Time-critical conditions: Assessment of burden and access to care using verbal autopsy in Agincourt, South Africa

BMJ Global Health, Volume 5, No. 4, Year 2020

Interpretation

Background Time-critical conditions (TCC) are estimated to cause substantial mortality in low and middle-income countries. However, quantification of deaths and identification of contributing factors to those deaths are challenging in settings with poor health records. Aim To use verbal autopsy (VA) data from the Agincourt health and sociodemographic surveillance system in rural South Africa to quantify the burden of deaths from TCC and to evaluate the barriers in seeking, reaching and receiving quality care for TCC leading to death. Methodology Deaths from 1993 to 2015 were analysed to identify causality from TCC. Deaths due to TCC were categorised as communicable, non-communicable, maternal, neonatal or injury-related. Proportion of deaths from TCC by age, sex, condition type and temporal trends was described. Deaths due to TCC from 2012 to 2015 were further examined by circumstances of mortality (CoM) indicators embedded in VA. Healthcare access, at illness onset and during the final day of life, as well as place of death, was extracted from free text summaries. Summaries were also analysed qualitatively using a Three Delays framework to identify barriers to healthcare. Results Of 15 305 deaths, 5885 (38.45%) were due to TCC. Non-communicable diseases were the most prevalent cause of death from TCC (2961/5885 cases, 50.31%). CoM indicators highlighted delays in a quarter of deaths due to TCC, most frequently in seeking care. The most common pattern of healthcare access was to die outwith a facility, having sought no healthcare (409/1324 cases, 30.89%). Issues in receipt of quality care were identified by qualitative analysis. Conclusion TCCs are responsible for a substantial burden of deaths in this rural South African population. Delays in seeking and receiving quality care were more prominent than those in reaching care, and thus further research and solution development should focus on healthcare-seeking behaviour and quality care provision. The study was based at the Rural Public Health and Health Transitions Research Unit of the Medical Research Council and University of the Witwatersrand in Agincourt, South Africa. The Agincourt unit has undertaken health and demographic surveillance surveys (HDSS) since 1992, including conducting annual VA on any deaths reported in the enumerated population.25 It is one of three HDSS sites in South Africa comprising the South African Population Research Infrastructure Network (SAPRIN), as well as being a member of the International Network for the Demographic Evaluation of Populations and Their Health (INDEPTH). Agincourt is located in a rural area of Mpumalanga, one of the nine provinces of South Africa, in the northeast of the country. Public health services in the country are managed by each province,26 with further division into health regions, Agincourt being in the Ehlanzeni region under the jurisdiction of the Mpumalanga Department of Health.27 Most interactions with healthcare in South Africa are based at the primary care level, in nurse-led clinics. District hospitals provide the majority of hospital-based emergency care and are usually staffed by non-specialist medical officers, with the ability to refer on to regional hospitals for more specialist input.28 In Mpumalanga there are 287 primary healthcare facilities, 23 district hospitals, 3 regional and 2 tertiary hospitals. Due to the rural nature of the region, there are also mobile clinics. Ambulance services in the region aim to provide prehospital medical services and interhospital transfers within national targets of 40 minutes in rural areas.27 We are not aware of any studies that have assessed whether these targets are met. This study employed mixed methodologies using VA data from 1993 to 2015. Data from 2015 were the most recent data available to us at the time of research. Data from 1992 were excluded as the VA process had not been fully established that year. The literature was scoped for pre-existing definitions of time-criticality and lists of conditions categorised as time-critical that were appropriate to our setting.1 3 8–11 Those conditions which were defined as ‘requiring prompt medical care within twelve hours from the onset of symptoms recognised by a layperson to prevent death’ by Hsiao et al were chosen as the basis for our study.10 This was due to their definition being used in a large VA project (the Million Death Study), its applicability to an LMIC setting and the availability of the precise list of conditions, using International Classification of Diseases, 10th Revision (ICD-10) codes. Study authors, who had extensive research or clinical expertise in the local area, refined the list and also reviewed causes of death in the VA data set not already classified by Hsiao et al. Consensus on which conditions to define as time-critical was met after discussion between authors. TCCs defined using ICD-10 were then mapped onto relevant VA codes. Deaths where likelihood of correct cause of death assigned by Inter-VA5 was less than or equal to 50% were excluded. Deaths were categorised as TCC or not. Numbers and percentages of deaths due to TCC were described according to age group, sex and time period. Deaths from TCC were disaggregated by condition type in the same manner as Hsiao et al: communicable, neonatal, maternal, non-communicable and injury-related.10 Deaths from TCC were divided into 10-year age groupings. For the age group below 10 years, deaths were also shown for neonates (<28 days), infants (≥28 days to <1 year) and from 1 to 5 years. Time was divided into 5-year periods, except the last time period (2013–2015), which encompassed 3 years. The 10 circumstances of mortality (CoM) indicators, as added to the VA process in 2012,18 were attributed to delays in seeking care (first delay), reaching care (second delay) and receiving quality care (third delay). An additional indicator from the main VA questions regarding patients discharged while still unwell was added to also indicate a delay in receiving quality care. Indicators of delays in seeking care were (1) doubts over the requirements for medical care, (2) use of traditional medicine, (3) lack of use of telephone to seek help, and (4) perceived prohibitive costs. Indicators of delays in reaching care were (1) lack of attendance to a facility, (2) lack of use of motorised transport to reach a facility, and (3) duration of over 2 hours to reach a facility. Indicators of delays in receiving quality care were (1) issues during admission, (2) issues with treatment, (3) issues pertaining to treatment and investigations, and (4) patients discharged while still unwell. As the CoM indicators were only recorded from 2012 and after, only deaths from 2012 onward were considered for this analysis. The number of time-critical deaths which experienced each delay was calculated. Quantitative analyses were done using SPSS statistics V.24.29 Non-parametric data are described as median (IQR). Proportions of deaths due to TCC are shown for age group, sex and 5-year time period. χ2 was used to test the association between TCC and sex; associations between TCC by age and year were tested using non-parametric linear regression, with age and year entered as continuous variables. As an exploratory study, no power calculation was performed. The free text portion of the VA interview is a summary of the discussion between interviewer and respondent. Notes are taken during the interview and the full narrative is written up by the interviewer following the interview; these contain details of the time leading up to each death that are not possible to capture on the binary responses of the VA questionnaire. These were examined to determine patterns in healthcare access prior to death. Only deaths due to TCC from 2012 to 2015 were analysed, in order to align with the CoM indicator analysis. Information on the following was extracted from the free text summaries: the initial healthcare type accessed (first healthcare act), all healthcare types accessed in the final day of life and the place of death. Cases were excluded if any one of these information points could not be determined. Those dying on the same day as attendance to hospital were classified as accessing the emergency department in the final day of life, whereas those with a hospital stay greater than 1 day prior to death were classified as inpatients. Free text summaries were further analysed using a qualitative combined inductive and deductive approach. A set of a priori codes related to the first, second and third delays of the Thaddeus and Maine framework were developed.20 Free text summaries were coded using NVivo V.1030 and an iterative process allowed for re-review with emergent codes, until saturation was reached.

