The influence of travel time to health facilities on stillbirths: A geospatial case-control analysis of facility-based data in Gombe, Nigeria

PLoS ONE, Volume 16, No. 1 January, Year 2021

Ukuhunyushwa

Access to quality emergency obstetric and newborn care (EmONC); having a skilled attendant at birth (SBA); adequate antenatal care; and efficient referral systems are considered the most effective interventions in preventing stillbirths. We determined the influence of travel time from mother’s area of residence to a tertiary health facility where women sought care on the likelihood of delivering a stillbirth. We carried out a prospective matched case-control study between 1st January 2019 and 31st December 2019 at the Federal Teaching Hospital Gombe (FTHG), Nigeria. All women who experienced a stillbirth after hospital admission during the study period were included as cases while controls were consecutive age-matched (ratio 1:1) women who experienced a live birth. We modelled travel time to health facilities. To determine how travel time to the nearest health facility and the FTHG were predictive of the likelihood of stillbirths, we fitted a conditional logistic regression model. A total of 318 women, including 159 who had stillborn babies (cases) and 159 age-matched women who had live births (controls) were included. We did not observe any significant difference in the mean travel time to the nearest government health facility for women who had experienced a stillbirth compared to those who had a live birth [9.3 mins (SD 7.3, 11.2) vs 6.9 mins (SD 5.1, 8.7) respectively, p = 0.077]. However, women who experienced a stillbirth had twice the mean travel time of women who had a live birth (26.3 vs 14.5 mins) when measured from their area of residence to the FTHG where deliveries occurred. Women who lived farther than 60 minutes were 12 times more likely of having a stillborn [OR = 12 (1.8, 24.3), p = 0.011] compared to those who lived within 15 minutes travel time to the FTHG. We have shown for the first time, the influence of travel time to a major tertiary referral health facility on the occurrence of stillbirths in an urban city in, northeast Nigeria. This study was conducted at the Federal Teaching Hospital, Gombe (FTHG), a major tertiary health facility located in Gombe City, the capital of Gombe State, northeast Nigeria. Gombe State shares borders with five other states, namely Adamawa, Bauchi, Borno, Taraba, and Yobe. Gombe State is predominantly rural, occupies a total land area of about 20,265sqkm, has an estimated population of 2.9 million people, a population density of 148 per km2, and an annual population growth rate of 4.05% [22, 23]. Most women access maternity services through state-funded public-sector primary and secondary health facilities. Gombe State has more than 600 public-sector and private health facilities spread across 11 Local Government Areas (the equivalent of a district) [24]. More than 90% of the health facilities in Gombe State are primary-level facilities offering basic preventative/curative care, while only about 4% are secondary and tertiary-level facilities offering specialised care [24]. Fig 1 shows the study area map. Note: This map was produced by the authors with administrative boundaries data from geoBoundaries [25]. The FTHG is the only tertiary hospital in Gombe (see Fig 1 for location). It has 450-bed capacity that offers specialised care, funded by the Federal (central) government, and receives referrals from Gombe and surrounding States. The hospital provides Comprehensive Emergency Obstetric and Newborn Care (CEmONC) and adequately staffed with obstetricians, gynaecologists, midwives, anaesthetists as well as neonatologists. They perform safe blood transfusion, caesarean sections, assisted vaginal delivery, and resuscitation of the newborn. The hospital records averagely 2,400 deliveries annually, with 27% of all births delivered by caesarean section. Between 2010 and 2018, the FTHG annual SBR ranged from 42 per 1000 births (95% CI:34,51) to 65 per 1000 births (95% CI: 55,76), with 52% of all stillbirths being intrapartum [26]. We carried out a prospective case-control study at the Obstetrics department of the FTHG between 1st January 2019 and 31st December 2019. All women who experienced a stillbirth after hospital admission during the study period were included as cases while controls were consecutive age-matched women who experienced a live birth. The case to control ratio was 1:1 (i.e. individual matching), and age-matched controls were within two years standard deviation of their respective cases. Women whose pregnancies culminated in multiple births were excluded. Although we did not have a predetermined sample size, we consider that our sample can be representative of a larger population as we included all stillbirths (considered rare events) that occurred over a year period with their appropriate controls in our study setting–a major referral facility. Written informed consent was obtained from all eligible women after delivery before a pre-tested, researcher administered questionnaire (S1 File) was used to collect information. The questionnaire was developed, pre-tested and adapted based on stillbirth data from our study setting [26]. In our study setting teenage marriage is common, thus, we considered the participants who were below the age of 18 years (the traditional age of consenting in our setting) as ‘emancipated minors’ because all of them were already married [27]. Informed consent (rather than assent) was thus sought from these participants in a similar manner to those women who were 18 years or older. Data collected includes their obstetric history, social, economic, and demographic characteristics. Also, their mode of transport to