Patterns and Predictors of HIV Comorbidity among Adolescents and Young Adults in South Africa.

Adolescents and young adults (AYA) are identified as a high-risk group for HIV acquisition. However, health services are generally not sensitive to the needs of this priority population. In addition, multimorbidity (having more than one disease in an individual) is not well studied among AYA, as it is typically associated with older individuals. This paper reports on commonly co-occurring disease conditions and disease patterns in AYA, aged 15-24 years, who took part in the 2016 South African Demographic and Health Survey. Chi-squared tests and logistic regression were used to examine the weighted prevalence of disease among those with/without HIV, and the risk factors associated with HIV. Latent class analysis (LCA) was conducted to identify common co-occurring diseases. Of the 1787 individuals included in our analysis, the weighted prevalence of HIV was 8.7%. Hypertension (30.5%), anaemia (35.8%) and diabetes (2.0%) were more prevalent among those with HIV. HIV and anaemia, hypertension and anaemia, and HIV and hypertension comprise the largest disease burden of co-occurring diseases. Co-morbidity was high among those with HIV, emphasizing the need for integrated care of HIV and non-communicable diseases.

Multimorbidity, Treatment, and Determinants among Chronic Patients Attending Primary Health Facilities in Tshwane, South Africa.

The growing burden of non-communicable diseases amidst the largest burden of HIV in South Africa leads to disease combinations of multimorbidity with the complexity of care. We conducted a cross-sectional study to assess multimorbidity, medication adherence, and associated factors among out-patients with chronic diseases in primary health care (PHC) facilities in Tshwane, South Africa. A structured questionnaire was used to collect data on comorbidities and medication adherence, along with socio-demographic and lifestyle factors. Logistic regression models were used to analyse the determinants of multimorbidity and medication adherence. In all 400 patients with chronic diseases (mean age: 47 ± 12 years) living in poor environments, common chronic conditions were hypertension (62%), diabetes (45%), HIV (44%), TB (33%), hypercholesterolemia (18%), and gout (13%). The proportion of concordant comorbidity (i.e., diseases with similar risk profiles and management) was 72%, more than 28% of discordant comorbidity (i.e., diseases not related in pathogenesis or management). Most patients had two coexisting chronic conditions (75%), while few had more than two chronic conditions (23%) and single-occurring conditions (2%). Prevalence rates for common multimorbidity patterns were 25% (HIV and TB), 17% (hypertension and diabetes), 9% (hypertension, diabetes, and hypercholesterolemia), and 2% (hypertension diabetes and HIV), while medication adherence was estimated at 74%. In multivariate analysis, multimorbidity was associated with an older age and lower socio-economic status, while medication non-adherence was associated with a younger age and socio-economic factors. The study highlights the presence of multimorbidity among primary care patients attributed to hypertension, diabetes, HIV, and TB in South Africa with non-adherence to medication in one-third of patients. Policies are needed for education on multimorbidity with a need to optimize lifestyle modifications, perhaps proactive outreach or nursing contact with high-risk patients with public-health-sensitive conditions, such as HIV and/or TB, as well as patients with a history of non-adherence to medications. Considerations should be given to the development of a medication adherence scale for multiple chronic conditions beyond assessing adherence to a single index medication.

Multimorbidity in South Africa: Is the health system ready?

Background: Multimorbidity is likely to be a significant contributor to ill health and inequality in South Africa and yet has been largely overlooked. Purpose: This paper focuses on the findings of a recent large study that highlighted emerging issues - namely (i) the high levels of multimorbidity among three key groups - older adults, women, and the wealthy; (ii) discordant and concordant disease clusters among the multimorbid. Research Design: Narrative. Study Sample and Data Collection: Not applicable. Results: We discuss the implications of each emerging issue for health systems policy and practice. Conclusion: Although key policies are identified, many of these policies are not implemented and are therefore not part of routine practice, leaving much space for improvement.

A systematic method for comparing multimorbidity in national surveys.

