Burden of mortality linked to community-nominated priorities in rural South Africa

Global Health Action, Volume 15, No. 1, Year 2022

Interpretation

Background: Community knowledge is a critical input for relevant health programmes and strategies. How community perceptions of risk reflect the burden of mortality is poorly understood. Objective: To determine the burden of mortality reflecting community-nominated health risk factors in rural South Africa, where a complex health transition is underway. Methods: Three discussion groups (total 48 participants) representing a cross-section of the community nominated health priorities through a Participatory Action Research process. A secondary analysis of Verbal Autopsy (VA) data was performed for deaths in the same community from 1993 to 2015 (n = 14,430). Using population attributable fractions (PAFs) extracted from Global Burden of Disease data for South Africa, deaths were categorised as ‘attributable at least in part’ to community-nominated risk factors if the PAF of the risk factor to the cause of death was >0. We also calculated ‘reducible mortality fractions’ (RMFs), defined as the proportions of each and all community-nominated risk factor(s) relative to all possible risk factors for deaths in the population . Results: Three risk factors were nominated as the most important health concerns locally: alcohol abuse, drug abuse, and lack of safe water. Of all causes of deaths 1993–2015, over 77% (n = 11,143) were attributable at least in part to at least one community-nominated risk factor. Causes of attributable deaths, at least in part, to alcohol abuse were most common (52.6%, n = 7,591), followed by drug abuse (29.3%, n = 4,223), and lack of safe water (11.4%, n = 1,652). In terms of the RMF, alcohol use contributed the largest percentage of all possible risk factors leading to death (13.6%), then lack of safe water (7.0%), and drug abuse (1.3%) . Conclusion: A substantial proportion of deaths are linked to community-nominated risk factors. Community knowledge is a critical input to understand local health risks. The study was located in South Africa, as part of the VAPAR (Verbal Autopsy with Participatory Action Research, www.vapar.org) programme in which community stakeholders participate in identifying and collectively addressing health challenges in cooperative learning partnerships with the authorities [15]. South Africa is an upper-middle-income country with 66 years of life expectancy in 2019 [16]. There is substantial, entrenched inequality in South Africa in terms of socioeconomic and health status, and access to health services, resulting in a deeply uneven distribution of ill-health and diseases [17]. The study was progressed within the Agincourt Health and Socio-Demographic Surveillance System (HDSS), located in Mpumalanga province, close to the Mozambican border in northeast South Africa [18]. The province is relatively poor and rural, with high unemployment, limited water, sanitation, and electricity services [18,19]. Reflecting on the national situation, the disease burden in the study area is a combination of non-communicable diseases (NCDs), infectious diseases, maternal and child-related disorders with considerably high rate of road accidents and external causes of deaths [20–23], often described as a ‘quadruple burden’ of disease [24]. Age-adjusted HIV prevalence is considerably high in the study area: 19% among men and 26% among women [25]. Community-nominated health risk factors were determined by three community discussion groups (total 48 participants) representing rural villages across the Agincourt HDSS. We progressed a Participatory Action Research (PAR) process to identify and address local health concerns. PAR transforms the roles of passive research subjects into active co-researchers and changes agents through collective analysis, taking, and evaluating action and learning from action [26]. We re-engaged participants involved in earlier research across three villages in the Agincourt HDSS [27]. Villages were selected to vary by distance to health facilities and levels of child-headed households, and participants represented a cross-section of the community (traditional healers, community and religious leaders, community health volunteers and family members). In each village, we held an introductory workshop in which participants nominated a range of issues, collectively validating and prioritising them using ranking and voting. Participants also directed expansion of the participant base to include perspectives that may otherwise be excluded. Each village nominated the highest priority risk factor, hereafter considered as community-nominated risk factor(s). After the nomination, new participants were recruited and worked together, through a series of workshops, sharing, and systematising experiences to build consensus on the problem’s identified, and locally acceptable actions to address them. A total of 16 workshops were held in the common local language xiTsonga. Throughout, participants were supported to assume ownership and control of the process. These elements are described elsewhere [28–31]. Longitudinal VA data from 1993 to 2015, for which period the data was available for this analysis, were used to ascertain the probable cause of death of individuals living in the HDSS based upon results from the InterVA-5 algorithm. InterVA-5 assigns each death to up to three cause(s) and the likelihood of that cause [14]. In this analysis, we used the first and most probable cause of death and excluded all causes of death, where the most probable