Under-five mortality in the Democratic Republic of the Congo: secondary analyses of survey and conflict data by province

Bulletin of the World Health Organization, Volume 100, No. 7, Year 2022

Interpretação

Objective To compare coverage of key child health policy indicators across provinces and to explore their association with under-five mortality and level of conflict in the Democratic Republic of the Congo. Methods We made a secondary analysis of nationally representative data from 1380 health facilities and 20 792 households in 2017–2018. We analysed provincial-level data on coverage of 23 different indicators for improving common causes of childhood mortality, combined into mean scores for: newborn health, pneumonia, diarrhoea, malaria and safe environment. Using negative binomial regression we compared the scores with provincial-level under-five mortality. With binary logistic regression at the individual level we compared indicators (outcome) with living in a conflict-affected province (exposure). Findings All grouped coverage scores demonstrated large ranges across the 26 provinces: newborn health: 20% to 61%; pneumonia: 26% to 86%; diarrhoea: 25% to 63%; malaria: 22% to 53%; and safe environment: 4% to 53%. The diarrhoea score demonstrated the strongest association with under-five mortality (adjusted coefficient: −0.026; 95% confidence interval: −0.045 to −0.007). Conflict-affected provinces had both the highest as well as the lowest mortality rates and indicator coverages. The odds of coverage were higher in conflict-affected provinces for 13 out of 23 indicators, whereas in provinces unaffected by conflict only one indicator had higher odds of coverage. Conclusion Conflict alone is a poor predictor for child health. Ensuring that children in unaffected provinces are not neglected while addressing the needs of the most vulnerable in conflict settings is important. Prevent, protect and treat strategies for diarrhoeal disease could help improve equity in child survival. We performed a secondary analysis of data from nationally representative, cross-sectional surveys of health facilities and households in the Democratic Republic of the Congo in 2017–2018. The framework for the study was based on a review of three global action plans to identify key policy indicators for action on common causes of childhood mortality, under the broad themes of prevent, protect and treat. The Democratic Republic of the Congo has an estimated population of 85–100 million14,15 residing across 26 provinces and 516 health zones.16 Health care is offered by public and private operators including faith-based organizations.16 In addition, several NGOs and international organizations operate in the country.17 An estimated 40% of the country’s health-care spending comes from out-of-pocket expenditure, with international donors providing a similar proportion.18 Ethical approval for the study was obtained from the Swedish Ethical Review Authority (Dnr 2020–05190). Data collection and sampling procedures for the data sets have been described elsewhere.5,19,20 We describe here some important details about the data sets; further details are in the supplementary files in the authors’ data repository.21 We obtained data on health indicators and socioeconomic status from two national data sets. The Service and Provision Assessment 2017–201819 used stratified random probability sampling to select 1412 health facilities from a list of all 12 050 operational health facilities, excluding health posts. These facilities were surveyed between October 2017 and April 2018. Of the sampled health facilities, 32 (2.3%) were not surveyed, mainly due to security problems. We extracted data from the inventory section of the data (for example, on medications and equipment), and from the service provider questionnaire (for example on receipt of training in kangaroo mother care). The Multiple Cluster Indicator Survey 2018 household survey5 was designed to provide provincial estimates based on individual-level data using a sample frame based on the 1984 population census. A systematic random sample of 30 households was drawn from each of the 721 clusters giving an overall sample of 21 630 households, of which 20 792 (96.1%) were successfully interviewed between December 2017 and July 2018. Twelve clusters were not visited due to insecurity problems, mainly in Tanganyika and Maniema provinces. We used data from the questionnaires about the household, women and children younger than 5 years. We extracted data on relative socioeconomic status (continuous variable) based on household asset ownership and urban or rural setting. To obtain data on areas of conflict in the Democratic Republic of the Congo we used a third data set. The Uppsala Conflict Data Program Georeferenced Event Data Set contains global temporally and spatially disaggregated data of conflict events.22–25 For an event to be included it must have resulted in at least one death and the actor involved must have been involved in events that together accumulated to at least 25 deaths in one calendar year. We calculated annual levels of conflict for each province between 2013–2018 to match the time frame used to calculate the under-five mortality. We divided provinces into three different conflict categories, adapting the definition from Uppsala University regarding state-based violence: major conflict (if more than 1000 battle-related deaths had occurred in one of the calendar years), minor conflict (more than 25 battle-related deaths) and no conflict (25 deaths or fewer).26 We compiled a list of 47 key policy indicators for action on common causes of childhood mortality from the following