When it rains, it pours: detecting seasonal patterns in utilization of maternal healthcare in Mozambique using routine data

BMC Health Services Research, Volume 20, No. 1, Year 2020

Interprétation

Background: Climatic conditions and seasonal trends can affect population health, but typically, we consider the effect of climate on the epidemiology of communicable diseases. However, climate can also have an effect on access to care, particularly in remote rural areas of low- and middle-income countries. In this study, we investigate associations between the rainy season and the utilization of maternal health services in Mozambique. Methods: We examined patterns in the number of women receiving antenatal care (ANC) and delivering at a health facility for 2012–2019, using data from Mozambique’s Health Management Information Systems. We investigated the association between seasonality (rainfall) and maternal health service utilization (ANC and institutional delivery) at national and provincial level. We fit a negative binomial regression model for institutional delivery and used it to estimate the yearly reduction in institutional deliveries due to the rainy season, with other factors held constant. We used the Lives Saved Tool (LiST) to model increases in mortality due to this estimated decrease in institutional delivery associated with the rainy season. Results: In our national analysis, the rate of ANC visits was 1% lower during the rainy season, adjusting for year and province (IRR = 0.99, 95% CI: 0.96–1.03). The rate of institutional deliveries was 6% lower during the rainy season than the dry season, after adjusting for time and province (IRR = 0.94, 95% CI: 0.92–0.96). In provincial analyses, all provinces except for Maputo-Cidade, Maputo-Province, Nampula, and Niassa showed a statistically significantly lower rate of institutional deliveries in the rainy season. None were statistically significantly lower for ANC. We estimate that, due to reductions in institutional delivery attributable only to the rainy season, there were 74 additional maternal deaths and 726 additional deaths of children under the age of 1 month in 2021, that would not have died if the mothers had instead delivered at a facility. Conclusion: Fewer women deliver at a health facility during the rainy season in Mozambique than during the dry season. Barriers to receiving care during pregnancy and childbirth must be addressed using a multisectoral approach, considering the impact of geographical inequities. We obtained routine monthly count data from the national health management information system (HMIS) for at least four completed ANC visits (ANC4) and count data of women delivering at health facilities (institutional delivery) in each of the 11 provinces, including the capital, Maputo-Cidade. Data for January 2012 to December 2015 came from Mozambique’s HMIS, Modulo Basico. The HMIS transitioned to the DHIS-2-based system called Sistema de Informação para a Saúde–Monitoria e Avaliação (SIS-MA) in 2016, and data from January 2017 to August 2019 comes from SIS-MA. The first 5 months of 2012 were excluded to account for slow adoption of the HMIS system in Mozambique. Of note, there were delays in the implementation of the systems transition activities, and completeness of the data reported through the SIS-MA remains a concern [21]. We were not able to obtain data for the year of transition (2016). Meteorological data was derived from the Climate Hazards Group InfraRed Precipitation with Stations (CHIRPS) dataset via the USAID Famine Early Warning Systems Network [22]. The publicly available CHIRPS dataset contains daily rainfall estimates from rain gauge and satellite observations for each province, excluding the capital Maputo-Cidade. Because weather station density over Mozambique is low and rainfall data collected is therefore very dependent on proxy satellite data for large areas, rainfall data was used to determine seasons. Precipitation data was retrieved for the study period, including monthly rainfall, which ranged from 2.40 to 538.60 mm from January 2012 to August 2019. A binary seasonality predictor for rainy and dry season was created based on average rainfall per month (Additional File 1). We categorized the rainy season as January, February, March, and December, the months with the highest rainfall, and all other months as being the dry season. A binary predictor variable was created for SIS-MA after 2016 versus the older HMIS, Modulo Basico, to account for any changes due to the new system. Nampula, the province with the largest population, represented the reference category for provinces. We assessed (1) frequency of ANC visits, calculated as the monthly total number of pregnant women completing 4 ANC visits at a health facility, and (2) the total number of pregnant women delivering at a health facility each month as outcome variables. We fit separate regression models for each outcome to assess whether or not being in the rainy season would decrease the frequency of maternal health service-related visits. We conducted statistical analyses of the association between seasonality and counts of maternal health facility visits at the national and provincial level. We first looked at the distribution of completed ANC4 visits and institutional deliveries, including the frequency of zero counts, and examined summary statistics, such as mean, median, skewness, and variance. We checked for collinearity by investigating viariance inflation factors (VIF). As VIF for both models were well below 2.00, we assumed that collinearity to be negligible for the models fitted here. To determine the most appropriate approach for examining the association between rainy season and number of facility visits, we evaluated a number of regression models, including the Poisson model, a negative binomial mean-dispersion model, and a generalized linear model (GLM) assuming an overdispersed Poisson model. After evaluating the predictive performance of each model for each outcome using Akaike information criterion (AIC), we found that the negative binomial model showed the best model fit to the data. We fit the overall model for each outcome (counts of ANC; counts of institutional deliveries), adjusted for time (monthly), to account for unmeasured confounders that may vary over time, HMIS change, and region (as provinces). Accounting for heterogeneity across province in both frequency of facility visits and precipitation across Mozambique, provincial associations were estimated by stratifying by provinces in our model. We conducted all analyses using Stata version 15.1 (StataCorp, College Station, TX) [23]. Inferences of statistically significant effects were based on a-priori defined significance level of alpha = 0.05 or if the 95% confidence interval overlapped the null value of incident rate ratio (IRR) = 1.00. We then calculated the predicted number of institutional deliveries after 2012 using the fitted negative binomial model, under two scenarios: first, with months in their original rainy/dry season categorization; and second, with all months considered to be in the dry season. We summed the predicted counts for each scenario and took the difference as an estimate of how many women would not deliver at a facility because of the rain. We used the Lives Saved Tool (LiST) to model the increase in mortality due to this predicted decrease in utilization of institutional delivery associated with the rainy season. LiST is a mathematical modeling tool which allows users to model the impact of scaling up maternal, newborn, child health and nutrition (MNCH&N) interventions on mortality and nutritional outcomes [24]. Our predicted reduction in service utilization of institutional delivery corresponded to a 2% decrease. We therefore created a projection in LiST keeping utilization in 2020 at 64.8% and dropped it to 63.5% for 2021. We used 2020 for our base year and 2021 for our target year.

