Comparing causes of death between formal and informal neighborhoods in urban Africa: Evidence from ouagadougou health and demographic surveillance system

Global Health Action, Volume 7, No. 1, Year 2014

Interpretation

Background: The probable coexistence of two or more epidemiological profiles in urban Africa is poorly documented. In particular, very few studies have focused on the comparison of cause-specific mortality between two types of neighborhoods that characterize contemporary southern cities: formal neighborhoods, that is, structured or delineated settlements (planned estates) that have full access to public utilities (electricity and water services), and the informal neighborhoods, that is, spontaneous and unplanned peri-urban settlements where people live in slum-like conditions, often with little or no access to public utilities. Objective: To compare the causes of death between the formal and informal neighborhoods covered by the Ouagadougou Health and Demographic Surveillance Systems (HDSS). Design: The data used come from the INDEPTH pooled dataset which includes the contribution of Ouagadougou HDSS and are compiled for the INDEPTH Network Data repository. The data were collected between 2009 and 2011 using verbal autopsy (VA) questionnaires completed by four fieldworkers well trained in the conduction of VAs. The VA data were then interpreted using the InterVA-4 program (version 4.02) to arrive at the causes of death. Results: Communicable diseases are the leading cause of death among children (aged between 29 days and 14 years) in both formal and informal neighborhoods, contributing more than 75% to the mortality rate. Mortality rates from non-communicable diseases (NCDs) are very low before age 15 but are the leading causes from age 50, especially in formal neighborhoods. Mortality from injuries is very low, with no significant difference between the two neighborhoods. Conclusions: The fact that mortality from NCDs is higher among adults in formal neighborhoods seems consistent with the idea of a correlation between modern life and epidemiological transition. However, NCDs do affect informal neighborhoods as well. They consist mainly of cardiovascular diseases and neoplasms most of which are preventable and/or manageable through a change in lifestyle. A prevention program would certainly reduce the burden of these chronic diseases among adults and the elderly with a significant economic impact for families. This work contributes to enrich the scientific debate on intra-urban differences in the epidemiological profile through an analysis of causes of death. The latter are compared between the formal and informal neighborhoods covered by the Ouagadougou HDSS. Data used come from the INDEPTH pooled dataset which includes the contribution of Ouagadougou HDSS and compiled for the INDEPTH Network Data repository (17). The Ouagadougou HDSS is a platform for health research and interventions established in 2008 covering five neighborhoods of Ouagadougou (18). Two of these neighborhoods (Kilwin and Tanghin) are formal neighborhoods with full access to public services, while the other three (Nonghin, Polesgo, and Nioko 2) are spontaneous (such as slums) without access to such services. People living in informal areas are poorer on average, less educated, and born in rural areas in comparison with people living in formal settlements (19), which highlights the importance of rural outmigration to the growth of informal urban settlements. Households in the informal settlements are usually small, made up of single men or young nuclear families in search of affordable housing (19). After an initial census conducted between October 2008 and March 2009 in the five neighborhoods, fieldworkers make regular household visits for update rounds (with an average periodicity of 7 months), registering vital events (births and deaths, marriages, and migrations). As at November 2012, the population under surveillance by the Ouagadougou HDSS totaled 86,071 residents (defined as individuals present in the zone for at least 6 months). In case of death, a verbal autopsy (VA) questionnaire is completed with the next of kin to determine the circumstances that led to the death, including history of the illness and the specific symptoms that preceded death. It should be noted that although the data used come from the INDEPTH pooled dataset, not all INDEPTH members used the INDEPTH standard VA instrument. In 2012, a group of experts under the auspices of WHO reviewed the existing VA instruments in the world and proceeded to their simplification and their standardization to make the results comparable (20). A revised list of causes of death has been established by grouping all ICD-10 causes of death into 62 broad categories. These categories were chosen on the basis of their public health relevance and their potential for ascertainment from VA. A total of 245 indicators (questions) were included in the revised VA instrument. A matrix of these indicators is the input file for the InterVA-4 model used for processing VA data to produce CoD for analysis in this special issue; all the contributing HDSSs transformed their CoD data into this matrix for use in the version 4.02 of InterVA-4 (21). This model applies Bayesian probabilistic methods to VA data and arrives at possible causes of death (21). It generates a maximum of three likely causes of death per case with their associated partial likelihoods (between 0 and 1). For some cases, the input data are insufficient for InterVA-4 to generate any cause of death and such cases are classified by InterVA-4 into the ‘indeterminate’ cause of death. For each case where the sum of the partial likelihoods does not total 1, the difference between their sum and 1 is assigned to the ‘indeterminate’ cause. For this paper, all identified causes of death will be considered proportionate to their partial likelihoods in the calculation of the number of deaths from each cause. In this INDEPTH pooled dataset, data from Ouagadougou HDSS cover the period 2009–2011 and include 1,032 deaths recorded across 221,178 person-years. Of the 1,032 recorded deaths, 870 VAs were completed. These VA data are used to compare formal and informal neighborhoods in terms of causes of death. In the corresponding multisite papers presented in this special issue, the Ouagadougou results are presented as one site. This study examined mortality rates, proportion of deaths due to each cause, and the contribution of each cause to the all-cause mortality rate. Mortality rates are obtained by dividing the number of deaths by the number of person-years. Our estimates will not provide confidence intervals since the HDSS covers an entire non-sampled population. Due to small number of deaths involved, the mortality rates are calculated only for major groups of causes (CDs, NCDs, maternal and neonatal causes, injuries, and unspecified causes). These groups are predefined in the InterVA-4 model (version 4.02) used. CDs include diarrheal diseases, HIV/AIDS, non-obstetric sepsis, malaria, meningitis and encephalitis, respiratory infections, TB, and other infectious diseases. The most common NCDs are anemia, asthma, cardiovascular diseases, neoplasms, diabetes, renal failure, acute abdomen, epilepsy, and severe malnutrition. Maternal and neonatal mortality includes by implication pregnancy-/birth-related causes (pregnancy-induced hypertension, pregnancy-related sepsis, obstetric hemorrhage) and neonatal causes (prematurity, birth asphyxia, neonatal pneumonia, neonatal sepsis, and congenital malformation). To better portray the cause-specific mortality by age, we used the seven age groups predefined in InterVA-4 model (version 4.02), which correspond theoretically to different leading causes of death. Thus, children were grouped into four categories with different levels of exposure to various diseases: neonates (less than 28 days), infants (29 days–11 months), children between 1 and 5, and those between 5 and 15. Among adults, the elderly (65 and over) have been distinguished from people aged 50–64 and from those aged 15–49. Table 1 presents the person-years distribution by age group, sex, and neighborhood, although the small number of deaths here does not allow us to perform mortality analysis by sex. For each sex, formal and informal neighborhoods have close distributions. Regardless of gender and type of neighborhood, people aged 15–49 are the most represented, accounting for more than 50%, followed by those aged 5–14 representing just over 1 in 5. The proportion of people aged 15–49 is slightly higher in formal neighborhoods. There are relatively more children (1–11 months and 1–4 years) in informal neighborhoods while older people (50 years and older) are slightly more in formal neighborhoods. To control for this slight difference in age structure between formal and informal neighborhoods, we provide standardized mortality rates next to the crude mortality rate (all ages) for each type of neighborhood. For this purpose, we have used the structure of the two types of neighborhoods combined as the standard population. Distribution (%) of person-years by age group, sex, and neighborhood, 2009–2011