AI Digest

Study Justification:

– Time-critical conditions (TCC) cause significant mortality in low and middle-income countries, but quantifying deaths and identifying contributing factors is challenging in settings with poor health records.
– The aim of this study was to use verbal autopsy (VA) data from the Agincourt health and sociodemographic surveillance system in rural South Africa to quantify the burden of deaths from TCC and evaluate barriers to accessing quality care for TCC leading to death.

Study Highlights:

– The study analyzed deaths from 1993 to 2015 to identify causality from TCC and categorized them as communicable, non-communicable, maternal, neonatal, or injury-related.
– Non-communicable diseases were found to be the most prevalent cause of death from TCC.
– Delays in seeking care were more prominent than delays in reaching care, highlighting the need to focus on healthcare-seeking behavior and quality care provision.
– The study used a mixed methodology approach, combining quantitative analysis of VA data with qualitative analysis of free text summaries.

Study Recommendations:

– Further research and solution development should focus on improving healthcare-seeking behavior and ensuring the provision of quality care for time-critical conditions.
– Strategies to reduce delays in seeking care, such as increasing awareness and knowledge about TCC, promoting early recognition of symptoms, and addressing financial barriers, should be explored.
– Efforts should be made to improve the quality of care provided for TCC, including addressing issues during admission, treatment, and investigations.