likelihood was <50%. Using population attributable fractions (PAF) for South Africa extracted from Global Burden of Disease (GBD) data version 2019 [32], we estimated whether each VA classified cause of death was ‘attributable at least in part’ to the community-nominated risk factors. We also calculated the ‘reducible mortality fraction’ (RMF), defined as the relative proportions of the community-nominated risk factors contributing to all possible risk factors contributing to deaths. The GBD extraction was performed between 18.1.2021 and 21.1.2021. PAF is an estimate of the reduction in population mortality if an exposure to a certain risk factor is minimised, as previously described by the WHO [33]. GBD provides PAFs for multiple risk factors for each disease and enables the contribution of individual risk factors to be estimated for any disease. The GBD risk factor hierarchy is based on four levels [34]. Level 1 risk factors are environmental and occupational, behavioural, or metabolic; Level 2 consists of 20 risks or clusters of risks, such as air pollution, tobacco, alcohol use, dietary risks, hearing, vision, and intellectual disabilities; Level 3 contains 52 risks or clusters of risks; and Level 4 includes 69 specific risk factors. The causality between these risk-outcome estimates have been previously established [34]. Sex (binary), age at death (continuous), cause of death (CoD) and year of death (continuous) were obtained from the VA data. The main outcomes were (1) the burden of mortality that was attributable, at least in part, in Agincourt to each community-nominated risk factor and (2) the total RMF due to each community-nominated risk factor. Diseases attributable at least in part to community-nominated risk factors were identified from the GBD data tool, and PAFs from South Africa for all diseases between 1993 and 2015 were extracted covering the same period that VA data were available for this analysis. PAFs were extracted for all ages and both sexes. From these PAFs across all years, the median was used. Diseases for which PAFs were extracted were mapped onto corresponding VA cause of death codes (supplementary material 1). The mapping was based on the WHO Verbal Autopsy Manual’s classification of VA causes of deaths and their corresponding ICD-10 codes. These steps are illustrated in Figure 1. Flowchart of the study phases. (PAR = participatory action research, GBD = global burden of disease, PAF = population attributable fraction, VA = verbal autopsy). The total number (and %) of deaths occurring between 1993 and 2015 that were attributable at least in part to each community-nominated risk factor and the number of risk factors that each death was attributable at least in part to were described. Death was categorised as attributable at least in part to a community-nominated risk factor(s) if PAF for the cause of death was >0. If a death was assigned as Indeterminate, the PAF was assumed to be 0. To quantify the RMF: the relative proportion of each and all community-nominated risk factor(s) to all possible risk factors contributing to all deaths between 1993 and 2015, we first summed the PAFs of all possible risk factors to every death in the population (Figure 2). Second, we summed the PAFs of each and all community-nominated risk factor(s) to every death. Third, we divided the summed PAFs of each and all community-nominated risk factor(s) with the summed PAFs of all possible risk factors (example calculation is contained in Supplementary material 3). It is possible for PAFs to add up to >100% for any death [35]. However, our aim was not to produce absolute numbers, but rather to ascertain the relative contributions of community-nominated risk factors to deaths in the population. The formula for ‘reducible mortality fraction’: the relative proportion of the PAFs of all risk factors that were due to each and all community-nominated risk factor(s) (example calculation is contained in supplementary material 3). (PAF1 = the PAFs for a community-nominated risk factor for each cause of death multiplied by the number deaths due to that cause, PAF2 = the PAFs for all risk factors for each cause of death multiplied by the number deaths due to that cause, n = 14,430 = the total number of deaths in the population between 1993 and 2015). Results are presented for all community-nominated risk factors and each separately for the whole population and then disaggregated by sex, age category, and mortality category. All deaths and deaths from each risk factor were described according to seven age groups (neonatal (<28 days), infant (28 days–12 months), 1–5 years, 5–15 years, adult (15–50 years), mid-ager (50–65 years) and >65 years), five cause categories as categorised by VA (infectious and parasitic diseases; non-communicable diseases; pregnancy, childbirth, and puerperium-related disorders; neonatal and external causes of death, indeterminate), and over time. Categorical data were described as n (%); continuous data were described as mean (SD) where normally distributed or median (IQR) where not. The analysis was done using SPSS version 25. The research was a secondary analysis of VA data from Agincourt HDSS, which has been previously approved by the Committee for Research on Human Subjects at the University of Witwatersrand (Nos. M960720 & M110138). Consent (informed consent at individual and household level as well as community consent from traditional leaders) was secured at the start of surveillance in 1992 and is reaffirmed regularly. The principle of informed consent and right to refusal or withdrawal was fully respected.