documents: (i) Every Newborn action plan;27,28 (ii) Global Action Plan for the Prevention and Control of Pneumonia and Diarrhoea;29 and (iii) Global Technical Strategy for Malaria 2016–2030.30 We reviewed the national health facility and household surveys for available data on coverage of the identified indicators. We used data on 23 different indicators: 10 of the 15 indicators in the Every Newborn action plan,27 11 of the 18 indicators from the Global Action Plan for the Prevention and Control of Pneumonia and Diarrhoea29 and three of the 15 Global Technical Strategy for Malaria 2016–2030 indicators30 (Table 1). We excluded indicators if no data were available, the intervention was not implemented at the time of the survey, the indicator was not focused on the child (maternal indicators, for example) or too few observations were recorded. Details about the excluded indicators are in the supplementary files.32 We set the target coverage at 80% for all indicators, except exclusive breastfeeding (50%) and caesarean section (10%), using the district-level targets set out by the Global Action Plan for the Prevention and Control of Pneumonia and Diarrhoea and the International Vaccine Access Centre.31 DTP: diphtheria–tetanus–pertussis; Hep B: hepatitis B; Hib: Haemophilus influenzae type B. a Authors’ classification. b Service and Provision Assessment Survey 2017–2018 does not include a question on mask size. c Study definition differs from action plan definition. d We only chose zinc, to be consistent with the international vaccine access centre definition.31 Note: Data sources were the Multiple Indicator Cluster Survey, 2017–20185 and Service and Provision Assessment 2017–2018.19 We calculated the indicators according to the definitions on Table 1; some indicators were identical to the source reports whereas other differed in definition and were not reported in the reports. We then combined data for the available indicators into six grouped coverage scores covering common causes of childhood mortality, using the same method as the International Vaccine Access Center:31 (i) newborn health (using indicators from the Every Newborn action plan); (ii) pneumonia; (iii) diarrhoea; (iv) combined pneumonia and diarrhoea (each from the Global Action Plan for the Prevention and Control of Pneumonia and Diarrhoea); (v) malaria (from the Global Technical Strategy for Malaria 2016–2030); and (vi) safe environment. We generated overall grouped scores by adding the coverage for all included indicators and dividing by the number of indicators in each group (Box 1). Numerator: exclusive breastfeeding for 6 months, skilled birth attendance, early postnatal care contact for infants, essential newborn care, newborn resuscitation, kangaroo mother care, treatment of severe neonatal infection, chlorhexidine cord-cleansing, caesarean section, emergency obstetric care Denominator: number of indicators (10) Numerator: exclusive breastfeeding for 6 months, pentavalent vaccine coverage, measles vaccine coverage, pneumococcal vaccine coverage, oral rehydration therapy, zinc for the treatment of diarrhoea Denominator: number of indicators (6) Numerator: exclusive breastfeeding for 6 months, pentavalent vaccine coverage, measles vaccine coverage, pneumococcal vaccine coverage Denominator: number of indicators (4) Numerator: exclusive breastfeeding for 6 months, measles vaccine coverage, oral rehydration therapy, zinc for the treatment of diarrhoea Denominator: number of indicators (4) Numerator: insecticide-treated net, malaria testing, first-line malaria treatment Denominator: number of indicators (3) Numerator: access to improved drinking-water, access to handwashing with soap, access to an improved sanitation facility, access to clean fuel for cooking Denominator: number of indicators (4) a We did not include pneumonia care-seeking, pneumonia treatment and rotavirus vaccine coverage due to lack of data. a We did not include pneumonia care-seeking, pneumonia treatment and rotavirus vaccine coverage due to lack of data. a We did not include pneumonia care-seeking, pneumonia treatment and rotavirus vaccine coverage due to lack of data. Our primary outcome was provincial-level under-five mortality, calculated using the synthetic cohort probability method.33 We collapsed the indicator variables to provincial means and summed these into the six indicator grouped scores (Box 1) as the main exposure variables. We applied sample weights to adjust for sampling method for all data taken from the health facility and household data sets. All numerators and denominators presented here are raw data whereas some percentages are weighted. We performed negative binomial regression (due to overdispersion in the data), to estimate the associations between provincial-level under-five mortality and indicator coverage scores for both grouped and individual indicators. Due to collinearity, we analysed each indicator separately. We adjusted the negative binomial regressions for provincial level of conflict (none, minor or major conflict) and socioeconomic status, reporting the results as an adjusted coefficient. Due to low levels of missing data, we performed a complete case analysis. Differences in mean scores were compared using two-sample t-tests. We performed an individual-level analysis using logistic regression, to explore associations between being covered by an indicator (outcome) and living in a conflict-affected province (exposure), combining major and minor levels of conflict. We adjusted the analysis for household socioeconomic status. The analysis was performed using Stata version 16 (StataCorp, College Station, Texas, United States of America).