Key Role Players:

– Researchers and public health experts from the Rural Public Health and Health Transitions Research Unit of the Medical Research Council and University of the Witwatersrand in Agincourt, South Africa.
– The Agincourt health and sociodemographic surveillance system team.
– The South African Population Research Infrastructure Network (SAPRIN) and the International Network for the Demographic Evaluation of Populations and Their Health (INDEPTH).
– The Mpumalanga Department of Health and healthcare providers in the region.
– Local communities and community leaders.

Cost Items for Planning Recommendations:

– Research and data collection costs, including personnel salaries, data management, and analysis.
– Costs associated with implementing interventions to improve healthcare-seeking behavior, such as community education campaigns and training programs for healthcare providers.
– Costs related to improving the quality of care, including staff training, infrastructure improvements, and equipment procurement.
– Monitoring and evaluation costs to assess the effectiveness of interventions and measure outcomes.
– Costs for collaboration and coordination with relevant stakeholders, including meetings, workshops, and communication channels.

Strength of Evidence

The strength of evidence for this abstract is 7 out of 10.
The evidence in the abstract is strong, but there are some areas for improvement. The study provides a comprehensive analysis of deaths from time-critical conditions using verbal autopsy data. The methodology is clearly described, and the results highlight the burden of deaths from time-critical conditions in a rural South African population. The study also identifies delays in seeking and receiving quality care as significant barriers. However, the abstract could be improved by providing more specific details about the sample size, data collection methods, and statistical analysis. Additionally, it would be helpful to include information about the limitations of the study and potential implications for future research and healthcare policy.

Abstract

The study was based at the Rural Public Health and Health Transitions Research Unit of the Medical Research Council and University of the Witwatersrand in Agincourt, South Africa. The Agincourt unit has undertaken health and demographic surveillance surveys (HDSS) since 1992, including conducting annual VA on any deaths reported in the enumerated population.25 It is one of three HDSS sites in South Africa comprising the South African Population Research Infrastructure Network (SAPRIN), as well as being a member of the International Network for the Demographic Evaluation of Populations and Their Health (INDEPTH). Agincourt is located in a rural area of Mpumalanga, one of the nine provinces of South Africa, in the northeast of the country. Public health services in the country are managed by each province,26 with further division into health regions, Agincourt being in the Ehlanzeni region under the jurisdiction of the Mpumalanga Department of Health.27 Most interactions with healthcare in South Africa are based at the primary care level, in nurse-led clinics. District hospitals provide the majority of hospital-based emergency care and are usually staffed by non-specialist medical officers, with the ability to refer on to regional hospitals for more specialist input.28 In Mpumalanga there are 287 primary healthcare facilities, 23 district hospitals, 3 regional and 2 tertiary hospitals. Due to the rural nature of the region, there are also mobile clinics. Ambulance services in the region aim to provide prehospital medical services and interhospital transfers within national targets of 40 minutes in rural areas.27 We are not aware of any studies that have assessed whether these targets are met. This study employed mixed methodologies using VA data from 1993 to 2015. Data from 2015 were the most recent data available to us at the time of research. Data from 1992 were excluded as the VA process had not been fully established that year. The literature was scoped for pre-existing definitions of time-criticality and lists of conditions categorised as time-critical that were appropriate to our setting.1 3 8–11 Those conditions which were defined as ‘requiring prompt medical care within twelve hours from the onset of symptoms recognised by a layperson to prevent death’ by Hsiao et al were chosen as the basis for our study.10 This was due to their definition being used in a large VA project (the Million Death Study), its applicability to an LMIC setting and the availability of the precise list of conditions, using International Classification of Diseases, 10th Revision (ICD-10) codes. Study authors, who had extensive research or clinical expertise in the local area, refined the list and also reviewed causes of death in the VA data set not already classified by Hsiao et al. Consensus on which conditions to define as time-critical was met after discussion between authors. TCCs defined using ICD-10 were then mapped onto relevant VA codes. Deaths where likelihood of correct cause of death assigned by Inter-VA5 was less than or equal to 50% were excluded. Deaths were categorised as TCC or not. Numbers and percentages of deaths due to TCC were described according to age group, sex and time period. Deaths from TCC were disaggregated by condition type in the same manner as Hsiao et al: communicable, neonatal, maternal, non-communicable and injury-related.10 Deaths from TCC were divided into 10-year age groupings. For the age group below 10 years, deaths were also shown for neonates (<28 days), infants (≥28 days to <1 year) and from 1 to 5 years. Time was divided into 5-year periods, except the last time period (2013–2015), which encompassed 3 years. The 10 circumstances of mortality (CoM) indicators, as added to the VA process in 2012,18 were attributed to delays in seeking care (first delay), reaching care (second delay) and receiving quality care (third delay). An additional indicator from the main VA questions regarding patients discharged while still unwell was added to also indicate a delay in receiving quality care. Indicators of delays in seeking care were (1) doubts over the requirements for medical care, (2) use of traditional medicine, (3) lack of use of telephone to seek help, and (4) perceived prohibitive costs. Indicators of delays in reaching care were (1) lack of attendance to a facility, (2) lack of use of motorised transport to reach a facility, and (3) duration of over 2 hours to reach a facility. Indicators of delays in receiving quality care were (1) issues during admission, (2) issues with treatment, (3) issues pertaining to treatment and investigations, and (4) patients discharged while still unwell. As the CoM indicators were only recorded from 2012 and after, only deaths from 2012 onward were considered for this analysis. The number of time-critical deaths which experienced each delay was calculated. Quantitative analyses were done using SPSS statistics V.24.29 Non-parametric data are described as median (IQR). Proportions of deaths due to TCC are shown for age group, sex and 5-year time period. χ2 was used to test the association between TCC and sex; associations between TCC by age and year were tested using non-parametric linear regression, with age and year entered as continuous variables. As an exploratory study, no power calculation was performed. The free text portion of the VA interview is a summary of the discussion between interviewer and respondent. Notes are taken during the interview and the full narrative is written up by the interviewer following the interview; these contain details of the time leading up to each death that are not possible to capture on the binary responses of the VA questionnaire. These were examined to determine patterns in healthcare access prior to death. Only deaths due to TCC from 2012 to 2015 were analysed, in order to align with the CoM indicator analysis. Information on the following was extracted from the free text summaries: the initial healthcare type accessed (first healthcare act), all healthcare types accessed in the final day of life and the place of death. Cases were excluded if any one of these information points could not be determined. Those dying on the same day as attendance to hospital were classified as accessing the emergency department in the final day of life, whereas those with a hospital stay greater than 1 day prior to death were classified as inpatients. Free text summaries were further analysed using a qualitative combined inductive and deductive approach. A set of a priori codes related to the first, second and third delays of the Thaddeus and Maine framework were developed.20 Free text summaries were coded using NVivo V.1030 and an iterative process allowed for re-review with emergent codes, until saturation was reached.