the hospital and referral pathway before arriving at the FTHG for delivery were collected. Cases and their respective controls were approached with the study information after delivery and informed consent for participation in the study was sought. All participants were informed that participation in the study was voluntary and a decision not to participate will not impact the care they will normally receive post-delivery. There was a recruitment window lasting from the day of delivery until seven days afterwards to allow for some recovery from the stress of a stillbirth. We geocoded the town address of participants using their house address and a smartphone to enable spatial analysis. All addresses were geocoded to the town level for confidentiality and privacy purposes. Due to the larger size and population density of Gombe City, we geocoded suburbs, generally at one square kilometre spatial resolution as towns and used their centroids. All other locations where women came from were geocoded as towns using OpenStreetMap [26]. Therefore, the suburbs of Gombe City and the other locations had relatively similar sizes. For the location of health facilities, we included the geocoordinates of all government-run public health facilities near the residential areas of participants. The coordinates were obtained from an open-source spatial database of health facilities managed by the public health sector in sub-Saharan Africa curated by the WHO [28]. Travel time to health facilities was modelled in AccessMod5.0 [29]. Travel time was chosen to model physical geographic access because it is a better measure that incorporates elevation, road network, and travel speed among other factors that influence geographic accessibility compared to network and straight-line distances [30]. Furthermore, we used AccessMod5.0 because it is free software, simple to use and widely used for analysing geographic accessibility to health services [21, 31]. Travel times were modelled to two destinations, first to the nearest government health facility (i.e., public primary and secondary facilities, excluding dispensaries) then to FTHG (the major referral facility in Gombe) where all the cases and controls delivered their babies. To estimate travel times, we used land cover [32], roads and rivers [33], digital elevation model [34], and the location of health facilities [28]. The travel speed used to estimate travel times varied by road (primary = 100kmh-1, secondary = 50kmh-1, tertiary = 30kmh-1) and land cover type adapted from previous studies [31, 35]. We assumed 10 kmh-1 on tracks for motorbikes, tricycles and other types of improvised ambulances used to transport women in labour. Details of the travel speeds applied by landcover and type of road are included as Table in S1 Table. To avoid creating artificial bridges across water bodies, road segments that intersect water bodies but not fully crossing it due to digitising, conversion or other topological error were corrected using the “clean artefacts” option in AccessMod [29]. The clean artefact function removes only the artificial bridge and includes the other segments of the road in the model. The estimated travel times account for variations in walking and bicycling speed due to changing elevation when travelling towards a health facility. The corrections for walking speed due to changing elevation was implemented with the Tobler’s formula while bicycling speed was adjusted using a complex physical model based on velocity, power and resistance that are explained into details in the AccessMod user manual [29]. Finally, we extracted the average travel times within a kilometre distance of the woman’s residential town. Then, we calculated the extra time travelled using the difference between travel time to the nearest health facility and the FTHG. All statistical analyses were performed with Statistical Package for the Social Sciences (SPSS) (IBM, NY, version 24), figures were generated using ggplot2 in R and maps were created using ArcGIS® software (version 10.4) by Esri [36]. Freely available to use state outline data from geoBoundaries [25], and OpenStreetMap [33] basemaps were used to draw the map figures. We produced two maps, one showing stillbirth or live births layered on travel times and the second showing flows of women towards FTGH. Summary tables for maternal sociodemographic and geographic accessibility characteristics were generated, firstly for cases (APSB and IPSV) versus controls (live births) and then for cases alone. Categorical and continuous variables were summarised as proportions and means respectively. Cross-tabulations comparing cases versus controls and IPSB versus APSB were performed. Independent sample t-test was used to compare means between groups and chi-square/Fischer’s exact test for association between groups, with statistical significance defined as alpha less than 0.05 (two-sided). We fitted a conditional logistic regression model to predict the likelihood of stillbirths. The independent variables in the regression model were travelling time to the nearest health facility (at intervals of 5 mins), and FTHG (at intervals of 15 mins). The crude regression model was adjusted for known confounders, including the level of education, maternal occupation, parity, booking status, and mode of transport to the hospital on the day of delivery. The confounders were selected a priori based on the literature on predictors of stillbirths in sub-Saharan Africa. We report adjusted odds (AOR) ratios and 95% confidence interval (CI). This study was reviewed and approved by the Research and Ethics Committee (REC) of the Federal Teaching Hospital Gombe (NHREC/25/10/2013). Informed consent was sought from all study participants before participation in this study.

