Causes of death in the Taabo health and demographic surveillance system, Cǒte d’Ivoire, from 2009 to 2011

Global Health Action, Volume 8, No. 1, Year 2015

Interpretação

Background: Current vital statistics from governmental institutions in Cǒte d’Ivoire are incomplete. This problem is particularly notable for remote rural areas that have limited access to the health system. Objective: To record all deaths from 2009 to 2011 and to identify the leading causes of death in the Taabo health and demographic surveillance system (HDSS) in south-central Cǒte d’Ivoire. Design: Deaths recorded in the first 3 years of operation of the Taabo HDSS were investigated by verbal autopsy (VA), using the InterVA-4 model. InterVA-4 is based on theWorld Health Organization 2012 VA tool in terms of input indicators and categories of causes of death. Results: Overall, 948 deaths were recorded, of which 236 (24.9%) had incomplete VA data. Among the 712 deaths analyzed, communicable diseases represented the leading causes (58.9%), with most deaths attributed to malaria (n=129), acute respiratory tract infections (n=110), HIV/AIDS (n=80), and pulmonary tuberculosis (n=46). Non-communicable diseases accounted for 18.9% of the deaths and included mainly acute abdomen (n=38), unspecified cardiac diseases (n=15), and digestive neoplasms (n=13). Maternal and neonatal conditions accounted for 8.3% of deaths, primarily pneumonia (n=19) and birth asphyxia (n=16) in newborns. Among the 3.8% of deaths linked to trauma and injury, the main causes were assault (n=6), accidental drowning (n=4), contact with venomous plants/animals (n=4), and traffic-related accidents (n=4). No clear causes were determined in 10.0% of the analyzed deaths. Conclusions: Communicable diseases remain the predominant cause of death in rural Cǒte d’Ivoire. Based on these findings, measures are now being implemented in the Taabo HDSS. It will be interesting to monitor patterns of mortality and causes of death in the face of rapid demographic and epidemiological transitions in this part of West Africa. The Taabo HDSS was established in 2008 and the initial census revealed a population of 37,792. In December 2011 the population was 39,422, and in December 2013 it had reached 42,480 (13). The Taabo HDSS includes one small town (Taabo Cité, 7,514 inhabitants at the end of 2013), 13 main villages, and over 100 hamlets. The establishment of the Taabo HDSS, the longitudinal surveillance of demographic and health data, and the implementation of specific interventions and research projects were approved by the institutional research commissions of the Centre Suisse de Recherches Scientifiques en Côte d’Ivoire (Abidjan, Côte d’Ivoire) and the Swiss Tropical and Public Health Institute (Basel, Switzerland). Ethical clearance was obtained from the ethics committees in Côte d’Ivoire (reference no. 1086 MSHD/CNEF) and Basel (EKBB, reference no. 316/08). All households belonging to the Taabo HDSS are visited three times a year by trained field enumerators. They conduct demographic surveillance, including the registration and monitoring of migration, pregnancies, births, and deaths. Monitoring pregnancies helps enumerators obtain information on stillbirths, abortions, and neonatal deaths. All deaths of permanent residents are recorded and – as with other HDSS sites – whenever possible examined by VA techniques in order to determine the most likely causes of death (14–19). All applied registration and monitoring methods were developed and standardized in order to ensure that the data collected are of high quality and will allow for cross-site comparison (10, 20–23). As a member of the International Network for the Continuous Demographic Evaluation of Populations and Their Health (INDEPTH; http://indepth-network.org), the Taabo HDSS adheres to INDEPTH’s standards. Further details regarding the Taabo HDSS have been published elsewhere (13). The reporting of death is facilitated by key informants in the communities who observe and record any death occurring in the study area. The information is then transmitted to a Taabo HDSS VA supervisor or a field enumerator who informs a VA supervisor. Deaths that might have been missed by the key informants can subsequently be identified during demographic surveillance rounds. Whenever possible, the VA supervisor visits the household in which the death has occurred within 2 weeks and contacts the Taabo HDSS data center for verification of the event. Once it has been verified that the deceased was an HDSS resident, the VA supervisor completes a standardized VA questionnaire with one of the deceased’s close relatives. Initially, completed VA questionnaires were submitted to two physicians who independently determined the direct and underlying cause of death and coded it according to the WHO International Classification of Diseases, version 10 (ICD-10) (24). However, this practice proved relatively slow, somewhat expensive, and there was considerable inter-observer variation regarding the causes of death. Since 2012, the computer automated InterVA-4 model has been validated and admitted for use in research and civil registration, both within already enumerated populations and also as a stand-alone death registration tool (25–28). For the present analysis, the InterVA-4 model was employed. The InterVA-4 tool is a freely available standard computerization model for interpreting VA data and determining causes of death. It has been designed to use the VA input indicators defined in the 2012 WHO VA instrument and to deliver causes of death compatible with the ICD-10. The causes of death are categorized into 62 groups, as defined in the 2012 WHO VA instrument (25–28). The InterVA-4 model was developed on the basis of Bayes’ theorem and is therefore an application of the Bayesian approach for diagnostic help (25–29). If the event of interest (A) depends on different mutually exclusive causes C 1, C 2, …, C m (for instance, causes of death) and other factors S 1, S 2, …, S n (for instance, different signs and symptoms leading to death), then Bayes’ general theorem for each C i and S j can be stated as with P(!Ci) = (1−P(Ci)). For the complete set of causes of death C 1, C 2, …, C m, a set of probabilities for each C i can be computed using a normalizing assumption so that the total conditional probability of all causes sums up to unity: While using an initial set of unconditional probabilities for the causes of death C 1, C 2, …, C m(P(C i∣S 0)) and the matrix of the conditional probabilities P(S j∣C i) for indicators S 1, S 2, …, S n and causes of death C 1, C 2, …, C m, it is possible to apply the same calculation for each S 1, S 2, …, S n that applies to one particular death: In short, with the exception of a minority of interviews where information is contradictory or inconsistent and the cause of death has to be classified as undetermined despite a completed VA interview, usually up to a maximum of three most likely causes of death and their probabilities are estimated per case. Whenever these causes do not add up to 100%, the remaining percentage is classified as undetermined. Because we are working with probabilities, one death can thus contribute to the statistics for several possible causes of death, but never at more than 100%. These idiosyncrasies of InterVA-4 lead to minor rounding errors and explain small differences between the disaggregated data and the summed up totals as presented in this study. Like its predecessor version, InterVA-4 employs special procedures for HIV/AIDS and malaria, because these diseases vary greatly from one setting to another (26). For malaria, unconditional probability is applied to the causes of death, especially due to sickle cell disease as there is a close link between these two conditions (26, 30). For the present analysis, all identified causes of death were aggregated into 14 broad groups, as predefined by INDEPTH, using the statistical software package STATA version 12 (StataCorp, College Station, TX, USA).