Materials

N/A

Innovations

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

1. Mobile clinics: Implementing mobile clinics in rural areas to provide maternal health services, including prenatal care, postnatal care, and family planning. This would help overcome geographical barriers and bring healthcare services closer to the community.

2. Telemedicine: Introducing telemedicine services for maternal health, allowing pregnant women to consult with healthcare professionals remotely. This would be particularly beneficial for women who have limited access to healthcare facilities or live in remote areas.

3. Community health workers: Expanding the role of community health workers to include maternal health services. These trained individuals can provide education, support, and basic healthcare services to pregnant women in their communities, improving access to care and promoting healthy pregnancies.

4. Transportation services: Establishing reliable transportation services, such as ambulances or transportation vouchers, to ensure that pregnant women can reach healthcare facilities in a timely manner. This would address the issue of delays in reaching care, especially in emergency situations.

5. Health education programs: Implementing comprehensive health education programs that focus on maternal health, including prenatal care, nutrition, and birth preparedness. These programs can be conducted in schools, community centers, and through mobile platforms to reach a wider audience.

6. Quality improvement initiatives: Investing in quality improvement initiatives to enhance the quality of maternal healthcare services. This could involve training healthcare providers, improving infrastructure and equipment, and implementing standardized protocols for maternal care.

7. Financial incentives: Introducing financial incentives, such as cash transfers or insurance schemes, to encourage pregnant women to seek timely and appropriate maternal healthcare services. This would help address financial barriers that prevent women from accessing necessary care.

8. Partnerships and collaborations: Strengthening partnerships and collaborations between healthcare providers, community organizations, and government agencies to improve coordination and ensure comprehensive maternal health services are available and accessible to all women.

These are just a few potential innovations that could be considered to improve access to maternal health based on the information provided. It is important to note that the specific context and needs of the community should be taken into account when implementing any innovation.

