Effects of the COVID-19 pandemic on maternal and perinatal health service utilisation and outcomes in Mozambique: an interrupted time series analysis

BMJ Open, Volume 12, No. 11, Year 2022

Interprétation

Objectives To measure the effects of the COVID-19 pandemic on maternal and perinatal health services and outcomes in Mozambique. Design This is an observational study analysing routine service delivery data using interrupted time series analysis. We used 43 months of district-level panel data with April 2020 as the point of interruption, adjusting for seasonality and population growth to analyse service utilisation outcomes. Setting The 222 public health facilities in Nampula Province, Mozambique, from January 2018 to July 2021. Outcome measures The change in the number of antenatal care (ANC) visits and facility deliveries, and the change in the rate of adverse birth outcomes at pandemic onset and over time compared with expected levels and trends, respectively. Results There were no significant disruptions to ANC at pandemic onset. Following this, there was a significant monthly increase of 29.8 (18.2-41.4) first ANC visits and 11.3 (5.5-17.2) ANC visits within the first trimester per district above prepandemic trends. There was no significant change in the number of fourth ANC visits completed. At the onset of COVID-19, districts experienced a significant decrease of 71.1 (-110.5 to -31.7) facility deliveries, but the rate then increased significantly above prepandemic trends. There was no significant increase in any adverse birth outcomes during the pandemic. Conversely, districts observed a significant monthly decrease of 5.3 uterine rupture cases (-9.9 to -0.6) and 19.2 stillbirths (-33.83 to -4.58) per 100 000 facility deliveries below prepandemic trends. There was a significant drop of 23.5 cases of neonatal sepsis/100 000 facility deliveries per district at pandemic onset. Conclusion Despite pandemic interference, Nampula Province saw no disruptions to ANC, only temporary disruptions to facility deliveries and no increases in adverse birth outcomes. ANC visits surprisingly increased, and the rates of uterine rupture, stillbirth and neonatal sepsis decreased, suggesting that Nampula Province may offer insights about health system resilience. The first case of COVID-19 in Mozambique was confirmed on 22 March 2020, with the government announcing a state of emergency on 30 March 2020.11 12 Policy measures to limit COVID-19 transmission, and their enforcement, have evolved throughout the pandemic in response to the rapidly changing situation. In Mozambique, such measures have included restrictions on social gatherings, limiting public transportation and school closures.13 Similar to other sub-Saharan African countries, the prevalence of COVID-19 cases and deaths has remained relatively low in Mozambique.14 These pandemic dynamics may be due to limited testing, a young population, pre-existing immunity and early adoption of mitigation measures.15 Over the last two decades, maternal mortality has improved in Mozambique but remains high, with a maternal mortality ratio estimated at 408 in 2011.16 Haemorrhage represents the most common direct cause of maternal death in the country, followed by pre-eclampsia and eclampsia.17 This study takes place specifically in Nampula Province, located in northern Mozambique and home to over 6 million residents, making it the most populous province nationwide.18 Facility delivery rates in Nampula Province have risen considerably from 53% in 201116 to 74% in 2017.19 However, the province continues to face widespread poverty, major health and gender inequities, insufficient numbers of health workers, poor health system infrastructure and persistent commodity shortages.16 18 20 We extracted routine service data reported monthly by health facilities to the national health management information system (HMIS), from January 2018 to July 2021, for which we had full access. The start of the study period was selected to limit inclusion of secular trends related to changes in data collection or other health system shocks. Data from all public health facilities providing antenatal or maternity services during the study period in Nampula Province were included in the analysis. This includes 222 health facilities (9 hospitals and 213 primary care facilities) across the 23 districts in Nampula Province. There are few private health facilities offering maternity services in Nampula Province and they do not report to the HMIS, as such, they were not included in this analysis. We assessed four indicators of service utilisation including number of clients attending a first ANC visit (ANC-1), number of clients attending ANC-1 within 12 weeks of pregnancy (early ANC), number of clients attending a fourth ANC visit (ANC-4) and number of health facility deliveries. We examined seven maternal health outcomes (number of cases of severe pre-eclampsia/eclampsia, postpartum haemorrhage, uterine rupture, obstructed labour, sepsis, caesarean delivery and death) and three perinatal outcomes (number of stillbirths, cases of neonatal sepsis and asphyxia). The use of HMIS data presents some challenges, including lack of completeness and reporting errors. To limit the effect of these issues, we examined all data entries above the 95th percentile for each outcome variable, for each health facility type, and identified logical inconsistencies (eg, if the number of