AI Digest

Study Justification:

– The study aims to document and compare the causes of death between formal and informal neighborhoods in urban Africa.
– This research contributes to the scientific debate on intra-urban differences in the epidemiological profile.
– It provides valuable insights into the health disparities and challenges faced by residents in informal settlements.
– The findings can inform policy and interventions to address the specific health needs of different neighborhoods.

Highlights:

– Communicable diseases are the leading cause of death among children in both formal and informal neighborhoods.
– Non-communicable diseases become the leading causes of death from age 50, especially in formal neighborhoods.
– Mortality from injuries is low and similar in both types of neighborhoods.
– The study suggests a correlation between modern life and the higher mortality from non-communicable diseases in formal neighborhoods.
– However, non-communicable diseases also affect informal neighborhoods, mainly cardiovascular diseases and neoplasms.
– Prevention programs targeting lifestyle changes could significantly reduce the burden of chronic diseases in both types of neighborhoods.

Recommendations:

– Implement prevention programs focused on lifestyle changes to reduce the burden of non-communicable diseases in both formal and informal neighborhoods.
– Improve access to public utilities (electricity and water services) in informal settlements to enhance living conditions and health outcomes.
– Strengthen healthcare services in both types of neighborhoods, with a particular focus on communicable diseases in children and non-communicable diseases in adults.
– Invest in research and surveillance systems to continuously monitor and address the health disparities between formal and informal neighborhoods.

