Brazilian Institute of Geography and Statistics (IBGE), Level 8, 500 Republic of Chile Avenue, Rio de Janeiro, RJ, 20031-170, Brazil.
Ministry of Health, SRTVN 701, Via W5 Norte, PO700 Building, 6th floor-DASNT, Brasilia, DF, 70723-040, Brazil.
Popul Health Metr. 2020 Sep 4;18(1):22. doi: 10.1186/s12963-020-00223-2.
In Brazil, both the Civil Registry (CR) and Ministry of Health (MoH) Mortality Information System (SIM) are sources of routine mortality data, but neither is 100% complete. Deaths from these two sources can be linked to facilitate estimation of completeness of mortality reporting and measurement of adjusted mortality indicators using generalized linear modeling (GLM).
The 2015 and 2016 CR and SIM data were linked using deterministic methods. GLM with covariates of the deceased's sex, age, state of residence, cause of death and place of death, and municipality-level education decile and population density decile, was used to estimate total deaths and completeness nationally, subnationally and by population sub-group, and to identify the characteristics of unreported deaths. The empirical completeness method and Global Burden of Disease (GBD) 2017 estimates were comparators at the national and state level.
Completeness was 98% for SIM and 95% for CR. The vast majority of deaths in Brazil were captured by either system and 94% were reported by both sources. For each source, completeness was lowest in the north. SIM completeness was consistently high across all sub-groups while CR completeness was lowest for deaths at younger ages, outside facilities, and in the lowest deciles of municipality education and population density. There was no clear municipality-level relationship in SIM and CR completeness, suggesting minimal dependence between sources. The empirical completeness method model 1 and GBD completeness estimates were each, on average, less than three percentage points different from GLM estimates at the state level. Life expectancy was lowest in the northeast and 7.5 years higher in females than males.
GLM using socio-economic and demographic covariates is a valuable tool to accurately estimate completeness from linked data sources. Close scrutiny of the quality of variables used to link deaths, targeted identification of unreported deaths in poorer, northern states, and closer coordination of the two systems will help Brazil achieve 100% death reporting completeness. The results also confirm the validity of the empirical completeness method.
在巴西，民事登记处（CR）和卫生部（MoH）死因信息系统（SIM）都是常规死亡率数据的来源，但都没有达到 100%的完整性。可以将这两个来源的死亡数据进行链接，以方便估计死亡率报告的完整性，并使用广义线性模型（GLM）来衡量调整后的死亡率指标。
使用确定性方法将 2015 年和 2016 年的 CR 和 SIM 数据进行链接。使用带有死者性别、年龄、居住地、死因和死亡地点以及市教育程度和人口密度十分位数等协变量的 GLM，来估计全国、次国家和人口亚组的总死亡人数和完整性，并确定未报告死亡人数的特征。国家和州一级的经验完整性方法和全球疾病负担（GBD）2017 估计值是比较器。
SIM 的完整性为 98%，CR 为 95%。巴西的绝大多数死亡都被这两个系统所记录，其中 94%都由两个系统共同报告。对于每个系统，北部的完整性最低。SIM 的完整性在所有亚组中都保持一致，而 CR 的完整性在年轻人群、外部设施以及市教育和人口密度最低的十分位数中最低。SIM 和 CR 的完整性在市一级没有明显的关系，这表明两个来源之间的依赖性很小。经验完整性方法模型 1 和 GBD 完整性估计值平均比 GLM 州级估计值低不到三个百分点。预期寿命在东北部最低，女性比男性高 7.5 岁。
使用社会经济和人口统计学协变量的 GLM 是一种从链接数据源准确估计完整性的有价值的工具。仔细检查用于链接死亡的变量的质量，有针对性地确定在较贫穷的北部州未报告的死亡人数，并加强两个系统的协调，将有助于巴西实现 100%的死亡报告完整性。结果还证实了经验完整性方法的有效性。
