Spatial distribution of mortality: Evidence from DIMAMO HDSS, Limpopo Province, South Africa

Spatial distribution of mortality: Evidence from DIMAMO HDSS, Limpopo Province, South Africa | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Spatial distribution of mortality: Evidence from DIMAMO HDSS, Limpopo Province, South Africa Katlego Mothapo, Kagiso P Seakamela, Reneilwe G Mashaba, Cairo B Ntimana, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6564367/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 08 Dec, 2025 Read the published version in BMC Public Health → Version 1 posted 12 You are reading this latest preprint version Abstract Background There has been a general decrease in mortality globally due to improved healthcare and modernization. However, rural areas of African countries remain unexplored on this subject. It is against this background that the study aimed to determine the spatial distribution of mortality in Limpopo province, South Africa, from 2014 to 2023. Methods This descriptive paper sampled from a cohort of 115,000 participants surveillance data. Conducted in DIMAMO HDSS located approximately 35 km northeast of Polokwane in Limpopo Province. The University of Limpopo Research Ethics Committee has approved this study. Mortality data were collected through Computer-Assisted Personal Interviews (CAPI). To examine spatial patterns of mortality, the study employed descriptive statistics and spatial analysis techniques to identify geographical variations in mortality rates. Results The study included about 92,430 (males, 40,609 and females, 51,821). The prevalence of unemployment in the area was 67.1%, with females having the highest unemployment percentage(74.8%). Under-5 mortality was elevated in all clusters (Cluster 1: 11.49 per 1000 person years, Cluster 2: 12.37 per 1000 person years, Cluster 3: 2.92). Adult mortality is stated to have peaked in the age category 50 – 54, with those aged above 80 having the highest level of mortality. The year 2020 had the highest mortality rates compared to other years. Conclusion The present study found an overall decrease in mortality rates in the area. However, there were variations among the village clusters, with some experiencing high mortality rates. Regarding age and mortality, the present study found that under-5 mortality was elevated in all clusters. Factors such as unemployment, marital status, and education level were associated with mortality. Mortality was highest in 2020 during the COVID-19 pandemic. Mortality spatial distribution COVID-19 Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Introduction Approximately 7.6 deaths per 1000 people occur worldwide each year, with injuries, cancer, and cardiovascular illnesses being the leading causes of death [ 1 ]. Previous studies reported that, in developed countries, death rates have decreased due to better living conditions and medical improvements [ 2 , 3 ]. Meanwhile, high mortality rates were reported in low- and middle-income countries (LMICs) acerbated by infectious diseases, maternal and child health issues, and limited access to high-quality healthcare services [ 4 – 7 ]. It has been established in the literature that Sub-Saharan Africa has a greater prevalence of mortality [ 8 – 10 ]. This mortality was reported to be due to a combination of non-communicable diseases (NCDs) (diabetes and hypertension), infectious diseases (AIDS, TB), and starvation [ 11 – 13 ]. South Africa, on the other hand, reported a mortality rate of 8.7 per 1,000 people [ 14 ]. The cause of mortality for the South African population was slightly different from that of other sub-Saharan African Countries. For instance, no deaths were reported to result from starvation however, HIV/AIDS and non-communicable diseases were common [ 14 ]. In addition, violence and traffic accidents form part of the main causes of death in South Africa. Although there are studies that explore the subject of mortality in rural South Africa, this demographic remains fairly unexplored, especially in the Limpopo Province. Socioeconomic inequalities, environmental problems, and limited access to healthcare in rural South African communities are reported to predispose them to a higher risk of mortality compared to those in urban areas [ 15 , 16 ]. The findings of this study, although local, may assist health departments in creating tailored interventions and ultimately serve as a measure of South Africa's progress in ending preventable deaths and achieving SDG 3.2. The choropleth mapping and village-level clustering employed in the present study may set a precedent for future research in the DIMAMO HDSS on the subject. Against this background, the study aimed to determine the spatial distribution of mortality in Limpopo province, South Africa, from 2014 to 2023. Material and methods Study Area The DIMAMO Population Health Research Centre (PHRC), formerly known as the Dikgale Health and Demographic Surveillance System (HDSS), was established in 1996 with an initial population of approximately 8,000 residents, covering all villages under Chief Dikgale [17]. In 2018, the surveillance area expanded to include villages under the Mamabolo and Mothiba (Figure 1) Tribal Authorities, increasing the total population to approximately 100,000 with 11 health facilities (10 clinics and 1 tertiary hospital) and leading to the site’s renaming to DIMAMO PHRC. By 2022, the population under surveillance had grown to 115,000 (Figure 2). The site is located approximately 35 km northeast of Polokwane in Limpopo Province, within the Polokwane Local Municipality of Capricorn District. It is in close proximity to the University of Limpopo (Turfloop campus) and lies between the coordinates 29.65° and 29.85°E and 23.65° and 23.90°S [18]. The region is characterized by low socio-economic status and is predominantly inhabited by individuals of African ancestry, primarily from the Ba-Pedi ethnic group. Ethics The study was conducted following the Declaration of Helsinki guidelines. This study has been approved by the University of Limpopo Research Ethics Committee (TREC) (TREC/58/2020: IR). A trained research assistant or data capture explained the study's objectives, and written consent was obtained from each participant before they were enrolled. All questionnaires and consent forms were translated into the local language to ensure that participants fully understood. Upon understanding the study protocol, the participants gave written informed consent to take part in the study. Data Collection Mortality data were collected through Computer-Assisted Personal Interviews (CAPI) conducted by trained fieldworkers during household survey updates. These updates captured village-level death counts, ensuring systematic and comprehensive mortality reporting. The data collection process adhered to standardized demographic surveillance protocols to enhance accuracy and reliability. The questionnaire used in the study has previously been published elsewhere [19]. Mortality Data and Rates The dataset includes annual mortality counts (deaths), person-years at risk, and calculated mortality rates per 1,000 person-years for different Functional Catchment Areas (FCA) within the DIMAMO HDSS. Mortality rates were determined using the following formula: This approach allowed for an assessment of temporal mortality trends across different years (2020–2025) and spatial variations in mortality rates between FCAs. Data Analysis To examine spatial patterns of mortality, the study employed descriptive statistics and spatial analysis techniques to identify geographical variations in mortality rates. A key component of the spatial analysis involved generating choropleth maps to visualize mortality distribution across the surveillance area. Village Clustering The DIMAMO HDSS surveillance area consists of 57 villages, which were grouped into three clusters to facilitate spatial analysis. To define these clusters, we applied the Nearest Neighbourhood Clustering method, which groups villages based on their spatial proximity while ensuring that each cluster contains a roughly equal number of households. The Nearest Neighbourhood Clustering Method is a spatial clustering technique that groups data points (such as villages, households, or individuals) based on their proximity to one another. It works by identifying clusters of locations that are geographically closer together than would be expected under a random distribution. This method minimizes spatial dispersion within clusters and enhances comparability across different regions, allowing for a balanced assessment of mortality trends. Classification for Choropleth Maps To classify mortality rates for mapping, we used the geometric interval classification method. This method determines class breaks based on a geometric progression, ensuring that each class range increases proportionally. Geometric intervals are particularly useful when dealing with skewed data distributions, as they prevent overrepresentation of extreme values while maintaining meaningful distinctions between areas with different mortality rates. The use of geometric intervals enhances visualization by ensuring a balanced representation of variations in mortality rates, making it easier to identify high-mortality clusters and regional disparities. Additionally, trend analysis was conducted to evaluate changes in mortality rates over time. Data were processed and analyzed using statistical and GIS-based