maternal deaths was higher than the number of deliveries reported by a health facility in a given month). We then reviewed these entries with Ministry of Health staff at the health facility and district level, comparing the HMIS data to the facility registers, to verify and correct any errors. We assumed zero values for health facilities that do not offer certain services (antenatal, intrapartum or surgical). There were less than 4% missing data for ANC-1, ANC-4 and facility deliveries during the study period, with little change during the pandemic period (see online supplemental figure S1). Reporting of early ANC increased over time, but this was independent of the pandemic. Since the service utilisation outcome variables had a small degree of missing data, we performed linear interpolation of missing values based on values before and after the missing points at each health facility for its period of operation. bmjopen-2022-062975supp001.pdf Health outcome data (maternal and perinatal outcomes) were almost exclusively reported by health facilities when there were cases to report, such that fewer than 0.5% of facilities reported zero cases for any given month in the prepandemic period (online supplemental table S1). Health facilities are required to report on a monthly basis, and all facilities submitted data each month during the study period, indicating that they did not miss a reporting period. Given this, we assumed that missing values for these outcomes were zero. Completeness of health outcomes did not change in response to the onset of the pandemic but did increase around November to December 2020 (online supplemental figure S1). Monthly health facility data were then aggregated to the district level to account for the health system networks of care within districts. District counts of adverse maternal and perinatal outcomes were calculated as rates using the number of health facility deliveries reported in a given district-month per 100 000 deliveries. Annual district-level population projections from the 2017 Mozambique census were linked to the routine service statistics dataset by district to account for population growth.18 Data were imported into Stata V.15 for preparation and analysis.21 Descriptive analyses were conducted to characterise the mean monthly volume of visits or cases as well as the mean rate of cases in the prepandemic and pandemic periods. We performed an interrupted time series regression analysis for each outcome of interest with April 2020 as the point of interruption, since Mozambique issued a state of emergency on 30 March 2020.22 This model uses district-level panel data and provides ordinary least-squares estimates with robust standard errors for the trends and changes in trends before and after interruption.23 We observed strong evidence of seasonality in the time series of each outcome variable (see online supplemental figure S2). As such, we adjusted for seasonality by including a fixed effect for each calendar month from February through December, with January as the reference. This accounted for effects associated with the same month over different years that may affect timing of pregnancy, care-seeking and quality of care. The correlation between the calendar month and time index variables was small (0.06) and not statistically significant. The models of service utilisation outcomes also included a covariate to adjust for population growth. Models of health outcomes are per 100 000 facility deliveries. For health outcomes, we used the model where Ytd represents the average of district d at time t (month); Xtd is a postinterruption indicator (0 for pre; 1 for post); Ttd is an index to represent the time in months, with t={1,…,43}; and Monthk is a dummy indicator representing the kth calendar month of observation, with k={2,…,12}. Here, β0 represents the average number of services provided/cases reported across districts at the beginning of the pre-COVID-19 period; β1 represents the average monthly change in the number of services provided/cases reported during the prepandemic period; β2 represents the change in the outcome in the first pandemic month; β3 represents the difference in the outcome trend between the pandemic and the prepandemic periods; γ2 – γ12 represent the changes associated with the months February through December, with January as the reference; and єtd is an error term that follows a second-order autoregressive process (lag chosen based on Cumby-Huizinga tests for time series autocorrelation24). For service utilisation outcomes, we fit a model as just described, with the addition of a variable to represent the annual district estimate of the population of reproductive-age women to account for population growth. Linear combinations were used to estimate the difference in trends across the two periods. We used the model for each outcome to obtain a prediction of the counterfactual—the expected trend had there not been an interruption. We calculated the difference between the model prediction and the actual observation for each month. For health outcome variables, this was calculated in rates per 100 000 deliveries as well as the absolute number of cases. The sum of the monthly differences represents the cumulative effect of the interruption over the months observed. Results are considered significant at the p<0.05 level for two-sided comparisons. This research was done without patient or public involvement.