I-AI Digest

Study Justification:

The study aimed to investigate the influence of travel time from a mother’s residence to a tertiary health facility on the likelihood of delivering a stillbirth. This is important because access to quality emergency obstetric and newborn care, skilled attendants at birth, adequate antenatal care, and efficient referral systems are crucial in preventing stillbirths. By understanding the impact of travel time on stillbirths, policymakers and healthcare providers can develop strategies to improve access to healthcare facilities and reduce stillbirth rates.

Study Highlights:

– The study was conducted at the Federal Teaching Hospital Gombe (FTHG) in Gombe, Nigeria, between January 2019 and December 2019.
– A total of 318 women, including 159 who had stillborn babies (cases) and 159 age-matched women who had live births (controls), were included in the study.
– The study found that women who experienced a stillbirth had twice the mean travel time to the FTHG compared to women who had a live birth.
– Women who lived farther than 60 minutes from the FTHG were 12 times more likely to have a stillborn baby compared to those who lived within 15 minutes travel time.
– The study highlights the importance of reducing travel time to major tertiary referral health facilities in urban areas to prevent stillbirths.

Recommendations for Lay Readers:

– Improve access to quality emergency obstetric and newborn care by reducing travel time to health facilities.
– Increase the number of skilled attendants at birth and ensure adequate antenatal care for pregnant women.
– Strengthen referral systems to ensure efficient and timely transfer of high-risk pregnancies to appropriate healthcare facilities.
– Invest in infrastructure and transportation to improve accessibility to healthcare facilities.

Recommendations for Policy Makers:

– Develop policies and strategies to reduce travel time to major tertiary health facilities, especially in urban areas.
– Increase funding for the construction and maintenance of healthcare facilities in underserved areas.
– Improve transportation infrastructure to facilitate easier access to healthcare facilities.
– Train and deploy more skilled healthcare providers, particularly in rural areas.
– Strengthen referral systems and establish protocols for timely and appropriate transfer of high-risk pregnancies.

Key Role Players:

– Ministry of Health: Responsible for developing and implementing policies to improve access to healthcare facilities.
– Healthcare Providers: Including doctors, nurses, midwives, and other healthcare professionals who play a crucial role in providing quality obstetric and newborn care.
– Local Government Authorities: Responsible for infrastructure development and transportation planning.
– Community Leaders: Engage with the community to raise awareness about the importance of accessing healthcare facilities and support initiatives to improve accessibility.