AI Digest

Study Justification:

– The study aimed to compare the coverage of key child health policy indicators across provinces in the Democratic Republic of the Congo (DRC) and explore their association with under-five mortality and level of conflict.
– The study was conducted to provide insights into the disparities in child health indicators and mortality rates across provinces, and to identify potential strategies for improving child survival in the DRC.

Study Highlights:

– The study analyzed nationally representative data from health facilities and households in the DRC in 2017-2018.
– Coverage scores for key child health indicators varied significantly across the 26 provinces, indicating disparities in healthcare access and quality.
– The study found that the coverage score for diarrhoea demonstrated the strongest association with under-five mortality.
– Conflict-affected provinces had both the highest and lowest mortality rates and indicator coverages, highlighting the complex relationship between conflict and child health outcomes.
– The study concluded that conflict alone is a poor predictor for child health and emphasized the importance of addressing the needs of vulnerable populations in conflict settings while ensuring that children in unaffected provinces are not neglected.
– The study recommended implementing prevent, protect, and treat strategies for diarrhoeal disease to improve equity in child survival.

Recommendations for Lay Reader and Policy Maker:

– Ensure equitable access to healthcare services and improve the coverage of key child health indicators across all provinces in the DRC.
– Develop targeted interventions to address the specific needs of conflict-affected provinces, while also prioritizing the health of children in unaffected provinces.
– Implement comprehensive strategies to prevent, protect, and treat diarrhoeal disease, as it was found to have a significant impact on under-five mortality.
– Strengthen healthcare systems and infrastructure to provide quality healthcare services for children in the DRC.
– Increase investment in child health programs and allocate resources effectively to address the identified gaps and improve child survival.

Key Role Players:

– Ministry of Health, Democratic Republic of the Congo
– Provincial Health Departments
– Health facilities and healthcare providers
– Non-governmental organizations (NGOs) and international organizations working in child health
– Community health workers and volunteers
– Researchers and academics specializing in child health

Cost Items for Planning Recommendations:

– Healthcare infrastructure development and maintenance
– Training and capacity building for healthcare providers
– Procurement and distribution of essential medicines and vaccines
– Health education and awareness campaigns
– Monitoring and evaluation systems
– Research and data collection
– Coordination and collaboration between stakeholders
– Advocacy and policy development initiatives

Força da prova

The strength of evidence for this abstract is 8 out of 10.
The evidence in the abstract is strong because it is based on a secondary analysis of nationally representative data from 1380 health facilities and 20,792 households. The study used negative binomial regression and binary logistic regression to analyze the data and found significant associations between coverage of key child health policy indicators, under-five mortality, and level of conflict in the Democratic Republic of the Congo. To improve the evidence, the study could consider including a larger sample size and conducting a longitudinal study to establish causality.