AI Digest

Study Justification:

– Current vital statistics in Côte d’Ivoire are incomplete, especially in remote rural areas with limited access to healthcare.
– The study aims to record all deaths from 2009 to 2011 and identify the leading causes of death in the Taabo health and demographic surveillance system (HDSS) in south-central Côte d’Ivoire.
– This information is crucial for understanding the health needs of the population and implementing effective interventions.

Study Highlights:

– Overall, 948 deaths were recorded, with 236 having incomplete data.
– Communicable diseases were the leading causes of death, with malaria, acute respiratory tract infections, HIV/AIDS, and pulmonary tuberculosis being the most common.
– Non-communicable diseases accounted for a significant portion of deaths, including acute abdomen, unspecified cardiac diseases, and digestive neoplasms.
– Maternal and neonatal conditions also contributed to a percentage of deaths, primarily pneumonia and birth asphyxia.
– Trauma and injury-related deaths were also observed, with assault, accidental drowning, contact with venomous plants/animals, and traffic-related accidents being the main causes.
– 10% of deaths could not be attributed to a clear cause.

Recommendations:

– Based on the findings, measures should be implemented to address the high burden of communicable diseases, especially malaria and respiratory infections.
– Efforts should also be made to improve access to healthcare and prevention strategies for non-communicable diseases.
– Maternal and neonatal health interventions should focus on reducing pneumonia and birth asphyxia.
– Strategies to prevent trauma and injury-related deaths, such as improving road safety and awareness, should be implemented.
– Further monitoring of mortality patterns and causes of death is recommended to understand the impact of demographic and epidemiological transitions.

Key Role Players:

– Researchers and scientists involved in the study and analysis of the data.
– Health policymakers and government officials responsible for implementing interventions and policies based on the study findings.
– Healthcare providers and community health workers involved in delivering healthcare services and interventions.
– Community leaders and key informants who play a role in reporting deaths and providing information for the study.

