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

  • International Journal of Health Geographics, Volume 19, No. 1, Year 2020

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Study Justification:
– The study aimed to investigate the relationship between travel time to health facilities and the use of maternal care services by pregnant women.
– It sought to compare self-reported travel times with modelled travel times using GIS technology.
– The study aimed to determine if women seek care at the closest health facility and if modelled access accurately predicts vulnerability in different spatial settings.
Study Highlights:
– Modelled geographical access to health facilities was generally lower than self-reported access.
– In India and Pakistan, modelled access closely matched self-reported travel times.
– In Mozambique, there were significant differences between modelled and self-reported access.
– The study concluded that modelling access can successfully predict potential vulnerability in populations.
– Differences between modelled and self-reported travel times were partially due to women not seeking care at their closest facilities.
– Modelling assumptions may be influenced by spatio-temporal and socio-cultural factors.
Study Recommendations:
– Consider the geography of the relationship between modelled and self-reported access.
– Recognize that modelling assumptions may vary across different spatial settings.
– Take into account the disproportionate variations in differences in access across different areas.
Key Role Players:
– Researchers and data analysts
– Health policymakers and administrators
– Community health workers
– Health facility staff
– Women’s advocacy groups
Cost Items for Planning Recommendations:
– Research and data analysis costs
– Training and capacity-building for health workers
– Implementation of community-level interventions
– Monitoring and evaluation activities
– Communication and awareness campaigns
– Infrastructure improvements for health facilities
– Transportation and logistics for maternal care services
– Support for women’s advocacy groups and community engagement initiatives

The strength of evidence for this abstract is 7 out of 10.
The evidence in the abstract is moderately strong. The study compares self-reported travel times to health facilities with modelled travel times in Mozambique, India, and Pakistan. Bland-Altman analysis is used to compare the differences between the two measures. The study finds that modelled access generally underestimates self-reported access, but this relationship varies geographically. The study concludes that modelling assumptions are likely modified by spatio-temporal and/or socio-cultural settings. To improve the evidence, the study could provide more details on the methodology used for modelling access and the specific limitations of the model. Additionally, including a larger sample size and conducting further statistical analysis could strengthen the findings.

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 (H0: 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 (H0: 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, https://clinicaltrials.gov/ct2/show/NCT01911494

This study was conducted as part of the Community Level Interventions for Pre-eclampsia (CLIP) trials. The study is a population-level cross sectional secondary analysis from the CLIP cluster randomized controlled trials [13] that introduces evidence-based interventions applied primarily at the community level to reduce maternal and perinatal mortality and morbidity in the intervention clusters resulting from the failure to identify and manage pre-eclampsia.. The study draws evidence from Maputo and Gaza provinces southern Mozambique, Karnataka, India and Sindh, Pakistan (Fig. 1). The trials were designed to address the excess maternal and perinatal mortality in low- and middle-income countries (LMICs) with participants all from a non-masked parallel assignment intervention model. CLIP Mozambique, India and Pakistan study sites Our study population includes all women aged 15–49 years who participated in the CLIP trials between September 2013 and May 2018. Samples of pregnant or postpartum women with complete data on where the woman came from to access care, where care was sought, as well as paired data on both self-reported and geographically modelled travel time to care facilities in the three study sites were included in the study. The final participating samples included into the study were 555 women in Mozambique, 517 in India and 159 pregnant women in Pakistan. These women qualified for inclusion to answer the first objective of the study. Of these, 265 women in Mozambique, 293 women in India and all the 159 women in Pakistan qualified to answer the second objective of the study. Figure 2 below shows the inclusion/exclusion sample flow in this analysis. Study sites with final participating samples per study site

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Based on the information provided, it seems that the study aims to analyze travel times to health facilities for pregnant women seeking maternal care in Mozambique, India, and Pakistan. The study compares self-reported travel times with modelled geographical access to determine if women seek care at the closest health facility. The study also explores the assumptions made in modelling access and how they may vary across different spatial settings.

In terms of innovations to improve access to maternal health, based on the study’s objectives, here are some potential recommendations:

1. Improve transportation infrastructure: Enhancing road networks and transportation systems can reduce travel times to health facilities, making it easier for pregnant women to access maternal care.

2. Establish more health facilities: Increasing the number of health facilities, particularly in areas with limited access, can ensure that pregnant women have closer options for receiving maternal care.

3. Implement mobile health clinics: Mobile clinics can bring maternal health services closer to remote or underserved areas, reducing the need for women to travel long distances for care.

4. Utilize telemedicine and teleconsultations: Implementing telemedicine services can enable pregnant women to access healthcare professionals remotely, reducing the need for physical travel to health facilities.

5. Strengthen community-level interventions: Empowering local communities to provide basic maternal health services, such as antenatal care and postnatal support, can reduce the need for women to travel long distances for routine care.

6. Improve data collection and modelling techniques: Enhancing the accuracy and reliability of data collection and modelling methods can help better predict travel times and access to health facilities, enabling more effective planning and resource allocation.

It’s important to note that these recommendations are based on the study’s objectives and the information provided. Further research and analysis may be required to determine the most appropriate and effective innovations for improving access to maternal health in specific contexts.
AI Innovations Description
The study titled “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” aims to improve access to maternal health by comparing travel times to health facilities as reported by women seeking maternal care with modelled travel times in a GIS environment.

The study found that modelled geographical access to health facilities is generally lower than self-reported access. However, there is a geographical variation in this relationship. In India and Pakistan, the modelled access compared fairly with self-reported travel times, while in Mozambique, there were significant differences between the two measures of access.

The study concludes that modelling access can successfully predict potential vulnerability in populations. However, differences between modelled and self-reported travel times are partially due to women not seeking care at their closest facilities. It emphasizes that modelling assumptions should not be viewed through a geographically static lens and are likely modified by spatio-temporal and socio-cultural settings.

The study was conducted as part of the Community Level Interventions for Pre-eclampsia (CLIP) trials, which aim to reduce maternal and perinatal mortality and morbidity in low- and middle-income countries. The study included women from Maputo and Gaza provinces in Mozambique, Karnataka in India, and Sindh in Pakistan.

Overall, the study provides valuable insights into the factors influencing access to maternal health and highlights the need for tailored interventions that consider the specific spatial and cultural contexts of different regions.


Statistics:
Citations: 6
Identifiers:
DOI: 10.1186/s12942-020-0197-5
Research Areas:
Community Interventions, Health System and Policy, Maternal and Child Health, Quality of Care, Sexual and Reproductive Health, Social Determinants
Study Countries:
Mozambique
Study Design:
Cross Sectional Study, randomised-control-trial
Participants Gender:
Female
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