.06]，死亡：β=1.03[1.01-1.05]）、应对（感染：β=1.06[1.00-1.13]，死亡：β=1.05[1.00-1.10]）、卫生系统（感染：β=1.06[1.03-1.10]，死亡：β=1.05[1.03-1.07]）和风险环境（感染：β=1.27[1.15-1.41]，死亡：β=1.15[1.08-1.23]）与数据完整性的两个结果均相关。GHS 指数对感染完成率（低收入：β=1.18[1.04-1.34]，中低收入：β=1.41[1.16-1.71]）和死亡率完成率（低收入：β=1.19[1.09-1.31]，中低收入：β=1.25[1.10-1.43]）的影响在中低收入国家最大。调整国家间数据完整性差异后，GHS 指数每增加 10%，SARS-CoV-2 感染率在 100 天和 300 天分别降低 13.5%（4.80-21.4%）和 9.10%（1.07-16.5%）。对于年龄标准化 COVID-19 死亡率，GHS 指数每增加 10%，在 100 天和 300 天分别降低 15.7%（5.19-25.0%）和 10.3%（-0.00-19.5%）。
结果支持大流行前的假设，即大流行防范能力较强的国家 SARS-CoV-2 感染和死亡率数据完整性更高，COVID-19 疾病负担较低。需要更多基于直接测量的 COVID-19 影响高质量数据。
