Unmasking inequalities: Sub-national maternal and child mortality data from two urban slums in Lagos, Nigeria tells the story

PLoS ONE, Volume 12, No. 5, Year 2017

Interprétation

Introduction: Nigeria has one of the highest maternal mortality ratios in the world as well as high perinatal mortality. Unfortunately, the country does not have the resources to assess this critical indicator with the conventional health information system and measuring its progress toward the goal of ending preventable maternal deaths is almost impossible. Médecins Sans Frontières (MSF) conducted a cross-sectional study to assess maternal and perinatal mortality in Makoko Riverine and Badia East, two of the most vulnerable slums of Lagos. Materials and methods: The study was a cross-sectional, community-based household survey. Nearly 4,000 households were surveyed. The sisterhood method was utilized to estimate maternal mortality and the preceding births technique was used to estimate newborn and child mortality. Questions regarding health seeking behavior were posed to female interviewees and self-reported data were collected. Results: Data was collected from 3963 respondents for a total of 7018 sisters ever married. The maternal mortality ratio was calculated at 1,050/100,000 live births (95% CI: 894-1215), and the lifetime risk of maternal death at 1:18. The neonatal mortality rate was extracted from 1967 pregnancies reported and was estimated at 28.4/1,000; infant mortality at 43.8/1,000 and under-five mortality at 103/1,000. Living in Badia, giving birth at home and belonging to the Egun ethnic group were associated with higher perinatal mortality. Half of the last pregnancies were reportedly delivered in private health facilities. Proximity to home was the main influencing factor (32.4%) associated with delivery at the health facility. Discussion: The maternal mortality ratio found in these urban slum populations within Lagos is extremely high, compared to the figure estimated for Lagos State of 545 per 100,000 live births. Urgent attention is required to address these neglected and vulnerable neighborhoods. Efforts should be invested in obtaining data from poor, marginalized, and hard-to-reach populations in order to identify pockets of marginalization needing additional resources and tailored approaches to guarantee equitable treatment and timely access to quality health services for vulnerable groups. This study demonstrates the importance of sub-regional, disaggregated data to identify and redress inequities that exist among poor, remote, vulnerable populations – as in the urban slums of Lagos. We conducted a cross-sectional, community-based household survey, with the primary objective of estimating the maternal mortality ratio through the direct sisterhood method [16]. We also aimed to estimate perinatal mortality and to assess women’s health seeking behavior around pregnancy and childbirth. Perinatal mortality was assessed through the preceding births technique [17, 18, 19]. Data were collected from February to March 2012 within the framework of the ongoing activities of MSF-OCBA in Lagos. Ethical approval for this study was obtained from Lagos University Teaching Hospital, the Lagos State Ministry of Health and MSF’s Ethical Review Board. The survey targeted all males and females living in the urban slum communities of Makoko Riverine and Badia East between the ages of 15 and 49 years, who voluntarily agreed to participate in the study. The sampling frame was the population of each of the 2 catchment communities (Badia East and Makoko Riverine) and the sampling unit was the household. Data were obtained from approximately 4,000 households, randomly selected in two stages. Following a probability proportional to size approach, systematic random sampling was used to select households within the study communities. By proportion, as originally planned, 2,666 households (66.5%) were to be randomly selected in Makoko Riverine and 1,334 (33.5%) in Badia East, for an overall total of 4,000 households. However, after the study began, these proportions were adjusted slightly–increased for Makoko Riverine (to 3,015) and decreased for Badia East (to 948)–to take into account the unfortunate and unforeseen demolition of houses and consequent displacement of persons that occurred during the survey in Badia East, as well as the likely initial overestimation of the population residing full-time in Badia, as