AI Digest

Study Justification:

– Community knowledge is crucial for developing relevant health programs and strategies.
– Understanding how community perceptions of risk align with the burden of mortality is important.
– This study aimed to determine the burden of mortality related to community-nominated health risk factors in rural South Africa.

Study Highlights:

– Three risk factors were identified as the most important health concerns: alcohol abuse, drug abuse, and lack of safe water.
– Over 77% of deaths from 1993 to 2015 were attributable, at least in part, to these community-nominated risk factors.
– Alcohol abuse was the most common risk factor, followed by drug abuse and lack of safe water.
– The study highlights the significant contribution of community-nominated risk factors to the burden of mortality in the population.

Study Recommendations:

– Community knowledge should be considered a critical input in understanding local health risks.
– Health programs and strategies should prioritize addressing alcohol abuse, drug abuse, and lack of safe water.
– Efforts should be made to reduce the prevalence of these risk factors to decrease the burden of mortality.

Key Role Players:

– Community stakeholders: Traditional healers, community and religious leaders, community health volunteers, and family members.
– Authorities: Government health departments, local health facilities, and policymakers.

Cost Items for Planning Recommendations:

– Public awareness campaigns: Costs associated with developing and implementing campaigns to educate the community about the risks of alcohol abuse, drug abuse, and lack of safe water.
– Treatment and rehabilitation services: Funding for the establishment and maintenance of programs to address alcohol and drug abuse, including counseling, rehabilitation centers, and support services.
– Infrastructure development: Investment in improving access to safe water, including the construction of water supply systems and sanitation facilities.
– Monitoring and evaluation: Budget for ongoing monitoring and evaluation of the effectiveness of interventions targeting the identified risk factors.
Please note that the provided information is based on the description of the study and may not include all details.

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 a Participatory Action Research process and a secondary analysis of Verbal Autopsy data. The study includes a large sample size (48 participants) and covers a long period of time (1993-2015). The results show that a substantial proportion of deaths in the community are attributable to community-nominated risk factors. To improve the evidence, the abstract could provide more details on the methodology used in the Participatory Action Research process and the analysis of Verbal Autopsy data.