OBJECTIVE: Due to gaps in the literature, we developed a systematic method to assess multimorbidity using national surveys. The objectives of this study were thus to identify methods used to define and measure multimorbidity, to create a pre-defined list of disease conditions, to identify potential national surveys to include, to select disease conditions for each survey, and to analyse and compare the survey findings. RESULTS: We used the count method to define multimorbidity. We created a pre-defined list of disease conditions by examining international literature and using local data on the burden of disease. We assessed national surveys, reporting on more than one disease condition in people 15 years and older, for inclusion. For each survey, the prevalence of multimorbidity was calculated, the disease patterns among the multimorbid population were assessed using a latent class analysis and logistic regression was used to identify sociodemographic and behavioural factors associated with multimorbidity. The prevalence of multimorbidity varied for each survey from 2.7 to 20.7%. We used a systematic and transparent method to interrogate multimorbidity in national surveys. While the prevalence in each survey differs, they collectively indicate that multimorbidity increases in older age groups and tends to be higher among women.

Decolonising multimorbidity? research gaps in low and middle-income countries.

Multimorbidity is defined as the co-existence of multiple health conditions in one person. However, its use in research has been predominantly applied to non-communicable diseases, because research was conducted almost exclusively in developed countries. More recently, infectious diseases of long duration, such as human immunodeficiency virus (HIV), have also been included in the conceptualization of multimorbidity. While multimorbidity is a growing area of research globally; much less is known about the phenomenon in low and middle-income countries (LMICs) where disease burdens are heavily impacted by HIV. Health systems and services tend to be constrained in LMICs and information on disease patterns are important to better prioritize services. This commentary aims to describe the changing conceptualization of multimorbidity, the dearth of research into multimorbidity in LMICs and how the knowledge generated by research in LMICs can contribute to the global understanding of multimorbidity. LMICs can play a key role in the implementation of integration research.

One in five South Africans are multimorbid: An analysis of the 2016 demographic and health survey.

Multimorbidity is a global research priority, yet relatively little is known about it in low and middle income countries. South Africa has the largest burden of HIV worldwide but also has a growing burden of non-communicable diseases; potentially leading to uncommon disease combinations. Information about the prevalence of multimorbidity and factors associated with it can assist in healthcare planning and targeting groups of people for interventions. This study aimed to determine the prevalence of multimorbidity by age and sex, as well as factors associated with multimorbidity in people 15 years and older. This study analyses the nationally representative 2016 South African Demographic Health Survey. The sample included 10 336 people who participated in the Adult Health questionnaire and approximately 7 961 people who provided biomarkers. Multivariate logistic regression was used to measure the association of multimorbidity with age, sex, living in an urban or rural area, education level, wealth level, employment status, body mass index, current alcohol or tobacco use. All analyses were conducted using STATA 15. Multimorbidity was present in 20.7% (95% CI: 19.5%- 21.9%) of participants; in 14.8% (95% CI: 13.4% - 16.3%) of males and 26.2% (95% CI: 24.7-27.7%) of females. Multimorbidity increased with age; with the highest odds in the 55-64 years old age group (OR: 24.910, 95% CI: 14.901-41.641, p < 0.001) compared to those aged 15-24 years. The odds of multimorbidity was also higher in young females compared to young males (OR: 2.734, 95% CI: 1.50-4.99, p = 0.001). Possessing tertiary education (OR: 0.722, 95% CI: 0.537-0.97, p = 0.031), being employed (OR: 0.813, 95% CI: 0.675-0.979, p = 0.029) or currently using alcohol (OR: 0.815, 95% CI: 0.686-0.968, p = 0.02) was protective against multimorbidity. Multimorbidity is prevalent within the South African population, with females and older adults being most affected. However, multimorbidity is also observed in younger adults and most likely driven by the high prevalence of HIV and hypertension.

Multimorbidity Patterns in a National HIV Survey of South African Youth and Adults.