Key Role Players:

– Researchers and scientists specializing in public health and epidemiology.
– Local government authorities and policymakers responsible for urban planning and healthcare services.
– Community leaders and organizations working in informal settlements.
– Healthcare professionals, including doctors, nurses, and community health workers.
– Non-governmental organizations (NGOs) and international development agencies supporting health initiatives in urban Africa.

Cost Items for Planning Recommendations:

– Funding for research and data collection, including the use of verbal autopsy questionnaires and the InterVA-4 program.
– Investments in healthcare infrastructure and services, such as clinics, hospitals, and community health centers.
– Budget for prevention programs, including awareness campaigns, health education, and lifestyle interventions.
– Resources for improving access to public utilities in informal settlements, such as electricity and water services.
– Support for capacity building and training of healthcare professionals and community health workers.
– Funding for ongoing surveillance systems and monitoring of health outcomes in formal and informal neighborhoods.

Strength of Evidence

The strength of evidence for this abstract is 8 out of 10.
The evidence in the abstract is strong because it is based on data collected from the Ouagadougou Health and Demographic Surveillance System (HDSS) and the INDEPTH pooled dataset. The data were collected using verbal autopsy questionnaires completed by well-trained fieldworkers. The causes of death were determined using the InterVA-4 program. The abstract provides specific details about the methodology and the results obtained. To improve the evidence, the abstract could include information about the sample size and the representativeness of the population under surveillance. Additionally, it could mention any limitations or potential biases in the data collection process.

Abstract

This work contributes to enrich the scientific debate on intra-urban differences in the epidemiological profile through an analysis of causes of death. The latter are compared between the formal and informal neighborhoods covered by the Ouagadougou HDSS. Data used come from the INDEPTH pooled dataset which includes the contribution of Ouagadougou HDSS and compiled for the INDEPTH Network Data repository (17). The Ouagadougou HDSS is a platform for health research and interventions established in 2008 covering five neighborhoods of Ouagadougou (18). Two of these neighborhoods (Kilwin and Tanghin) are formal neighborhoods with full access to public services, while the other three (Nonghin, Polesgo, and Nioko 2) are spontaneous (such as slums) without access to such services. People living in informal areas are poorer on average, less educated, and born in rural areas in comparison with people living in formal settlements (19), which highlights the importance of rural outmigration to the growth of informal urban settlements. Households in the informal settlements are usually small, made up of single men or young nuclear families in search of affordable housing (19). After an initial census conducted between October 2008 and March 2009 in the five neighborhoods, fieldworkers make regular household visits for update rounds (with an average periodicity of 7 months), registering vital events (births and deaths, marriages, and migrations). As at November 2012, the population under surveillance by the Ouagadougou HDSS totaled 86,071 residents (defined as individuals present in the zone for at least 6 months). In case of death, a verbal autopsy (VA) questionnaire is completed with the next of kin to determine the circumstances that led to the death, including history of the illness and the specific symptoms that preceded death. It should be noted that although the data used come from the INDEPTH pooled dataset, not all INDEPTH members used the INDEPTH standard VA instrument. In 2012, a group of experts under the auspices of WHO reviewed the existing VA instruments in the world and proceeded to their simplification and their standardization to make the results comparable (20). A revised list of causes of death has been established by grouping all ICD-10 causes of death into 62 broad categories. These categories were chosen on the basis of their public health relevance and their potential for ascertainment from VA. A total of 245 indicators (questions) were included in the revised VA instrument. A matrix of these indicators is the input file for the InterVA-4 model used for processing VA data to produce CoD for analysis in this special issue; all the contributing HDSSs transformed their CoD data into this matrix for use in the version 4.02 of InterVA-4 (21). This model applies Bayesian probabilistic methods to VA data and arrives at possible causes of death (21). It generates a maximum of three likely causes of death per case with their associated partial likelihoods (between 0 and 1). For some cases, the input data are insufficient for InterVA-4 to generate any cause of death and such cases are classified by InterVA-4 into the ‘indeterminate’ cause of death. For each case where the sum of the partial likelihoods does not total 1, the difference between their sum and 1 is assigned to the ‘indeterminate’ cause. For this paper, all identified causes of death will be considered proportionate to their partial likelihoods in the calculation of the number of deaths from each cause. In this INDEPTH pooled dataset, data from Ouagadougou HDSS cover the period 2009–2011 and include 1,032 deaths recorded across 221,178 person-years. Of the 1,032 recorded deaths, 870 VAs were completed. These VA data are used to compare formal and informal neighborhoods in terms of causes of death. In the corresponding multisite papers presented in this special issue, the Ouagadougou results are presented as one site. This study examined mortality rates, proportion of deaths due to each cause, and the contribution of each cause to the all-cause mortality rate. Mortality rates are obtained by dividing the number of deaths by the number of person-years. Our estimates will not provide confidence intervals since the HDSS covers an entire non-sampled population. Due to small number of deaths involved, the mortality rates are calculated only for major groups of causes (CDs, NCDs, maternal and neonatal causes, injuries, and unspecified causes). These groups are predefined in the InterVA-4 model (version 4.02) used. CDs include diarrheal diseases, HIV/AIDS, non-obstetric sepsis, malaria, meningitis and encephalitis, respiratory infections, TB, and other infectious diseases. The most common NCDs are anemia, asthma, cardiovascular diseases, neoplasms, diabetes, renal failure, acute abdomen, epilepsy, and severe malnutrition. Maternal and neonatal mortality includes by implication pregnancy-/birth-related causes (pregnancy-induced hypertension, pregnancy-related sepsis, obstetric hemorrhage) and neonatal causes (prematurity, birth asphyxia, neonatal pneumonia, neonatal sepsis, and congenital malformation). To better portray the cause-specific mortality by age, we used the seven age groups predefined in InterVA-4 model (version 4.02), which correspond theoretically to different leading causes of death. Thus, children were grouped into four categories with different levels of exposure to various diseases: neonates (less than 28 days), infants (29 days–11 months), children between 1 and 5, and those between 5 and 15. Among adults, the elderly (65 and over) have been distinguished from people aged 50–64 and from those aged 15–49. Table 1 presents the person-years distribution by age group, sex, and neighborhood, although the small number of deaths here does not allow us to perform mortality analysis by sex. For each sex, formal and informal neighborhoods have close distributions. Regardless of gender and type of neighborhood, people aged 15–49 are the most represented, accounting for more than 50%, followed by those aged 5–14 representing just over 1 in 5. The proportion of people aged 15–49 is slightly higher in formal neighborhoods. There are relatively more children (1–11 months and 1–4 years) in informal neighborhoods while older people (50 years and older) are slightly more in formal neighborhoods. To control for this slight difference in age structure between formal and informal neighborhoods, we provide standardized mortality rates next to the crude mortality rate (all ages) for each type of neighborhood. For this purpose, we have used the structure of the two types of neighborhoods combined as the standard population. Distribution (%) of person-years by age group, sex, and neighborhood, 2009–2011