confirmed by MSF community outreach workers, in consultation with local community leaders in Badia East. Since estimating the maternal mortality ratio was the primary objective of this study, by estimating a total fertility rate (TFR) of 5.4, this sample size was chosen to be large enough to detect a MMR of 500 with a margin of error of 20% and a confidence interval of 95% [20]. One female or male who met the inclusion criteria of the study was randomly selected per household for the interview. In cases where no eligible respondent was present in the household, data collectors marked the house for a return visit. If, after one further attempt, still no eligible respondent was found at home, data collectors went on to the next nearest household with an eligible respondent present. In case of female household respondents, they were queried regarding their previous pregnancies through the preceding birth technique (within a recall period of 5 years) to estimate perinatal mortality, neonatal mortality, and infant mortality. Questions related to women’s health seeking behavior during pregnancy, delivery, and after delivery were also asked in relation to their last pregnancy (within a recall period of 2 years). Both men and women were asked questions about socio-economic status, survival or deaths of their adult sisters and births and deaths in the household over the preceding year, in order to estimate maternal mortality, under-five mortality, and crude (household) mortality rates of the previous year. Interviewers administered the questionnaires only after obtaining written informed consent from the interviewee. All questionnaires were anonymous. In accordance with the study objectives of estimating maternal and perinatal mortality specifically in Lagos, data were collected only on births and deaths that took place in Lagos. Thus, to avoid confounding factors of differing health systems, policies, and contexts beyond Lagos, births and deaths that occurred outside of Lagos, whether in another state of Nigeria or in another country such as Benin Republic or Togo, were excluded from the analysis. All questionnaires were pre-tested, piloted in the field, translated, and back-translated into the most common local languages in the study communities (Egun, Pidgin, and Yoruba). A data management strategy and field manual were developed to ensure clear and appropriate procedures were followed, including quality control. The quantitative data from household survey forms were double entered into EpiData version 3.1 statistical package (Lauritsen JM. (Ed.) EpiData Data Entry, Data Management and basic Statistical Analysis System. Odense Denmark, EpiData Association, 2000–2008). Completed data were initially exported to SPSS version 16 for first level data cleaning before being subsequently exported to STATA version 13 (StataCorp. 2013. Stata Statistical Software: Release 13. College Station, TX: StataCorp LP) for analysis. Results were reported as proportions for the descriptive, univariate analysis and chi-square test was used to determine whether there is an association (or relationship) between two categorical variables. Preference for the place of delivery has been modeled in a logistic regression with ethnic group, neighborhood of residence, illiteracy and working as a sex worker to assess any possible correlation: in this case we included the predictor in the model if the test had a p value ≤0.3. With the same approach we also investigated any possible association with PNM. All the hypotheses tested were 2-tailed, and we considered statistical significance only in the presence of p values <0.05. The calculation of the maternal mortality ratio was based on the sisterhood method expounded by Graham et al. [21]:MMR = 100,000 x (1- [1- total lifetime risk](1/Total Fertility Rate)). In our study, early neonatal death was defined as: death of a liveborn infant occurring fewer than 7 completed days from the time of birth out of the total live births; late neonatal death as: death of a liveborn infant occurring after 7 completed days of age but before 28 completed days out of the total live births; infant mortality: the number of deaths of children under one year of age out of the total live births; perinatal mortality rate: any death from the 22nd week of gestation up to the first week of life out of the total live births. We expressed these indicators as the number of such deaths per 1000 live births