Résumé de l’IA

Study Justification:

The study aims to measure the effects of the COVID-19 pandemic on maternal and perinatal health services and outcomes in Mozambique, specifically in Nampula Province. This is important because understanding the impact of the pandemic on maternal and perinatal health is crucial for informing policy and healthcare interventions to mitigate any negative effects.

Highlights:

– The study found no significant disruptions to antenatal care (ANC) at the onset of the pandemic.
– Following the onset of the pandemic, there was a significant increase in the number of first ANC visits and ANC visits within the first trimester per district, suggesting improved access to early prenatal care.
– There was a significant decrease in facility deliveries at the onset of the pandemic, but the rate then increased significantly above pre-pandemic trends.
– There were no significant increases in adverse birth outcomes during the pandemic. In fact, the rates of uterine rupture, stillbirth, and neonatal sepsis decreased.
– Despite the pandemic, Nampula Province demonstrated resilience in its health system, with no disruptions to ANC and improvements in certain outcomes.

Recommendations:

Based on the study findings, the following recommendations can be made:

1. Strengthen access to and utilization of antenatal care services, particularly early ANC visits, to ensure continued positive maternal and perinatal health outcomes.
2. Address the temporary disruptions to facility deliveries by implementing strategies to ensure consistent access to safe delivery services during pandemics or other health emergencies.
3. Investigate the factors contributing to the decrease in adverse birth outcomes during the pandemic and identify strategies to sustain these improvements in the long term.
4. Learn from the resilience demonstrated by Nampula Province’s health system and apply these insights to strengthen health systems in other regions.

Key Role Players:

To address the recommendations, the following key role players are needed:

1. Ministry of Health: Responsible for policy development, coordination, and implementation of interventions to improve maternal and perinatal health services.
2. District Health Authorities: Responsible for overseeing health facilities and implementing interventions at the local level.
3. Health Facility Staff: Including doctors, nurses, midwives, and other healthcare providers who deliver maternal and perinatal health services.
4. Community Health Workers: Engaged in community outreach and education to promote maternal and perinatal health and facilitate access to services.
5. Non-Governmental Organizations (NGOs): Involved in supporting and implementing maternal and perinatal health programs and interventions.

Cost Items for Planning Recommendations:

While the actual cost will depend on the specific interventions and strategies implemented, the following cost items should be considered in planning:

1. Training and Capacity Building: Costs associated with training healthcare providers, community health workers, and other relevant personnel on best practices for maternal and perinatal health.
2. Infrastructure and Equipment: Costs for improving health facility infrastructure, including maternity wards, delivery rooms, and equipment necessary for safe deliveries.
3. Supplies and Medications: Costs for ensuring an adequate supply of essential maternal and perinatal health supplies, including medications, contraceptives, and equipment.
4. Community Outreach and Education: Costs for community engagement activities, including awareness campaigns, health education materials, and training for community health workers.
5. Monitoring and Evaluation: Costs for data collection, analysis, and monitoring of maternal and perinatal health indicators to assess the impact of interventions and guide future planning.
Please note that the above cost items are general categories and the actual cost estimates will depend on the context and specific interventions implemented.

Force de la preuve

The strength of evidence for this abstract is 8 out of 10.
The evidence in the abstract is based on an observational study using interrupted time series analysis. The study analyzes routine service delivery data from 222 public health facilities in Nampula Province, Mozambique, over a period of 43 months. The study examines the effects of the COVID-19 pandemic on maternal and perinatal health service utilization and outcomes. The findings show no significant disruptions to antenatal care (ANC) visits, an increase in ANC visits within the first trimester, temporary disruptions to facility deliveries followed by an increase, and a decrease in adverse birth outcomes such as uterine rupture, stillbirths, and neonatal sepsis. The evidence is based on a large dataset and uses statistical analysis to compare trends before and after the pandemic. However, it is important to note that the study is observational and cannot establish causation. To improve the evidence, future research could consider conducting a randomized controlled trial or a prospective cohort study to further investigate the effects of the pandemic on maternal and perinatal health outcomes.

