Multilevel and spatial determinants of multimorbidity and optimal co-care delivery model in South Africa
South Africa, 2018 - 2019
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AHRI.Vukuzazi.GeoSpartial.Multimorbidity.2019.V1.0
Multilevel and spatial determinants of multimorbidity and optimal co-care delivery model in South Africa
| Name | Country code |
|---|---|
| South Africa | ZA |
We aim to identify the interactive effects and associations of the key individual, familial, household, and community determinants of communicable and non-communicable disease multimorbidity in rural kwazulu-natal. We will use state-of-the-art multi-level and spatial modelling techniques to understand the complex mechanisms and spatial distribution of the epidemics and establish a multilevel analytical, methodological, and theoretical framework to investigate emerging multimorbidity epidemics in other similar in ssa. The specific objectives are as following:
Aim 1. To quantify the spatial distribution of individual and multimorbid communicable and non-communicable disease epidemiology in rural kwazulu-natal. We posit that the spatial distribution and geographic density of prevalence of HIV, TB and NCDs (hypertension, diabetes and obesity) epidemics are heterogeneous with overlapping hot-spot areas within the surveillance area characterized by urbanicity, and social and economic activities. Understanding the geospatial distribution will inform the development of targeted interventions for disease prevention, management, and treatment.
Aim 2. To measure the relative and interactive contributions of individual, familial, household, and community factors on disease multimorbidity. We hypothesize that household and community factors substantially contribute to the presence of multimorbidity accounting for biological, individual or familiar factors and that key causal pathways exist across different comorbidity conditions. Mtulilevel regression models will allow quantifying the effects of different individual and contextual determinants and their interactions on multimorbidity as well as the level of clustering within household members or at the community-level.
Aim 3. To evaluate an optimal co-care delivery model for multimorbidity using agent-based simulation model (i.E. EMOD HIV/TB). We posit that provision of prevention and treatment for multimorbidity can be optimized through the co-care delivery model at both individual an population levels. We aim to adapt an existing EMOD HIV/TB model to interact with other multiple comorbidity conditions such as diabetes and hypertension. Model parameters for progression to different comorbidity conditions will be determined and calibrated to the key factors and epidemiological data from aim 1 and 2 as well as the longitudinal population-based demographic and hiv surveillance data. We will also estimate costs and effectiveness (i.e. disability-adjusted life year) for different scenariors of co-care delivery models.
Version
V1.0
Scope
Producers and sponsors
| Name | Affiliation |
|---|---|
| Thumbi Ndung'u | Africa Health Research Institute |
| Mark Siedner | Africa Health Research Institute |
| Emily Wong | Africa Health Research Institute |
| Name |
|---|
| Africa Health Research Institute |
| Name |
|---|
| Vukuzazi Study Participants |
Data collection
| Start | End |
|---|---|
| 2018-05-25 | 2019-11-19 |
Data Access
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Metadata production
DDI.AHRI.Vukuzazi.GeoSpartial.Multimorbidity.2019.V1.0
| Name |
|---|
| Africa Health Research Institute |