QALY losses for chronic diseases and its social distribution in the general population: results from the Belgian Health Interview Survey

Symptom-based drug prediction of lifestyle-related chronic diseases using unsupervised machine learning techniques

BACKGROUND AND OBJECTIVES

Lifestyle-related diseases (LSDs) impose a substantial economic burden on patients and health care services. LSDs are chronic in nature and can directly affect the heart and lungs. Therapeutic interventions only based on symptoms can be crucial for prompt treatment initiation in LSDs, as symptoms are the first information available to clinicians. So, this work aims to apply unsupervised machine learning (ML) techniques for developing models to predict drugs from symptoms for LSDs, with a specific focus on pulmonary and heart diseases.

METHODS

The drug-disease and disease-symptom associations of 143 LSDs, 1271 drugs, and 305 symptoms were used to compute direct associations between drugs and symptoms. ML models with four different algorithms - K-Means, Bisecting K-Means, Mean Shift, and Balanced Iterative Reducing and Clustering using Hierarchies (BIRCH) - were developed to cluster the drugs using symptoms as features. The optimal model was saved in a server for the development of a web application. A web application was developed to perform the prediction based on the optimal model.

RESULTS

The Bisecting K-means model showed the best performance with a silhouette coefficient of 0.647 and generated 138 drug clusters. The drugs within the optimal clusters showed good similarity based on i) gene ontology annotations of the gene targets, ii) chemical ontology annotations, and iii) maximum common substructure of the drugs. In the web application, the model also provides a confidence score for each predicted drug while predicting from a new set of input symptoms.

CONCLUSION

In summary, direct associations between drugs and symptoms were computed, and those were used to develop a symptom-based drug prediction tool for LSDs with unsupervised ML models. The ML-based prediction can provide a second opinion to clinicians to aid their decision-making for early treatment of LSD patients. The web application (URL - http://bicresources.jcbose.ac.in/ssaha4/sdldpred) can provide a simple interface for all end-users to perform the ML-based prediction.

The Impacts of COVID-19 Restrictions on Quality Adjusted Life Years (QALY): Heterogeneous effects and post-pandemic recovery

OBJECTIVES

Estimate the effects of non-pharmacological interventions used to prevent the spread of COVID-19 on the quality of life, measured by Quality Adjusted Life Years (QALYs).

METHODS

A survey on 1,506 heads of households from Chile in May of 2022. Respondents were asked basic socioeconomic questions and a version of the EQ-5D-5L questionnaire that was used to calculate the evolution of HRQoLs. Comparisons of means in HRQoLs measures before the pandemic, at the peak of restrictions, and at the moment of the survey were performed.

RESULTS

The average HRQoL of the population before the pandemic was similar to other countries in the region (0.96). At the peak of restrictions (June 2020-August 2021), the average HRQoL decreased to 0.87 (-9%). At the time of survey (May 2022), the average HQRoL was 0.91 (4%). Assuming the recovery trend continued, pre-pandemic HRQoLs would be reached by January 2024. Altogether, the pandemic would have reduced QALYs by 0.2 in average. The effect is larger and the recovery slower among women. Our estimates imply that the restrictions to manage the pandemic came at a cost of 2.4 months of life years for the average (surviving) person, 1.8 months for men and 3.4 for women.

CONCLUSIONS

Our results suggest that COVID-19 had worse effects on life quality than previously thought. These effects are more significant among women than among men. Efforts to improve life quality and speed up its recovery could have large positive consequences for the population.

Exploring Interactions Between Sex, Pain Characteristics, Disability, and Quality of Life in People With Chronic Spinal Pain: A Structural Equation Model