Résumé de l’IA

Study Justification:

– The study aims to investigate the association between the rainy season and the utilization of maternal health services in Mozambique.
– It is important to understand the impact of climatic conditions on access to care, especially in remote rural areas of low- and middle-income countries.
– By identifying seasonal patterns in maternal healthcare utilization, policymakers can develop targeted interventions to address barriers to care during the rainy season.

Study Highlights:

– The study found that the rate of institutional deliveries was 6% lower during the rainy season compared to the dry season.
– The rate of ANC visits was 1% lower during the rainy season, although this difference was not statistically significant.
– Most provinces in Mozambique showed a statistically significant lower rate of institutional deliveries during the rainy season.
– The study estimated that reductions in institutional delivery due to the rainy season resulted in 74 additional maternal deaths and 726 additional deaths of children under 1 month in 2021.

Study Recommendations:

– Barriers to receiving care during pregnancy and childbirth must be addressed using a multisectoral approach.
– Policymakers should consider the impact of geographical inequities in access to maternal healthcare.
– Targeted interventions should be developed to improve access to maternal health services during the rainy season, particularly in provinces with significant reductions in institutional deliveries.

Key Role Players:

– Ministry of Health: Responsible for developing and implementing policies to improve maternal healthcare access.
– Local Health Authorities: Responsible for coordinating healthcare services at the provincial and district levels.
– Non-Governmental Organizations (NGOs): Can provide support and resources for implementing interventions to improve maternal healthcare access.
– Community Health Workers: Play a crucial role in reaching remote rural areas and providing education and support to pregnant women.

Cost Items for Planning Recommendations:

– Infrastructure Development: Funding may be required to improve healthcare facilities in remote rural areas, including the construction or renovation of health centers.
– Training and Capacity Building: Resources are needed to train healthcare providers and community health workers on maternal healthcare services and interventions.
– Transportation and Logistics: Budget should be allocated for transportation of pregnant women to healthcare facilities during the rainy season, including the provision of ambulances or other means of transportation.
– Information and Communication Technology: Investment in digital health solutions can improve access to maternal healthcare services, such as telemedicine or mobile health applications.
– Community Engagement and Awareness: Funding is needed for community outreach programs and campaigns to raise awareness about the importance of maternal healthcare and address cultural or social barriers.
Please note that the cost items provided are general categories and the actual cost will depend on the specific context and implementation strategies.