AI Innovations Description

The study mentioned in the description focuses on time-critical conditions (TCC) and their impact on maternal health in rural South Africa. The researchers used verbal autopsy (VA) data to quantify the burden of deaths from TCC and identify barriers to seeking, reaching, and receiving quality care for TCC leading to death.

Based on the findings of the study, here is a recommendation that can be developed into an innovation to improve access to maternal health:

1. Strengthening healthcare-seeking behavior: Develop community-based interventions to raise awareness about the importance of seeking timely and appropriate healthcare during pregnancy and childbirth. This can include educational campaigns, community health workers, and mobile clinics to provide accessible and culturally appropriate maternal health services.

2. Improving transportation infrastructure: Address the delays in reaching care by improving transportation infrastructure in rural areas. This can involve providing reliable and affordable transportation options, such as ambulances or transportation vouchers, to ensure that pregnant women can reach healthcare facilities in a timely manner.

3. Enhancing quality of care: Focus on improving the quality of care provided during pregnancy and childbirth. This can be achieved through training healthcare providers, ensuring the availability of essential equipment and supplies, and implementing quality assurance mechanisms to monitor and improve the delivery of maternal health services.

4. Strengthening health information systems: Enhance the health information systems to improve the collection, analysis, and use of data related to maternal health. This can help identify gaps in access to care, monitor progress, and inform evidence-based decision-making for improving maternal health outcomes.

By implementing these recommendations, it is possible to develop innovative solutions that address the barriers to accessing maternal health services and ultimately improve maternal health outcomes in rural areas.

AI Innovations Methodology

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

1. Mobile Clinics: Implementing mobile clinics that can reach remote areas and provide essential maternal health services such as prenatal care, vaccinations, and postnatal care.

2. Telemedicine: Utilizing telemedicine technologies to connect pregnant women in rural areas with healthcare professionals who can provide remote consultations, monitor their health, and offer guidance.

3. Community Health Workers: Training and deploying community health workers who can provide education, support, and basic healthcare services to pregnant women in their communities.

4. Transportation Support: Establishing transportation systems or subsidies to help pregnant women in remote areas access healthcare facilities for prenatal visits, delivery, and emergency care.

5. Maternal Health Education: Conducting community-based education programs to raise awareness about the importance of maternal health, pregnancy complications, and available healthcare services.

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

1. Baseline Data Collection: Gather data on the current state of maternal health access in the target area, including information on healthcare facilities, transportation infrastructure, healthcare utilization rates, and maternal health outcomes.

2. Modeling the Recommendations: Use mathematical modeling techniques to simulate the potential impact of each recommendation on improving access to maternal health. This could involve estimating the number of additional pregnant women who would receive care, the reduction in travel time to healthcare facilities, or the increase in healthcare utilization rates.

3. Data Validation: Validate the model’s predictions by comparing them with real-world data from similar interventions or pilot studies conducted in other settings. This step helps ensure the accuracy and reliability of the simulation results.

4. Sensitivity Analysis: Perform sensitivity analysis to assess the robustness of the simulation results by varying key parameters such as population size, healthcare facility capacity, or transportation availability. This analysis helps identify the most influential factors and potential limitations of the recommendations.

5. Impact Assessment: Evaluate the simulated impact of the recommendations on improving access to maternal health by measuring indicators such as the number of pregnant women receiving prenatal care, the reduction in maternal mortality rates, or the increase in healthcare utilization rates. This assessment provides insights into the potential effectiveness of the recommendations.

6. Policy and Implementation Planning: Based on the simulation results, develop policy recommendations and an implementation plan for the selected interventions. Consider factors such as cost-effectiveness, scalability, and sustainability to ensure the recommendations can be effectively implemented in the target area.

7. Monitoring and Evaluation: Continuously monitor and evaluate the implementation of the recommendations to assess their real-world impact on improving access to maternal health. Adjustments and improvements can be made based on the findings to optimize the interventions and achieve the desired outcomes.

Author

Dima Health

Statistics:

Citations: 10

Identifiers:

DOI: 10.1136/bmjgh-2020-002289

Research Areas:

Disparities, Health System and Policy, Maternal Access, Maternal and Child Health, Noncommunicable Diseases, Quality of Care, Social Determinants, Violence and Injury

Study Countries:

South Africa

Study Design:

Cross Sectional Study, Exploratory Study, Narrative Study

Study Approach:

Mixed-methods, Qualitative, Quantitative