Cardiometabolic multimorbidity and associated patterns of healthcare utilization and quality of life: Results from the Study on Global AGEing and Adult Health (SAGE) Wave 2 in Ghana

Understanding the patterns of multimorbidity, defined as the co-occurrence of more than one chronic condition, is important for planning health system capacity and response. This study assessed the association of different cardiometabolic multimorbidity combinations with healthcare utilization and quality of life (QoL). Data were from the World Health Organization (WHO) study on global AGEing and adult health Wave 2 (2015) conducted in Ghana. We analysed the clustering of cardiometabolic diseases including angina, stroke, type 2 diabetes, and hypertension with unrelated conditions such as asthma, chronic lung disease, arthritis, cataract and depression. The clusters of adults with cardiometabolic multimorbidity were identified using latent class analysis and agglomerative hierarchical clustering algorithms. We used negative binomial regression to determine the association of multimorbidity combinations with outpatient visits. The association of multimorbidity clusters with hospitalization and QoL were assessed using multivariable logistic and linear regressions. Data from 3,128 adults aged over 50 years were analysed. We identified four distinct classes of multimorbidity: relatively "healthy class" with no multimorbidity (47.9%): abdominal obesity only (40.7%): cardiometabolic and arthritis class comprising participants with hypertension, type 2 diabetes, stroke, abdominal and general obesity, arthritis and cataract (5.7%); and cardiopulmonary and depression class including participants with angina, chronic lung disease, asthma, and depression (5.7%). Relative to the class with no multimorbidity, the cardiopulmonary and depression class was associated with a higher frequency of outpatient visits [β = 0.3; 95% CI 0.1 to 0.6] and higher odds of hospitalization [aOR = 1.9; 95% CI 1.0 to 3.7]. However, cardiometabolic and arthritis class was associated with a higher frequency of outpatient visits [β = 0.8; 95% CI 0.3 to 1.2] and not hospitalization [aOR = 1.1; 95% CI 0.5 to 2.9]. The mean QoL scores was lowest among participants in the cardiopulmonary and depression class [β = -4.8; 95% CI -7.3 to -2.3] followed by the cardiometabolic and arthritis class [β = -3.9; 95% CI -6.4 to -1.4]. Our findings show that cardiometabolic multimorbidity among older persons in Ghana cluster together in distinct patterns that differ in healthcare utilization. This evidence may be used in healthcare planning to optimize treatment and care.

Assessing the Readiness to Provide Integrated Management of Cardiovascular Diseases and Type 2 Diabetes in Kenya: Results from a National Survey

Introduction

Integrated chronic disease management is the desired core function of a responsive healthcare system. However, many challenges surround its implementation in Sub-Saharan Africa. The current study assessed the readiness of healthcare facilities to provide integrated management of cardiovascular diseases (CVDs) and type 2 diabetes in Kenya.

Methods

We used data from a nationally representative cross-sectional survey of 258 public and private health facilities conducted in Kenya between 2019 and 2020. Data were collected using a standardised facility assessment questionnaire and observation checklists modified from the World Health Organization Package of Essential Non-communicable Diseases. The primary outcome was the readiness to provide integrated care for CVDs and diabetes-defined as the mean availability of tracer items comprising trained staff and clinical guidelines, diagnostic equipment, essential medicines, diagnosis, treatment and follow-up. A cut-off threshold of ≥70% was used to classify facilities as 'ready'. Gardner-Altman plots and modified Poisson regression were used to examine the facility characteristics associated with care integration readiness.

Results

Of the surveyed facilities, only a quarter (24.1%) were ready to provide integrated care for CVDs and type 2 diabetes. Care integration readiness was lower in public versus private facilities [aPR = 0.6; 95% CI 0.4 to 0.9], and primary healthcare facilities were less likely to be ready compared to hospitals [aPR = 0.2; 95% CI 0.1 to 0.4]. Facilities located in Central Kenya [aPR = 0.3; 95% CI 0.1 to 0.9], and the Rift Valley region [aPR = 0.4; 95% CI 0.1 to 0.9], were less likely to be ready compared to the capital Nairobi.

Conclusions

There are gaps in the readiness of healthcare facilities particularly primary healthcare facilities in Kenya to provide integrated care services for CVDs and diabetes. Our findings inform the review of current supply-side interventions for integrated management of CVDs and type 2 diabetes, especially in lower-level public health facilities in Kenya.

Effect of Patient Support Groups for Hypertension on Blood Pressure among Patients with and Without Multimorbidity: Findings from a Cohort Study of Patients on a Home-Based Self-Management Program in Kenya

Introduction

Patient support group interventions have been widely used to manage chronic diseases in Kenya. However, the potential benefits of these groups on patient health outcomes, and how this is influenced by multimorbidity, have not been rigorously evaluated.

Objective

We assessed the effect of a patient support group intervention on blood pressure (BP) management and the potential moderating effect of multimorbidity among low- and middle-income patients with hypertension in Kenya.

Methods

We analysed data from a non-randomized, quasi-experimental study of 410 patients with hypertension on a home-based self-management program conducted from September 2019 to September 2020. The program included the formation and participation in patient support groups. Using a modified STEPS questionnaire, data were collected on BP, anthropometry and other measurements at enrolment and after 12 months of follow-up. Multimorbidity was defined as the simultaneous presence of hypertension and at least one or more related conditions with similar pathophysiology (concordant multimorbidity) or unrelated chronic conditions (discordant multimorbidity). Propensity score (PS) weighting was used to adjust for baseline differences among 243 patients who participated in the support groups and 167 who did not. We estimated the effects of patient support groups and moderating effects of multimorbidity on BP management using multivariable ordinary linear regression weighted by PS.