Cost Items for Planning Recommendations:

– Construction and maintenance of healthcare facilities.
– Training and deployment of skilled healthcare providers.
– Infrastructure development, including roads and transportation systems.
– Awareness campaigns and community engagement initiatives.
– Monitoring and evaluation of the implemented interventions.
Please note that the cost estimates provided are for planning purposes and may vary depending on the specific context and resources available.

Amandla Obufakazi

The strength of evidence for this abstract is 8 out of 10.
The evidence in the abstract is strong, but there are some areas for improvement. The study design is prospective and includes a matched case-control analysis, which provides a robust method for examining the influence of travel time on stillbirths. The sample size is adequate, with 159 cases and 159 controls. The statistical analysis includes a conditional logistic regression model, which accounts for confounding variables. However, there are a few areas that could be improved. Firstly, the abstract does not provide information on the representativeness of the sample and whether the findings can be generalized to the larger population. Secondly, the abstract does not mention any limitations of the study, such as potential biases or confounding factors that were not accounted for. Lastly, the abstract does not provide any information on the strength of the associations found (e.g., effect sizes, p-values). To improve the evidence, the authors could provide more information on the representativeness of the sample, acknowledge any limitations of the study, and include effect sizes and p-values in the abstract.

Abstract

This study was conducted at the Federal Teaching Hospital, Gombe (FTHG), a major tertiary health facility located in Gombe City, the capital of Gombe State, northeast Nigeria. Gombe State shares borders with five other states, namely Adamawa, Bauchi, Borno, Taraba, and Yobe. Gombe State is predominantly rural, occupies a total land area of about 20,265sqkm, has an estimated population of 2.9 million people, a population density of 148 per km2, and an annual population growth rate of 4.05% [22, 23]. Most women access maternity services through state-funded public-sector primary and secondary health facilities. Gombe State has more than 600 public-sector and private health facilities spread across 11 Local Government Areas (the equivalent of a district) [24]. More than 90% of the health facilities in Gombe State are primary-level facilities offering basic preventative/curative care, while only about 4% are secondary and tertiary-level facilities offering specialised care [24]. Fig 1 shows the study area map. Note: This map was produced by the authors with administrative boundaries data from geoBoundaries [25]. The FTHG is the only tertiary hospital in Gombe (see Fig 1 for location). It has 450-bed capacity that offers specialised care, funded by the Federal (central) government, and receives referrals from Gombe and surrounding States. The hospital provides Comprehensive Emergency Obstetric and Newborn Care (CEmONC) and adequately staffed with obstetricians, gynaecologists, midwives, anaesthetists as well as neonatologists. They perform safe blood transfusion, caesarean sections, assisted vaginal delivery, and resuscitation of the newborn. The hospital records averagely 2,400 deliveries annually, with 27% of all births delivered by caesarean section. Between 2010 and 2018, the FTHG annual SBR ranged from 42 per 1000 births (95% CI:34,51) to 65 per 1000 births (95% CI: 55,76), with 52% of all stillbirths being intrapartum [26]. We carried out a prospective case-control study at the Obstetrics department of the FTHG between 1st January 2019 and 31st December 2019. All women who experienced a stillbirth after hospital admission during the study period were included as cases while controls were consecutive age-matched women who experienced a live birth. The case to control ratio was 1:1 (i.e. individual matching), and age-matched controls were within two years standard deviation of their respective cases. Women whose pregnancies culminated in multiple births were excluded. Although we did not have a predetermined sample size, we consider that our sample can be representative of a larger population as we included all stillbirths (considered rare events) that occurred over a year period with their appropriate controls in our study setting–a major referral facility. Written informed consent was obtained from all eligible women after delivery before a pre-tested, researcher administered questionnaire (S1 File) was used to collect information. The questionnaire was developed, pre-tested and adapted based on stillbirth data from our study setting [26]. In our study setting teenage marriage is common, thus, we considered the participants who were below the age of 18 years (the traditional age of consenting in our setting) as ‘emancipated minors’ because all of them were already married [27]. Informed consent (rather than assent) was thus sought from these participants in a similar manner to those women who were 18 years or older. Data collected includes their obstetric history, social, economic, and demographic characteristics. Also, their mode of transport to the hospital and referral pathway before arriving at the FTHG for delivery were collected. Cases and their respective controls were approached with the study information after delivery and informed consent for participation in the study was