Introduction: Information pertaining to multimorbidity is frequently informed by studies from high income countries and it is unclear how these findings relate to low and middle income countries, where the burden of infectious disease is high. South Africa has a quadruple burden of disease which includes a high HIV prevalence and a growing burden of non-communicable diseases. This study aimed to analyse the prevalence and patterns (disease classes or clusters) of multimorbidity in South Africa. Methods: A secondary analysis of individuals over the age of 15 years who participated in the Fifth South African National HIV Prevalence, Incidence, Behavior and Communication Survey, 2017 (SABSSM 2017) was done. Six disease conditions were identified in the analysis (cancer, diabetes, heart disease, hypertension/high blood pressure, tuberculosis, and HIV). Chi-square tests were used to test for the differences in disease prevalence by sex. Common disease patterns were identified using a latent class analysis. Results: The sample included 27,896 participants, of which 1,837 had comorbidity or multimorbidity. When taking population-weighting into account, multimorbidity was present in 5.9% (95% CI: 5.4-6.4) of the population The prevalence of multimorbidity tended to be higher among females and increased with age, reaching 21.9% in the oldest age group (70+). The analyses identified seven distinct disease classes in the population. The largest class was "Diabetes and Hypertension" (36.3%), followed by "HIV and Hypertension" (31.0%), and "Heart disease and Hypertension" (14.5%). The four smaller classes were: "HIV, Diabetes, and Heart disease" (6.9%), "TB and HIV" (6.3%), "Hypertension, TB, and Cancer" (2.8%), and "All diseases except HIV" (2.2%). Conclusion: As the South African population continues to age, the prevalence of multimorbidity is likely to increase which will further impact the health care system. The prevalence of multimorbidity in the population was relatively low but reached up to 20% in the oldest age groups. The largest disease cluster was the combination of diabetes and hypertension; followed by HIV and hypertension. The gains in improving adherence to antiretrovirals amongst treatment-experienced people living with HIV, should be expanded to include compliance with lifestyle/behavioral modifications to blood pressure and glucose control, as well as adherence to anti-hypertension and anti-diabetic medication. There is an urgent need to improve the early diagnosis and treatment of disease in the South African population.

Aging with HIV: Increased Risk of HIV Comorbidities in Older Adults.

With improved access to antiretroviral treatment (ART), adults with HIV live longer to reach older age. The number of older adults living with HIV is increasing steadily, giving rise to a new population of interest in HIV research and for invigorated considerations in health service delivery and policy. We analysed the profile of comorbidities in older people (50 years and older) living with HIV in South Africa. We conducted a secondary analysis of all individuals over 15 years who tested HIV positive in the Fifth South African National HIV Prevalence, Incidence, Behaviour and Communication Survey, 2017. We conducted multivariate logistic regression to determine the factors associated with having HIV comorbidity using Stata 15.0 software. We entered 3755 people living with HIV into the analysis, of whom 18.3% (n = 688) were 50 years or older. Older adults had four times greater odds (OR = 4.7 (3.1-7.0)) of having an HIV comorbidity compared to younger adults. Being female (OR = 1.6 (1.1-2.4)) and living in an urban area (OR = 2.6 (1.8-3.7)) increased the odds of HIV comorbidity. Older adults with HIV require comprehensive health care to deal with multimorbidity, to maximise the benefits gained by advances in HIV therapies.

Multimorbidity patterns in South Africa: A latent class analysis.

Introduction: South Africa has the largest burden of HIV worldwide and has a growing burden of non-communicable diseases; the combination of which may lead to diseases clustering in ways that are not seen in other regions. This study sought to identify common disease classes and sociodemographic and lifestyle factors associated with each disease class. Methods: Data were analyzed from the South African Demographic and Health Survey 2016. A latent class analysis (LCA) was conducted using nine disease conditions. Sociodemographic and behavioral factors associated with each disease cluster were explored. All analysis was conducted in Stata 15 and the LCA Stata plugin was used to conduct the latent class and regression analysis. Results: Multimorbid participants were included (n = 2 368). Four disease classes were identified: (1) HIV, Hypertension and Anemia (comprising 39.4% of the multimorbid population), (2) Anemia and Hypertension (23.7%), (3) Cardiovascular-related (19.9%) and (4) Diabetes and Hypertension (17.0%). Age, sex, and lifestyle risk factors were associated with class membership. In terms of age, with older adults were less likely to belong to the first class (HIV, Hypertension and Anemia). Males were more likely to belong to Class 2 (Anemia and Hypertension) and Class 4 (Diabetes and Hypertension). In terms of alcohol consumption, those that consumed alcohol were less likely to belong to Class 4 (Diabetes and Hypertension). Current smokers were more likely to belong to Class 3 (Cardiovascular-related). People with a higher body mass index tended to belong to Class 3 (Cardiovascular-related) or the Class 4 (Diabetes and Hypertension). Conclusion: This study affirmed that integrated care is urgently needed, evidenced by the largest disease class being an overlap of chronic infectious diseases and non-communicable diseases. This study also highlighted the need for hypertension to be addressed. Tackling the risk factors associated with hypertension could avert an epidemic of multimorbidity.

