Evaluation of the Association Between Health State Utilities and Obesity in Sub-Saharan Africa: Evidence From World Health Organization Study on Global AGEing and Adult Health Wave 2

The Hidden Toll of Psychological Distress in Australian Adults and Its Impact on Health-Related Quality of Life Measured as Health State Utilities

BACKGROUND

Psychological distress (PD) is a major health problem that affects all aspects of health-related quality of life including physical, mental and social health, leading to a substantial human and economic burden. Studies have revealed a concerning rise in the prevalence of PD and various mental health conditions among Australians, particularly in female individuals. There is a scarcity of studies that estimate health state utilities (HSUs), which reflect the overall health-related quality of life in individuals with PD. No such studies have been conducted in Australia thus far.

OBJECTIVE

We aimed to evaluate the age-specific, sex-specific and PD category-specific HSUs (disutilities) in Australian adults with PD to inform healthcare decision making in the management of PD.

METHODS

Data on age, sex, SF-36/SF6D responses, Kessler psychological distress (K10) scale scores and other characteristics of N = 15,139 participants (n = 8149 female individuals) aged >15 years were derived from the latest wave (21) of the nationally representative Household, Income and Labor Dynamics in Australia survey. Participants were grouped into the severity categories of no (K10 score: 10-19), mild (K10: 20-24), moderate (K10: 25-29) and severe PD (K10: 30-50). Both crude and adjusted HSUs were calculated from participants' SF-36 profiles, considering potential confounders such as smoking, marital status, remoteness, education and income levels. The calculations were based on the SF-6D algorithm and aligned with Australian population norms. Additionally, the HSUs were stratified by age, sex and PD categories. Disutilities of PD, representing the mean difference between HSUs of people with PD and those without, were also calculated for each group.

RESULTS

The average age of individuals was 46.130 years (46% male), and 31% experienced PD in the last 4 weeks. Overall, individuals with PD had significantly lower mean HSUs than those likely to be no PD, 0.637 (95% confidence interval [CI] 0.636, 0.640) vs 0.776 (95% CI 0.775, 0.777) i.e. disutility: -0.139 [95% CI -0.139, -0.138]). Mean disutilities of -0.108 (95% CI -0.110, -0.104), -0.140 (95% CI -0.142, -0.138), and -0.188 (95% CI -0.190, -0.187) were observed for mild PD, moderate PD and severe PD, respectively. Disutilities of PD also differed by age and sex groups. For instance, female individuals had up to 0.049 points lower mean HSUs than male individuals across the three classifications of PD. There was a clear decline in health-related quality of life with increasing age, demonstrated by lower mean HSUs in older population age groups, that ranged from 0.818 (95% CI 0.817, 0.818) for the 15-24 years age group with no PD to 0.496 (95% CI 0.491, 0.500) for the 65+ years age group with severe PD). Across all ages and genders, respondents were more likely to report issues in certain dimensions, notably vitality, and these responses did not uniformly align with ageing.

CONCLUSIONS

The burden of PD in Australia is substantial, with a significant impact on female individuals and older individuals. Implementing age-specific and sex-specific healthcare interventions to address PD among Australian adults may greatly alleviate this burden. The PD state-specific HSUs calculated in our study can serve as valuable inputs for future health economic evaluations of PD in Australia and similar populations.

Health-related quality of life among adults living with chronic non-communicable diseases in the Ho Municipality of Ghana: a health facility-based cross-sectional study

BACKGROUND

Morbidity and mortality rates from chronic non-communicable diseases (CNCDs) are increasing globally. In Ghana, CNCDs account for 43% of all deaths. We examined the Health-Related Quality of Life (HRQoL) and associated factors among adults living with CNCDs in the Ho Municipality.

METHODS

This was a health facility-based descriptive cross-sectional study among 432 adults living with cancer, diabetes, chronic kidney disease (CKD), stroke, and hypertension in the Ho Municipality of Ghana. The study adopted the EQ-5D-5L instrument and the Ugandan value set to compute respondents' HRQoL index. Quantile regression models were used in analysing the data with STATA v17.0 at 95% Confidence Intervals, and statistical significance set at p < 0.05.

RESULTS

63.7% of our respondents reported having a problem across the five dimensions of the EQ-5D-5L. The most problems were reported in the dimensions "Anxiety/Depression" (94.4%) and "Pain/Discomfort" (91.4%). Divorced/separated respondents (aOR=-0.52, 95% CI=-0.71, -0.33) and those living with comorbidities (aOR=-0.95, 95% CI=-0.15, -0.04,) were less likely to report high index for HRQoL. However, respondents diagnosed with CKD (aOR = 0.26, 95% CI = 0.10, 0.42), diabetes (aOR = 0.28, 95% CI = 0.11, 0.45), hypertension (aOR = 0.35, 95% CI = 0.19, 0.50) and stroke (aOR = 0.26, 95% CI = 0.11, 0.40) were more likely to report higher index than those diagnosed with cancer.

CONCLUSION

Our study revealed elevated proportions of reported problems in the "Anxiety/Depression" and "Pain/Discomfort" dimensions, indicating noteworthy concerns in these areas of HRQoL. The prevalent issues reported across HRQoL dimensions are cause for concern, posing potential exacerbation of health conditions. We advocate for collaborative efforts from the Ministry of Health, Ghana Health Service, and relevant stakeholders to scrutinize and implement interventions targeting social and psychological factors. These efforts should specifically address contributors to diminished health-related quality of life, particularly among less educated, divorced, and comorbid individuals.

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.

Loss of Health State Utilities Attributable to Obesity

OBJECTIVES

This study aimed to quantify the association between the loss of health state utilities (LHSU) and obesity, considering different obesity categories. This is relevant to interventions economic evaluations and for public policy decision planning.