Abstract

The study was located in South Africa, as part of the VAPAR (Verbal Autopsy with Participatory Action Research, www.vapar.org) programme in which community stakeholders participate in identifying and collectively addressing health challenges in cooperative learning partnerships with the authorities [15]. South Africa is an upper-middle-income country with 66 years of life expectancy in 2019 [16]. There is substantial, entrenched inequality in South Africa in terms of socioeconomic and health status, and access to health services, resulting in a deeply uneven distribution of ill-health and diseases [17]. The study was progressed within the Agincourt Health and Socio-Demographic Surveillance System (HDSS), located in Mpumalanga province, close to the Mozambican border in northeast South Africa [18]. The province is relatively poor and rural, with high unemployment, limited water, sanitation, and electricity services [18,19]. Reflecting on the national situation, the disease burden in the study area is a combination of non-communicable diseases (NCDs), infectious diseases, maternal and child-related disorders with considerably high rate of road accidents and external causes of deaths [20–23], often described as a ‘quadruple burden’ of disease [24]. Age-adjusted HIV prevalence is considerably high in the study area: 19% among men and 26% among women [25]. Community-nominated health risk factors were determined by three community discussion groups (total 48 participants) representing rural villages across the Agincourt HDSS. We progressed a Participatory Action Research (PAR) process to identify and address local health concerns. PAR transforms the roles of passive research subjects into active co-researchers and changes agents through collective analysis, taking, and evaluating action and learning from action [26]. We re-engaged participants involved in earlier research across three villages in the Agincourt HDSS [27]. Villages were selected to vary by distance to health facilities and levels of child-headed households, and participants represented a cross-section of the community (traditional healers, community and religious leaders, community health volunteers and family members). In each village, we held an introductory workshop in which participants nominated a range of issues, collectively validating and prioritising them using ranking and voting. Participants also directed expansion of the participant base to include perspectives that may otherwise be excluded. Each village nominated the highest priority risk factor, hereafter considered as community-nominated risk factor(s). After the nomination, new participants were recruited and worked together, through a series of workshops, sharing, and systematising experiences to build consensus on the problem’s identified, and locally acceptable actions to address them. A total of 16 workshops were held in the common local language xiTsonga. Throughout, participants were supported to assume ownership and control of the process. These elements are described elsewhere [28–31]. Longitudinal VA data from 1993 to 2015, for which period the data was available for this analysis, were used to ascertain the probable cause of death of individuals living in the HDSS based upon results from the InterVA-5 algorithm. InterVA-5 assigns each death to up to three cause(s) and the likelihood of that cause [14]. In this analysis, we used the first and most probable cause of death and excluded all causes of death, where the most probable likelihood was 0. If a death was assigned as Indeterminate, the PAF was assumed to be 0. To quantify the RMF: the relative proportion of each and all community-nominated risk factor(s) to all possible risk factors contributing to all deaths between 1993 and 2015, we first summed the PAFs of all possible risk factors to every death in the population (Figure 2). Second, we summed the PAFs of each and all community-nominated risk factor(s) to every death. Third, we divided the summed PAFs of each and all community-nominated risk factor(s) with the summed PAFs of all possible risk factors (example calculation is contained in Supplementary material 3). It is possible for PAFs to add up to >100% for any death [35]. However, our aim was not to produce absolute numbers, but rather to ascertain the relative contributions of community-nominated risk factors to deaths in the population. The formula for ‘reducible mortality fraction’: the relative proportion of the PAFs of all risk factors that were due to each and all community-nominated risk factor(s) (example calculation is contained in supplementary material 3). (PAF1 = the PAFs for a community-nominated risk factor for each cause of death multiplied by the number deaths due to that cause, PAF2 = the PAFs for all risk factors for each cause of death multiplied by the number deaths due to that cause, n = 14,430 = the total number of deaths in the population between 1993 and 2015). Results are presented for all community-nominated risk factors and each separately for the whole population and then disaggregated by sex, age category, and mortality category. All deaths and deaths from each risk factor were described according to seven age groups (neonatal (65 years), five cause categories as categorised by VA (infectious and parasitic diseases; non-communicable diseases; pregnancy, childbirth, and puerperium-related disorders; neonatal and external causes of death, indeterminate), and over time. Categorical data were described as n (%); continuous data were described as mean (SD) where normally distributed or median (IQR) where not. The analysis was done using SPSS version 25. The research was a secondary analysis of VA data from Agincourt HDSS, which has been previously approved by the Committee for Research on Human Subjects at the University of Witwatersrand (Nos. M960720 & M110138). Consent (informed consent at individual and household level as well as community consent from traditional leaders) was secured at the start of surveillance in 1992 and is reaffirmed regularly. The principle of informed consent and right to refusal or withdrawal was fully respected.

Materials

https://efashare.b-cdn.net/materials/4756e1fd565cf6abde745e72215166e5e992d1cf88c9c50980a0510416626b35/ZGHA_A_2013599_SM6496.docx

Innovations

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

1. Mobile Health Clinics: Implementing mobile health clinics that can travel to rural areas, where access to healthcare facilities is limited. These clinics can provide essential maternal health services, including prenatal care, postnatal care, and family planning.

2. Telemedicine: Introducing telemedicine services that allow pregnant women in remote areas to consult with healthcare professionals through video calls or phone calls. This can provide access to medical advice, monitoring, and support without the need for travel.

3. Community Health Workers: Training and deploying community health workers who can provide basic maternal health services and education within their communities. These workers can conduct prenatal visits, provide health information, and refer women to healthcare facilities when necessary.

4. Maternal Health Vouchers: Implementing a voucher system that provides pregnant women with access to essential maternal health services. These vouchers can cover the cost of prenatal care, delivery, and postnatal care, ensuring that financial barriers do not prevent women from accessing necessary care.

5. Health Education Programs: Developing and implementing health education programs that focus on maternal health and target communities with limited access to healthcare. These programs can provide information on prenatal care, nutrition, hygiene practices, and birth preparedness.

6. Transportation Support: Establishing transportation support systems to help pregnant women reach healthcare facilities for prenatal visits, delivery, and postnatal care. This can include providing transportation vouchers, organizing community transportation services, or partnering with local transportation providers.

7. Maternal Waiting Homes: Establishing maternal waiting homes near healthcare facilities for pregnant women who live far away. These homes provide a safe and comfortable place for women to stay during the final weeks of pregnancy, ensuring they are close to the facility when it’s time to give birth.

8. Mobile Apps for Maternal Health: Developing mobile applications that provide pregnant women with information, reminders, and tools for tracking their pregnancy. These apps can also include features for connecting with healthcare professionals and accessing telemedicine services.

9. Maternal Health Hotlines: Setting up hotlines or helplines dedicated to maternal health, where women can call to ask questions, seek advice, or report any concerns. Trained professionals can provide guidance and support over the phone.