Force de la preuve

The strength of evidence for this abstract is 7 out of 10.
The evidence in the abstract is moderately strong. The study used routine data from Mozambique’s Health Management Information Systems to investigate the association between the rainy season and the utilization of maternal health services. The study employed a negative binomial regression model and estimated the reduction in institutional deliveries due to the rainy season. The results showed a statistically significant lower rate of institutional deliveries during the rainy season compared to the dry season. The study also estimated the additional maternal and child deaths associated with the decrease in institutional deliveries. However, the abstract does not provide information on potential limitations of the study, such as data quality issues or confounding factors. To improve the strength of the evidence, the authors could include a discussion of the limitations and potential sources of bias in the study, as well as suggestions for future research to address these limitations.

Abstrait

We obtained routine monthly count data from the national health management information system (HMIS) for at least four completed ANC visits (ANC4) and count data of women delivering at health facilities (institutional delivery) in each of the 11 provinces, including the capital, Maputo-Cidade. Data for January 2012 to December 2015 came from Mozambique’s HMIS, Modulo Basico. The HMIS transitioned to the DHIS-2-based system called Sistema de Informação para a Saúde–Monitoria e Avaliação (SIS-MA) in 2016, and data from January 2017 to August 2019 comes from SIS-MA. The first 5 months of 2012 were excluded to account for slow adoption of the HMIS system in Mozambique. Of note, there were delays in the implementation of the systems transition activities, and completeness of the data reported through the SIS-MA remains a concern [21]. We were not able to obtain data for the year of transition (2016). Meteorological data was derived from the Climate Hazards Group InfraRed Precipitation with Stations (CHIRPS) dataset via the USAID Famine Early Warning Systems Network [22]. The publicly available CHIRPS dataset contains daily rainfall estimates from rain gauge and satellite observations for each province, excluding the capital Maputo-Cidade. Because weather station density over Mozambique is low and rainfall data collected is therefore very dependent on proxy satellite data for large areas, rainfall data was used to determine seasons. Precipitation data was retrieved for the study period, including monthly rainfall, which ranged from 2.40 to 538.60 mm from January 2012 to August 2019. A binary seasonality predictor for rainy and dry season was created based on average rainfall per month (Additional File 1). We categorized the rainy season as January, February, March, and December, the months with the highest rainfall, and all other months as being the dry season. A binary predictor variable was created for SIS-MA after 2016 versus the older HMIS, Modulo Basico, to account for any changes due to the new system. Nampula, the province with the largest population, represented the reference category for provinces. We assessed (1) frequency of ANC visits, calculated as the monthly total number of pregnant women completing 4 ANC visits at a health facility, and (2) the total number of pregnant women delivering at a health facility each month as outcome variables. We fit separate regression models for each outcome to assess whether or not being in the rainy season would decrease the frequency of maternal health service-related visits. We conducted statistical analyses of the association between seasonality and counts of maternal health facility visits at the national and provincial level. We first looked at the distribution of completed ANC4 visits and institutional deliveries, including the frequency of zero counts, and examined summary statistics, such as mean, median, skewness, and variance. We checked for collinearity by investigating viariance inflation factors (VIF). As VIF for both models were well below 2.00, we assumed that collinearity to be negligible for the models fitted here. To determine the most appropriate approach for examining the association between rainy season and number of facility visits, we evaluated a number of regression models, including the Poisson model, a negative binomial mean-dispersion model, and a generalized linear model (GLM) assuming an overdispersed Poisson model. After evaluating the predictive performance of each model for each outcome using Akaike information criterion (AIC), we found that the negative binomial model showed the best model fit to the data. We fit the overall model for each outcome (counts of ANC; counts of institutional deliveries), adjusted for time (monthly), to account for unmeasured confounders that may vary over time, HMIS change, and region (as provinces). Accounting for heterogeneity across province in both frequency of facility visits and precipitation across Mozambique, provincial associations were estimated by stratifying by provinces in our model. We conducted all analyses using Stata version 15.1 (StataCorp, College Station, TX) [23]. Inferences of statistically significant effects were based on a-priori defined significance level of alpha = 0.05 or if the 95% confidence interval overlapped the null value of incident rate ratio (IRR) = 1.00. We then calculated the predicted number of institutional deliveries after 2012 using the fitted negative binomial model, under two scenarios: first, with months in their original rainy/dry season categorization; and second, with all months considered to be in the dry season. We summed the predicted counts for each scenario and took the difference as an estimate of how many women would not deliver at a facility because of the rain. We used the Lives Saved Tool (LiST) to model the increase in mortality due to this predicted decrease in utilization of institutional delivery associated with the rainy season. LiST is a mathematical modeling tool which allows users to model the impact of scaling up maternal, newborn, child health and nutrition (MNCH&N) interventions on mortality and nutritional outcomes [24]. Our predicted reduction in service utilization of institutional delivery corresponded to a 2% decrease. We therefore created a projection in LiST keeping utilization in 2020 at 64.8% and dropped it to 63.5% for 2021. We used 2020 for our base year and 2021 for our target year.