Multimorbidity in South Africa: a systematic review of prevalence studies.

OBJECTIVES: To review prevalence studies of multimorbidity in South Africa to identify prevalence estimates, common disease clusters and factors associated with multimorbidity. DESIGN: Systematic review. SETTING: South Africa (general community and healthcare facilities). DATA SOURCES: Articles were retrieved from electronic databases (PubMed, Web of Science, Scopus, CINAHL, Science Direct and JSTOR). ELIGIBILITY CRITERIA: Studies addressing the prevalence of multimorbidity in South Africa were eligible for inclusion. A systematic search was done in various databases up to December 2020. A risk of bias assessment was conducted for each article using a modified checklist. STUDY SELECTION: Two researchers independently screened titles and abstracts; assessed the risk of bias of each study and extracted data. Included studies were described using a narrative synthesis. RESULTS: In total, 1407 titles were retrieved; of which 10 articles were included in the narrative synthesis. Six studies had a low risk of bias and three had a moderate risk of bias. One study was not assessed for risk of bias, because there was no criteria that apply to routine health information systems. Three of the included studies were population-based surveys, four were community-based cohorts and three cross-sectional studies of health facility data. The prevalence of multimorbidity was low to moderate (3%-23%) in studies that included younger people or had a wide range of selected age groups; and moderate to high (30%-87%) in studies of older adults. The common disease clusters were hypertension and diabetes, hypertension and HIV, and TB and HIV. CONCLUSION: All studies indicated that multimorbidity is a norm in South Africa, especially among older adults. Hypertension is the main driver of multimorbidity. Research on multimorbidity in South Africa needs to be strengthened with high-quality study designs. PROSPERO REGISTRATION NUMBER: CRD42020196895.

Can non-fatal burden estimates from the Global Burden of Disease study be used locally? An investigation using models of stroke and diabetes for South Africa.

Background: The Global Burden of Disease (GBD) approach estimates disease burden by combining fatal (years of life lost) and non-fatal burden prevalence-based years of life lived with disability (PYLDs) estimates. Although South Africa has data to estimate mortality, prevalence data to estimate non-fatal burden are sparse. PYLD estimates from the GBD study for South Africa can potentially be used. However, there is a divergence in mortality estimates for South Africa between the second South African National Burden of Disease (SANBD2) and 2013 GBD studies. Objective: We investigated the feasibility of utilising GBD PYLD estimates for stroke and diabetes by exploring different disease modelling scenarios. Method: DisMod II software-generated South African stroke and diabetes PYLDs for 2010 from models using local epidemiological parameters and demographic data for people 20-79 years old. We investigated the impact on PYLD estimates of 1) differences in the cause-of-death envelope, 2) differences in the cause-specific mortality estimates (increase/decrease by 15% for stroke and 30% for diabetes), and 3) difference using local disease parameters compared to other country or region parameters. Differences were expressed as ratios, average ratios and ratio ranges. Results: Using the GBD cause-of-death envelope (16% more deaths than SANBD2) and holding other parameters constant yielded age-specific ratios of PYLDs for stroke and diabetes ranging between 0.89 and 1.07 (average 0.98) for males. Similar results were observed for females. A 15% change in age-specific stroke mortality showed little difference in the ratio comparison of PYLDs (range 0.98-1.02) while a 30% change in age-specific diabetes mortality resulted in a ratio range of 0.96-1.07 for PYLDs depending on age. Conclusion: This study showed that GBD non-fatal burden estimates (PYLDs) can be used for stroke and diabetes non-fatal burden in the SANBD2 study.