Is the closest health facility the one used in pregnancy care-seeking? A cross-sectional comparative analysis of self-reported and modelled geographical access to maternal care in Mozambique, India and Pakistan
Is the closest health facility the one used in pregnancy care-seeking? A cross-sectional comparative analysis of self-reported and modelled geographical access to maternal care in Mozambique, India and Pakistan
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DOI：
10.1186/s12942-020-0197-5
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发表时间：
2020-02-03
影响因子：
4.9
通讯作者：
von Dadelszen, Peter
中科院分区：
文献类型：
--
作者：
Makacha, Liberty;Makanga, Prestige Tatenda;von Dadelszen, Peter
背景前往护理的旅行时间是众所周知的影响卫生服务的吸收。一般来说，需要较长时间才能到保健设施的孕妇最不可能在设施中分娩。目前尚不清楚，如果模型访问预测公平的脆弱性，在不同的空间环境中寻求孕产妇保健的妇女。目的本横截面分析旨在（一）比较旅行时间，以照顾在GIS环境中建模与自我报告的旅行时间的妇女寻求孕产妇护理在社区一级干预先兆子痫：莫桑比克，印度和巴基斯坦;和（二）调查的假设，妇女将寻求照顾在最近的卫生设施。方法对妇女进行采访，以获得估计的旅行时间的卫生设施（R）。还对到最近设施的旅行时间进行了建模（P）（最近设施工具（ArcGIS）），并对到寻求护理的设施的时间进行了估计（A）（路线网络层查找器（ArcGIS））。Bland-Altman分析比较了模拟和自我报告的旅行时间之间差异的空间变化。分析了到最近设施的旅行时间（P）与到实际使用设施的模拟旅行时间（A）之间的差异。使用中位数的对数转换数据比较图和箱形图叠加分布。结果模拟的地理访问（P）一般低于自我报告的访问（R），但有一个地理这种关系。在印度和巴基斯坦，潜在访问（P）与自我报告的旅行时间（R）[P（H-0：平均差异= 0）]相比相当，<.001，一致性界限分别为：[-273.81; 56.40]和[-264.10; 94.25]。在莫桑比克，两种获取措施之间的平均差异显著不同于0 [P（H-0：平均差异= 0）= 0.31，一致性界限：[-187.26; 199.96]]。结论模型访问成功地预测潜在的脆弱性人群。模拟旅行时间（P）和自我报告的旅行时间（R）之间的差异部分是由于妇女没有在最近的设施寻求护理。不应通过地理上静止的透镜来看待建模访问。建模假设可能会因时空和/或社会文化背景而改变。访问的地理分层揭示了差异的不成比例的变化，强调跨空间设置的假设的不同性质。试用注册www.example.com，NCT 01911494。2013年7月30日登记，
Background Travel time to care is known to influence uptake of health services. Generally, pregnant women who take longer to transit to health facilities are the least likely to deliver in facilities. It is not clear if modelled access predicts fairly the vulnerability in women seeking maternal care across different spatial settings. Objectives This cross-sectional analysis aimed to (i) compare travel times to care as modelled in a GIS environment with self-reported travel times by women seeking maternal care in Community Level Interventions for Pre-eclampsia: Mozambique, India and Pakistan; and (ii) investigate the assumption that women would seek care at the closest health facility. Methods Women were interviewed to obtain estimated travel times to health facilities (R). Travel time to the closest facility was also modelled (P) (closest facility tool (ArcGIS)) and time to facility where care was sought estimated (A) (route network layer finder (ArcGIS)). Bland-Altman analysis compared spatial variation in differences between modelled and self-reported travel times. Variations between travel times to the nearest facility (P) with modelled travel times to the actual facilities accessed (A) were analysed. Log-transformed data comparison graphs for medians, with box plots superimposed distributions were used. Results Modelled geographical access (P) is generally lower than self-reported access (R), but there is a geography to this relationship. In India and Pakistan, potential access (P) compared fairly with self-reported travel times (R) [P (H-0: Mean difference = 0)] < .001, limits of agreement: [- 273.81; 56.40] and [- 264.10; 94.25] respectively. In Mozambique, mean differences between the two measures of access were significantly different from 0 [P (H-0: Mean difference = 0) = 0.31, limits of agreement: [- 187.26; 199.96]]. Conclusion Modelling access successfully predict potential vulnerability in populations. Differences between modelled (P) and self-reported travel times (R) are partially a result of women not seeking care at their closest facilities. Modelling access should not be viewed through a geographically static lens. Modelling assumptions are likely modified by spatio-temporal and/or socio-cultural settings. Geographical stratification of access reveals disproportionate variations in differences emphasizing the varied nature of assumptions across spatial settings. Trial registration ClinicalTrials.gov, NCT01911494. Registered 30 July 2013,