sought. All participants were informed that participation in the study was voluntary and a decision not to participate will not impact the care they will normally receive post-delivery. There was a recruitment window lasting from the day of delivery until seven days afterwards to allow for some recovery from the stress of a stillbirth. We geocoded the town address of participants using their house address and a smartphone to enable spatial analysis. All addresses were geocoded to the town level for confidentiality and privacy purposes. Due to the larger size and population density of Gombe City, we geocoded suburbs, generally at one square kilometre spatial resolution as towns and used their centroids. All other locations where women came from were geocoded as towns using OpenStreetMap [26]. Therefore, the suburbs of Gombe City and the other locations had relatively similar sizes. For the location of health facilities, we included the geocoordinates of all government-run public health facilities near the residential areas of participants. The coordinates were obtained from an open-source spatial database of health facilities managed by the public health sector in sub-Saharan Africa curated by the WHO [28]. Travel time to health facilities was modelled in AccessMod5.0 [29]. Travel time was chosen to model physical geographic access because it is a better measure that incorporates elevation, road network, and travel speed among other factors that influence geographic accessibility compared to network and straight-line distances [30]. Furthermore, we used AccessMod5.0 because it is free software, simple to use and widely used for analysing geographic accessibility to health services [21, 31]. Travel times were modelled to two destinations, first to the nearest government health facility (i.e., public primary and secondary facilities, excluding dispensaries) then to FTHG (the major referral facility in Gombe) where all the cases and controls delivered their babies. To estimate travel times, we used land cover [32], roads and rivers [33], digital elevation model [34], and the location of health facilities [28]. The travel speed used to estimate travel times varied by road (primary = 100kmh-1, secondary = 50kmh-1, tertiary = 30kmh-1) and land cover type adapted from previous studies [31, 35]. We assumed 10 kmh-1 on tracks for motorbikes, tricycles and other types of improvised ambulances used to transport women in labour. Details of the travel speeds applied by landcover and type of road are included as Table in S1 Table. To avoid creating artificial bridges across water bodies, road segments that intersect water bodies but not fully crossing it due to digitising, conversion or other topological error were corrected using the “clean artefacts” option in AccessMod [29]. The clean artefact function removes only the artificial bridge and includes the other segments of the road in the model. The estimated travel times account for variations in walking and bicycling speed due to changing elevation when travelling towards a health facility. The corrections for walking speed due to changing elevation was implemented with the Tobler’s formula while bicycling speed was adjusted using a complex physical model based on velocity, power and resistance that are explained into details in the AccessMod user manual [29]. Finally, we extracted the average travel times within a kilometre distance of the woman’s residential town. Then, we calculated the extra time travelled using the difference between travel time to the nearest health facility and the FTHG. All statistical analyses were performed with Statistical Package for the Social Sciences (SPSS) (IBM, NY, version 24), figures were generated using ggplot2 in R and maps were created using ArcGIS® software (version 10.4) by Esri [36]. Freely available to use state outline data from geoBoundaries [25], and OpenStreetMap [33] basemaps were used to draw the map figures. We produced two maps, one showing stillbirth or live births layered on travel times and the second showing flows of women towards FTGH. Summary tables for maternal sociodemographic and geographic accessibility characteristics were generated, firstly for cases (APSB and IPSV) versus controls (live births) and then for cases alone. Categorical and continuous variables were summarised as proportions and means respectively. Cross-tabulations comparing cases versus controls and IPSB versus APSB were performed. Independent sample t-test was used to compare means between groups and chi-square/Fischer’s exact test for association between groups, with statistical significance defined as alpha less than 0.05 (two-sided). We fitted a conditional logistic regression model to predict the likelihood of stillbirths. The independent variables in the regression model were travelling time to the nearest health facility (at intervals of 5 mins), and FTHG (at intervals of 15 mins). The crude regression model was adjusted for known confounders, including the level of education, maternal occupation, parity, booking status, and mode of transport to the hospital on the day of delivery. The confounders were selected a priori based on the literature on predictors of stillbirths in sub-Saharan Africa. We report adjusted odds (AOR) ratios and 95% confidence interval (CI). This study was reviewed and approved by the Research and Ethics Committee (REC) of the Federal Teaching Hospital Gombe (NHREC/25/10/2013). Informed consent was sought from all study participants before participation in this study.