Materials

N/A

Innovations

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

1. Mobile health clinics: Implementing mobile health clinics that can travel to informal neighborhoods, providing essential maternal health services such as prenatal care, vaccinations, and postnatal care.

2. Telemedicine: Utilizing telemedicine technology to connect healthcare providers with pregnant women in informal neighborhoods, allowing them to receive virtual consultations, advice, and monitoring without the need for physical travel.

3. Community health workers: Training and deploying community health workers in informal neighborhoods to provide education, support, and basic healthcare services to pregnant women, ensuring they have access to necessary care and information.

4. Public-private partnerships: Establishing partnerships between public healthcare systems and private healthcare providers to expand access to maternal health services in informal neighborhoods, leveraging the resources and expertise of both sectors.

5. Health education programs: Implementing targeted health education programs in informal neighborhoods to raise awareness about maternal health, pregnancy complications, and the importance of seeking timely care, empowering women to make informed decisions about their health.

6. Improved infrastructure: Investing in the development and improvement of infrastructure in informal neighborhoods, including access to clean water, sanitation facilities, and electricity, which are essential for ensuring safe and hygienic maternal health practices.

7. Financial incentives: Introducing financial incentives or subsidies for pregnant women in informal neighborhoods to encourage them to seek regular prenatal care and access necessary maternal health services.

8. Collaborative networks: Establishing collaborative networks between healthcare providers, community organizations, and local authorities to coordinate efforts and resources, ensuring comprehensive and accessible maternal health services in informal neighborhoods.

These innovations aim to address the specific challenges faced by pregnant women in informal neighborhoods, such as limited access to healthcare facilities, lack of awareness, and socioeconomic barriers. By implementing these recommendations, it is possible to improve access to maternal health and reduce maternal mortality rates in these communities.