Cost Items for Planning Recommendations:

– Funding for research and data collection.
– Resources for implementing interventions, such as healthcare infrastructure, medical supplies, and personnel.
– Training and capacity building for healthcare providers and community health workers.
– Awareness campaigns and community engagement activities.
– Monitoring and evaluation of interventions to assess their effectiveness.
Please note that the cost items mentioned are general categories and not actual cost estimates. The actual cost will depend on various factors and would require a detailed budgeting process.

Força da prova

The strength of evidence for this abstract is 7 out of 10.
The evidence in the abstract is relatively strong, but there are some areas for improvement. The study design is clearly described, and the data collection methods are explained in detail. The sample size is adequate, with 712 deaths analyzed. The leading causes of death are identified and categorized. However, there are some limitations to consider. 24.9% of the deaths had incomplete verbal autopsy data, which may affect the accuracy of the cause of death determination. Additionally, the abstract does not provide information on the representativeness of the study population or the generalizability of the findings. To improve the evidence, it would be helpful to address the limitations by ensuring complete data collection for verbal autopsies and providing information on the population characteristics and the extent to which the findings can be applied to other settings.

Resumo

The Taabo HDSS was established in 2008 and the initial census revealed a population of 37,792. In December 2011 the population was 39,422, and in December 2013 it had reached 42,480 (13). The Taabo HDSS includes one small town (Taabo Cité, 7,514 inhabitants at the end of 2013), 13 main villages, and over 100 hamlets. The establishment of the Taabo HDSS, the longitudinal surveillance of demographic and health data, and the implementation of specific interventions and research projects were approved by the institutional research commissions of the Centre Suisse de Recherches Scientifiques en Côte d’Ivoire (Abidjan, Côte d’Ivoire) and the Swiss Tropical and Public Health Institute (Basel, Switzerland). Ethical clearance was obtained from the ethics committees in Côte d’Ivoire (reference no. 1086 MSHD/CNEF) and Basel (EKBB, reference no. 316/08). All households belonging to the Taabo HDSS are visited three times a year by trained field enumerators. They conduct demographic surveillance, including the registration and monitoring of migration, pregnancies, births, and deaths. Monitoring pregnancies helps enumerators obtain information on stillbirths, abortions, and neonatal deaths. All deaths of permanent residents are recorded and – as with other HDSS sites – whenever possible examined by VA techniques in order to determine the most likely causes of death (14–19). All applied registration and monitoring methods were developed and standardized in order to ensure that the data collected are of high quality and will allow for cross-site comparison (10, 20–23). As a member of the International Network for the Continuous Demographic Evaluation of Populations and Their Health (INDEPTH; http://indepth-network.org), the Taabo HDSS adheres to INDEPTH’s standards. Further details regarding the Taabo HDSS have been published elsewhere (13). The reporting of death is facilitated by key informants in the communities who observe and record any death occurring in the study area. The information is then transmitted to a Taabo HDSS VA supervisor or a field enumerator who informs a VA supervisor. Deaths that might have been missed by the key informants can subsequently be identified during demographic surveillance rounds. Whenever possible, the VA supervisor visits the household in which the death has occurred within 2 weeks and contacts the Taabo HDSS data center for verification of the event. Once it has been verified that the deceased was an HDSS resident, the VA supervisor completes a standardized VA questionnaire with one of the deceased’s close relatives. Initially, completed VA questionnaires were submitted to two physicians who independently determined the direct and underlying cause of death and coded it according to the WHO International Classification of Diseases, version 10 (ICD-10) (24). However, this practice proved relatively slow, somewhat expensive, and there was considerable inter-observer variation regarding the causes of death. Since 2012, the computer automated InterVA-4 model has been validated and admitted for use in research and civil registration, both within already enumerated populations and also as a stand-alone death registration tool (25–28). For the present analysis, the InterVA-4 model was employed. The InterVA-4 tool is a freely available standard computerization model for interpreting VA data and determining causes of death. It has been designed to use the VA input indicators defined in the 2012 WHO VA instrument and to deliver causes of death compatible with the ICD-10. The causes of death are categorized into 62 groups, as defined in the 2012 WHO VA instrument (25–28). The InterVA-4 model was developed on the basis of Bayes’ theorem and is therefore an application of the Bayesian approach for diagnostic help (25–29). If the event of interest (A) depends on different mutually exclusive causes C 1, C 2, …, C m (for instance, causes of death) and other factors S 1, S 2, …, S n (for instance, different signs and symptoms leading to death), then Bayes’ general theorem for each C i and S j can be stated as with P(!Ci) = (1−P(Ci)). For the complete set of causes of death C 1, C 2, …, C m, a set of probabilities for each C i can be computed using a normalizing assumption so that the total conditional probability of all causes sums up to unity: While using an initial set of unconditional probabilities for the causes of death C 1, C 2, …, C m(P(C i∣S 0)) and the matrix of the conditional probabilities P(S j∣C i) for indicators S 1, S 2, …, S n and causes of death C 1, C 2, …, C m, it is possible to apply the same calculation for each S 1, S 2, …, S n that applies to one particular death: In short, with the exception of a minority of interviews where information is contradictory or inconsistent and the cause of death has to be classified as undetermined despite a completed VA interview, usually up to a maximum of three most likely causes of death and their probabilities are estimated per case. Whenever these causes do not add up to 100%, the remaining percentage is classified as undetermined. Because we are working with probabilities, one death can thus contribute to the statistics for several possible causes of death, but never at more than 100%. These idiosyncrasies of InterVA-4 lead to minor rounding errors and explain small differences between the disaggregated data and the summed up totals as presented in this study. Like its predecessor version, InterVA-4 employs special procedures for HIV/AIDS and malaria, because these diseases vary greatly from one setting to another (26). For malaria, unconditional probability is applied to the causes of death, especially due to sickle cell disease as there is a close link between these two conditions (26, 30). For the present analysis, all identified causes of death were aggregated into 14 broad groups, as predefined by INDEPTH, using the statistical software package STATA version 12 (StataCorp, College Station, TX, USA).