tools to facilitate spatial visualization and interpretation. Results The present study included about 92,430 records, of which 40,609 were males and 51,821 were females. The majority (49%) of individuals were divorced/separated, with males having the highest percentage (53.5%) than females (45.1%). In addition, about 30.7% of the population were never married. About 14.4% were married, with about 5.6% in polygamous marriages. The prevalence of unemployment in the area was 67.1%, with females having the highest unemployment percentage (74.8%). The prevalence of individuals with no formal education was 4.0%. More women (9.3%) had post-secondary school qualifications compared to males (8.3%) (Table 1). Table 1: Socio-demographic profile of included participants Variables Male (n = 40609) Female (n = 51821 ) Total (n = 92430) Marital status Never Married 10244 (25.2%) 18230 (34.8%) 28474 (30.7%) Married 6194 (15.2%) 7121 (13.6%) 13315 (14.4%) Polygamous Marriage 2391 (5.9%) 2844 (5.4%) 5235 (5.6%) Divorced/Separated 21780 (53.5%) 23626 (45.1%) 45406 (49.0%) Widowed 92 (0.2%) 508 (1.0%) 600 (0.6%) Employment Status Employed 6224 (39.1%) 4520 (25.2%) 10744 (31.2%) Unemployed 9706 (60.9%) 13385 (74.8%) 23091 (67.1%) Education level Pre-Secondary 6214 (26.0%) 6743 (25.7%) 12957 (25.8%) Secondary 14885 (62.4%) 15889 (60.4%) 30774 (61.3%) Post-secondary 1988 (8.3%) 2440 (9.3%) 4428 (8.8%) No-formal schooling 774 (3.2%) 1215 (4.6%) 1989 (4.0%) Table 2 presents participants' characteristics by cluster. There was no significant difference in terms of marital and employment status in the clusters. However, cluster 2 had the highest level of post-secondary education (9.4%) compared to cluster 2 (8.2%) and cluster 3 (7.4%). On the other hand, cluster 2 had a 6.3% prevalence of no formal education. Table 2: Participants' characteristics by clusters Variables Cluster 1 Cluster 2 Cluster 3 Marital status Never Married 9606 (30.5%) 8747 (30.3%) 10122 (31.0%) Married 4677 (14.8%) 4327 (15.0%) 4311 (13.2%) Polygamous Marriage 2314 (7.3%) 1763 (6.1%) 1158 (3.5%) Divorced/Separated 14685 (46.6%) 13828 (48.0%) 16893 (51.7%) Widowed 223 (0.7%) 166 (0.6%) 211 (0.6%) Employment Status Employed 7150 (31.8%) 1780 (31.6%) 1814 (31.5%) Unemployed 15299 (68.2%) 3846 (68.4%) 3946 (68.5%) Education level Pre-Secondary 7547 (23.9%) 2571 (28.4%) 2840 (29.8%) Secondary 19857 (62.9%) 5170 (57.1%) 5747 (60.3%) Post-secondary 2978 (9.4%) 742 (8.2%) 708 (7.4%) No-formal schooling 1178 (3.7%) 571 (6.3%) 240 (2.5%) Table 3 highlights the mortality rates by village clusters. Clusters 2 and 3 had had highest mortality rates at 6.35 per 1000 person-years and 6.65 per 1000 person-years, respectively. Cluster 1 had the lowest mortality rate at 2.87 per 1000 person-years. Table 3: Overall mortality rates Clusters Death count Person Years Rates/1000 1 1259 438921 2.87 2 1249 196835 6.35 3 1244 187156 6.65 Figure 3 and Table S1 present the mortality rates by cluster and sex. Males have a slightly higher mortality rate in two clusters (1 and 2) compared to females. This is indicated by a mortality rate of 6.81 per 1000 person years in males compared to 6.51 per 1000 person years in females in class 3 and 2.95 per 1000 person years (males) and 2.78 per 1000 person years (females) in class 1. In class 2, females had a slightly higher mortality rate (6.46 per 1000 person years) than males (6.21 per 1000 person years). In general, cluster 1 had the lowest mortality rate compared to other clusters. Figure 4 presents the mortality rate by age category. The graph indicates that mortality is high in the age category 0-4 (cluster 1: 11.49 per 1000 person years, Cluster 2: 12.37 per 1000 person years, Cluster 3: 2.92). However, the mortality rate remained low in all clusters for the age category (5-54). The mortality rates are high in age groups 60 years and older. The three clusters of mortality for individuals over 54 years start peaking at different points. For instance, cluster 1 peaks in the age category of 60-64 years, cluster 2 peaks in the age category of 50-54, while cluster 3 peaks in the 55-59 year range. Figure 5 presents the spatial distribution of mortality by sex. Females were found to have a higher mortality rate in all clusters compared to males. For instance, in class 1, females had a mortality rate of between 2.78-6.08 compared to males (2.96-5.19). Cluster 3 had the highest level of mortality for both sexes compared to other clusters. Relating to spatial distribution or mortality by category, there was a variation across the three clusters. However, cluster 3 had the highest mortality rates of child mortality (0-4) and individuals aged 85 years and older, with ranges of between 11.51 - 12.37 per 1000 person years and between 90.03 – 115.38 per 1000 person years, respectively. Compared to the other years, 2020 had the highest mortality rate. Discussion The present study included about 92,430 records, of which 40,609 were males and 51,821 were females. The majority of individuals were divorced/separated, with males having a higher percentage than females. Individuals who fell in the never married/single/separated marital category have been reported to have a high risk of mortality [ 19 ]. This is further corroborated by the findings of the present study, where clusters with low prevalence of separated/divorced individuals also had low mortality rates. A study by Metsä-Simola et al. [ 20 ], investigated time patterns of post-divorce excess mortality amongst 252,641 married individuals until the date of divorce, and death was reported the follows [ 20 ]. Excess mortality was highest immediately after divorce in men, followed by a decline over 8 years [ 20 ]. The same trend was observed in women, but with a lower risk compared to men [ 20 ]. Post-divorce social and economic factors changes were associated with excess mortality [ 20 ]. Furthermore, a study by Sbarra et al. [ 21 ], reported that the plausible mechanisms for high mortality rates post-divorce included the following factors: social selection, resource disruptions, changes in health behaviours, and chronic psychological distress The prevalence of unemployment in the area was 67.1%, with females having the highest unemployment percentage (74.8%). However, there was no difference in employment status among the three clusters. It is noteworthy that unemployment has been associated with a 4.31% increased risk of mortality [ 22 ]. Unemployment is reported to increase mortality in working age groups due to psychological stress and poverty risk [ 23 ]. In addition, unemployed individuals are reported to engage in unhealthy coping behaviours and lifestyles such as substance abuse and a sedentary lifestyle [ 24 ]. A study conducted by Muthelo et al.[ 25 ], reported a high trend of Crystal Meth, “nyaope,” and Cannabis (marijuana) usage in cluster 1 of the HDSS. These trends may be influenced by the high unemployment and low socioeconomic status in the area [ 26 ]. In the present study, mortality trends varied across the three clusters, with males having slightly higher rates than women. The findings of the present study align with those reported in the literature. Several studies have highlighted that men have a significantly higher mortality risk compared to females [ 27 , 28 ]. This is beside females having a higher prevalence of chronic conditions than men [ 27 , 28 ]. Studies published around the DIMAMO HDSS have revealed that men had a high prevalence of factors linked with mortality, such as smoking and alcohol consumption [ 29 ], waist/hip ratio [ 30 ], and visceral adiposity [ 31 ]. The present study further found that mortality rates were highest in all clusters for the year 2020, a factor that could’ve been influenced by the COVID-19 pandemic. This hypothesis is tenable given the high prevalence of COVID-19 hesitancy that was reported in the area [ 32 ]. Relating to age and mortality, the present study found that, under 5 mortality was elevated in all clusters (Cluster 1: 11.49 per 1000 person years, Cluster 2: 12.37 per 1000 person years, Cluster 3: 2.92). The under-5 mortality reported in the present study is lower than the 37–40 deaths per 1,000 national estimates [ 33 ]. Although this trend was lower than the national estimate, cluster 2 had the highest estimates when compared with clusters 1 and 3, besides the cluster having the highest number of health facilities. This may suggest a discrepancy in health usage and access for the infant's caregivers. Factors such as a mother’s educational level, lower socio-economic status, and inadequate prenatal care are reported to be contributing factors [ 34 – 36 ]. Furthermore, the present study reported a prevalence of individuals with no formal education to be 4.0%, a factor linked with under-5 mortality. Nonetheless, the South African National Department of Health is committed to reducing under-5 mortality rates in line with the Sustainable Development Goal (SDG) targets [ 33 ]. Adult mortality is stated to peak in the age category 50–54, with those aged above 80 having the highest level of mortality. This is due possibly to the high prevalence of chronic conditions such as diabetes mellitus and hypertension in this age group. Significance of the study The present study found that mortality risk was high amongst under-5 children, unemployed adults, and divorced males, thus highlighting the need for targeted interventions for these demographic groups. This is important information, especially