Findings

Participation in support groups significantly reduced systolic BP by 5.4 mmHg compared to non-participation in the groups [β = -5.4; 95% CI -1.9 to -8.8]. However, among participants in the support group intervention, the mean systolic BP at follow-up assessment for those with concordant multimorbidity was 8.8 mmHg higher than those with no multimorbidity [β = 8.8; 95% CI 0.8 to 16.8].

Conclusion

Although patient support groups are potentially important adjuncts to home-based self-care, multimorbidity attenuates their effectiveness. There is a need to tailor patient support group interventions to match the needs of the people living with multimorbidity in low- and middle-income settings in Kenya.

Multimorbidity of cardiometabolic diseases: a cross-sectional study of patterns, clusters and associated risk factors in sub-Saharan Africa

OBJECTIVE

To determine the patterns of cardiometabolic multimorbidity and associated risk factors in sub-Saharan Africa (SSA).

DESIGN

We used data from the WHO STEPwise approach to non-communicable disease risk factor surveillance cross-sectional surveys conducted between 2014 and 2017.

PARTICIPANTS

The participants comprised 39, 658 respondents aged 15-69 years randomly selected from nine SSA countries using a multistage stratified sampling design.

PRIMARY OUTCOME MEASURE

Using latent class analysis and agglomerative hierarchical clustering algorithms, we analysed the clustering of cardiometabolic diseases (CMDs) including high blood sugar, hypercholesterolaemia, hypertension and cardiovascular diseases (CVDs) such as heart attack, angina and stroke. Clusters of lifestyle risk factors: harmful salt intake, physical inactivity, obesity, tobacco and alcohol use were also computed. Prevalence ratios (PR) from modified Poisson regression were used to assess the association of cardiometabolic multimorbidity with sociodemographic and lifestyle risk factors.

RESULTS

Two distinct classes of CMDs were identified: relatively healthy group with minimal CMDs (95.2%) and cardiometabolic multimorbidity class comprising participants with high blood sugar, hypercholesterolaemia, hypertension and CVDs (4.8%). The clusters of lifestyle risk factors included alcohol, tobacco and harmful salt consumption (27.0%), and physical inactivity and obesity (5.8%). The cardiometabolic multimorbidity cluster exhibited unique sociodemographic and lifestyle risk profiles. Being female (PR=1.7, 95% CI (1.5 to 2.0), middle-aged (35-54 years) (3.9 (95% CI 3.2 to 4.8)), compared with age 15-34 years, employed (1.2 (95% CI 1.1 to 1.4)), having tertiary education (2.5 (95% CI 2.0 to 3.3)), vs no formal education and clustering of physical inactivity and obesity (2.4 (95% CI 2.0 to 2.8)) were associated with a higher likelihood of cardiometabolic multimorbidity.

CONCLUSION

Our findings show that cardiometabolic multimorbidity and lifestyle risk factors cluster in distinct patterns with a disproportionate burden among women, middle-aged, persons in high socioeconomic positions, and those with sedentary lifestyles and obesity. These results provide insights for health systems response in SSA to focus on these clusters as potential targets for integrated care.

Theory driven analysis of social class and health outcomes using UK nationally representative longitudinal data

BACKGROUND

Social class is frequently used as a means of ranking the population to expose inequalities in health, but less often as a means of understanding the social processes of causation. We explored how effectively different social class mechanisms could be measured by longitudinal cohort data and whether those measures were able to explain health outcomes.

METHODS

Using a theoretically informed approach, we sought to map variables within the National Child Development Study (NCDS) to five different social class mechanisms: social background and early life circumstances; habitus and distinction; exploitation and domination; location within market relations; and power relations. Associations between the SF-36 physical, emotional and general health outcomes at age 50 years and the social class measures within NCDS were then assessed through separate multiple linear regression models. R2 values were used to quantify the proportion of variance in outcomes explained by the independent variables.

RESULTS

We were able to map the NCDS variables to the each of the social class mechanisms except 'Power relations'. However, the success of the mapping varied across mechanisms. Furthermore, although relevant associations between exposures and outcomes were observed, the mapped NCDS variables explained little of the variation in health outcomes: for example, for physical functioning, the R2 values ranged from 0.04 to 0.10 across the four mechanisms we could map.

CONCLUSIONS

This study has demonstrated both the potential and the limitations of available cohort studies in measuring aspects of social class theory. The relatively small amount of variation explained in the outcome variables in this study suggests that these are imperfect measures of the different social class mechanisms. However, the study lays an important foundation for further research to understand the complex interactions, at various life stages, between different aspects of social class and subsequent health outcomes.

Theorising social class and its application to the study of health inequalities

The literature on health inequalities often uses measures of socio-economic position pragmatically to rank the population to describe inequalities in health rather than to understand social and economic relationships between groups. Theoretical considerations about the meaning of different measures, the social processes they describe, and how these might link to health are often limited. This paper builds upon Wright's synthesis of social class theories to propose a new integrated model for understanding social class as applied to health. This model incorporates several social class mechanisms: social background and early years' circumstances; Bourdieu's habitus and distinction; social closure and opportunity hoarding; Marxist conflict over production (domination and exploitation); and Weberian conflict over distribution. The importance of discrimination and prejudice in determining the opportunities for groups is also explicitly recognised, as is the relationship with health behaviours. In linking the different social class processes we have created an integrated theory of how and why social class causes inequalities in health. Further work is required to test this approach, to promote greater understanding of researchers of the social processes underlying different measures, and to understand how better and more comprehensive data on the range of social class processes these might be collected in the future.