10. Partnerships with Traditional Birth Attendants: Collaborating with traditional birth attendants in rural communities to improve their knowledge and skills in safe delivery practices. This can help ensure that women who choose to give birth at home have access to skilled birth attendants who can provide safe and hygienic care.

It’s important to note that the specific context and needs of the community should be considered when implementing these innovations.

AI Innovations Description

Based on the information provided, the recommendation to improve access to maternal health would be to prioritize and address the community-nominated risk factors identified in the study. These risk factors include alcohol abuse, drug abuse, and lack of safe water.

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

1. Community Engagement: Engage with the local community, including community leaders, health volunteers, and residents, to raise awareness about the importance of maternal health and the identified risk factors. Encourage community participation and involvement in finding solutions.

2. Education and Awareness: Conduct educational campaigns and workshops to increase knowledge and awareness about the negative impact of alcohol and drug abuse on maternal health. Provide information on the importance of safe water for maternal and child health.

3. Prevention and Intervention Programs: Develop and implement prevention and intervention programs targeting alcohol and drug abuse. These programs can include counseling services, support groups, and rehabilitation programs. Collaborate with local healthcare providers, NGOs, and community organizations to ensure comprehensive support.

4. Infrastructure Development: Work towards improving access to safe water by implementing infrastructure development projects. This may involve building or repairing water supply systems, promoting water treatment methods, and ensuring proper sanitation facilities in the community.

5. Collaboration and Partnerships: Collaborate with relevant stakeholders, such as government agencies, healthcare providers, NGOs, and community-based organizations, to leverage resources and expertise. Establish partnerships to implement and sustain the innovation effectively.

6. Monitoring and Evaluation: Regularly monitor and evaluate the impact of the innovation on maternal health outcomes. Collect data on key indicators, such as maternal mortality rates, substance abuse rates, and access to safe water. Use this data to assess the effectiveness of the innovation and make necessary adjustments.

By addressing the community-nominated risk factors and implementing innovative solutions, access to maternal health can be improved, leading to better health outcomes for mothers and their children.

AI Innovations Methodology

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

1. Increase awareness and education: Implement community-based education programs to raise awareness about maternal health, including the importance of prenatal care, safe delivery practices, and postnatal care. This can be done through workshops, community meetings, and the use of local media channels.

2. Strengthen healthcare infrastructure: Improve the availability and quality of healthcare facilities in rural areas by investing in the construction and renovation of clinics and hospitals. This includes ensuring access to essential equipment, medications, and skilled healthcare providers.

3. Enhance transportation services: Develop and expand transportation services to ensure that pregnant women can easily access healthcare facilities. This can involve providing ambulances or other means of transportation for emergency situations, as well as improving road infrastructure in remote areas.

4. Promote community engagement: Engage community members, including traditional healers, religious leaders, and community health volunteers, in promoting maternal health. Encourage their involvement in health campaigns, referrals to healthcare facilities, and the dissemination of accurate health information.

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

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

2. Collect baseline data: Gather data on the current status of maternal health in the target area, including the number of healthcare facilities, the availability of transportation services, and the level of community engagement.

3. Implement interventions: Introduce the recommended interventions in a phased approach, taking into account the available resources and the specific needs of the community. Monitor the implementation process and collect data on the interventions’ coverage and effectiveness.

4. Analyze data: Analyze the collected data to assess the impact of the interventions on the defined indicators. Compare the baseline data with the post-intervention data to determine any improvements in access to maternal health.

5. Evaluate outcomes: Evaluate the outcomes of the interventions based on the analyzed data. Assess the extent to which the recommendations have improved access to maternal health and identify any areas that require further attention or modification.

6. Adjust and refine: Based on the evaluation outcomes, make adjustments and refinements to the interventions as needed. Continuously monitor and evaluate the impact of the recommendations to ensure ongoing improvement in access to maternal health.

By following this methodology, it will be possible to simulate the impact of the recommendations on improving access to maternal health and make informed decisions on how to further enhance maternal healthcare services in the target area.

Author

Dima Health

Identifiers:

DOI: 10.1080/16549716.2021.2013599

Research Areas:

Community Interventions, Environmental, Health System and Policy, Infectious Diseases, Maternal Access, Maternal and Child Health, Noncommunicable Diseases, Quality of Care, Sexual and Reproductive Health, Social Determinants, Substance Abuse

Study Countries:

South Africa

Study Design:

Cohort Study, Cross Sectional Study

Study Approach:

Quantitative

Participants Gender:

Male & Female