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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.
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