Abstrait

The first case of COVID-19 in Mozambique was confirmed on 22 March 2020, with the government announcing a state of emergency on 30 March 2020.11 12 Policy measures to limit COVID-19 transmission, and their enforcement, have evolved throughout the pandemic in response to the rapidly changing situation. In Mozambique, such measures have included restrictions on social gatherings, limiting public transportation and school closures.13 Similar to other sub-Saharan African countries, the prevalence of COVID-19 cases and deaths has remained relatively low in Mozambique.14 These pandemic dynamics may be due to limited testing, a young population, pre-existing immunity and early adoption of mitigation measures.15 Over the last two decades, maternal mortality has improved in Mozambique but remains high, with a maternal mortality ratio estimated at 408 in 2011.16 Haemorrhage represents the most common direct cause of maternal death in the country, followed by pre-eclampsia and eclampsia.17 This study takes place specifically in Nampula Province, located in northern Mozambique and home to over 6 million residents, making it the most populous province nationwide.18 Facility delivery rates in Nampula Province have risen considerably from 53% in 201116 to 74% in 2017.19 However, the province continues to face widespread poverty, major health and gender inequities, insufficient numbers of health workers, poor health system infrastructure and persistent commodity shortages.16 18 20 We extracted routine service data reported monthly by health facilities to the national health management information system (HMIS), from January 2018 to July 2021, for which we had full access. The start of the study period was selected to limit inclusion of secular trends related to changes in data collection or other health system shocks. Data from all public health facilities providing antenatal or maternity services during the study period in Nampula Province were included in the analysis. This includes 222 health facilities (9 hospitals and 213 primary care facilities) across the 23 districts in Nampula Province. There are few private health facilities offering maternity services in Nampula Province and they do not report to the HMIS, as such, they were not included in this analysis. We assessed four indicators of service utilisation including number of clients attending a first ANC visit (ANC-1), number of clients attending ANC-1 within 12 weeks of pregnancy (early ANC), number of clients attending a fourth ANC visit (ANC-4) and number of health facility deliveries. We examined seven maternal health outcomes (number of cases of severe pre-eclampsia/eclampsia, postpartum haemorrhage, uterine rupture, obstructed labour, sepsis, caesarean delivery and death) and three perinatal outcomes (number of stillbirths, cases of neonatal sepsis and asphyxia). The use of HMIS data presents some challenges, including lack of completeness and reporting errors. To limit the effect of these issues, we examined all data entries above the 95th percentile for each outcome variable, for each health facility type, and identified logical inconsistencies (eg, if the number of maternal deaths was higher than the number of deliveries reported by a health facility in a given month). We then reviewed these entries with Ministry of Health staff at the health facility and district level, comparing the HMIS data to the facility registers, to verify and correct any errors. We assumed zero values for health facilities that do not offer certain services (antenatal, intrapartum or surgical). There were less than 4% missing data for ANC-1, ANC-4 and facility deliveries during the study period, with little change during the pandemic period (see online supplemental figure S1). Reporting of early ANC increased over time, but this was independent of the pandemic. Since the service utilisation outcome variables had a small degree of missing data, we performed linear interpolation of missing values based on values before and after the missing points at each health facility for its period of operation. bmjopen-2022-062975supp001.pdf Health outcome data (maternal and perinatal outcomes) were almost exclusively reported by health facilities when there were cases to report, such that fewer than 0.5% of facilities reported zero cases for any given month in the prepandemic period (online supplemental table S1). Health facilities are required to report on a monthly basis, and all facilities submitted data each month during the study period, indicating that they did not miss a reporting period. Given this, we assumed that missing values for these outcomes were zero. Completeness of health outcomes did not change in response to the onset of the pandemic but did increase around November to December 2020 (online supplemental figure S1). Monthly health facility data were then aggregated to the district level to account for the health system networks of care within districts. District counts of adverse maternal and perinatal outcomes were calculated as rates using the number of health facility deliveries reported in a given district-month per 100 000 deliveries. Annual district-level population projections from the 2017 Mozambique census were linked to the routine service statistics dataset by district to account for population growth.18 Data were imported into Stata V.15 for preparation and analysis.21 Descriptive analyses were conducted to characterise the mean monthly volume of visits or cases as well as the mean rate of cases in the prepandemic and pandemic periods. We performed an interrupted time series regression analysis for each outcome of interest with April 2020 as the point of interruption, since Mozambique issued a state of emergency on 30 March 2020.22 This model uses district-level panel data and provides ordinary least-squares estimates with robust standard errors for the trends and changes in trends before and after interruption.23 We observed strong evidence of seasonality in the time series of each outcome variable (see online supplemental figure S2). As such, we adjusted for seasonality by including a fixed effect for each calendar month from February through December, with January as the reference. This accounted for effects associated with the same month over different years that may affect timing of pregnancy, care-seeking and quality of care. The correlation between the calendar month and time index variables was small (0.06) and not statistically significant. The models of service utilisation outcomes also included a covariate to adjust for population growth. Models of health outcomes are per 100 000 facility deliveries. For health outcomes, we used the model where Ytd represents the average of district d at time t (month); Xtd is a postinterruption indicator (0 for pre; 1 for post); Ttd is an index to represent the time in months, with t={1,…,43}; and Monthk is a dummy indicator representing the kth calendar month of observation, with k={2,…,12}. Here, β0 represents the average number of services provided/cases reported across districts at the beginning of the pre-COVID-19 period; β1 represents the average monthly change in the number of services provided/cases reported during the prepandemic period; β2 represents the change in the outcome in the first pandemic month; β3 represents the difference in the outcome trend between the pandemic and the prepandemic periods; γ2 – γ12 represent the changes associated with the months February through December, with January as the reference; and єtd is an error term that follows a second-order autoregressive process (lag chosen based on Cumby-Huizinga tests for time series autocorrelation24). For service utilisation outcomes, we fit a model as just described, with the addition of a variable to represent the annual district estimate of the population of reproductive-age women to account for population growth. Linear combinations were used to estimate the difference in trends across the two periods. We used the model for each outcome to obtain a prediction of the counterfactual—the expected trend had there not been an interruption. We calculated the difference between the model prediction and the actual observation for each month. For health outcome variables, this was calculated in rates per 100 000 deliveries as well as the absolute number of cases. The sum of the monthly differences represents the cumulative effect of the interruption over the months observed. Results are considered significant at the p<0.05 level for two-sided comparisons. This research was done without patient or public involvement.