Matériaux

Innovations

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

1. Telemedicine: Implementing telemedicine services can allow pregnant women in remote rural areas to access healthcare professionals and receive prenatal care remotely. This can help overcome geographical barriers and improve access to maternal health services.

2. Mobile clinics: Setting up mobile clinics that travel to remote areas can bring maternal health services closer to the communities that need them. These clinics can provide prenatal care, delivery services, and postnatal care, ensuring that women have access to essential healthcare throughout their pregnancy and childbirth.

3. Community health workers: Training and deploying community health workers who are knowledgeable about maternal health can help bridge the gap between healthcare facilities and communities. These workers can provide education, counseling, and basic healthcare services to pregnant women, ensuring that they receive the necessary care and support.

4. Cash transfer programs: Implementing cash transfer programs specifically targeted at pregnant women can help alleviate financial barriers to accessing maternal health services. By providing financial support, these programs can enable women to afford transportation to healthcare facilities, cover the cost of services, and overcome other financial challenges that may prevent them from seeking care.

5. Mobile applications: Developing mobile applications that provide information and resources on maternal health can empower women with knowledge about pregnancy, childbirth, and postnatal care. These apps can also include features such as appointment reminders, medication tracking, and emergency contact information, making it easier for women to manage their healthcare and seek timely assistance when needed.

6. Public-private partnerships: Collaborating with private healthcare providers and organizations can help expand the availability and accessibility of maternal health services. By leveraging the resources and expertise of both the public and private sectors, innovative solutions can be developed to improve access to quality maternal healthcare.

It’s important to note that the specific context and needs of Mozambique should be taken into consideration when implementing these innovations. Local stakeholders, including healthcare providers, policymakers, and community members, should be involved in the design and implementation process to ensure that the innovations are culturally appropriate, sustainable, and effectively address the barriers to accessing maternal health services.

AI Innovations Description

Based on the provided description, the recommendation to improve access to maternal health in Mozambique is to address the barriers to receiving care during pregnancy and childbirth using a multisectoral approach that considers the impact of geographical inequities. This recommendation is based on the findings that fewer women deliver at a health facility during the rainy season compared to the dry season, leading to potential negative health outcomes such as increased maternal and child mortality.

To develop this recommendation into an innovation, the following steps can be taken:

1. Conduct a comprehensive assessment: Conduct a thorough assessment of the barriers and challenges faced by pregnant women in accessing maternal health services during the rainy season. This assessment should consider factors such as geographical location, infrastructure limitations, transportation issues, and cultural beliefs.

2. Stakeholder engagement: Engage with relevant stakeholders including government agencies, healthcare providers, community leaders, and non-governmental organizations to gather insights and perspectives on the barriers and potential solutions. Collaborate with these stakeholders to develop a shared vision and commitment to improving access to maternal health.

