Exley Josephine, Bhattacharya Antoinette, Hanson Claudia, Shuaibu Abdulrahman, Umar Nasir, Marchant Tanya
Department of Disease Control, London School of Hygiene & Tropical Medicine, London, United Kingdom.
Department of Public Health Sciences-Global Health, Karolinska Institutet, Stockholm, Sweden.
PLOS Glob Public Health. 2022 Apr 21;2(4):e0000359. doi: 10.1371/journal.pgph.0000359. eCollection 2022.
Estimating effective coverage of childbirth care requires linking population based data sources to health facility data. For effective coverage to gain widespread adoption there is a need to focus on the feasibility of constructing these measures using data typically available to decision makers in low resource settings. We estimated effective coverage of childbirth care in Gombe State, northeast Nigeria, using two different combinations of facility data sources and examined their strengths and limitations for decision makers. Effective coverage captures information on four steps: access, facility inputs, receipt of interventions and process quality. We linked data from the 2018 Nigerian Demographic and Health Survey (NDHS) to two sources of health facility data: (1) comprehensive health facility survey data generated by a research project; and (2) District Health Information Software 2 (DHIS2). For each combination of data sources, we examined which steps were feasible to calculate, the size of the drop in coverage between steps and the resulting estimate of effective coverage. Analysis included 822 women with a recent live birth, 30% of whom attended a facility for childbirth. Effective coverage was low: 2% based on the project data and less than 1% using the DHIS2. Linking project data with NDHS, it was feasible to measure all four steps; using DHIS2 it was possible to estimate three steps: no data was available to measure process quality. The provision of high quality care is suboptimal in this high mortality setting where access and facility readiness to provide care, crucial foundations to the provision of high quality of care, have not yet been met. This study demonstrates that partial effective coverage measures can be constructed from routine data combined with nationally representative surveys. Advocacy to include process of care indicators in facility summary reports could optimise this data source for decision making.
估算分娩护理的有效覆盖率需要将基于人群的数据源与卫生设施数据相联系。为了使有效覆盖率得到广泛应用，有必要关注利用低资源环境中决策者通常可获取的数据来构建这些指标的可行性。我们使用两种不同的卫生设施数据源组合估算了尼日利亚东北部贡贝州的分娩护理有效覆盖率，并探讨了它们对决策者而言的优势和局限性。有效覆盖率涵盖四个方面的信息：可及性、设施投入、干预措施的接受情况以及过程质量。我们将2018年尼日利亚人口与健康调查（NDHS）的数据与两个卫生设施数据源相联系：（1）一个研究项目生成的综合卫生设施调查数据；以及（2）地区卫生信息软件2（DHIS2）。对于每种数据源组合，我们考察了哪些方面的计算是可行的、各方面之间覆盖率下降的幅度以及最终得出的有效覆盖率估算值。分析纳入了822名近期有活产的妇女，其中30%在医疗机构分娩。有效覆盖率较低：基于项目数据为2%，使用DHIS2则不到1%。将项目数据与NDHS相联系时，测量所有四个方面是可行的；使用DHIS2时，可以估算三个方面：没有数据可用于测量过程质量。在这个高死亡率的环境中，高质量护理的提供并不理想，在提供高质量护理的关键基础——可及性和设施准备情况方面尚未得到满足。本研究表明，可以从常规数据与具有全国代表性的调查相结合的数据中构建部分有效覆盖率指标。倡导在医疗机构总结报告中纳入护理过程指标，可优化这一用于决策的数据源。