Matériaux

Innovations

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

1. Telemedicine: Implementing telemedicine services can allow pregnant women to receive remote consultations and follow-up care, reducing the need for in-person visits and improving access to healthcare, especially in remote or underserved areas.

2. Mobile health (mHealth) applications: Developing mobile applications that provide information and resources related to maternal health, such as prenatal care guidelines, nutrition advice, and appointment reminders, can empower pregnant women to take control of their health and access necessary information easily.

3. Community health workers: Expanding the role of community health workers can help bridge the gap between healthcare facilities and pregnant women in rural or hard-to-reach areas. These trained individuals can provide education, support, and basic healthcare services to pregnant women, improving access to maternal health services.

4. Transportation solutions: Addressing transportation barriers by providing affordable and reliable transportation options, such as community-based transportation services or partnerships with ride-sharing companies, can ensure that pregnant women can access healthcare facilities for prenatal care and delivery.

5. Health information systems: Strengthening health information systems, such as the national health management information system (HMIS), can improve data collection and reporting, leading to better monitoring of maternal health indicators and informed decision-making for resource allocation and service planning.

6. Maternal health financing models: Exploring innovative financing models, such as health insurance schemes or conditional cash transfer programs, can help reduce financial barriers to accessing maternal health services and ensure that pregnant women receive the care they need without facing financial hardship.

These are just a few examples of innovations that can be considered to improve access to maternal health. It’s important to assess the local context, resources, and needs to determine the most suitable innovations for implementation.

AI Innovations Description

Based on the provided description, the study conducted an observational analysis of routine service delivery data to measure the effects of the COVID-19 pandemic on maternal and perinatal health services and outcomes in Mozambique, specifically in Nampula Province. The study analyzed data from January 2018 to July 2021, focusing on indicators such as antenatal care (ANC) visits, facility deliveries, and various maternal and perinatal health outcomes.

The study found that there were no significant disruptions to ANC visits at the onset of the pandemic. However, there was a significant monthly increase in first ANC visits and ANC visits within the first trimester per district above prepandemic trends. The number of fourth ANC visits completed did not significantly change. At the onset of COVID-19, there was a significant decrease in facility deliveries, but the rate then increased significantly above prepandemic trends. There were no significant increases in adverse birth outcomes during the pandemic. Interestingly, the rates of uterine rupture, stillbirth, and neonatal sepsis decreased during the pandemic.

Based on these findings, the study suggests that Nampula Province in Mozambique may offer insights into health system resilience. Despite pandemic interference, there were no disruptions to ANC services, and there were temporary disruptions to facility deliveries. The study highlights the importance of maintaining access to maternal health services during times of crisis and suggests that strategies to improve access and utilization of ANC services can contribute to positive maternal and perinatal health outcomes.