Izinto zokwakha

Emisha

The study titled “The influence of travel time to health facilities on stillbirths: A geospatial case-control analysis of facility-based data in Gombe, Nigeria” investigated the impact of travel time on maternal health outcomes, specifically stillbirths. The study was conducted at the Federal Teaching Hospital Gombe (FTHG) in Nigeria and included 318 women who experienced stillbirths and 318 age-matched women who had live births.

The key findings of the study are as follows:

1. Travel time to the nearest government health facility did not significantly differ between women who experienced stillbirths and those who had live births.
2. However, women who experienced stillbirths had twice the mean travel time compared to women who had live births when measured from their area of residence to the FTHG.
3. Women who lived farther than 60 minutes from the FTHG were 12 times more likely to have a stillborn baby compared to those who lived within 15 minutes travel time to the FTHG.

Based on these findings, the study highlights the importance of reducing travel time to tertiary health facilities for pregnant women to improve maternal health outcomes, specifically reducing the risk of stillbirths. The study suggests that interventions should focus on improving access to emergency obstetric and newborn care (EmONC), skilled attendants at birth (SBA), and efficient referral systems.

To develop this recommendation into an innovation to improve access to maternal health, several strategies can be considered:

1. Telemedicine and Teleconsultation: Implementing telemedicine services can provide remote access to healthcare professionals, allowing pregnant women to receive medical advice, consultations, and monitoring without the need for travel. This can help reduce travel time and improve access to timely and appropriate care.

2. Mobile Clinics and Outreach Programs: Setting up mobile clinics or organizing regular outreach programs in remote areas can bring healthcare services closer to pregnant women, reducing the need for long-distance travel. These clinics can provide antenatal care, basic obstetric services, and referrals to higher-level facilities when necessary.

3. Improving Transportation Infrastructure: Investing in improving transportation infrastructure, such as roads and public transportation systems, can reduce travel time and make it easier for pregnant women to reach healthcare facilities. This can include building new roads, improving existing ones, and providing affordable and reliable transportation options specifically for pregnant women.

4. Community-Based Maternal Health Programs: Empowering and training community health workers to provide basic maternal healthcare services within their communities can improve access to care, especially in remote areas. These workers can provide antenatal care, health education, and referrals to higher-level facilities when needed.

5. Strengthening Primary Healthcare Facilities: Enhancing the capacity and resources of primary healthcare facilities can ensure that pregnant women have access to quality care closer to their homes. This can include training healthcare providers, improving infrastructure and equipment, and ensuring the availability of essential maternal health services.

By implementing these innovative strategies, access to maternal health can be improved, reducing travel time and ultimately reducing the risk of stillbirths and improving overall maternal and newborn health outcomes.

AI Innovations Description

The study titled “The influence of travel time to health facilities on stillbirths: A geospatial case-control analysis of facility-based data in Gombe, Nigeria” provides valuable insights into the impact of travel time on maternal health outcomes, specifically stillbirths. The study was conducted at the Federal Teaching Hospital Gombe (FTHG) in Nigeria and included 318 women who experienced stillbirths and 318 age-matched women who had live births.

