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Abstract：
BACKGROUND: All-cause excess mortality is a comprehensive measure of the combined direct and indirect effects of COVID-19 on mortality. Estimates are usually derived from Civil Registration and Vital Statistics (CRVS) systems, but these do not include non-registered deaths, which may be affected by changes in vital registration coverage over time.
METHODS: Our analytical framework and empirical strategy account for registered mortality and under-registration. This provides a better estimate of the actual mortality impact of the first wave of the COVID-19 pandemic in Peru. We use population and crude mortality rate projections from Peru\'s National Institute of Statistics and Information (INEI, in Spanish), individual-level registered COVID-19 deaths from the Ministry of Health (MoH), and individual-level registered deaths by region and age since 2017 from the National Electronic Deaths Register (SINADEF, in Spanish).We develop a novel framework combining different estimates and using quasi-Poisson models to estimate total excess mortality across regions and age groups. Also, we use logistic mixed-effects models to estimate the coverage of the new SINADEF system.
RESULTS: We estimate that registered mortality underestimates national mortality by 37•1% (95% CI 23% - 48•5%) across 26 regions and nine age groups. We estimate total all-cause excess mortality during the period of analysis at 173,099 (95% CI 153,669 - 187,488) of which 108,943 (95% CI 96,507 - 118,261) were captured by the vital registration system. Deaths at age 60 and over accounted for 74•1% (95% CI 73•9% - 74•7%) of total excess deaths, and there were fewer deaths than expected in younger age groups. Lima region, on the Pacific coast and including the national capital, accounts for the highest share of excess deaths, 87,781 (95% CI 82,294 - 92,504), while in the opposite side regions of Apurimac and Huancavelica account for less than 300 excess deaths.
CONCLUSIONS: Estimating excess mortality in low- and middle-income countries (LMICs) such as Peru must take under-registration of mortality into account. Combining demographic trends with data from administrative registries reduces uncertainty and measurement errors. In countries like Peru, this is likely to produce significantly higher estimates of excess mortality than studies that do not take these effects into account.
BACKGROUND: None.
METHODS: Our analytical framework and empirical strategy account for registered mortality and under-registration. This provides a better estimate of the actual mortality impact of the first wave of the COVID-19 pandemic in Peru. We use population and crude mortality rate projections from Peru\'s National Institute of Statistics and Information (INEI, in Spanish), individual-level registered COVID-19 deaths from the Ministry of Health (MoH), and individual-level registered deaths by region and age since 2017 from the National Electronic Deaths Register (SINADEF, in Spanish).We develop a novel framework combining different estimates and using quasi-Poisson models to estimate total excess mortality across regions and age groups. Also, we use logistic mixed-effects models to estimate the coverage of the new SINADEF system.
RESULTS: We estimate that registered mortality underestimates national mortality by 37•1% (95% CI 23% - 48•5%) across 26 regions and nine age groups. We estimate total all-cause excess mortality during the period of analysis at 173,099 (95% CI 153,669 - 187,488) of which 108,943 (95% CI 96,507 - 118,261) were captured by the vital registration system. Deaths at age 60 and over accounted for 74•1% (95% CI 73•9% - 74•7%) of total excess deaths, and there were fewer deaths than expected in younger age groups. Lima region, on the Pacific coast and including the national capital, accounts for the highest share of excess deaths, 87,781 (95% CI 82,294 - 92,504), while in the opposite side regions of Apurimac and Huancavelica account for less than 300 excess deaths.
CONCLUSIONS: Estimating excess mortality in low- and middle-income countries (LMICs) such as Peru must take under-registration of mortality into account. Combining demographic trends with data from administrative registries reduces uncertainty and measurement errors. In countries like Peru, this is likely to produce significantly higher estimates of excess mortality than studies that do not take these effects into account.
BACKGROUND: None.
摘要：
背景：全因超额死亡率是COVID-19对死亡率的直接和间接综合影响的综合指标。估计通常来自民事登记和生命统计(CRVS)系统，但这些不包括未登记的死亡，随着时间的推移，这可能会受到重要登记覆盖范围变化的影响。
方法：我们的分析框架和经验策略说明了登记死亡率和登记不足。这为秘鲁第一波COVID-19大流行的实际死亡率影响提供了更好的估计。我们使用秘鲁国家统计和信息研究所的人口和粗死亡率预测(INEI，西班牙语)，卫生部(MoH)登记的个人层面的COVID-19死亡，以及自2017年以来按地区和年龄从国家电子死亡登记册(SINADEF，在西班牙语中）。我们开发了一个新的框架，结合不同的估计，并使用准泊松模型来估计各地区和年龄组的总超额死亡率。此外，我们使用逻辑混合效应模型来估计新的SINADEF系统的覆盖率。
结果：我们估计，在26个地区和9个年龄组中，登记死亡率低估了全国死亡率37•1%（95%CI23%-48•5%）。我们估计分析期间的全因超额总死亡率为173,099（95％CI153,669-187,488），其中108,943（95％CI96,507-118,261）被生命登记系统捕获。60岁及以上的死亡人数占总超额死亡人数的74•1%（95%CI73•9%-74•7%），年轻年龄组的死亡人数少于预期。利马地区,在太平洋沿岸，包括国家首都，占超额死亡的最高比例，87,781（95%CI82,294-92,504），而在Apurimac和Huancavelica的相对侧区域，死亡人数不足300。
结论：估计秘鲁等低收入和中等收入国家（LMICs）的超额死亡率必须考虑死亡率登记不足。将人口趋势与行政登记处的数据相结合可以减少不确定性和测量误差。在秘鲁这样的国家,与未考虑这些影响的研究相比,这可能产生更高的超额死亡率估计值.
背景：无。
方法：我们的分析框架和经验策略说明了登记死亡率和登记不足。这为秘鲁第一波COVID-19大流行的实际死亡率影响提供了更好的估计。我们使用秘鲁国家统计和信息研究所的人口和粗死亡率预测(INEI，西班牙语)，卫生部(MoH)登记的个人层面的COVID-19死亡，以及自2017年以来按地区和年龄从国家电子死亡登记册(SINADEF，在西班牙语中）。我们开发了一个新的框架，结合不同的估计，并使用准泊松模型来估计各地区和年龄组的总超额死亡率。此外，我们使用逻辑混合效应模型来估计新的SINADEF系统的覆盖率。
结果：我们估计，在26个地区和9个年龄组中，登记死亡率低估了全国死亡率37•1%（95%CI23%-48•5%）。我们估计分析期间的全因超额总死亡率为173,099（95％CI153,669-187,488），其中108,943（95％CI96,507-118,261）被生命登记系统捕获。60岁及以上的死亡人数占总超额死亡人数的74•1%（95%CI73•9%-74•7%），年轻年龄组的死亡人数少于预期。利马地区,在太平洋沿岸，包括国家首都，占超额死亡的最高比例，87,781（95%CI82,294-92,504），而在Apurimac和Huancavelica的相对侧区域，死亡人数不足300。
结论：估计秘鲁等低收入和中等收入国家（LMICs）的超额死亡率必须考虑死亡率登记不足。将人口趋势与行政登记处的数据相结合可以减少不确定性和测量误差。在秘鲁这样的国家,与未考虑这些影响的研究相比,这可能产生更高的超额死亡率估计值.
背景：无。