Résumé de l’IA

Study Justification:

– Nigeria has one of the highest maternal mortality ratios and perinatal mortality rates in the world.
– Conventional health information systems in Nigeria are unable to accurately assess these indicators.
– The study aimed to estimate maternal and perinatal mortality in two vulnerable slums in Lagos, Nigeria.
– The study provides important data on the health seeking behavior of women during pregnancy and childbirth.

Highlights:

– The maternal mortality ratio in the urban slums of Lagos was found to be extremely high at 1,050/100,000 live births.
– The lifetime risk of maternal death was estimated at 1 in 18.
– The neonatal mortality rate was 28.4/1,000, infant mortality rate was 43.8/1,000, and under-five mortality rate was 103/1,000.
– Living in certain slum areas, giving birth at home, and belonging to a specific ethnic group were associated with higher perinatal mortality.
– Half of the last pregnancies were reportedly delivered in private health facilities, with proximity to home being the main influencing factor.

Recommendations:

– Urgent attention is needed to address the high maternal and perinatal mortality in these vulnerable slum populations.
– Efforts should be made to obtain data from poor, marginalized, and hard-to-reach populations to identify and address inequities.
– Additional resources and tailored approaches are needed to ensure equitable treatment and timely access to quality health services for vulnerable groups.

Key Role Players:

– Local government authorities
– Ministry of Health
– Non-governmental organizations (NGOs)
– Community leaders and representatives
– Healthcare providers and facilities

Cost Items for Planning Recommendations:

– Data collection and analysis
– Training and capacity building for healthcare providers
– Infrastructure development and improvement
– Health education and awareness campaigns
– Access to affordable healthcare services
– Monitoring and evaluation of interventions
– Community engagement and participation initiatives

Force de la preuve

N/A

Abstrait

We conducted a cross-sectional, community-based household survey, with the primary objective of estimating the maternal mortality ratio through the direct sisterhood method [16]. We also aimed to estimate perinatal mortality and to assess women’s health seeking behavior around pregnancy and childbirth. Perinatal mortality was assessed through the preceding births technique [17, 18, 19]. Data were collected from February to March 2012 within the framework of the ongoing activities of MSF-OCBA in Lagos. Ethical approval for this study was obtained from Lagos University Teaching Hospital, the Lagos State Ministry of Health and MSF’s Ethical Review Board. The survey targeted all males and females living in the urban slum communities of Makoko Riverine and Badia East between the ages of 15 and 49 years, who voluntarily agreed to participate in the study. The sampling frame was the population of each of the 2 catchment communities (Badia East and Makoko Riverine) and the sampling unit was the household. Data were obtained from approximately 4,000 households, randomly selected in two stages. Following a probability proportional to size approach, systematic random sampling was used to select households within the study communities. By proportion, as originally planned, 2,666 households (66.5%) were to be randomly selected in Makoko Riverine and 1,334 (33.5%) in Badia East, for an overall total of 4,000 households. However, after the study began, these proportions were adjusted slightly–increased for Makoko Riverine (to 3,015) and decreased for Badia East (to 948)–to take into account the unfortunate and unforeseen demolition of houses and consequent displacement of persons that occurred during the survey in Badia East, as well as the likely initial overestimation of the population residing full-time in Badia, as confirmed by MSF community outreach workers, in consultation with local community leaders in Badia East. Since estimating the maternal mortality ratio was the primary objective of this study, by estimating a total fertility rate (TFR) of 5.4, this sample size was chosen to be large enough to detect a MMR of 500 with a margin of error of 20% and a confidence interval of 95% [20]. One female or male who met the inclusion criteria of the study was randomly selected per household for the interview. In cases where no eligible respondent was present in the household, data collectors marked the house for a return visit. If, after one further attempt, still no eligible respondent was found at home, data collectors went on to the next nearest household with an eligible respondent present. In case of female household respondents, they were queried regarding their previous pregnancies through the preceding birth technique (within a recall period of 5 years) to estimate perinatal mortality, neonatal mortality, and infant mortality. Questions related to women’s health seeking behavior during pregnancy, delivery, and after delivery were also asked in relation to their last pregnancy (within a recall period of 2 years). Both men and women were asked questions about socio-economic status, survival or deaths of their adult sisters and births and deaths in the household over the preceding year, in order to estimate maternal mortality, under-five mortality, and crude (household) mortality rates of the previous year. Interviewers administered the questionnaires only after obtaining written informed consent from the interviewee. All questionnaires were anonymous. In accordance with the study objectives of estimating maternal and perinatal mortality specifically in Lagos, data were collected only on births and deaths that took place in Lagos. Thus, to avoid confounding factors of differing health systems, policies, and contexts beyond Lagos, births and deaths that occurred outside of Lagos, whether in another state of Nigeria or in another country such as Benin Republic or Togo, were excluded from the analysis. All questionnaires were pre-tested, piloted in the field, translated, and back-translated into the most common local languages in the study communities (Egun, Pidgin, and Yoruba). A data management strategy and field manual were developed to ensure clear and appropriate procedures were followed, including quality control. The quantitative data from household survey forms were double entered into EpiData version 3.1 statistical package (Lauritsen JM. (Ed.) EpiData Data Entry, Data Management and basic Statistical Analysis System. Odense Denmark, EpiData Association, 2000–2008). Completed data were initially exported to SPSS version 16 for first level data cleaning before being subsequently exported to STATA version 13 (StataCorp. 2013. Stata Statistical Software: Release 13. College Station, TX: StataCorp LP) for analysis. Results were reported as proportions for the descriptive, univariate analysis and chi-square test was used to determine whether there is an association (or relationship) between two categorical variables. Preference for the place of delivery has been modeled in a logistic regression with ethnic group, neighborhood of residence, illiteracy and working as a sex worker to assess any possible correlation: in this case we included the predictor in the model if the test had a p value ≤0.3. With the same approach we also investigated any possible association with PNM. All the hypotheses tested were 2-tailed, and we considered statistical significance only in the presence of p values <0.05. The calculation of the maternal mortality ratio was based on the sisterhood method expounded by Graham et al. [21]:MMR = 100,000 x (1- [1- total lifetime risk](1/Total Fertility Rate)). In our study, early neonatal death was defined as: death of a liveborn infant occurring fewer than 7 completed days from the time of birth out of the total live births; late neonatal death as: death of a liveborn infant occurring after 7 completed days of age but before 28 completed days out of the total live births; infant mortality: the number of deaths of children under one year of age out of the total live births; perinatal mortality rate: any death from the 22nd week of gestation up to the first week of life out of the total live births. We expressed these indicators as the number of such deaths per 1000 live births