The key findings of the study are as follows:

To develop this recommendation into an innovation to improve access to maternal health, several strategies can be considered:

AI Innovations Methodology

The methodology used in the study titled “The influence of travel time to health facilities on stillbirths: A geospatial case-control analysis of facility-based data in Gombe, Nigeria” involved a prospective matched case-control study conducted at the Federal Teaching Hospital Gombe (FTHG) in Nigeria. The study period was from January 1, 2019, to December 31, 2019.

The study included a total of 318 women who experienced stillbirths (cases) and 318 age-matched women who had live births (controls). The travel time from the women’s area of residence to the nearest government health facility and to the FTHG was modeled using AccessMod5.0 software. Travel time was chosen as a measure of physical geographic access, taking into account factors such as elevation, road network, and travel speed.

To determine the impact of travel time on stillbirths, a conditional logistic regression model was fitted. The model included variables such as travel time to the nearest health facility and the FTHG, as well as confounders such as level of education, maternal occupation, parity, booking status, and mode of transport to the hospital on the day of delivery.

The key findings of the study were as follows:

1. There was no significant difference in the mean travel time to the nearest government health facility between women who experienced stillbirths and those who had live births.
2. However, women who experienced stillbirths had twice the mean travel time compared to women who had live births when measured from their area of residence to the FTHG.
3. Women who lived farther than 60 minutes from the FTHG were 12 times more likely to have a stillborn baby compared to those who lived within 15 minutes travel time to the FTHG.

The study concluded that reducing travel time to tertiary health facilities is crucial in improving maternal health outcomes and reducing the risk of stillbirths. The study recommended interventions such as improving access to emergency obstetric and newborn care (EmONC), skilled attendants at birth (SBA), and efficient referral systems.

To simulate the impact of these recommendations on improving access to maternal health, several strategies can be considered:

1. Telemedicine and Teleconsultation: Implementing telemedicine services can provide remote access to healthcare professionals, allowing pregnant women to receive medical advice, consultations, and monitoring without the need for travel. This can help reduce travel time and improve access to timely and appropriate care.

2. Mobile Clinics and Outreach Programs: Setting up mobile clinics or organizing regular outreach programs in remote areas can bring healthcare services closer to pregnant women, reducing the need for long-distance travel. These clinics can provide antenatal care, basic obstetric services, and referrals to higher-level facilities when necessary.

3. Improving Transportation Infrastructure: Investing in improving transportation infrastructure, such as roads and public transportation systems, can reduce travel time and make it easier for pregnant women to reach healthcare facilities. This can include building new roads, improving existing ones, and providing affordable and reliable transportation options specifically for pregnant women.

4. Community-Based Maternal Health Programs: Empowering and training community health workers to provide basic maternal healthcare services within their communities can improve access to care, especially in remote areas. These workers can provide antenatal care, health education, and referrals to higher-level facilities when needed.

5. Strengthening Primary Healthcare Facilities: Enhancing the capacity and resources of primary healthcare facilities can ensure that pregnant women have access to quality care closer to their homes. This can include training healthcare providers, improving infrastructure and equipment, and ensuring the availability of essential maternal health services.

By simulating the implementation of these strategies, it is possible to assess their potential impact on improving access to maternal health and reducing travel time, ultimately leading to improved maternal and newborn health outcomes.

Umbhali

Dima Health

Statistics:

Citations: 8

Identifiers:

DOI: 10.1371/journal.pone.0245297

Research Areas:

Community Interventions, Environmental, Health System and Policy, Maternal Access, Maternal and Child Health, Quality of Care, Social Determinants, Workforce

Study Countries:

Nigeria

Study Design:

Case-Control Study, Cohort Study, Cross Sectional Study

Study Approach:

Quantitative

Participants Gender:

Female

Yabelana ngalokhu: