An assessment of geographical access and factors influencing travel time to emergency obstetric care in the urban state of Lagos, Nigeria

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Study Justification:
– Previous efforts to estimate travel time to emergency obstetric care in low- and middle-income countries have been based on spatial models or self-reported travel time, both with known inaccuracies.
– This study aims to estimate more realistic travel times for pregnant women in emergency situations using Google Maps.
– The study also aims to determine system-level factors that influence travel time and use these estimates to assess geographical accessibility and coverage of emergency obstetric care in Lagos State, Nigeria.
Study Highlights:
– Data on demographics, obstetric history, and travel to emergency obstetric care facilities were collected from hospital records.
– Bivariate and multivariate analyses were used to test associations between travel and health system-related factors with reaching the facility within 60 minutes.
– Mean travel times were compared and geographical coverage was mapped to identify areas with predominantly long travel times to facilities.
– The study found that the majority of pregnant women in Lagos State were able to reach emergency obstetric care facilities within the recommended travel time benchmarks.
– However, there were identified “hotspots” where pregnant women had to travel more than 60 minutes to reach a facility.
– The study also found that travel time and distance to tertiary referral hospitals were significantly higher than secondary facilities.
Recommendations for Lay Reader and Policy Maker:
– The findings suggest that actions should be taken to address geographical gaps in emergency obstetric care access in Lagos State.
– Stakeholders should use the study’s approach as a guide to comprehensively explore geographical inequities in emergency obstetric care access in urban/peri-urban settings.
– Contextualized interventions should be implemented to improve access to emergency obstetric care in identified “hotspot” areas.
– Consideration should be given to improving the availability and accessibility of secondary facilities, particularly in remote towns.
– Efforts should be made to reduce travel time and distance to tertiary referral hospitals.
Key Role Players:
– Lagos State Government
– Lagos State Ministry of Health
– Lagos State Bureau of Statistics
– Public CEmOC facilities in Lagos State
– Private hospitals in Lagos State
– Military hospitals in Lagos State
– Research team and research assistants
Cost Items for Planning Recommendations:
– Infrastructure development and improvement of secondary health care facilities
– Training and capacity building for health care providers
– Transportation services for pregnant women in remote areas
– Information and communication technology (ICT) infrastructure for accurate travel time estimation
– Data collection and analysis tools and software
– Public awareness campaigns on emergency obstetric care services and facilities

The strength of evidence for this abstract is 8 out of 10.
The evidence in the abstract is strong because the study collected data from hospital records and used Google Maps to estimate travel times. The study also conducted bivariate and multivariate analyses to test associations between travel and health system-related factors. However, to improve the evidence, the study could have included a larger sample size and conducted a longer data collection period to increase the generalizability of the findings.

Previous efforts to estimate the travel time to comprehensive emergency obstetric care (CEmOC) in low- and middle-income countries (LMICs) have either been based on spatial models or self-reported travel time, both with known inaccuracies. The study objectives were to estimate more realistic travel times for pregnant women in emergency situations using Google Maps, determine system-level factors that influence travel time and use these estimates to assess CEmOC geographical accessibility and coverage in Lagos state, Nigeria. Data on demographics, obstetric history and travel to CEmOC facilities of pregnant women with an obstetric emergency, who presented between 1st November 2018 and 31st December 2019 at a public CEmOC facility were collected from hospital records. Estimated travel times were individually extracted from Google Maps for the period of the day of travel. Bivariate and multivariate analyses were used to test associations between travel and health system-related factors with reaching the facility >60 minutes. Mean travel times were compared and geographical coverage mapped to identify ‘hotspots’ of predominantly >60 minutes travel to facilities. For the 4005 pregnant women with traceable journeys, travel time ranges were 2-240 minutes (without referral) and 7-320 minutes (with referral). Total travel time was within the 60 and 120 minute benchmark for 80 and 96% of women, respectively. The period of the day of travel and having been referred were significantly associated with travelling >60 minutes. Many pregnant women living in the central cities and remote towns typically travelled to CEmOC facilities around them. We identified four hotspots from which pregnant women travelled >60 minutes to facilities. Mean travel time and distance to reach tertiary referral hospitals were significantly higher than the secondary facilities. Our findings suggest that actions taken to address gaps need to be contextualized. Our approach provides a useful guide for stakeholders seeking to comprehensively explore geographical inequities in CEmOC access within urban/peri-urban LMIC settings.

The study was conducted in Lagos State, southwest Nigeria. The Lagos State Bureau of Statistics estimated that 25.6 million people resided in Lagos State in 2019: a density of 6871 residents per square kilometre (km) (LASG, 2019). The state is further divided into 20 local government areas (LGA). Lagos state is highly urbanized and has a mix of different geographical terrains, including city and suburb, metropolis and slums, as well as land and riverine areas. The central areas form the Lagos metropolis, which is surrounded by several suburbs. In contrast, the extreme western and eastern parts of the state are made up of less built-up towns (Figure 1). Map of Lagos state showing variation in geographical settlements and location of the public CEmOC facilities. Compared to the national maternal mortality ratio (MMR) of 512 maternal deaths per 100 000 live births (year—2017) (National Population Commission, ICF International, 2019), MMR in Lagos State has been estimated as 450 (95% CI 360–530) per 100 000 live births (Oye-Adeniran et al., 2011). When disaggregated by LGAs, MMR ranges from 356 per 100 000 live births in Ikeja LGA to 826 per 100 000 live births in Alimosho LGA. Similar to global patterns, hypertension, spontaneous abortions and ectopic pregnancies were the most commonly reported causes of death during pregnancy, while haemorrhage and prolonged or obstructed labour were more commonly reported as causes of death during childbirth in Lagos (Odeyemi et al., 2014; Okonofua et al., 2017). In terms of available CEmOC facilities, there are 24 public CEmOC facilities in Lagos State, including 20 secondary health care facilities (general hospitals/maternal childcare centres) and four tertiary health care facilities (teaching hospitals/apex referral centres), all of which are expected to provide CEmOC services 24 hours a day (Figure 1 and Supplementary Data). There is also a complement of three military hospitals and about 35 private hospitals that can be classified as CEmOC facilities with specialists who can provide CEmOC services 24 hours a day, as per the database of the State Ministry of Health. However, for this study, we focus only on public sector hospitals providing CEmOC, as they form the bedrock of universal health coverage in LMICs (Sachs, 2012). In any case, as per 2018 Nigeria Demographic Health Survey (NDHS), excluding home delivery (59.0%), twice the number of women in Nigeria deliver in public hospitals (26.4%) compared to private hospitals (13.0%) (National Population Commission, ICF International, 2019). Data for this study were collected over 6 months and based on a review of patient records of all pregnant women who presented in the obstetric emergency rooms of all 24 public CEmOC facilities in Lagos state with any major pregnancy and childbirth complication between 1st November 2018 and 30th October 2019. However, some facilities were being built or renovated during this one-year study period. First, the Institute of Maternal and Child Health (IMCH, commonly referred to as Àyìnkę House) was only re-opened for service after a 9-year closure for renovation on 24th April 2019 (Okoghenun, 2016; Ugvodaga, 2019). As such, we could only extract 3 months of data from the facility (1st July 2019 to 30th September 2019). Second, Eti-Osa Maternal and Child Care Centre (MCC) was newly built and commissioned (Bassey, 2019). Data collected from this facility were for the period 1st September 2019 to 31st December 2019. The data were mined from the records by members of the research team supported by trained research assistants who were qualified medical doctors conversant with the patient records system in Lagos public health facilities. Using a pre-tested data extraction tool, we collected data on demographic characteristics, obstetric history, travel to reach the health facility (month of the year, day of the week—weekday or weekend and period of day when the journey to the facility commenced—morning, afternoon, evening or night), street name of women’s self-reported start location (place of residence, unless otherwise stated), other facilities visited en route (referral points) if any and the destination facility (one of the 24) from clerking notes recorded in the patient folders. In cases where clarification was needed, we solicited the support of the medical doctors in charge of the obstetric emergency room. We geo-coded the origin, any facilities visited along the way (referral points) and destination locations for each woman. The points of origin were based on the stated street name of the women’s self-reported start location, most commonly her home. We used Google Maps to find the exact location and selected the relevant coordinates if the street was discoverable on the platform. For streets that were difficult to find, we used local persons who were familiar with the various communities to check for any spelling errors and re-attempted to locate the street. If, despite our best efforts, we could not locate the street, the record of the woman, along with those that did not have an address marked in their patient files, was labelled untraceable (4% of the sample). We identified the exact entry point of the obstetric emergency ward for each destination CEmOC facility by visiting the obstetric emergency ward of each facility and geocoding its location. Geographical coordinates of the CEmOC facilities were collected using a free mobile application, ‘Easy GPS’ (TopoGrafix, Stow, Massachusetts, USA), which automatically logged longitude and latitude values of the CEmOC facilities (see Supplementary Data). In cases in which pregnant women went to a referral point on her path to a CEmOC facility, we used the same approach as was used for geocoding the points of origin to geocode such referral points. However, for those who had multiple referral points, we only traced their journeys from their places of residence to the facility from where they were referred to the final destination. Stopovers made to informal settings (e.g. church or mosque) were not geo-coded. For pregnant women whose journeys could be traced, estimated travel time between the origin and destination (including referral points) were extracted from Google Maps using the ‘typical time of travel’ tool for the time and day that the woman commenced her journey to the CEmOC facility. Motorized vehicle was used as means of transport in Google Maps, as private cars (25%) followed by taxis (21%) are the most popular means of transportation to health facilities in Lagos, as per the 2018 NDHS (National Population Commission, ICF International, 2019). While journeys that required travel by boat were identified (0.14% of the sample), these could not be traced on Google Maps. To collect travel time estimates for the period of the day when journey to the facility commenced, we used 9.00 a.m., 3.00 p.m., 6.00 p.m. and 9.00 p.m. time slots for morning, afternoon, evening or night journeys, respectively. For journeys in which we could not tell the time of the day that women commenced their journeys to the facility (33% of the sample), travel time was extracted for the afternoon (3.00 p.m.), as it offered a middle-ground estimate in between the two known peak periods for travel in Lagos (6.30 a.m. and 11.30 am—morning peak period and 3.00 p.m. and 7.30 pm—evening peak period) (Asiyanbola et al., 2012). Most up to date (as of 2017) shapefiles capturing administrative boundaries, population, road networks, and water bodies within Lagos were retrieved from the State’s Ministry of Urban and Regional Planning. These files formed the platform for which the geographical analysis was conducted. Categorical variables, which included demographic data, obstetric and travel history of the included women, were summarized using frequencies and proportions and presented in summary tables. Continuous variables were summarized using means and medians with their interquartile ranges (IQR). Individual-level, pregnancy-related and health systems-related factors as groups of independent factors that can be associated with travel time (Sacks et al., 2016; Geleto et al., 2018; Ahmed et al., 2019; Banke-Thomas et al., 2019). Individual-level and pregnancy-related factors relate to socio-demographic and obstetric history, respectively, while health system factors comprise referral, skilled health personnel and type of facilities providing care. In addition, the season, day, period of the day when the journey took places and road conditions may also impact women’s total travel time to reach care. As our study objective was focused on system-level factors, we did not report individual-level and pregnancy-related factors as part of our analysis. We used Chi-square test (bivariate analysis) to test the null hypothesis that there is no association between day, period of day or health systems factors with reaching (or not reaching) the destination CEmOC facility within the 60 minutes. The choice of 60 minutes as benchmark, as opposed to 120 minutes, was based on the established evidence that pregnant women with obstetric emergencies can escalate in less than 2 hours (Khan and El-Rafaey, 2006; UNFPA, 2012) and that further delays could occur upon reaching the CEmOC facilities (Thaddeus and Maine, 1994; Gabrysch and Campbell, 2009). As such, a narrower window of travel will be helpful for effective service planning and policymaking decisions. In any case, other authors have used the 60 minute travel time benchmark for analysis (Chowdhury et al., 2017; Niyitegeka et al., 2017; Ouma et al., 2018). For our analysis, associations between the independent and dependent variables were tested at a 95% confidence interval (CI), with a P-value of significance set at ≤0.05. Multivariate analysis was conducted to identify the factors associated with the travel time category to reach health facilities. Using the actual travel time and the distance to reach each facility, a linear regression model was conducted to show any statistically significant differences in mean travel time and distance to CEmOC facilities. Linear regression was also conducted to compare mean travel time and distance to CEmOC facilities for women living in the area surrounding the newly established facility before and after its commissioning. In cases where specific data were not retrieved from the patient records, such missing data were excluded from the analysis. All statistical analyses were done using STATA SE 15.0® (StataCorp, College Station, Texas, USA). For visualization, we visually identified locations of high concentration of long travel time of >60 or >120 minutes to the destination facility. Data points were disaggregated by day of the week, period of the day, and referral. All maps were drawn with the ‘gg’ package, including the tile server for Stamen Maps, in R version 4.0.2 (R Development Core Team, Auckland, New Zealand). Data layers were projected into the spatial reference frame, WGS84/ UTM Zone 35S. Ethical approval for this study was obtained from the Research and Ethics Committees of the Lagos State University Teaching Hospital (LASUTH) (LREC/06/10/1226) and Lagos University Teaching Hospital (LUTH) (ADM/DCST/HREC/APP/2880). Social approval for the study was received from the Lagos State Government (LSHSC/2222/VOLII/107). As this study was based on patient records, we minimized the risk of patient identification by not collecting data on patient names and specific street numbers. In mapping, we selected the mid-point of streets of origin to ensure anonymity. Random displacements of the sort are typically used in similar large surveys (Burgert et al., 2013).

Based on the provided information, here are some potential innovations that could improve access to maternal health:

1. Telemedicine: Implementing telemedicine services can allow pregnant women in remote areas to consult with healthcare professionals without the need for physical travel. This can help address geographical barriers and provide timely medical advice and support.

2. Mobile health (mHealth) applications: Developing mobile applications that provide information on prenatal care, emergency obstetric care, and nearby healthcare facilities can empower pregnant women to make informed decisions and easily access the care they need.

3. Emergency transportation services: Establishing dedicated emergency transportation services, such as ambulances or community-based transportation networks, can ensure that pregnant women with obstetric emergencies can reach healthcare facilities quickly and safely.

4. Community health workers: Training and deploying community health workers who can provide basic prenatal care, education, and referrals in underserved areas can improve access to maternal health services and reduce travel time for pregnant women.

5. Public-private partnerships: Collaborating with private healthcare providers to expand the availability of comprehensive emergency obstetric care facilities can increase access to timely and quality maternal healthcare services.

6. Improved road infrastructure: Investing in road infrastructure, especially in remote areas, can reduce travel time and improve access to healthcare facilities for pregnant women.

7. Awareness campaigns: Conducting awareness campaigns to educate pregnant women and their families about the importance of prenatal care, emergency obstetric care, and the available healthcare facilities can encourage early and regular healthcare-seeking behavior.

It is important to note that the specific context and needs of the target population should be considered when implementing these innovations to ensure their effectiveness and sustainability.
AI Innovations Description
Based on the description provided, the recommendation to improve access to maternal health in Lagos State, Nigeria is to focus on addressing the identified geographical inequities in comprehensive emergency obstetric care (CEmOC) access. Here are some steps that can be taken to develop this recommendation into an innovation:

1. Conduct a comprehensive assessment: Conduct a detailed assessment of the current state of maternal health access in Lagos State, including an analysis of geographical settlements, population distribution, and the location of existing CEmOC facilities. This assessment should also consider factors such as travel time, transportation options, and referral systems.

2. Identify hotspots and gaps: Use the assessment findings to identify areas with limited access to CEmOC facilities, known as “hotspots.” These hotspots are locations where pregnant women have to travel more than 60 minutes to reach a facility. Additionally, identify any gaps in the distribution of CEmOC facilities, particularly in areas with high population density or remote towns.

3. Develop targeted interventions: Based on the identified hotspots and gaps, develop targeted interventions to improve access to maternal health. This could include establishing new CEmOC facilities in underserved areas, improving transportation infrastructure to reduce travel time, and strengthening referral systems to ensure timely access to emergency obstetric care.

4. Collaborate with stakeholders: Engage with relevant stakeholders, including government agencies, healthcare providers, community organizations, and NGOs, to collaborate on implementing the identified interventions. This collaboration will help ensure that resources and expertise are effectively utilized to address the identified gaps in access to maternal health.

5. Monitor and evaluate: Implement a monitoring and evaluation framework to assess the impact of the interventions on improving access to maternal health. Regularly collect data on travel time, utilization of CEmOC facilities, and maternal health outcomes to measure the effectiveness of the interventions and make any necessary adjustments.

By following these steps, the recommendation to address geographical inequities in CEmOC access can be developed into an innovation that improves access to maternal health in Lagos State, Nigeria.
AI Innovations Methodology
Based on the provided description, here are some potential recommendations for improving access to maternal health:

1. Strengthen transportation infrastructure: Improve road networks and transportation systems to ensure efficient and reliable access to maternal health facilities. This could involve building new roads, improving existing ones, and implementing public transportation systems that cater specifically to pregnant women.

2. Establish satellite clinics: Set up smaller healthcare facilities in remote or underserved areas to provide basic maternal health services. These clinics can offer prenatal care, emergency obstetric care, and referrals to larger hospitals when necessary.

3. Mobile health clinics: Utilize mobile clinics equipped with medical professionals and necessary equipment to reach remote areas and provide maternal health services. These clinics can travel to different locations on a regular schedule, ensuring that pregnant women in hard-to-reach areas have access to essential care.

4. Telemedicine services: Implement telemedicine programs that allow pregnant women to consult with healthcare professionals remotely. This can help address barriers such as distance and transportation by providing virtual access to medical advice, monitoring, and support.

To simulate the impact of these recommendations on improving access to maternal health, a methodology could include the following steps:

1. Define the target population: Identify the specific group of pregnant women who would benefit from improved access to maternal health services. Consider factors such as geographical location, socioeconomic status, and existing healthcare infrastructure.

2. Collect baseline data: Gather data on the current state of access to maternal health services, including travel times, distance to facilities, and any existing barriers or challenges. This data can be obtained through surveys, interviews, or analysis of existing healthcare records.

3. Develop a simulation model: Create a mathematical or computational model that simulates the impact of the recommended interventions on access to maternal health services. This model should take into account factors such as population distribution, transportation networks, and healthcare facility locations.

4. Input intervention parameters: Define the specific parameters of each recommended intervention, such as the number and location of satellite clinics, the frequency and routes of mobile health clinics, or the availability of telemedicine services. Incorporate these parameters into the simulation model.

5. Run simulations: Use the simulation model to simulate different scenarios and assess the impact of the interventions on access to maternal health services. This can include measuring changes in travel times, distance to facilities, and the proportion of pregnant women who can reach care within a specified time frame.

6. Analyze results: Evaluate the simulation results to determine the effectiveness of the recommended interventions in improving access to maternal health services. Identify any potential limitations or challenges that may arise from implementing these interventions.

7. Refine and iterate: Based on the analysis of the simulation results, refine the intervention parameters and run additional simulations to further optimize the recommendations. Iterate this process until the desired level of improvement in access to maternal health services is achieved.

By following this methodology, stakeholders can gain insights into the potential impact of different interventions and make informed decisions on how to allocate resources and implement strategies to improve access to maternal health.

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