Faculty of Science and Technology, Surveying and Geomatics, Midlands State University, Gweru, Zimbabwe.
Department of Obstetrics and Gynaecology, University of British Columbia, Vancouver, Canada.
Int J Health Geogr. 2020 Feb 3;19(1):1. doi: 10.1186/s12942-020-0197-5.
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.
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.
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.
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: 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: Mean difference = 0) = 0.31, limits of agreement: [- 187.26; 199.96]].
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, https://clinicaltrials.gov/ct2/show/NCT01911494.
人们出行前往医疗机构所花费的时间会影响他们对医疗服务的使用。通常来说,前往医疗机构用时较长的孕妇最不可能在医疗机构分娩。目前尚不清楚,在不同的空间环境下,利用模型预测的可达性是否能够较为准确地预测女性对孕产妇保健服务的需求。
本横断面分析旨在:(i)比较地理信息系统(GIS)环境中建模的出行时间与在社区级干预预防子痫前期:莫桑比克、印度和巴基斯坦的孕产妇保健服务中自我报告的出行时间;(ii)检验一个假设,即女性会选择前往最近的医疗机构就诊。
对女性进行访谈,以获取她们前往医疗机构的估计出行时间(R)。还对最近的医疗机构的出行时间进行建模(P)(最近的医疗机构工具(ArcGIS)),并估算女性前往就诊的实际医疗机构的出行时间(A)(路径网络层查找器(ArcGIS))。Bland-Altman 分析比较了模型预测和自我报告出行时间之间的空间差异。分析了最近的医疗机构出行时间(P)与实际就诊医疗机构出行时间(A)之间的差异。使用对数转换数据的中位数比较图,并叠加箱线图。
建模的地理可达性(P)通常低于自我报告的可达性(R),但这种关系具有一定的地理特征。在印度和巴基斯坦,潜在可达性(P)与自我报告的出行时间(R)相当接近[P(H:平均差值=0)] < .001,一致性界限:[-273.81;56.40]和[-264.10;94.25]。在莫桑比克,这两种可达性测量方法的平均差异显著不为 0 [P(H:平均差值=0)=0.31,一致性界限:[-187.26;199.96]]。
模型预测可达性成功地预测了人群中潜在的脆弱性。模型预测的可达性(P)与自我报告的出行时间(R)之间的差异部分是由于女性未选择前往最近的医疗机构就诊所致。对可达性的建模不应从地理静态的角度来看待。建模的假设可能会受到时空和/或社会文化背景的影响。可达性的地理分层揭示了差异的比例变化,强调了空间设置的不同假设性质。
ClinicalTrials.gov,NCT01911494。2013 年 7 月 30 日注册,https://clinicaltrials.gov/ct2/show/NCT01911494。