3. Design context-specific interventions: Develop innovative interventions that address the identified barriers and challenges. These interventions could include strategies such as improving transportation infrastructure, establishing mobile health clinics, providing incentives for healthcare providers to work in rural areas during the rainy season, and implementing community-based education and awareness programs.

4. Technology integration: Explore the use of technology to overcome barriers to access. This could involve leveraging mobile health applications to provide information and reminders about antenatal care visits, implementing telemedicine services for remote consultations, and using data analytics to identify areas with the greatest need for maternal health services during the rainy season.

5. Capacity building: Invest in training and capacity building for healthcare providers, community health workers, and other relevant stakeholders to ensure they have the knowledge and skills to deliver quality maternal health services during the rainy season. This could include training on emergency obstetric care, disaster preparedness, and culturally sensitive care.

6. Monitoring and evaluation: Establish a robust monitoring and evaluation system to track the impact of the implemented interventions. Regularly assess the utilization of maternal health services during the rainy season, measure changes in maternal and child health outcomes, and gather feedback from beneficiaries to continuously improve the interventions.

By implementing these recommendations and fostering innovation, it is possible to improve access to maternal health services during the rainy season in Mozambique and reduce the negative impact of seasonal patterns on maternal and child health outcomes.

AI Innovations Methodology

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

1. Strengthening healthcare infrastructure: Invest in improving and expanding healthcare facilities, particularly in remote rural areas where access is limited. This can include building new clinics, upgrading existing facilities, and ensuring they are equipped with necessary resources and skilled healthcare professionals.

2. Mobile health clinics: Implement mobile health clinics that can travel to remote areas, providing essential maternal health services such as antenatal care and delivery assistance. These clinics can reach women who have limited access to healthcare facilities due to geographical barriers.

3. Telemedicine and telehealth services: Utilize technology to provide remote consultations, advice, and support to pregnant women. This can include telemedicine platforms, where women can consult with healthcare professionals through video calls or phone calls, and telehealth apps that provide information and guidance on maternal health.

4. Community health workers: Train and deploy community health workers who can provide basic maternal health services, education, and support in their communities. These workers can bridge the gap between healthcare facilities and remote areas, ensuring that women receive necessary care and information.

5. Transportation support: Address transportation barriers by providing transportation services or subsidies for pregnant women to access healthcare facilities. This can include organizing community transport systems or partnering with local transportation providers to ensure women can reach healthcare facilities safely and timely.

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 women receiving antenatal care, the number of institutional deliveries, and maternal and infant mortality rates.

2. Collect baseline data: Gather data on the current status of these indicators, including information on healthcare utilization, geographical barriers, and existing healthcare infrastructure.

3. Define the simulation model: Develop a simulation model that incorporates the potential impact of the recommended interventions on the identified indicators. This model should consider factors such as population demographics, geographical distribution, and the effectiveness of the interventions.

4. Input intervention parameters: Determine the parameters for each recommended intervention, such as the number of new healthcare facilities to be built, the coverage and frequency of mobile health clinics, the number of community health workers to be trained and deployed, and the availability of transportation support.

5. Run simulations: Use the simulation model to project the potential impact of the interventions over a specified time period. This can involve running multiple scenarios to assess the effectiveness of different combinations of interventions.

6. Analyze results: Analyze the simulation results to determine the projected changes in the identified indicators. Assess the impact of the interventions on improving access to maternal health, including changes in healthcare utilization, reduction in maternal and infant mortality rates, and improvements in geographical equity.

7. Refine and iterate: Refine the simulation model and intervention parameters based on the analysis of the results. Iterate the simulation process to further optimize the interventions and assess their long-term sustainability and scalability.

By following this methodology, policymakers and healthcare stakeholders can gain insights into the potential impact of recommended interventions on improving access to maternal health and make informed decisions on resource allocation and implementation strategies.

Auteur

Dima Health

Identifiers:

DOI: 10.1186/s12913-020-05807-0

Research Areas:

Community Interventions, Environmental, Food Security, Genetics and Genomics, Health System and Policy, Maternal Access, Maternal and Child Health, Quality of Care, Sexual and Reproductive Health, Social Determinants

Study Countries:

Mozambique

Study Design:

Cross Sectional Study

Study Approach:

Quantitative

Participants Gender:

Female

Partagez ceci :