AI Innovations Description

The recommendation that can be developed into an innovation to improve access to maternal health based on the provided description is to implement a prevention program focused on reducing the burden of non-communicable diseases (NCDs) among adults and the elderly in both formal and informal neighborhoods.

The description highlights that NCDs, such as cardiovascular diseases and neoplasms, are the leading causes of death among adults in formal neighborhoods, but they also affect informal neighborhoods. These diseases are preventable and/or manageable through a change in lifestyle.

By implementing a prevention program that educates and empowers individuals in both formal and informal neighborhoods about the importance of a healthy lifestyle, including regular physical activity, balanced nutrition, and avoiding risk factors such as smoking and excessive alcohol consumption, the burden of NCDs can be reduced. This program can also provide access to affordable healthcare services and screenings for early detection and management of NCDs.

Improving access to maternal health can be achieved by integrating maternal health services into this prevention program. This can include providing prenatal care, promoting safe delivery practices, and offering postnatal support and education. By addressing the health needs of women during pregnancy and childbirth, the program can contribute to reducing maternal mortality and improving overall maternal health outcomes.

Furthermore, the prevention program can also focus on improving access to healthcare services in informal neighborhoods, where people often have limited access to public utilities and face slum-like conditions. This can involve establishing mobile clinics or community health centers in these areas, providing essential healthcare services, and raising awareness about the importance of seeking timely medical care.

Overall, the recommendation is to develop and implement a prevention program that targets NCDs and integrates maternal health services, with a specific focus on improving access to healthcare in informal neighborhoods. This innovation has the potential to improve the overall health outcomes and access to maternal health services in urban Africa.

AI Innovations Methodology

The study mentioned focuses on comparing causes of death between formal and informal neighborhoods in urban Africa, specifically in Ouagadougou. The objective is to understand the differences in cause-specific mortality between these two types of neighborhoods.

To improve access to maternal health in these neighborhoods, the following innovations could be considered:

1. Mobile Clinics: Implementing mobile clinics that travel to both formal and informal neighborhoods can provide essential maternal health services, including prenatal care, vaccinations, and postnatal care. This approach ensures that healthcare services are brought directly to the communities, making them more accessible to pregnant women.

2. Community Health Workers: Training and deploying community health workers in both formal and informal neighborhoods can help bridge the gap in access to maternal health services. These workers can provide education, support, and referrals for pregnant women, ensuring they receive the necessary care throughout their pregnancy.

3. Telemedicine: Utilizing telemedicine technology can improve access to maternal health services, especially in areas where healthcare facilities are limited. Pregnant women can consult with healthcare professionals remotely, reducing the need for travel and increasing access to expert advice and guidance.

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

1. Baseline Data Collection: Gather data on the current state of maternal health access in both formal and informal neighborhoods. This includes information on the number of healthcare facilities, healthcare providers, and the availability of essential maternal health services.

2. Define Metrics: Identify key metrics to measure the impact of the recommendations, such as the number of pregnant women receiving prenatal care, the number of births attended by skilled healthcare professionals, and the reduction in maternal mortality rates.

3. Simulation Modeling: Develop a simulation model that incorporates the baseline data and simulates the impact of the recommendations over a specific time period. The model should consider factors such as population demographics, healthcare facility capacity, and the effectiveness of the recommended interventions.

4. Data Analysis: Analyze the simulation results to assess the projected impact of the recommendations on improving access to maternal health. This includes evaluating the changes in the identified metrics and identifying any potential challenges or limitations.

5. Sensitivity Analysis: Conduct sensitivity analysis to understand the robustness of the simulation results. This involves testing the model with different assumptions and parameters to assess the variability in the projected outcomes.

6. Recommendations and Implementation: Based on the simulation results, provide recommendations for implementing the identified innovations to improve access to maternal health. Consider factors such as cost-effectiveness, scalability, and sustainability.

By following this methodology, stakeholders can gain insights into the potential impact of the recommended innovations and make informed decisions on how to improve access to maternal health in both formal and informal neighborhoods.

Authors & Co-Authors

Statistics:

Citations: 8

Authors: 4

Identifiers:

DOI: 10.3402/gha.v7.25523

Research Areas:

Cancer, Community Interventions, Environmental, Food Security, Health System and Policy, Infectious Diseases, Maternal and Child Health, Noncommunicable Diseases, Quality of Care, Sexual and Reproductive Health, Violence and Injury

Study Design:

Cross Sectional Study

Participants Gender:

Male