Resumo

We performed a secondary analysis of data from nationally representative, cross-sectional surveys of health facilities and households in the Democratic Republic of the Congo in 2017–2018. The framework for the study was based on a review of three global action plans to identify key policy indicators for action on common causes of childhood mortality, under the broad themes of prevent, protect and treat. The Democratic Republic of the Congo has an estimated population of 85–100 million14,15 residing across 26 provinces and 516 health zones.16 Health care is offered by public and private operators including faith-based organizations.16 In addition, several NGOs and international organizations operate in the country.17 An estimated 40% of the country’s health-care spending comes from out-of-pocket expenditure, with international donors providing a similar proportion.18 Ethical approval for the study was obtained from the Swedish Ethical Review Authority (Dnr 2020–05190). Data collection and sampling procedures for the data sets have been described elsewhere.5,19,20 We describe here some important details about the data sets; further details are in the supplementary files in the authors’ data repository.21 We obtained data on health indicators and socioeconomic status from two national data sets. The Service and Provision Assessment 2017–201819 used stratified random probability sampling to select 1412 health facilities from a list of all 12 050 operational health facilities, excluding health posts. These facilities were surveyed between October 2017 and April 2018. Of the sampled health facilities, 32 (2.3%) were not surveyed, mainly due to security problems. We extracted data from the inventory section of the data (for example, on medications and equipment), and from the service provider questionnaire (for example on receipt of training in kangaroo mother care). The Multiple Cluster Indicator Survey 2018 household survey5 was designed to provide provincial estimates based on individual-level data using a sample frame based on the 1984 population census. A systematic random sample of 30 households was drawn from each of the 721 clusters giving an overall sample of 21 630 households, of which 20 792 (96.1%) were successfully interviewed between December 2017 and July 2018. Twelve clusters were not visited due to insecurity problems, mainly in Tanganyika and Maniema provinces. We used data from the questionnaires about the household, women and children younger than 5 years. We extracted data on relative socioeconomic status (continuous variable) based on household asset ownership and urban or rural setting. To obtain data on areas of conflict in the Democratic Republic of the Congo we used a third data set. The Uppsala Conflict Data Program Georeferenced Event Data Set contains global temporally and spatially disaggregated data of conflict events.22–25 For an event to be included it must have resulted in at least one death and the actor involved must have been involved in events that together accumulated to at least 25 deaths in one calendar year. We calculated annual levels of conflict for each province between 2013–2018 to match the time frame used to calculate the under-five mortality. We divided provinces into three different conflict categories, adapting the definition from Uppsala University regarding state-based violence: major conflict (if more than 1000 battle-related deaths had occurred in one of the calendar years), minor conflict (more than 25 battle-related deaths) and no conflict (25 deaths or fewer).26 We compiled a list of 47 key policy indicators for action on common causes of childhood mortality from the following documents: (i) Every Newborn action plan;27,28 (ii) Global Action Plan for the Prevention and Control of Pneumonia and Diarrhoea;29 and (iii) Global Technical Strategy for Malaria 2016–2030.30 We reviewed the national health facility and household surveys for available data on coverage of the identified indicators. We used data on 23 different indicators: 10 of the 15 indicators in the Every Newborn action plan,27 11 of the 18 indicators from the Global Action Plan for the Prevention and Control of Pneumonia and Diarrhoea29 and three of the 15 Global Technical Strategy for Malaria 2016–2030 indicators30 (Table 1). We excluded indicators if no data were available, the intervention was not implemented at the time of the survey, the indicator was not focused on the child (maternal indicators, for example) or too few observations were recorded. Details about the excluded indicators are in the supplementary files.32 We set the target coverage at 80% for all indicators, except exclusive breastfeeding (50%) and caesarean section (10%), using the district-level targets set out by the Global Action Plan for the Prevention and Control of Pneumonia and Diarrhoea and the International Vaccine Access Centre.31 DTP: diphtheria–tetanus–pertussis; Hep B: hepatitis B; Hib: Haemophilus influenzae type B. a Authors’ classification. b Service and Provision Assessment Survey 2017–2018 does not include a question on mask size. c Study definition differs from action plan definition. d We only chose zinc, to be consistent with the international vaccine access centre definition.31 Note: Data sources were the Multiple Indicator Cluster Survey, 2017–20185 and Service and Provision Assessment 2017–2018.19 We calculated the indicators according to the definitions on Table 1; some indicators were identical to the source reports whereas other differed in definition and were not reported in the reports. We then combined data for the available indicators into six grouped coverage scores covering common causes of childhood mortality, using the same method as the International Vaccine Access Center:31 (i) newborn health (using indicators from the Every Newborn action plan); (ii) pneumonia; (iii) diarrhoea; (iv) combined pneumonia and diarrhoea (each from the Global Action Plan for the Prevention and Control of Pneumonia and Diarrhoea); (v) malaria (from the Global Technical Strategy for Malaria 2016–2030); and (vi) safe environment. We generated overall grouped scores by adding the coverage for all included indicators and dividing by the number of indicators in each group (Box 1). Numerator: exclusive breastfeeding for 6 months, skilled birth attendance, early postnatal care contact for infants, essential newborn care, newborn resuscitation, kangaroo mother care, treatment of severe neonatal infection, chlorhexidine cord-cleansing, caesarean section, emergency obstetric care Denominator: number of indicators (10) Numerator: exclusive breastfeeding for 6 months, pentavalent vaccine coverage, measles vaccine coverage, pneumococcal vaccine coverage, oral rehydration therapy, zinc for the treatment of diarrhoea Denominator: number of indicators (6) Numerator: exclusive breastfeeding for 6 months, pentavalent vaccine coverage, measles vaccine coverage, pneumococcal vaccine coverage Denominator: number of indicators (4) Numerator: exclusive breastfeeding for 6 months, measles vaccine coverage, oral rehydration therapy, zinc for the treatment of diarrhoea Denominator: number of indicators (4) Numerator: insecticide-treated net, malaria testing, first-line malaria treatment Denominator: number of indicators (3) Numerator: access to improved drinking-water, access to handwashing with soap, access to an improved sanitation facility, access to clean fuel for cooking Denominator: number of indicators (4) a We did not include pneumonia care-seeking, pneumonia treatment and rotavirus vaccine coverage due to lack of data. a We did not include pneumonia care-seeking, pneumonia treatment and rotavirus vaccine coverage due to lack of data. a We did not include pneumonia care-seeking, pneumonia treatment and rotavirus vaccine coverage due to lack of data. Our primary outcome was provincial-level under-five mortality, calculated using the synthetic cohort probability method.33 We collapsed the indicator variables to provincial means and summed these into the six indicator grouped scores (Box 1) as the main exposure variables. We applied sample weights to adjust for sampling method for all data taken from the health facility and household data sets. All numerators and denominators presented here are raw data whereas some percentages are weighted. We performed negative binomial regression (due to overdispersion in the data), to estimate the associations between provincial-level under-five mortality and indicator coverage scores for both grouped and individual indicators. Due to collinearity, we analysed each indicator separately. We adjusted the negative binomial regressions for provincial level of conflict (none, minor or major conflict) and socioeconomic status, reporting the results as an adjusted coefficient. Due to low levels of missing data, we performed a complete case analysis. Differences in mean scores were compared using two-sample t-tests. We performed an individual-level analysis using logistic regression, to explore associations between being covered by an indicator (outcome) and living in a conflict-affected province (exposure), combining major and minor levels of conflict. We adjusted the analysis for household socioeconomic status. The analysis was performed using Stata version 16 (StataCorp, College Station, Texas, United States of America).