Matériaux

N/A

Innovations

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

1. Mobile health clinics: Implementing mobile health clinics that can travel to urban slums, such as Makoko Riverine and Badia East, to provide maternal health services. This would bring healthcare closer to the community, making it more accessible for pregnant women.

2. Telemedicine: Introducing telemedicine services that allow pregnant women in urban slums to consult with healthcare professionals remotely. This would enable them to receive medical advice and guidance without the need to travel long distances to healthcare facilities.

3. Community health workers: Training and deploying community health workers in urban slums to provide education, support, and basic healthcare services to pregnant women. These workers can act as a bridge between the community and formal healthcare system, ensuring that pregnant women receive the necessary care and information.

4. Improving transportation infrastructure: Investing in transportation infrastructure, such as roads and public transportation, to improve access to healthcare facilities for pregnant women in urban slums. This would reduce barriers to accessing timely and quality maternal healthcare.

5. Strengthening referral systems: Establishing and strengthening referral systems between primary healthcare centers in urban slums and higher-level healthcare facilities. This would ensure that pregnant women with complications can be quickly and efficiently referred to appropriate facilities for specialized care.

6. Community-based health financing: Implementing community-based health financing schemes, such as health insurance or savings groups, to help pregnant women in urban slums afford the cost of maternal healthcare services. This would reduce financial barriers and improve access to essential care.

7. Public-private partnerships: Collaborating with private healthcare providers to expand access to maternal health services in urban slums. This could involve subsidizing services or establishing partnerships to ensure that pregnant women can access quality care at affordable prices.

8. Health education and awareness campaigns: Conducting health education and awareness campaigns in urban slums to educate pregnant women and their families about the importance of antenatal care, skilled birth attendance, and postnatal care. This would help increase awareness and encourage women to seek timely and appropriate care.

These innovations aim to address the challenges faced by pregnant women in urban slums, such as limited access to healthcare facilities, financial constraints, and lack of awareness. By implementing these recommendations, it is hoped that access to maternal health services can be improved, leading to a reduction in maternal and perinatal mortality rates in these vulnerable populations.

AI Innovations Description

Based on the information provided, here is a recommendation that can be developed into an innovation to improve access to maternal health:

1. Implement targeted interventions in vulnerable urban slums: Given the high maternal mortality ratio and perinatal mortality rates in the urban slums of Lagos, it is crucial to focus on these marginalized populations. Develop and implement targeted interventions that specifically address the unique challenges faced by women in these communities, such as lack of access to quality healthcare facilities and limited awareness about maternal health.