Materiais

N/A

Inovações

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

1. Mobile Health (mHealth) Applications: Develop and implement mobile applications that provide pregnant women and new mothers with access to important health information, reminders for prenatal and postnatal care appointments, and emergency contact numbers.

2. Telemedicine: Establish telemedicine services that allow pregnant women in remote rural areas to consult with healthcare professionals through video calls or phone calls, reducing the need for travel and improving access to medical advice and support.

3. Community Health Workers: Train and deploy community health workers in remote rural areas to provide maternal health education, conduct regular check-ups, and assist with prenatal and postnatal care. These workers can also serve as a bridge between the community and healthcare facilities.

4. Transportation Solutions: Develop innovative transportation solutions, such as mobile clinics or ambulances, to ensure that pregnant women have access to timely and safe transportation to healthcare facilities for prenatal care, delivery, and emergency situations.

5. Maternal Health Vouchers: Implement a voucher system that provides pregnant women with subsidized or free access to maternal health services, including prenatal care, delivery, and postnatal care. This can help overcome financial barriers and improve access to essential healthcare services.

6. Health Information Systems: Establish a comprehensive health information system that collects and analyzes data on maternal health outcomes, enabling policymakers and healthcare providers to identify gaps and implement targeted interventions to improve access and quality of care.

7. Public-Private Partnerships: Foster collaborations between the government, private sector, and non-profit organizations to improve access to maternal health services. This can involve leveraging private sector resources and expertise to expand healthcare infrastructure, provide training for healthcare professionals, and increase availability of essential maternal health supplies.

8. Maternity Waiting Homes: Set up maternity waiting homes near healthcare facilities to accommodate pregnant women who live far away and need to travel for delivery. These homes provide a safe and comfortable environment for women to stay during the final weeks of pregnancy, ensuring timely access to skilled birth attendants and emergency obstetric care.

9. Health Education Campaigns: Launch targeted health education campaigns to raise awareness about the importance of prenatal care, safe delivery practices, and postnatal care. These campaigns can be conducted through various channels, including radio, television, community meetings, and social media.

10. Integration of Maternal Health Services: Integrate maternal health services with other healthcare services, such as family planning, immunization, and HIV/AIDS prevention and treatment, to provide comprehensive care to women and their families. This can improve efficiency and ensure that women receive all the necessary services in one location.

AI Innovations Description

Based on the information provided, one recommendation to improve access to maternal health in the Taabo health and demographic surveillance system in Côte d’Ivoire is to implement targeted interventions and programs to address the leading causes of maternal and neonatal deaths identified in the study.