To develop this recommendation into an innovation to improve access to maternal health, stakeholders could consider implementing the following:

1. Telehealth and mobile health solutions: Utilize technology to provide remote ANC consultations, education, and support to pregnant women, especially in areas with limited access to healthcare facilities.

2. Community-based interventions: Establish community health worker programs to provide ANC services, health education, and referrals in remote or underserved areas. This can help bridge the gap between communities and healthcare facilities.

3. Transportation support: Improve transportation infrastructure and provide transportation subsidies or vouchers to pregnant women, ensuring they can access healthcare facilities for ANC visits and facility deliveries.

4. Strengthening health system capacity: Invest in training and deploying more skilled healthcare providers, particularly midwives, to improve the quality and availability of maternal health services.

5. Health education and awareness campaigns: Conduct targeted campaigns to raise awareness about the importance of ANC visits, facility deliveries, and early pregnancy care, addressing cultural and social barriers that may hinder access to maternal health services.

6. Integration of maternal health services: Integrate maternal health services with other healthcare programs, such as family planning and immunization, to provide comprehensive care and improve access for women.

These recommendations can be tailored and implemented based on the specific context and needs of the target population, considering factors such as geographical location, cultural practices, and available resources. Continuous monitoring and evaluation of the implemented interventions will be crucial to assess their effectiveness and make necessary adjustments for sustained improvements in access to maternal health.

AI Innovations Methodology

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

1. Strengthening Health System Infrastructure: Invest in improving the infrastructure of health facilities in Nampula Province, Mozambique, to ensure they have the necessary resources and equipment to provide quality maternal health services.

2. Increasing Health Workforce Capacity: Address the shortage of health workers in Nampula Province by recruiting and training more healthcare professionals, particularly midwives and obstetricians, to provide comprehensive maternal health care.

3. Enhancing Community-Based Maternal Health Programs: Implement community-based programs that focus on educating and empowering women and their families about maternal health, including the importance of antenatal care visits, facility deliveries, and early recognition of complications.

4. Improving Transportation and Accessibility: Address transportation barriers by improving road infrastructure and providing transportation options for pregnant women in remote areas to access health facilities for antenatal care and delivery.

5. Strengthening Health Information Systems: Enhance the health information systems in Nampula Province to ensure accurate and timely data collection, monitoring, and evaluation of maternal health services. This will help identify gaps and track progress towards improving access to maternal health.

To simulate the impact of these recommendations on improving access to maternal health, a methodology could be developed as follows:

1. Define Key Indicators: Identify key indicators that reflect access to maternal health, such as the number of antenatal care visits, facility deliveries, maternal mortality rates, and perinatal outcomes.

2. Baseline Data Collection: Collect baseline data on the identified indicators before implementing the recommendations. This data will serve as a reference point for comparison.

3. Implement Recommendations: Implement the recommended interventions, such as strengthening health system infrastructure, increasing health workforce capacity, implementing community-based programs, improving transportation, and enhancing health information systems.

4. Data Collection during Implementation: Continuously collect data on the identified indicators during the implementation phase. This data will help monitor the progress and identify any changes resulting from the interventions.

5. Analyze Data: Analyze the collected data using appropriate statistical methods, such as interrupted time series analysis. This analysis will help determine the impact of the recommendations on the identified indicators.

6. Compare Results: Compare the results obtained after implementing the recommendations with the baseline data to assess the impact of the interventions on improving access to maternal health.

7. Adjust and Refine Interventions: Based on the analysis of the data, adjust and refine the interventions as needed to further improve access to maternal health.

8. Continuous Monitoring and Evaluation: Establish a system for continuous monitoring and evaluation to track the long-term impact of the interventions and make necessary adjustments to ensure sustained improvements in access to maternal health.

By following this methodology, policymakers and healthcare providers can assess the effectiveness of the recommendations and make informed decisions to further enhance access to maternal health services in Nampula Province, Mozambique.

Auteurs et co-auteurs

Statistics:

Authors: 5

Identifiers:

DOI: 10.1136/bmjopen-2022-062975

Research Areas:

COVID, Health System and Policy, Maternal Access, Maternal and Child Health, Quality of Care, Sexual and Reproductive Health

Study Countries:

Mozambique

Study Design:

Cross Sectional Study, Grounded Theory

Study Approach:

Quantitative

Participants Gender:

Female

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