Materiais

N/A

Inovações

Based on the provided information, it seems that the study focused on analyzing coverage of key child health policy indicators and their association with under-five mortality and level of conflict in the Democratic Republic of the Congo. The study used secondary analysis of nationally representative data from health facilities and households.

To improve access to maternal health, some potential recommendations based on the study findings could include:

1. Strengthening healthcare infrastructure: Investing in the improvement and expansion of healthcare facilities, particularly in conflict-affected provinces, can help increase access to maternal health services.

2. Enhancing healthcare workforce: Increasing the number of skilled healthcare providers, such as midwives and obstetricians, and ensuring their deployment in areas with high maternal mortality rates can improve access to quality maternal healthcare.

3. Promoting community-based interventions: Implementing community-based programs that focus on maternal health education, awareness, and support can help reach women in remote or conflict-affected areas who may have limited access to healthcare facilities.

4. Improving availability of essential maternal health services: Ensuring the availability of essential maternal health services, such as antenatal care, skilled birth attendance, emergency obstetric care, and postnatal care, can significantly reduce maternal mortality rates.

5. Strengthening health information systems: Enhancing data collection and reporting systems can help monitor maternal health indicators and identify areas with low coverage or high mortality rates, enabling targeted interventions and resource allocation.

6. Addressing socio-economic factors: Addressing underlying socio-economic factors, such as poverty, gender inequality, and lack of education, can contribute to improving access to maternal health services and reducing maternal mortality rates.

It is important to note that these recommendations are based on the provided study description and may need to be further tailored and contextualized to the specific needs and challenges of the Democratic Republic of the Congo.

AI Innovations Description

The study conducted a secondary analysis of data from health facilities and households in the Democratic Republic of the Congo to compare coverage of key child health policy indicators across provinces and explore their association with under-five mortality and level of conflict. The study identified several important findings:

1. Coverage of key child health policy indicators varied significantly across provinces. For example, coverage of newborn health ranged from 20% to 61%, pneumonia from 26% to 86%, diarrhoea from 25% to 63%, malaria from 22% to 53%, and safe environment from 4% to 53%.

2. The study found that the diarrhoea score demonstrated the strongest association with under-five mortality. Improving strategies for preventing, protecting, and treating diarrhoeal disease could help improve child survival.

3. Conflict-affected provinces had both the highest and lowest mortality rates and indicator coverages. This suggests that conflict alone is not a reliable predictor for child health.

Based on these findings, a recommendation to improve access to maternal health could be to prioritize interventions that address diarrhoeal disease in conflict-affected provinces. This could involve implementing preventive measures such as promoting handwashing with soap, improving access to clean drinking water, and providing education on proper sanitation practices. Additionally, efforts should be made to ensure that children in unaffected provinces are not neglected and have access to necessary healthcare services.

AI Innovations Methodology

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

1. Strengthen Health Facilities: Enhance the capacity and resources of health facilities in the Democratic Republic of the Congo to provide comprehensive maternal health services. This can include improving infrastructure, ensuring availability of essential medical supplies and equipment, and training healthcare providers.

2. Community-Based Interventions: Implement community-based interventions to increase awareness and access to maternal health services. This can involve training community health workers to provide basic maternal health services, conducting health education campaigns, and establishing referral systems between communities and health facilities.

3. Mobile Health Technologies: Utilize mobile health technologies, such as mobile apps and SMS messaging, to provide information and reminders about antenatal care visits, vaccinations, and other important maternal health services. This can help overcome barriers to accessing healthcare in remote areas.

4. Financial Support: Implement financial support mechanisms, such as conditional cash transfers or health insurance schemes, to reduce the financial burden of maternal healthcare on families. This can help ensure that cost does not become a barrier to accessing essential services.

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

1. Define Key Indicators: Identify key indicators that measure access to maternal health services, such as the number of antenatal care visits, percentage of births attended by skilled healthcare providers, and maternal mortality rate.

2. Data Collection: Collect baseline data on the identified indicators from health facilities, households, and other relevant sources. This can involve surveys, interviews, and analysis of existing data.

3. Intervention Implementation: Implement the recommended interventions, such as strengthening health facilities, community-based interventions, mobile health technologies, and financial support mechanisms.

4. Monitoring and Evaluation: Continuously monitor and evaluate the impact of the interventions on the identified indicators. This can involve collecting data at regular intervals, analyzing the data, and comparing it to the baseline data.

5. Statistical Analysis: Use statistical analysis techniques, such as regression analysis or difference-in-differences analysis, to assess the impact of the interventions on the identified indicators. This can help determine the effectiveness of the interventions in improving access to maternal health services.

6. Policy Recommendations: Based on the findings of the analysis, provide policy recommendations to further improve access to maternal health services. This can involve scaling up successful interventions, addressing any identified challenges or barriers, and advocating for policy changes at the national or regional level.

By following this methodology, policymakers and healthcare stakeholders can gain insights into the potential impact of different interventions on improving access to maternal health services and make informed decisions to prioritize and implement the most effective strategies.

Autor

Dima Health

Identifiers:

DOI: 10.2471/BLT.22.287915

ISSN: 00429686

Research Areas:

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

Study Countries:

Congo

Study Design:

Cohort Study, Cross Sectional Study, Grounded Theory, randomised-control-trial

Study Approach:

Quantitative

Participants Gender:

Female

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