In people with nonspecific chronic spinal pain (nCSP), disability and quality of life are associated with clinical, cognitive, psychophysical, and demographic variables. However, evidence regarding the interactions between these variables is only limited to this population. Therefore, this study aims to explore path models explaining the multivariate contributions of such variables to disability and quality of life in people with nCSP. This secondary analysis uses baseline data from a randomized controlled trial including 120 participants with nCSP. Structural equation modeling was used to explore path models for the Pain Disability Index (PDI), the Short Form 36-item physical (SF-36 PC), and mental (SF-36 MC) component scores. All models included sex, pain catastrophizing, kinesiophobia, hypervigilance, and pain intensity. Additionally, the PDI and SF-36 PC models included pressure pain thresholds (PPTs) at the dominant pain site (ie, neck or low back). Significant associations were found between sex, pain cognitions, pain intensity, and PPTs. Only pain catastrophizing significantly directly influenced the PDI (P ≤ .001) and SF-36 MC (P = .014), while the direct effects on the SF-36 PC from kinesiophobia (P = .008) and pain intensity (P = .006) were also significant. However, only the combined effect of all pain cognitions on the SF-36 PC was mediated by pain intensity (P = .019). Our findings indicate that patients' pain-related cognitions have an adverse effect on their physical health-related quality of life via a negative influence on their pain intensity in people with nCSP. PERSPECTIVE: This secondary analysis details a network analysis confirming significant interactions between sex, pain cognitions, pain intensity, and PPTs in relation to disability and health-related quality of life in people with chronic spinal pain. Moreover, its findings establish the importance of pain cognitions and pain intensity for these outcomes. TRIALS REGISTRATION: Clinicaltrials.gov (NCT02098005).

Productivity-adjusted life-years and correlates of uncontrolled hypertension at two health facilities in Zambia

BACKGROUND

Hypertension has in the recent past surfaced as one of the conditions that has a significant impact on workforce productivity in emerging economies. Zambia is no different and has in the recent past recorded increasing cases. Despite the impact of hypertension being of great importance in regards to productivity, we have scarcity of data and studies on hypertension-related Productivity-Adjusted Life-Years (PALYs) in Zambia and Africa at large. This study assessed the impact of hypertension on PALYs lost and socioeconomic factors associated with nonadherence to antihypertensive medication (NATAM).

METHODS

This was a cross-sectional study of 198 participants from Livingstone University Teaching Hospital and Maramba Clinic situated in Livingstone, Zambia. Structured questionnaires were used to collect data. Productivity index multiplied by years lived was used to calculate PALYs and descriptive statistics were used to summarize sociodemographic, clinical and economic variables. Multivariable logistic regression was used to determine factors associated with NATAM.

RESULTS

The participants had a median age (interquartile range (IQR)) of 49 years (41, 59) and 60.1% (n = 119) were females while 39.9% (n = 79) were male. Our estimated PALYs lost per person due to hypertension were 0.2 (IQR 0.0, 2.7). Cumulative PALYs value lost due to the burden of hypertension was estimated to be at $871,239.58 in gross domestic product (GDP). The prevalence of NATAM was 48% (n = 95). The factors that were significantly associated with NATAM were age (odds ratio (OR) 0.94; 95% confidence interval (CI) 0.90, 0.98), female sex (OR 2.52; 95%CI 1.18, 5.40), self-employment (OR 2.57; 95%CI 1.02, 6.45) and absenteeism from work (OR 3.60; 95%CI 1.16, 11.22).

CONCLUSIONS

Findings in our study highlight a high economic loss of PALYs due to hypertension with a potential to impact GDP negatively. We also found that NATAM reduced productivity and income among individuals of working age further impacting PALYs lost due to hypertension. The factors associated with NATAM were age, sex, employment status and absenteeism from work. This study underscores the need for interventions targeting young people, females, self-employed individuals, and absentees at work to improve adherence to antihypertensive drugs in order to reduce PALYs lost due to hypertension.

Double Stigma in Portuguese Lesbian, Gay, and Bisexual Older Adults: A Study of Health Status

The double stigma-sexual stigma and ageism-can affect the health status of lesbian, gay, and bisexual (LGB) older adults, but little is known about this topic in Portugal and internationally. The aim of this study was to assess the health status and the prevalence of chronic diseases of Portuguese LGB older adults, as well as to assess the relationship between double stigma and health status. A sample of 280 Portuguese LGB older adults completed a chronic disease questionnaire, the Homosexuality-Related Stigma Scale, the Ambivalent Ageism Scale, and the SF-12 Short Form Health Survey. A structural equation model was performed using the double stigma variables as independent variables and health status as outcome. The mental health status of Portuguese LGB older adults was lower when compared to people from studies from more than 10 countries. Higher sexual self-stigma, sexual enacted stigma related to health care, and benevolent ageism significantly explained the worse general health status. The double stigma significantly affects the health status of these older adults in a profile of internalization of sexual stigma and benevolent age discrimination, and not necessarily by a hostile/aggressive profile. More research is needed on the double stigma.