2. Strengthen the health information system: Improve the conventional health information system in Nigeria to accurately assess maternal mortality and measure progress towards reducing preventable maternal deaths. This can be achieved by investing in data collection, analysis, and reporting mechanisms that capture sub-national, disaggregated data from poor, marginalized, and hard-to-reach populations. This will help identify pockets of marginalization and allocate additional resources and tailored approaches to ensure equitable treatment and timely access to quality health services for vulnerable groups.

3. Enhance community-based healthcare services: Establish and strengthen community-based healthcare services in the urban slums of Lagos. This can include setting up mobile clinics, training community health workers, and providing essential maternal health services closer to where women live. By bringing healthcare services to the doorstep of these communities, it will improve access to antenatal care, skilled birth attendance, and postnatal care, ultimately reducing maternal and perinatal mortality rates.

4. Promote awareness and education: Launch targeted awareness campaigns to educate women and their families about the importance of seeking timely and quality maternal healthcare. This can be done through community engagement programs, health education sessions, and the use of local media channels. By increasing awareness and knowledge about maternal health, women will be empowered to make informed decisions and seek appropriate care during pregnancy and childbirth.

5. Collaborate with local and international organizations: Foster partnerships and collaborations with local and international organizations, such as Médecins Sans Frontières (MSF), to leverage their expertise, resources, and networks. By working together, it will be possible to pool resources, share best practices, and implement innovative solutions to improve access to maternal health in the urban slums of Lagos.

Overall, the recommendation is to focus on targeted interventions, strengthen the health information system, enhance community-based healthcare services, promote awareness and education, and collaborate with relevant stakeholders. By implementing these strategies, it is possible to develop innovative solutions that will improve access to maternal health and reduce maternal and perinatal mortality rates in vulnerable urban slum populations.

AI Innovations Methodology

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

1. Strengthening healthcare infrastructure: Investing in the development and improvement of healthcare facilities, particularly in vulnerable areas such as urban slums, can help ensure that pregnant women have access to quality maternal healthcare services.

2. Increasing availability of skilled healthcare providers: Training and deploying more skilled healthcare providers, such as midwives and obstetricians, in areas with high maternal mortality rates can improve access to essential maternal health services.

3. Promoting community-based interventions: Implementing community-based interventions, such as mobile clinics or community health workers, can help reach pregnant women who may face barriers to accessing healthcare due to geographical or socio-economic factors.

4. Enhancing transportation services: Improving transportation infrastructure and providing transportation support, such as ambulances or transportation vouchers, can help pregnant women in remote or underserved areas reach healthcare facilities in a timely manner.

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

1. Define the indicators: Identify key indicators that reflect access to maternal health, such as the number of pregnant women receiving antenatal care, the number of deliveries attended by skilled birth attendants, or the distance to the nearest healthcare facility.

2. Collect baseline data: Gather data on the current status of the selected indicators in the target population or area. This can be done through surveys, interviews, or existing data sources.

3. Introduce the recommendations: Simulate the implementation of the recommended interventions by adjusting the relevant indicators based on the expected impact. For example, increase the number of skilled healthcare providers or improve transportation services.

4. Analyze the impact: Compare the baseline data with the simulated data to assess the impact of the recommendations on the selected indicators. This can be done through statistical analysis or modeling techniques.

5. Evaluate the results: Interpret the findings to determine the effectiveness of the recommendations in improving access to maternal health. Identify any gaps or areas for further improvement.

6. Refine and iterate: Based on the evaluation results, refine the recommendations and repeat the simulation process to continuously improve access to maternal health.

It is important to note that the methodology may vary depending on the specific context and available data sources. Additionally, involving relevant stakeholders, such as healthcare providers and community members, in the simulation process can help ensure the accuracy and relevance of the findings.

Auteurs et co-auteurs

Statistics:

Citations: 17

Authors: 6

Identifiers:

DOI: 10.1371/journal.pone.0177190

Research Areas:

Community Interventions, Health System and Policy, Maternal and Child Health, Quality of Care, Sexual and Reproductive Health, Social Determinants

Study Countries:

Benin, Multi-Countries, Nigeria, Togo

Study Design:

Cross Sectional Study

Study Approach:

Quantitative

Participants Gender:

Male & Female

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