in a rural setting where there is limited reporting on the subject of mortality. These findings, although local, may assist the health departments in creating tailor made interventions and ultimately act as a measure of how South Africa is progressing in ending preventable death and achieving SDG 3.2. The choropleth mapping and village-level clustering used in the present study for future research in the DIMAMO HDSS. Limitations The present study used all-cause mortality data, which lacked details about the actual cause of death, thus making it difficult to deduce conditions or circumstances that contributed to the rates of mortality in the area. This is further observed with the spike in mortality observed in 2020. The authors speculated the reason for the spike based on COVID-19 mortality trends and prior studies of vaccine hesitancy conducted in the HDSS. The data set used did not have behavioral and environmental factors, which may also have an influence on mortality outcomes. These findings may not be generalizable to other areas due to differences in social, economic, environmental factors, and health access and utilization specific to the DIMAMO HDSS site in Limpopo Province. Conclusion The present study included about 92,430 records, of which 40,609 were males and 51,821 were females. The present study found that there was, in overall, a decrease in mortality rates in the area. However, there were some variations amongst the village clusters, with some experiencing high mortality rates. Relating to age and mortality, the present study found that under-5 mortality was elevated in all clusters. Factors such as unemployment, marital status, and education level were associated with mortality. Mortality was highest in 2020 during the COVID-19 pandemic. Integrating spatial analysis with demographic surveillance data, the present study provides insight for epidemiologists, the Department of Health, and policymakers to create interventions that are community-specific to reduce preventable deaths in DIMAMO HDSS. Declarations Acknowledgments: The authors would like to acknowledge the DIMAMO HDSS from the University of Limpopo for providing a platform to conduct research. Ethics statement: The study was conducted following the Declaration of Helsinki guidelines. This study has been approved by the University of Limpopo Research Ethics Committee (TREC) (TREC/58/2020: IR). A trained research assistant or data capture explained the study's objectives, and written consent was obtained from each participant before they were enrolled. All questionnaires and consent forms were translated into the local language to ensure that participants fully understood. Upon understanding the study protocol, the participants gave written informed consent to take part in the study. Data availability statement: Data used in the manuscript will be provided upon request from the corresponding author (email: [email protected] ). Consent for publication: Not applicable Author contributions :KM, CBN, JT, KPS, and RGM were responsible for conceptualization, methodology, data collection, analysis, and original draft preparation. KM, CBN, JT, KPS, and RGM validated the tool, supervised, and visualized project administration, writing, review, and editing. 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Cite Share Download PDF Status: Published Journal Publication published 08 Dec, 2025 Read the published version in BMC Public Health → Version 1 posted Editorial decision: Revision requested 05 Sep, 2025 Reviews received at journal 20 Aug, 2025 Reviewers agreed at journal 23 Jun, 2025 Reviews received at journal 18 Jun, 2025 Reviewers agreed at journal 16 Jun, 2025 Reviewers agreed at journal 16 Jun, 2025 Reviewers agreed at journal 12 Jun, 2025 Reviewers invited by journal 11 Jun, 2025 Editor assigned by journal 10 Jun, 2025 Editor invited by journal 07 May, 2025 Submission checks completed at journal 06 May, 2025 First submitted to journal 06 May, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6564367","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":470216496,"identity":"1299ac88-c970-43f1-be58-4c3de2eae4c2","order_by":0,"name":"Katlego Mothapo","email":"","orcid":"","institution":"University of Limpopo","correspondingAuthor":false,"prefix":"","firstName":"Katlego","middleName":"","lastName":"Mothapo","suffix":""},{"id":470216497,"identity":"b0da19cc-1e25-4feb-a677-d12bb97ed9b9","order_by":1,"name":"Kagiso P Seakamela","email":"","orcid":"","institution":"University of Limpopo","correspondingAuthor":false,"prefix":"","firstName":"Kagiso","middleName":"P","lastName":"Seakamela","suffix":""},{"id":470216498,"identity":"93c0958b-71bf-4ea1-9168-a60620b9bd44","order_by":2,"name":"Reneilwe G Mashaba","email":"","orcid":"","institution":"University of Limpopo","correspondingAuthor":false,"prefix":"","firstName":"Reneilwe","middleName":"G","lastName":"Mashaba","suffix":""},{"id":470216499,"identity":"5c28a51d-fd15-4242-912d-be170d297db4","order_by":3,"name":"Cairo B Ntimana","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA9klEQVRIiWNgGAWjYFACxgYQmQBmJxjYgEQaD+DVwYas5UFBGtgQAlogFFgL44MPh8E8vFrM5ZtbN/zcYZdnPiP54YMEg/N2a9sPA22psYnGpcWyjbHtZu+Z5GKZG2nGBgkGt5O3nUkEajmWltuAQ4vBMca2G7xtzIkzJHLYJEBazA4AtTA2HMar5ebftnqQFvYfCQbnks3OPySs5TZv22GwLcBAPmBndoOgLYltt2XbjhdL8DwzBjosOcHsBtCWBHx+OXz82c23bdV5EuzJDz/++GNnb3Y+/eGDDzU2OLVggESwygRilYOAPSmKR8EoGAWjYGQAAPTUaEodFbWbAAAAAElFTkSuQmCC","orcid":"","institution":"University of Limpopo","correspondingAuthor":true,"prefix":"","firstName":"Cairo","middleName":"B","lastName":"Ntimana","suffix":""},{"id":470216500,"identity":"87589fdd-b252-47ff-8710-a983f407341d","order_by":4,"name":"Joseph Tlouyamma","email":"","orcid":"","institution":"University of Limpopo","correspondingAuthor":false,"prefix":"","firstName":"Joseph","middleName":"","lastName":"Tlouyamma","suffix":""}],"badges":[],"createdAt":"2025-04-30 11:08:12","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6564367/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6564367/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s12889-025-25449-3","type":"published","date":"2025-12-08T15:57:04+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":84702945,"identity":"7297b9e1-ea60-4403-9e94-4dcdba2a18a2","added_by":"auto","created_at":"2025-06-16 11:53:27","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":459010,"visible":true,"origin":"","legend":"\u003cp\u003eDIMAMO Surveillance Site\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-6564367/v1/8de5e30e1fbfad8f8e871405.png"},{"id":84702951,"identity":"fff62f0c-415f-4839-a971-1e427cdd0e33","added_by":"auto","created_at":"2025-06-16 11:53:27","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":357863,"visible":true,"origin":"","legend":"\u003cp\u003eDIMAMO population pyramid\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-6564367/v1/ad7dff4b9015637aad7cf825.png"},{"id":84703772,"identity":"541efc10-1afd-44d2-bd76-b4c8c30b5093","added_by":"auto","created_at":"2025-06-16 12:01:27","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":125913,"visible":true,"origin":"","legend":"\u003cp\u003eMortality rates by cluster and sex\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-6564367/v1/d92de2adbfce3b5cd3576ea1.png"},{"id":84702958,"identity":"581a3af7-8d79-454e-ab75-8c456cdc2e37","added_by":"auto","created_at":"2025-06-16 11:53:28","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":80359,"visible":true,"origin":"","legend":"\u003cp\u003eMortality by age category\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-6564367/v1/5aa90b1e7bd094a38da2e8de.png"},{"id":84702938,"identity":"817b8440-c93e-4ec5-b446-2d9a3d874fec","added_by":"auto","created_at":"2025-06-16 11:53:26","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":746728,"visible":true,"origin":"","legend":"\u003cp\u003eSpatial distribution of mortality by sex.\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-6564367/v1/d2dba80092d2720e9481178c.png"},{"id":84702968,"identity":"151357da-5a24-4301-871c-fb9a3d0c3f72","added_by":"auto","created_at":"2025-06-16 11:53:29","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":3475376,"visible":true,"origin":"","legend":"\u003cp\u003eTrend of mortality by year\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-6564367/v1/7a68b1b641314d7f8caf0474.png"},{"id":98243524,"identity":"44343243-3655-4061-8f9a-18f1d6306aa4","added_by":"auto","created_at":"2025-12-15 16:08:34","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":4959282,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6564367/v1/a1c9b5fc-fb25-4961-841d-ce866cdd6427.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Spatial distribution of mortality: Evidence from DIMAMO HDSS, Limpopo Province, South Africa","fulltext":[{"header":"Introduction","content":"\u003cp\u003eApproximately 7.6 deaths per 1000 people occur worldwide each year, with injuries, cancer, and cardiovascular illnesses being the leading causes of death [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Previous studies reported that, in developed countries, death rates have decreased due to better living conditions and medical improvements [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Meanwhile, high mortality rates were reported in low- and middle-income countries (LMICs) acerbated by infectious diseases, maternal and child health issues, and limited access to high-quality healthcare services [\u003cspan additionalcitationids=\"CR5 CR6\" citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIt has been established in the literature that Sub-Saharan Africa has a greater prevalence of mortality [\u003cspan additionalcitationids=\"CR9\" citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. This mortality was reported to be due to a combination of non-communicable diseases (NCDs) (diabetes and hypertension), infectious diseases (AIDS, TB), and starvation [\u003cspan additionalcitationids=\"CR12\" citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. South Africa, on the other hand, reported a mortality rate of 8.7 per 1,000 people [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. The cause of mortality for the South African population was slightly different from that of other sub-Saharan African Countries. For instance, no deaths were reported to result from starvation however, HIV/AIDS and non-communicable diseases were common [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. In addition, violence and traffic accidents form part of the main causes of death in South Africa.\u003c/p\u003e \u003cp\u003eAlthough there are studies that explore the subject of mortality in rural South Africa, this demographic remains fairly unexplored, especially in the Limpopo Province. Socioeconomic inequalities, environmental problems, and limited access to healthcare in rural South African communities are reported to predispose them to a higher risk of mortality compared to those in urban areas [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe findings of this study, although local, may assist health departments in creating tailored interventions and ultimately serve as a measure of South Africa's progress in ending preventable deaths and achieving SDG 3.2. The choropleth mapping and village-level clustering employed in the present study may set a precedent for future research in the DIMAMO HDSS on the subject. Against this background, the study aimed to determine the spatial distribution of mortality in Limpopo province, South Africa, from 2014 to 2023.\u003c/p\u003e"},{"header":"Material and methods","content":"\u003cp\u003e\u003cstrong\u003eStudy Area\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe DIMAMO Population Health Research Centre (PHRC), formerly known as the Dikgale Health and Demographic Surveillance System (HDSS), was established in 1996 with an initial population of approximately 8,000 residents, covering all villages under Chief Dikgale [17]. In 2018, the surveillance area expanded to include villages under the Mamabolo and Mothiba (Figure 1) Tribal Authorities, increasing the total population to approximately 100,000 with 11 health facilities (10 clinics and 1 tertiary hospital) and leading to the site\u0026rsquo;s renaming to DIMAMO PHRC. By 2022, the population under surveillance had grown to 115,000 (Figure 2).\u003c/p\u003e\n\u003cp\u003eThe site is located approximately 35 km northeast of Polokwane in Limpopo Province, within the Polokwane Local Municipality of Capricorn District. It is in close proximity to the University of Limpopo (Turfloop campus) and lies between the coordinates 29.65\u0026deg; and 29.85\u0026deg;E and 23.65\u0026deg; and 23.90\u0026deg;S [18]. The region is characterized by low socio-economic status and is predominantly inhabited by individuals of African ancestry, primarily from the Ba-Pedi ethnic group.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study was conducted following the Declaration of Helsinki guidelines.\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eThis study has been approved by the University of Limpopo Research Ethics Committee (TREC) (TREC/58/2020: IR). A trained research assistant or data capture explained the study\u0026apos;s objectives, and written consent was obtained from each participant before they were enrolled. All questionnaires and consent forms were translated into the local language to ensure that participants fully understood. Upon understanding the study protocol, the participants gave written informed consent to take part in the study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Collection\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMortality data were collected through Computer-Assisted Personal Interviews (CAPI) conducted by trained fieldworkers during household survey updates. These updates captured village-level death counts, ensuring systematic and comprehensive mortality reporting. The data collection process adhered to standardized demographic surveillance protocols to enhance accuracy and reliability. The questionnaire used in the study has previously been published elsewhere [19].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMortality Data and Rates\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe dataset includes annual mortality counts (deaths), person-years at risk, and calculated mortality rates per 1,000 person-years for different Functional Catchment Areas (FCA) within the DIMAMO HDSS. Mortality rates were determined using the following formula:\u003c/p\u003e\n\u003cp\u003e\u003cimg 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7Tlpgv/IFdQZEr355pvRlye1Fu4Vbkq65rEuJo/M7Dj//PPj0cP4j4VNJQjatLyfeuqpsEVBQlG48sorw/+JgOuZ4umhZ6JKIeOeoYeGsmSIijeRe++9d9z0sCZCPMV/rRW8gSvOhofgyy+/nDIDViHE8MJLDC+W3ricHnR6fWmfpstznpoo9EIqz3vF91S1oWdlBHLDMMwe4E03Z1dSB5/fnkhy05l6hZuSr2rSnffggw8OXded/wgZIHxJM71HfFBsoocIuOHS4SF6Jm6++eb4Lw/KCvY+KAsoJryVtLHhKSPtTWF+v7fx8G7CvqUwAeTSJycnN95NNLQDKB0MGfMyZ9NSeXmjbTN7kqlsL/w3m4yy4WxP2oamPde//PJL9JUze/bs6OuS2nuanV8VKHK90pcygkBOM03F9mK4OsxwM3JTImwZ+hnGL13m7HhYE4ZPoE9WerEPMVsMgwe5zCYDRYS3DVv4i96biVaSjLruWpSpXIPX1PWq/QshhgNsB+kNwG6E6bhmH8iWto2hG3ptp3KdLNrxVKDnDELTHuh0FCFtlw8cOBB9h0lf4KpsNCGnFP3zzz/R18V/PbotfSkjYF+u9LQ1XDX8ly1zpNrdRHRN1UHvgl94zM/8GDZSpQPNHmE/meOeudk8zK7JDddce+21Y+ahX3LJJdHXP+k8+tQORQgx88mtp1EGbSM9s2UvFrSbvIhO1gtTGfRke7799tvoO4xvR1Fe0jykaxoxNJ2SDoenwzRnn3129HVJZ2lC+tJXJ8Or6FsZYTjGa3L0lPQ6hJFqoLt27Yq+Lql2N5F2EGX4T4wPOzw86Q3Vq2LYFK6ZDtdgCJYO16CceLuNiYYhNH8f0ouVm/nEGPFUdbvmFgkSQkws3q5j/vz50dcee9nNCf+pZM2aNdHXJbWNTNuvO++8M/oOk8pGhqD8CyK9HL49Rm6kL6389z0stOupDY1/6aP97Ucm962MoDV65SPXU9IUuqiYZWGkY1T+v83G6Icjjjgi+rqg+VFhDDPYOiap9sg3BwjTz5LBTWmSvhQ/rZebYypsW8qGa3wZ5Yx7yQcPBevB1JGOZ6KoUhYM+7DlPkwfyrVr14Y0cBx4iLH5mSp7n3POOSf6ulg6hBAThykRtHepQG0D7QJDsIMehqfH2L9UojRYW0p76dt42t2cfSbtoX9JRJFgnSfaIJyXs5Rb+kJpPPnkk9HXZf369aPDNazN4mcwMtGjr16kTuHXYivcrVu3Lru6Gvs4jis73im00TDmOgWQXd2O61gYWznOr/TG8RRbTc/C4Ba6FVPLSM/BdSpnNF3pp5zNsT+XJ78SK/lLj1t+oO441KUvh51D/P2Qyzt5Lqtj0pWGt9VuIXec+Mqu4/Po69+7NI+58jKXxjnZcC2f57RuhRD9Y/KC1VKHBZ59VqbOyQj2cayqLeJY7lzvmrRnXpbmHO2TrUpdhl/Vusx5udcrlcpITliaS0EIpDdDmQBJHeemkDni8+H4X5bpKiFUJQSoTF9hXCOtnI4WGY5RcYQ1YUw4FB6OseUGM6rSQ7nWHTeapC+FdBC2ThEroypt3qXlWqa44cCXFw+Sr0srYxz5zT1k5MuXd5myRbz+QbawdQ/uZODrzitmQoiJwRR+3/4OEnvem7oq+URblrbH/G/TlhA/csO/GNE+0uY2bRORJbRl1v7i8LOvTh41ZRY/nYjFDILhD1bAnSrbCFEOXaKsS0M3aacBCIvuCSHGQ/c/M0dYlr1pdz9tHBMMOoJx3Mc6xfSib5sRMXygiFx22WXxnxgkNKqsSwNYwNt4qxAzAZQBWwiw13vbvqiOQsESCk1XYEbRN/uJ3CKMYnohZWQaw8OIAScPMsZEQIOA0JsqI01RDwZx6+IaMN5wTIjpDh8iBRSJXicUML3WmDNnTuN4mLE3MjISnimt+zMDCIM1YlqS2uQw/sdYYJkthRgsZj9SZvckxERDW8A9x/j+MGNtGLYNddDOEY7wPFNiZqCekWlMOvV33rx5wTahl6X4xeTD1LeOcAhj3PRk0bMlxGTBmhAPPfRQ8KcLaQ0Tfu2KdBVQD88LU1yxwWLdjM2bN4dnSswMpIxMY5hTzwNJ1yasWLGieOONN/qb6y0mFVtmmjVz+JoyDTFDajbubkoKWz6rwD6G4srG4xmzt6E6HOd4w2Uf/9y5c0ePYeRs8acflORafDyM8BYvYWwo0MMicsRvYdlyrle0muSRbZ3NAef4ePxaO1zD9uc+kFlXTp40vYQlrcTrv9bN+WbvQBjKAghr5ZGuB5TGjSNd1EcdaZnhiIt9+H358R/7C15OgM90WPgcaZ1busmTLzerV7umudy6S03z6hcZW7p0adhyfX8e12U9kUcffTSsJ/TDDz8M5Wc5RB/EHhIhxBRDdzPT9GzaHI8jw2zss3UI6F5nf6472p9DHEyx4z8Ov4+fLfvZ+mnUOD9NkHNsCqAN9zGsxP+0C932E96mJ/rrQFkecZzPfvbh6rrcOc51fPr9kJftT+OpKyePxeHzxHkW3sqKY8RL/uwY5eXD4nz8fljV4ra01U3VJB5/L3Bdm8KPI70pNpSBI3wVhCVNNqyD8/Hj0vr3ebX8GG3y6tNp+PM5T8x8pIwIMQSYEMf5xtr20/B7vEA2gecFYyocLB6ElgnlHDkhApxnyglwPvsI5/d7YZYKwFwefZo53gSfRsrBMGHn092mnLzw9XXg8+TDG3aM8jBFIYePxyBNZfF6TPCjkPj4Lb5c2Vn9tLEX8WnkvKp0+fpMaZNXU7L8PV6muIiZi5QRIYaAMsHhlQjD9yb4t1UabtufKhsWP86Ecg4EhYUj7jLBasIC569VJbhzefTX84pFHXaOF8LE6/+3LScTir6soUromoDFcX5ZeYEvG/xNsR4onBfOvuzS+Hze2/Qs+F6KOkXA6tOXudE0rz6d1D95Qikh7vT+ETMbKSNCDJi0QfbYft/ge2FqQx2+8fdDF+Dj90I5B8LUhDIOf04omCCq6rHhukZZHn2626ygaQqCKQ4msH1a25STT18qvK08ckLXrourE95ecbH4fBmVUab4+WunddQmXZ4yhSzF5yWnRDbNazoUZK5KgREzExmwCjFg/Ke5/cf1zCAS/BeA/X5m5nQUgvABx45QCOsupEaKPv6q2QqA8TMzFTrCI/wnPgwhMUY0MCY0w8jUWJRzoSPMxqwX4dOwaNGi6Ot+eNI48cQTo68eKw/SQXpY/KojtMesN9GmnHz6/MfWMOwkLOS+wvzJJ59EX1GcfPLJ0ZeHeDtKQ1iTA/gAGmkyo9AyysrUX5tVSz1t0mX4vJ511llhW8bu3bujb/wHIaFpXnfu3Bl9RTDsJgz4+0L8N5AyIsSA+frrr6OvKJYsWRJ9RfH6669HX1FcfPHF0dcVGkbnhaL4888/w8JR999/f3bBKB+/zVaogjiIr/NGHfcUxdNPPx19Y7/AvGDBgujrKiksuAepQuTT4AWnfcYcodV0sSvwCg0zNCiDTZs2xT1d2pTTgQMHom+s8P7www+jLy90/fL+TRbeIsyePXtGF8FDmdq+fXvwl1GmINhsIAR4OoPO573pgmDff/999BXFsmXLoi/Pxx9/HH3lSmSTvHpFCwXGpiD7z9uL/wZSRoQYMNYggwkVBDvTMYFeCi9QyoQ25yCYfY8A+PirBBPTKX1PR9nUyfQt3GCqsnHrrbdGX5d9+/ZF3+GeB5/Otutg+DSgALHkfiqQ25ZTCuHuuuuu+G+soghe+bKepDIoV5suSzpXrVoV/L3C1FrrncqVnQlz62logu+lqFNaDx48GLZpbw20yauVnylaXslsMt1ZzBykjAgxYKxBBgQkQm7jxo1B2CBMXn755Xi0i2/crcFmGAUBO3v27OLYY48N+wyLv04wobQQ1t64/dDMmjVroq8rYOxt17rTCWsLbG3dunWcgDLh6IW2X7QPoUbeyQP5r8MPpRBnTnFqU04+XaxnQc8C4dLeCNbisLh8D1FuCMcwpQXl0hSgXbt2hS35Xr58efCXsXLlyrC1D8Gxpse2bdvCuYaVnWHDI6aw2DorVXiFsUzhTOE65I+eMMq2TV79/WU9Mb6833777XH5EjOYYDkihBgIGB7yGOI6DfsYP0Z8ZbMzMPzrKBej4TvCdIxBpuHjzxkaejCU9HHiMHhNjSOBdBFfR8CEcGzLwlalgf/+Whg+NsEMbblu1QyWpuUEubSMjIyM1ovVidHU+JY4iM/XL+mmvDlWB+nIpcFfPy07f07Ta1lclFcdlKHVPeEt/23yWlZ+GNxa3G3uCTG9mcVPp9KFEAOAt9y1a9cGf6dBLi655JLgF9XwJv7aa68FI8mmNhFCiOFFwzRCDJAmhoCi26XPMujAUAmKSOfNWoqIEDMEKSNCDBBvL1JmcCm6dgcjIyPhOyXYI2Czwnd+hBAzAykjQgwIv64DyFCvnoULF+prrULMQGQzIoQQQoiBop4RIYQQQgwUKSNCCCGEGChSRoQQQggxUKSMCCGEEGKgSBkRQgghxECRMiKEEEKIgSJlRAghhBADRcqIEEIIIQaKlBEhhBBCDBQpI0IIIYQYKFJGhBBCCDFQpIwIIYQQYoAUxf8DnGBRyizEoJEAAAAASUVORK5CYII=\" width=\"547\" height=\"78\"\u003e\u003c/p\u003e\n\u003cp\u003eThis approach allowed for an assessment of temporal mortality trends across different years (2020\u0026ndash;2025) and spatial variations in mortality rates between FCAs.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo examine spatial patterns of mortality, the study employed descriptive statistics and spatial analysis techniques to identify geographical variations in mortality rates. A key component of the spatial analysis involved generating choropleth maps to visualize mortality distribution across the surveillance area.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eVillage Clustering\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe DIMAMO HDSS surveillance area consists of 57 villages, which were grouped into three clusters to facilitate spatial analysis. To define these clusters, we applied the Nearest Neighbourhood Clustering method, which groups villages based on their spatial proximity while ensuring that each cluster contains a roughly equal number of households. The Nearest Neighbourhood Clustering Method is a spatial clustering technique that groups data points (such as villages, households, or individuals) based on their proximity to one another. It works by identifying clusters of locations that are geographically closer together than would be expected under a random distribution. This method minimizes spatial dispersion within clusters and enhances comparability across different regions, allowing for a balanced assessment of mortality trends.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClassification for Choropleth Maps\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo classify mortality rates for mapping, we used the geometric interval classification method. This method determines class breaks based on a geometric progression, ensuring that each class range increases proportionally. Geometric intervals are particularly useful when dealing with skewed data distributions, as they prevent overrepresentation of extreme values while maintaining meaningful distinctions between areas with different mortality rates.\u003c/p\u003e\n\u003cp\u003eThe use of geometric intervals enhances visualization by ensuring a balanced representation of variations in mortality rates, making it easier to identify high-mortality clusters and regional disparities. Additionally, trend analysis was conducted to evaluate changes in mortality rates over time. Data were processed and analyzed using statistical and GIS-based tools to facilitate spatial visualization and interpretation.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eThe present study included about 92,430 records, of which 40,609 were males and 51,821 were females. The majority (49%) of individuals were divorced/separated, with males having the highest percentage (53.5%) than females (45.1%). In addition, about 30.7% of the population were never married. About 14.4% were married, with about 5.6% in polygamous marriages. The prevalence of unemployment in the area was 67.1%, with females having the highest unemployment percentage (74.8%). The prevalence of individuals with no formal education was 4.0%. More women (9.3%) had post-secondary school qualifications compared to males (8.3%) (Table 1).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 1: Socio-demographic profile of included participants\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"690\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 0px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eVariables\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 0px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMale (n = 40609)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 0px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eFemale (n = 51821 )\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 0px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTotal (n = 92430)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"5\" valign=\"top\" style=\"width: 0px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMarital status\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 0px;\"\u003e\n \u003cp\u003eNever Married\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 0px;\"\u003e\n \u003cp\u003e10244 (25.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 0px;\"\u003e\n \u003cp\u003e18230 (34.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 0px;\"\u003e\n \u003cp\u003e28474 (30.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 0px;\"\u003e\n \u003cp\u003eMarried\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 0px;\"\u003e\n \u003cp\u003e6194 (15.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 0px;\"\u003e\n \u003cp\u003e7121 (13.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 0px;\"\u003e\n \u003cp\u003e13315 (14.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 0px;\"\u003e\n \u003cp\u003ePolygamous Marriage\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 0px;\"\u003e\n \u003cp\u003e2391 (5.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 0px;\"\u003e\n \u003cp\u003e2844 (5.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 0px;\"\u003e\n \u003cp\u003e5235 (5.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 0px;\"\u003e\n \u003cp\u003eDivorced/Separated\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 0px;\"\u003e\n \u003cp\u003e21780 (53.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 0px;\"\u003e\n \u003cp\u003e23626 (45.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 0px;\"\u003e\n \u003cp\u003e45406 (49.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 0px;\"\u003e\n \u003cp\u003eWidowed\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 0px;\"\u003e\n \u003cp\u003e92 (0.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 0px;\"\u003e\n \u003cp\u003e508 (1.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 0px;\"\u003e\n \u003cp\u003e600 (0.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 0px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eEmployment Status\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 0px;\"\u003e\n \u003cp\u003eEmployed\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 0px;\"\u003e\n \u003cp\u003e6224 (39.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 0px;\"\u003e\n \u003cp\u003e4520 (25.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 0px;\"\u003e\n \u003cp\u003e10744 (31.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 0px;\"\u003e\n \u003cp\u003eUnemployed\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 0px;\"\u003e\n \u003cp\u003e9706 (60.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 0px;\"\u003e\n \u003cp\u003e13385 (74.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 0px;\"\u003e\n \u003cp\u003e23091 (67.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"4\" valign=\"top\" style=\"width: 0px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eEducation level\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 0px;\"\u003e\n \u003cp\u003ePre-Secondary\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 0px;\"\u003e\n \u003cp\u003e6214 (26.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 0px;\"\u003e\n \u003cp\u003e6743 (25.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 0px;\"\u003e\n \u003cp\u003e12957 (25.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 0px;\"\u003e\n \u003cp\u003eSecondary\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 0px;\"\u003e\n \u003cp\u003e14885 (62.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 0px;\"\u003e\n \u003cp\u003e15889 (60.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 0px;\"\u003e\n \u003cp\u003e30774 (61.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 0px;\"\u003e\n \u003cp\u003ePost-secondary\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 0px;\"\u003e\n \u003cp\u003e1988 (8.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 0px;\"\u003e\n \u003cp\u003e2440 (9.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 0px;\"\u003e\n \u003cp\u003e4428 (8.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 0px;\"\u003e\n \u003cp\u003eNo-formal schooling\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 0px;\"\u003e\n \u003cp\u003e774 (3.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 0px;\"\u003e\n \u003cp\u003e1215 (4.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 0px;\"\u003e\n \u003cp\u003e1989 (4.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eTable 2 presents participants\u0026apos; characteristics by cluster. There was no significant difference in terms of marital and employment status in the clusters. However, cluster 2 had the highest level of post-secondary education (9.4%) compared to cluster 2 (8.2%) and cluster 3 (7.4%). On the other hand, cluster 2 had a 6.3% prevalence of no formal education.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2: Participants\u0026apos; characteristics by clusters\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"689\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 0px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eVariables\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 0px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCluster 1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 0px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCluster 2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 0px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCluster 3\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"5\" valign=\"top\" style=\"width: 0px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMarital status\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 0px;\"\u003e\n \u003cp\u003eNever Married\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 0px;\"\u003e\n \u003cp\u003e9606 (30.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 0px;\"\u003e\n \u003cp\u003e8747 (30.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 0px;\"\u003e\n \u003cp\u003e10122 (31.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 0px;\"\u003e\n \u003cp\u003eMarried\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 0px;\"\u003e\n \u003cp\u003e4677 (14.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 0px;\"\u003e\n \u003cp\u003e4327 (15.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 0px;\"\u003e\n \u003cp\u003e4311 (13.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 0px;\"\u003e\n \u003cp\u003ePolygamous Marriage\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 0px;\"\u003e\n \u003cp\u003e2314 (7.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 0px;\"\u003e\n \u003cp\u003e1763 (6.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 0px;\"\u003e\n \u003cp\u003e1158 (3.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 0px;\"\u003e\n \u003cp\u003eDivorced/Separated\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 0px;\"\u003e\n \u003cp\u003e14685 (46.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 0px;\"\u003e\n \u003cp\u003e13828 (48.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 0px;\"\u003e\n \u003cp\u003e16893 (51.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 0px;\"\u003e\n \u003cp\u003eWidowed\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 0px;\"\u003e\n \u003cp\u003e223 (0.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 0px;\"\u003e\n \u003cp\u003e166 (0.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 0px;\"\u003e\n \u003cp\u003e211 (0.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 0px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eEmployment Status\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 0px;\"\u003e\n \u003cp\u003eEmployed\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 0px;\"\u003e\n \u003cp\u003e7150 (31.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 0px;\"\u003e\n \u003cp\u003e1780 (31.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 0px;\"\u003e\n \u003cp\u003e1814 (31.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 0px;\"\u003e\n \u003cp\u003eUnemployed\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 0px;\"\u003e\n \u003cp\u003e15299 (68.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 0px;\"\u003e\n \u003cp\u003e3846 (68.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 0px;\"\u003e\n \u003cp\u003e3946 (68.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"4\" valign=\"top\" style=\"width: 0px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eEducation level\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 0px;\"\u003e\n \u003cp\u003ePre-Secondary\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 0px;\"\u003e\n \u003cp\u003e7547 (23.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 0px;\"\u003e\n \u003cp\u003e2571 (28.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 0px;\"\u003e\n \u003cp\u003e2840 (29.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 0px;\"\u003e\n \u003cp\u003eSecondary\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 0px;\"\u003e\n \u003cp\u003e19857 (62.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 0px;\"\u003e\n \u003cp\u003e5170 (57.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 0px;\"\u003e\n \u003cp\u003e5747 (60.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 0px;\"\u003e\n \u003cp\u003ePost-secondary\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 0px;\"\u003e\n \u003cp\u003e2978 (9.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 0px;\"\u003e\n \u003cp\u003e742 (8.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 0px;\"\u003e\n \u003cp\u003e708 (7.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 0px;\"\u003e\n \u003cp\u003eNo-formal schooling\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 0px;\"\u003e\n \u003cp\u003e1178 (3.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 0px;\"\u003e\n \u003cp\u003e571 (6.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 0px;\"\u003e\n \u003cp\u003e240 (2.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eTable 3 highlights the mortality rates by village clusters. Clusters 2 and 3 had had highest mortality rates at 6.35 per 1000 person-years and 6.65 per 1000 person-years, respectively. Cluster 1 had the lowest mortality rate at 2.87 per 1000 person-years.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3: Overall mortality rates\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"615\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 150px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eClusters\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 97px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDeath count\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 187px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePerson Years\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 181px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eRates/1000\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 150px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 97px;\"\u003e\n \u003cp\u003e1259\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 187px;\"\u003e\n \u003cp\u003e438921\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 181px;\"\u003e\n \u003cp\u003e2.87\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 150px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 97px;\"\u003e\n \u003cp\u003e1249\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 187px;\"\u003e\n \u003cp\u003e196835\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 181px;\"\u003e\n \u003cp\u003e6.35\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 150px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 97px;\"\u003e\n \u003cp\u003e1244\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 187px;\"\u003e\n \u003cp\u003e187156\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 181px;\"\u003e\n \u003cp\u003e6.65\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eFigure 3 and Table S1 present the mortality rates by cluster and sex. Males have a slightly higher mortality rate in two clusters (1 and 2) compared to females. This is indicated by a mortality rate of 6.81 per 1000 person years in males compared to 6.51 per 1000 person years in females in class 3 and 2.95 per 1000 person years (males) and 2.78 per 1000 person years (females) in class 1. In class 2, females had a slightly higher mortality rate (6.46 per 1000 person years) than males (6.21 per 1000 person years). In general, cluster 1 had the lowest mortality rate compared to other clusters.\u003c/p\u003e\n\u003cp\u003eFigure 4 presents the mortality rate by age category. The graph indicates that mortality is high in the age category 0-4 (cluster 1: 11.49 per 1000 person years, Cluster 2: 12.37 per 1000 person years, Cluster 3: 2.92). However, the mortality rate remained low in all clusters for the age category (5-54). The mortality rates are high in age groups 60 years and older. The three clusters of mortality for individuals over 54 years start peaking at different points. For instance, cluster 1 peaks in the age category of 60-64 years, cluster 2 peaks in the age category of 50-54, while cluster 3 peaks in the 55-59 year range.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFigure 5 presents the spatial distribution of mortality by sex. Females were found to have a higher mortality rate in all clusters compared to males. For instance, in class 1, females had a mortality rate of between 2.78-6.08 compared to males (2.96-5.19). Cluster 3 had the highest level of mortality for both sexes compared to other clusters.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eRelating to spatial distribution or mortality by category, there was a variation across the three clusters. However, cluster 3 had the highest mortality rates of child mortality (0-4) and individuals aged 85 years and older, with ranges of between 11.51 - 12.37 per 1000 person years and between 90.03 \u0026ndash; 115.38 per 1000 person years, respectively. \u0026nbsp; Compared to the other years, 2020 had the highest mortality rate.\u0026nbsp;\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe present study included about 92,430 records, of which 40,609 were males and 51,821 were females. The majority of individuals were divorced/separated, with males having a higher percentage than females. Individuals who fell in the never married/single/separated marital category have been reported to have a high risk of mortality [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. This is further corroborated by the findings of the present study, where clusters with low prevalence of separated/divorced individuals also had low mortality rates. A study by Mets\u0026auml;-Simola et al. [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e], investigated time patterns of post-divorce excess mortality amongst 252,641 married individuals until the date of divorce, and death was reported the follows [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Excess mortality was highest immediately after divorce in men, followed by a decline over 8 years [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. The same trend was observed in women, but with a lower risk compared to men [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Post-divorce social and economic factors changes were associated with excess mortality [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Furthermore, a study by Sbarra et al. [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e], reported that the plausible mechanisms for high mortality rates post-divorce included the following factors: social selection, resource disruptions, changes in health behaviours, and chronic psychological distress\u003c/p\u003e \u003cp\u003eThe prevalence of unemployment in the area was 67.1%, with females having the highest unemployment percentage (74.8%). However, there was no difference in employment status among the three clusters. It is noteworthy that unemployment has been associated with a 4.31% increased risk of mortality [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. Unemployment is reported to increase mortality in working age groups due to psychological stress and poverty risk [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. In addition, unemployed individuals are reported to engage in unhealthy coping behaviours and lifestyles such as substance abuse and a sedentary lifestyle [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. A study conducted by Muthelo et al.[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e], reported a high trend of Crystal Meth, \u0026ldquo;nyaope,\u0026rdquo; and Cannabis (marijuana) usage in cluster 1 of the HDSS. These trends may be influenced by the high unemployment and low socioeconomic status in the area [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn the present study, mortality trends varied across the three clusters, with males having slightly higher rates than women. The findings of the present study align with those reported in the literature. Several studies have highlighted that men have a significantly higher mortality risk compared to females [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. This is beside females having a higher prevalence of chronic conditions than men [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. Studies published around the DIMAMO HDSS have revealed that men had a high prevalence of factors linked with mortality, such as smoking and alcohol consumption [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e], waist/hip ratio [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e], and visceral adiposity [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. The present study further found that mortality rates were highest in all clusters for the year 2020, a factor that could\u0026rsquo;ve been influenced by the COVID-19 pandemic. This hypothesis is tenable given the high prevalence of COVID-19 hesitancy that was reported in the area [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eRelating to age and mortality, the present study found that, under 5 mortality was elevated in all clusters (Cluster 1: 11.49 per 1000 person years, Cluster 2: 12.37 per 1000 person years, Cluster 3: 2.92). The under-5 mortality reported in the present study is lower than the 37\u0026ndash;40 deaths per 1,000 national estimates [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. Although this trend was lower than the national estimate, cluster 2 had the highest estimates when compared with clusters 1 and 3, besides the cluster having the highest number of health facilities. This may suggest a discrepancy in health usage and access for the infant's caregivers. Factors such as a mother\u0026rsquo;s educational level, lower socio-economic status, and inadequate prenatal care are reported to be contributing factors [\u003cspan additionalcitationids=\"CR35\" citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. Furthermore, the present study reported a prevalence of individuals with no formal education to be 4.0%, a factor linked with under-5 mortality. Nonetheless, the South African National Department of Health is committed to reducing under-5 mortality rates in line with the Sustainable Development Goal (SDG) targets [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. Adult mortality is stated to peak in the age category 50\u0026ndash;54, with those aged above 80 having the highest level of mortality. This is due possibly to the high prevalence of chronic conditions such as diabetes mellitus and hypertension in this age group.\u003c/p\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eSignificance of the study\u003c/h2\u003e \u003cp\u003eThe present study found that mortality risk was high amongst under-5 children, unemployed adults, and divorced males, thus highlighting the need for targeted interventions for these demographic groups. This is important information, especially in a rural setting where there is limited reporting on the subject of mortality. These findings, although local, may assist the health departments in creating tailor made interventions and ultimately act as a measure of how South Africa is progressing in ending preventable death and achieving SDG 3.2. The choropleth mapping and village-level clustering used in the present study for future research in the DIMAMO HDSS.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eLimitations\u003c/h2\u003e \u003cp\u003eThe present study used all-cause mortality data, which lacked details about the actual cause of death, thus making it difficult to deduce conditions or circumstances that contributed to the rates of mortality in the area. This is further observed with the spike in mortality observed in 2020. The authors speculated the reason for the spike based on COVID-19 mortality trends and prior studies of vaccine hesitancy conducted in the HDSS. The data set used did not have behavioral and environmental factors, which may also have an influence on mortality outcomes. These findings may not be generalizable to other areas due to differences in social, economic, environmental factors, and health access and utilization specific to the DIMAMO HDSS site in Limpopo Province.\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThe present study included about 92,430 records, of which 40,609 were males and 51,821 were females. The present study found that there was, in overall, a decrease in mortality rates in the area. However, there were some variations amongst the village clusters, with some experiencing high mortality rates. Relating to age and mortality, the present study found that under-5 mortality was elevated in all clusters. Factors such as unemployment, marital status, and education level were associated with mortality. Mortality was highest in 2020 during the COVID-19 pandemic. Integrating spatial analysis with demographic surveillance data, the present study provides insight for epidemiologists, the Department of Health, and policymakers to create interventions that are community-specific to reduce preventable deaths in DIMAMO HDSS.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgments:\u0026nbsp;\u003c/strong\u003eThe authors would like to acknowledge the DIMAMO HDSS \u0026nbsp; from the University of Limpopo for providing a platform to conduct research.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics statement:\u003c/strong\u003e The study was conducted following the Declaration of Helsinki guidelines.\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eThis study has been approved by the University of Limpopo Research Ethics Committee (TREC) (TREC/58/2020: IR). A trained research assistant or data capture explained the study\u0026apos;s objectives, and written consent was obtained from each participant before they were enrolled. All questionnaires and consent forms were translated into the local language to ensure that participants fully understood. Upon understanding the study protocol, the participants gave written informed consent to take part in the study.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability statement:\u0026nbsp;\u003c/strong\u003eData used in the manuscript will be provided upon request from the corresponding author (email: [email protected]).\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication:\u0026nbsp;\u003c/strong\u003eNot applicable\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e :KM, CBN, JT, KPS, and RGM were responsible for conceptualization, methodology, data collection, analysis, and original draft preparation. KM, CBN, JT, KPS, and RGM validated the tool, supervised, and visualized project administration, writing, review, and editing. All authors have read and agreed to the published version of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding:\u0026nbsp;\u003c/strong\u003eNo funding\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of interest:\u0026nbsp;\u003c/strong\u003eThe authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eOrganization WH. Tracking universal health coverage: 2023 global monitoring report [Internet]. World Health Organization; 2023 [cited 2025 Apr 25]. 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Int J Epidemiol. 2022;51(4):e206\u0026ndash;16. \u003c/li\u003e\n\u003cli\u003eLachonius M, Giang KW, P\u0026eacute;tursson P, Anger\u0026aring;s O, Skoglund K, Jeppsson A, et al. Marital status, educational level, and mid-term mortality risk in 5924 patients after transcatheter aortic valve implantation. Eur Heart J Open. 2024 Sep 1;4(5):oeae077. \u003c/li\u003e\n\u003cli\u003eMets\u0026auml;-Simola N, and Martikainen P. The short-term and long-term effects of divorce on mortality risk in a large Finnish cohort, 1990\u0026ndash;2003. Popul Stud. 2013 Mar 1;67(1):97\u0026ndash;110. \u003c/li\u003e\n\u003cli\u003eSbarra DA, Law RW, Portley RM. Divorce and Death: A Meta-Analysis and Research Agenda for Clinical, Social, and Health Psychology. Perspect Psychol Sci. 2011 Sep 1;6(5):454\u0026ndash;74. \u003c/li\u003e\n\u003cli\u003eKim JM, Son NH, Park EC, Nam CM, Kim TH, Cho WH. The Relationship Between Changes in Employment Status and Mortality Risk Based on the Korea Labor and Income Panel Study (2003-2008). Asia Pac J Public Health. 2015 Mar;27(2):NP993\u0026ndash;1001. \u003c/li\u003e\n\u003cli\u003eNeshat Ghojagh HM, Agheli L, Faraji Dizaji S, Kabir MJ, Taghvaee V. Economic instability, income, and unemployment effects on mortality: using SUR panel data in Iran. Int J Health Econ Manag. 2024 Dec 1;24(4):555\u0026ndash;70. \u003c/li\u003e\n\u003cli\u003eArena AF, Mobbs S, Sanatkar S, Williams D, Collins D, Harris M, et al. Mental health and unemployment: A systematic review and meta-analysis of interventions to improve depression and anxiety outcomes. J Affect Disord. 2023 Aug 15;335:450\u0026ndash;72. \u003c/li\u003e\n\u003cli\u003eMuthelo L, Mbombi MO, Mphekgwana P, Mabila LN, Dhau I, Tlouyamma J, et al. Exploring roles of stakeholders in combating substance abuse in the DIMAMO Surveillance Site, South Africa. Subst Abuse Res Treat. 2023;17:11782218221147498. \u003c/li\u003e\n\u003cli\u003eMphekgwana PM, Mbombi MO, Muthelo L, Tlouyamma J, Nemuramba R, Ntimana C, et al. Overweight Prevalence among Rural Adolescents by Household Head Obesity and Socio-Economic Status in Limpopo, South Africa. Children. 2022 Nov;9(11):1728. \u003c/li\u003e\n\u003cli\u003eChang Z, Lu J, Zhang Q, Wu H, Liang Z, Pan X, et al. Clinical biomarker profiles reveals gender differences and mortality factors in sepsis. Front Immunol. 2024;15:1413729. \u003c/li\u003e\n\u003cli\u003eLv Y, Cao X, Yu K, Pu J, Tang Z, Wei N, et al. Gender differences in all-cause and cardiovascular mortality among US adults: from NHANES 2005\u0026ndash;2018. Front Cardiovasc Med. 2024;11:1283132. \u003c/li\u003e\n\u003cli\u003eNtimana CB, Mashaba RG, Seakamela KP, Mphekgwana PM, Nemuramba R, Mothapo K, et al. Association Between Renal Dysfunction and Lipid Ratios in Rural Black South Africans. Int J Environ Res Public Health. 2025;22(3):324. \u003c/li\u003e\n\u003cli\u003eMashaba GR, Phoswa WN, Lebelo SL, Choma SS, Maimela E, Mokgalaboni K. A Longitudinal Cohort Assessing the Carotid Intima-Media Thickness Progression and Cardiovascular Risk Factors in a Rural Black South African Community. J Clin Med. 2025;14(3):1033. \u003c/li\u003e\n\u003cli\u003eNtimana CB, Mashaba RG, Seakamela KP, Maimela E, Masemola-Maphutha ML, Choma SSR. Comorbidities of Obesity in a Rural African Population Residing in Limpopo Province, South Africa: A Comparison between General and Central Obesity. Obesities. 2024 Sep;4(3):375\u0026ndash;88. \u003c/li\u003e\n\u003cli\u003eMbombi MO, Muthelo L, Mphekgwane P, Dhau I, Tlouyamma J, Nemuramba R, et al. Prevalence of COVID-19 vaccine hesitancy in a rural setting: a case study of DIMAMO health and demographic surveillance site, Limpopo province of South Africa. J Respir. 2022;2(2):101\u0026ndash;10. \u003c/li\u003e\n\u003cli\u003eBamford LJ, McKerrow NH, Barron P, Aung Y. Child mortality in South Africa: Fewer deaths, but better data are needed. S Afr Med J. 2018 May 7;108(3):25\u0026ndash;32. \u003c/li\u003e\n\u003cli\u003eVan Malderen C, Amouzou A, Barros AJD, Masquelier B, Van Oyen H, Speybroeck N. Socioeconomic factors contributing to under-five mortality in sub-Saharan Africa: a decomposition analysis. BMC Public Health. 2019 Jun 14;19(1):760. \u003c/li\u003e\n\u003cli\u003eRademeyer S. Levels and determinants of under-five mortality in South Africa. South Afr J Demogr. 2019;19(1):64\u0026ndash;89. \u003c/li\u003e\n\u003cli\u003eAvelino IC, Van-D\u0026uacute;nem J, Varandas L. Under-five mortality and social determinants in africa: a systematic review. Eur J Pediatr. 2025 Jan 24;184(2):150. \u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-public-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"pubh","sideBox":"Learn more about [BMC Public Health](http://bmcpublichealth.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/pubh/default.aspx","title":"BMC Public Health","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Mortality, spatial distribution, COVID-19","lastPublishedDoi":"10.21203/rs.3.rs-6564367/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6564367/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThere has been a general decrease in mortality globally due to improved healthcare and modernization. However, rural areas of African countries remain unexplored on this subject. It is against this background that the study aimed to determine the spatial distribution of mortality in Limpopo province, South Africa, from 2014 to 2023.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis descriptive paper sampled from a cohort of 115,000 participants surveillance data. Conducted in DIMAMO HDSS located approximately 35 km northeast of Polokwane in Limpopo Province. The University of Limpopo Research Ethics Committee has approved this study. Mortality data were collected through Computer-Assisted Personal Interviews (CAPI). To examine spatial patterns of mortality, the study employed descriptive statistics and spatial analysis techniques to identify geographical variations in mortality rates.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study included about 92,430 (males, 40,609 and females, 51,821). The prevalence of unemployment in the area was 67.1%, with females having the highest unemployment percentage(74.8%). Under-5 mortality was elevated in all clusters (Cluster 1: 11.49 per 1000 person years, Cluster 2: 12.37 per 1000 person years, Cluster 3: 2.92). Adult mortality is stated to have peaked in the age category 50 – 54, with those aged above 80 having the highest level of mortality. The year 2020 had the highest mortality rates compared to other years.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe present study found an overall decrease in mortality rates in the area. However, there were variations among the village clusters, with some experiencing high mortality rates. Regarding age and mortality, the present study found that under-5 mortality was elevated in all clusters. Factors such as unemployment, marital status, and education level were associated with mortality. Mortality was highest in 2020 during the COVID-19 pandemic.\u003c/p\u003e","manuscriptTitle":"Spatial distribution of mortality: Evidence from DIMAMO HDSS, Limpopo Province, South Africa","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-06-16 11:53:12","doi":"10.21203/rs.3.rs-6564367/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-09-05T06:06:11+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-08-20T09:24:06+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"284878789189340895838471284902063183476","date":"2025-06-24T03:28:29+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-06-18T14:23:14+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"270469039767371602583128568179981077314","date":"2025-06-17T02:00:23+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"171649381489335542796586868830650389297","date":"2025-06-16T15:53:08+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"99503461147925523393669952662753081660","date":"2025-06-12T07:49:50+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-06-11T13:30:29+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-06-10T05:15:23+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-05-07T10:15:02+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-05-06T10:59:00+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Public Health","date":"2025-05-06T10:57:49+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-public-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"pubh","sideBox":"Learn more about [BMC Public Health](http://bmcpublichealth.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/pubh/default.aspx","title":"BMC Public Health","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"e977e397-6a31-439a-961b-bd9c83d41dfb","owner":[],"postedDate":"June 16th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-12-15T16:01:18+00:00","versionOfRecord":{"articleIdentity":"rs-6564367","link":"https://doi.org/10.1186/s12889-025-25449-3","journal":{"identity":"bmc-public-health","isVorOnly":false,"title":"BMC Public Health"},"publishedOn":"2025-12-08 15:57:04","publishedOnDateReadable":"December 8th, 2025"},"versionCreatedAt":"2025-06-16 11:53:12","video":"","vorDoi":"10.1186/s12889-025-25449-3","vorDoiUrl":"https://doi.org/10.1186/s12889-025-25449-3","workflowStages":[]},"version":"v1","identity":"rs-6564367","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6564367","identity":"rs-6564367","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}