The study found that maternal and neonatal conditions accounted for 8.3% of deaths, primarily pneumonia and birth asphyxia in newborns. To address these specific causes, the following recommendations can be considered:

1. Strengthen antenatal care: Enhance access to and utilization of antenatal care services, including regular check-ups, screenings, and education on preventing and managing pneumonia and birth asphyxia. This can be achieved through community outreach programs, mobile clinics, and partnerships with local healthcare providers.

2. Improve skilled birth attendance: Promote the presence of skilled birth attendants during delivery to ensure safe and effective management of childbirth. This can be done by training and deploying more midwives and other skilled healthcare professionals to rural areas, as well as providing incentives for women to give birth in healthcare facilities.

3. Enhance newborn care practices: Educate mothers and caregivers on essential newborn care practices, such as immediate and exclusive breastfeeding, proper hygiene, and recognizing signs of illness. This can be achieved through community-based education programs, support groups, and home visits by trained healthcare workers.

4. Strengthen referral systems: Establish and improve referral systems to ensure timely access to emergency obstetric and neonatal care for high-risk pregnancies and complications. This can involve establishing clear protocols, training healthcare providers on emergency obstetric and neonatal care, and improving transportation infrastructure for timely transfers.

5. Increase community awareness: Conduct community awareness campaigns to promote the importance of maternal and neonatal health, dispel myths and misconceptions, and encourage early recognition and seeking of healthcare services. This can be done through various channels, including radio, television, community meetings, and engagement with local leaders.

By implementing these targeted interventions, it is expected that access to maternal health services will be improved, leading to a reduction in maternal and neonatal deaths in the Taabo health and demographic surveillance system in Côte d’Ivoire.

AI Innovations Methodology

To improve access to maternal health, here are some potential recommendations:

1. Mobile clinics: Implementing mobile clinics that travel to remote rural areas can provide essential maternal health services, including prenatal care, delivery assistance, and postnatal care. This can help overcome the limited access to healthcare facilities in these areas.

2. Telemedicine: Utilizing telemedicine technology can connect pregnant women in remote areas with healthcare professionals who can provide virtual consultations and guidance. This can help address the lack of healthcare providers in these areas and improve access to maternal health services.

3. Community health workers: Training and deploying community health workers who are knowledgeable about maternal health can help educate and support pregnant women in remote areas. These workers can provide basic prenatal care, conduct health screenings, and refer women to healthcare facilities when necessary.

4. Transportation support: Improving transportation infrastructure and providing transportation support, such as ambulances or transportation vouchers, can help pregnant women in remote areas reach healthcare facilities in a timely manner for prenatal care, delivery, and emergency obstetric care.

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

1. Define indicators: Identify key indicators to measure the impact of the recommendations, such as the number of pregnant women receiving prenatal care, the number of deliveries attended by skilled birth attendants, and the reduction in maternal mortality rates.

2. Data collection: Collect baseline data on the current access to maternal health services in the target area, including the number of healthcare facilities, the availability of skilled birth attendants, and the utilization of prenatal care services.

3. Modeling: Use a simulation model, such as a mathematical or statistical model, to simulate the impact of the recommendations on the identified indicators. The model should take into account factors such as population size, geographical distribution, and existing healthcare infrastructure.

4. Scenario analysis: Run different scenarios in the model to assess the potential impact of each recommendation individually and in combination. This can help determine the most effective combination of interventions to improve access to maternal health.

5. Evaluation: Evaluate the results of the simulation to determine the projected impact of the recommendations on improving access to maternal health. Compare the simulated outcomes with the baseline data to assess the effectiveness of the interventions.

6. Refinement and implementation: Based on the simulation results, refine the recommendations and develop an implementation plan. Monitor and evaluate the actual implementation of the interventions to assess their real-world impact on improving access to maternal health.

It is important to note that the methodology for simulating the impact of recommendations may vary depending on the specific context and available data.

Autor

Dima Health

Statistics:

Citations: 19

Identifiers:

DOI: 10.3402/gha.v8.27271

Research Areas:

Community Interventions, Health System and Policy, Infectious Diseases, Maternal and Child Health, Noncommunicable Diseases, Quality of Care, Social Determinants, Violence and Injury

Study Countries:

Ivory Coast, Multi-Countries

Study Design:

Cohort Study, Cross Sectional Study

Study Approach:

Quantitative

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