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雨下得很大:利用莫桑比克常规数据检测孕产妇保健利用的季节性模式。

When it rains, it pours: detecting seasonal patterns in utilization of maternal healthcare in Mozambique using routine data.

机构信息

Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA.

Instituto Nacional de Saúde, Ministério da Saúde, Maputo, Mozambique.

出版信息

BMC Health Serv Res. 2020 Oct 15;20(1):950. doi: 10.1186/s12913-020-05807-0.

DOI:10.1186/s12913-020-05807-0
PMID:33059682
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC7559485/
Abstract

BACKGROUND

Climatic conditions and seasonal trends can affect population health, but typically, we consider the effect of climate on the epidemiology of communicable diseases. However, climate can also have an effect on access to care, particularly in remote rural areas of low- and middle-income countries. In this study, we investigate associations between the rainy season and the utilization of maternal health services in Mozambique.

METHODS

We examined patterns in the number of women receiving antenatal care (ANC) and delivering at a health facility for 2012-2019, using data from Mozambique's Health Management Information Systems. We investigated the association between seasonality (rainfall) and maternal health service utilization (ANC and institutional delivery) at national and provincial level. We fit a negative binomial regression model for institutional delivery and used it to estimate the yearly reduction in institutional deliveries due to the rainy season, with other factors held constant. We used the Lives Saved Tool (LiST) to model increases in mortality due to this estimated decrease in institutional delivery associated with the rainy season.

RESULTS

In our national analysis, the rate of ANC visits was 1% lower during the rainy season, adjusting for year and province (IRR = 0.99, 95% CI: 0.96-1.03). The rate of institutional deliveries was 6% lower during the rainy season than the dry season, after adjusting for time and province (IRR = 0.94, 95% CI: 0.92-0.96). In provincial analyses, all provinces except for Maputo-Cidade, Maputo-Province, Nampula, and Niassa showed a statistically significantly lower rate of institutional deliveries in the rainy season. None were statistically significantly lower for ANC. We estimate that, due to reductions in institutional delivery attributable only to the rainy season, there were 74 additional maternal deaths and 726 additional deaths of children under the age of 1 month in 2021, that would not have died if the mothers had instead delivered at a facility.

CONCLUSION

Fewer women deliver at a health facility during the rainy season in Mozambique than during the dry season. Barriers to receiving care during pregnancy and childbirth must be addressed using a multisectoral approach, considering the impact of geographical inequities.

摘要

背景

气候条件和季节性趋势会影响人口健康,但通常情况下,我们认为气候会对传染病的流行病学产生影响。然而,气候也会影响获得医疗保健的机会,尤其是在低收入和中等收入国家的偏远农村地区。在这项研究中,我们调查了莫桑比克雨季与孕产妇保健服务利用之间的关联。

方法

我们利用莫桑比克卫生管理信息系统的数据,研究了 2012-2019 年期间接受产前护理 (ANC) 和在医疗机构分娩的妇女人数模式。我们在国家和省级层面调查了季节性(降雨)和孕产妇保健服务利用(ANC 和机构分娩)之间的关联。我们对机构分娩拟合了负二项回归模型,并使用该模型估计在其他因素保持不变的情况下,由于雨季导致的机构分娩每年减少量。我们使用 Lives Saved Tool (LiST) 对由于雨季导致的机构分娩减少而增加的死亡率进行建模。

结果

在我们的国家分析中,调整年份和省份后,雨季期间 ANC 就诊率降低了 1%(IRR=0.99,95%CI:0.96-1.03)。调整时间和省份后,雨季机构分娩率比旱季低 6%(IRR=0.94,95%CI:0.92-0.96)。在省级分析中,除马普托市、马普托省、楠普拉省和尼亚萨省外,所有省份的雨季机构分娩率均显著降低。在雨季 ANC 就诊率均无显著降低。我们估计,仅由于雨季导致机构分娩减少,2021 年就会有 74 名额外的孕产妇死亡和 726 名 1 个月以下儿童死亡,如果母亲在医疗机构分娩,这些死亡本可以避免。

结论

莫桑比克雨季期间到医疗机构分娩的妇女人数比旱季少。必须采取多部门方法解决怀孕期间和分娩时获得护理的障碍,同时考虑到地理不平等的影响。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/815d/7559485/f39967f35211/12913_2020_5807_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/815d/7559485/7f45eb9312b3/12913_2020_5807_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/815d/7559485/00da0ea5a88c/12913_2020_5807_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/815d/7559485/f39967f35211/12913_2020_5807_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/815d/7559485/7f45eb9312b3/12913_2020_5807_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/815d/7559485/00da0ea5a88c/12913_2020_5807_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/815d/7559485/f39967f35211/12913_2020_5807_Fig3_HTML.jpg

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