METHODS

Using data from the Chilean National Health Survey, this study uses linear regression models and counterfactual scenarios to calculate the prevalent burden, population averages, and total sum of LHSU attributable to obesity for the Chilean national level on people older than 15 years, year 2017. Adjustments for socioeconomic status and associated noncommunicable diseases (NCDs) are considered. Calculating the LHSU using these methods enables the approximation of loss of prevalent quality-adjusted life-years (QALYs).

RESULTS

The raw obesity LHSU burden was 9.1% (95% uncertainty interval [UI] 5.1-13). When adjustment is considered, the LHSU attributable to obesity reaches 4.6% (95% UI 0.6-8.5) being responsible for 121 045 prevalent QALYs. Socioeconomic status adjusted analysis of higher body mass index (BMI, in kg/m²) categories of obesity shows a dose-response effect for LHSU, being the BMI ≥ 40 category with the highest population average of attributable LHSU (10.1; 95% UI 5.5-14.5, scale 0 [full health] to 100 [dead]). Burden for BMI ≥ 35 categories showed the biggest change after NCD adjustment.

CONCLUSIONS

Obesity carries a significant burden of QALY loss. Policy decision-making addressing obesity should focus specially on the BMI ≥ 40 group. NCD comorbidity should be considered for policies addressing the BMI ≥ 35 group.

Monitoring Information-Seeking Patterns and Obesity Prevalence in Africa With Internet Search Data: Observational Study

BACKGROUND

The prevalence of chronic conditions such as obesity, hypertension, and diabetes is increasing in African countries. Many chronic diseases have been linked to risk factors such as poor diet and physical inactivity. Data for these behavioral risk factors are usually obtained from surveys, which can be delayed by years. Behavioral data from digital sources, including social media and search engines, could be used for timely monitoring of behavioral risk factors.

OBJECTIVE

The objective of our study was to propose the use of digital data from internet sources for monitoring changes in behavioral risk factors in Africa.

METHODS

We obtained the adjusted volume of search queries submitted to Google for 108 terms related to diet, exercise, and disease from 2010 to 2016. We also obtained the obesity and overweight prevalence for 52 African countries from the World Health Organization (WHO) for the same period. Machine learning algorithms (ie, random forest, support vector machine, Bayes generalized linear model, gradient boosting, and an ensemble of the individual methods) were used to identify search terms and patterns that correlate with changes in obesity and overweight prevalence across Africa. Out-of-sample predictions were used to assess and validate the model performance.

RESULTS

The study included 52 African countries. In 2016, the WHO reported an overweight prevalence ranging from 20.9% (95% credible interval [CI] 17.1%-25.0%) to 66.8% (95% CI 62.4%-71.0%) and an obesity prevalence ranging from 4.5% (95% CI 2.9%-6.5%) to 32.5% (95% CI 27.2%-38.1%) in Africa. The highest obesity and overweight prevalence were noted in the northern and southern regions. Google searches for diet-, exercise-, and obesity-related terms explained 97.3% (root-mean-square error [RMSE] 1.15) of the variation in obesity prevalence across all 52 countries. Similarly, the search data explained 96.6% (RMSE 2.26) of the variation in the overweight prevalence. The search terms yoga, exercise, and gym were most correlated with changes in obesity and overweight prevalence in countries with the highest prevalence.

CONCLUSIONS

Information-seeking patterns for diet- and exercise-related terms could indicate changes in attitudes toward and engagement in risk factors or healthy behaviors. These trends could capture population changes in risk factor prevalence, inform digital and physical interventions, and supplement official data from surveys.

Impact of overweight and obesity on life expectancy, quality-adjusted life years and lifetime costs in the adult population of Ghana

INTRODUCTION

Prior studies have revealed the increasing prevalence of obesity and its associated health effects among ageing adults in resource poor countries. However, no study has examined the long-term and economic impact of overweight and obesity in sub-Saharan Africa. Therefore, we quantified the long-term impact of overweight and obesity on life expectancy (LE), quality-adjusted life years (QALYs) and total direct healthcare costs.

METHODS

A Markov simulation model projected health and economic outcomes associated with three categories of body mass index (BMI): healthy weight (18.5≤BMI <25.0); overweight (25.0≤BMI < 30.0) and obese (BMI ≥30.0 kg/m2) in simulated adult cohorts over a 50-year time horizon from age fifty. Costs were estimated from government and patient perspectives, discounted 3% annually and reported in 2017 US$. Mortality rates from Ghanaian lifetables were adjusted by BMI-specific all-cause mortality HRs. Published input data were used from the 2014/2015 Ghana WHO Study on global AGEing and adult health data. Internal and external validity were assessed.

RESULTS

From age 50 years, average (95% CI) remaining LE for females were 25.6 (95% CI: 25.4 to 25.8), 23.5 (95% CI: 23.3 to 23.7) and 21.3 (95% CI: 19.6 to 21.8) for healthy weight, overweight and obesity, respectively. In males, remaining LE were healthy weight (23.0; 95% CI: 22.8 to 23.2), overweight (20.7; 95% CI: 20.5 to 20.9) and obesity (17.6; 95% CI: 17.5 to 17.8). In females, QALYs for healthy weight were 23.0 (95% CI: 22.8 to 23.2), overweight, 21.0 (95% CI: 20.8 to 21.2) and obesity, 19.0 (95% CI: 18.8 to 19.7). The discounted total costs per female were US$619 (95% CI: 616 to 622), US$1298 (95% CI: 1290 to 1306) and US$2057 (95% CI: 2043 to 2071) for healthy weight, overweight and obesity, respectively. QALYs and costs were lower in males.

CONCLUSION

Overweight and obesity have substantial health and economic impacts, hence the urgent need for cost-effective preventive strategies in the Ghanaian population.