Methods to assess the contribution of diseases to disability using cross-sectional studies: comparison of different versions of the attributable fraction and the attribution method

Metrological performances of the global chronic morbidity indicator of the Minimum European Health Module and implications for chronic disease prevalence and socioeconomic gradient estimations

BACKGROUND

Although the global chronic morbidity indicator (GCMI) of the Minimum European Health Module (MEHM) was not specifically designed to monitor chronic disease in the population, it is increasingly used for this purpose in Europe and elsewhere. However, its metrological characteristics have seldom been examined, with various sensitivity issues being raised. The present study investigated the metrological performances of the GCMI and analyzed its implications in terms of prevalence and demographic and socioeconomic gradients of chronic conditions in the population.

METHODS

We used data from two large French nationwide representative surveys with cross-sectional and longitudinal data conducted between 2010 and 2021. The surveys used MEHM and collected data on numerous chronic conditions and socioeconomic indicators. Criterion and predictive validity of the GCMI regarding chronic conditions and the resultant socioeconomic gradients were compared with indicators based on reports of individual chronic conditions.

RESULTS

GCMI sensitivity to capture chronic conditions varied from <20 to 80% depending on the chronic condition. Number of chronic conditions, gender, age and education were also associated with GCMI endorsement. However, the GCMI was predictive of mortality and activity limitations independently of individual conditions.

CONCLUSION

The varying lack of sensitivity depending on the chronic condition and the respondent's sociodemographic status may bias estimates of demographic and socioeconomic gradients compared with indicators based on reports of individual chronic conditions. Differences between GCMI and list-based approaches should be more cautiously considered when monitoring chronic conditions in the population. These approaches should be viewed as complementary rather than contradictory or interchangeable.

The potential impact fraction of population weight reduction scenarios on non-communicable diseases in Belgium: application of the g-computation approach

BACKGROUND

Overweight is a major risk factor for non-communicable diseases (NCDs) in Europe, affecting almost 60% of all adults. Tackling obesity is therefore a key long-term health challenge and is vital to reduce premature mortality from NCDs. Methodological challenges remain however, to provide actionable evidence on the potential health benefits of population weight reduction interventions. This study aims to use a g-computation approach to assess the impact of hypothetical weight reduction scenarios on NCDs in Belgium in a multi-exposure context.

METHODS

Belgian health interview survey data (2008/2013/2018, n = 27 536) were linked to environmental data at the residential address. A g-computation approach was used to evaluate the potential impact fraction (PIF) of population weight reduction scenarios on four NCDs: diabetes, hypertension, cardiovascular disease (CVD), and musculoskeletal (MSK) disease. Four scenarios were considered: 1) a distribution shift where, for each individual with overweight, a counterfactual weight was drawn from the distribution of individuals with a "normal" BMI 2) a one-unit reduction of the BMI of individuals with overweight, 3) a modification of the BMI of individuals with overweight based on a weight loss of 10%, 4) a reduction of the waist circumference (WC) to half of the height among all people with a WC:height ratio greater than 0.5. Regression models were adjusted for socio-demographic, lifestyle, and environmental factors.

RESULTS

The first scenario resulted in preventing a proportion of cases ranging from 32.3% for diabetes to 6% for MSK diseases. The second scenario prevented a proportion of cases ranging from 4.5% for diabetes to 0.8% for MSK diseases. The third scenario prevented a proportion of cases, ranging from 13.6% for diabetes to 2.4% for MSK diseases and the fourth scenario prevented a proportion of cases ranging from 36.4% for diabetes to 7.1% for MSK diseases.

CONCLUSION

Implementing weight reduction scenarios among individuals with excess weight could lead to a substantial and statistically significant decrease in the prevalence of diabetes, hypertension, cardiovascular disease (CVD), and musculoskeletal (MSK) diseases in Belgium. The g-computation approach to assess PIF of interventions represents a straightforward approach for drawing causal inferences from observational data while providing useful information for policy makers.

Disease Attribution to Multiple Exposures Using Aggregate Data

BACKGROUND

Identifying which exposures cause disease and quantifying their impacts is essential in promoting and monitoring public health. When multiple exposures are involved, measuring individual contributions becomes challenging.

METHODS

The authors propose a disease attribution method based on aggregate data or summary statistics of individual-level data, possibly from multiple data sources.

RESULTS

Using the proposed method, the burden of disease is apportioned to the independent and interaction effects of each of its major risk factors and all the other factors as a whole. This scheme guarantees that 100% is the total share of the burden.

CONCLUSION

The calculation is simple and straightforward; therefore, it is recommended for use in studies on disease burden.

Estimating risk factor attributable burden - challenges and potential solutions when using the comparative risk assessment methodology

Background

Burden of disease analyses quantify population health and provide comprehensive overviews of the health status of countries or specific population groups. The comparative risk assessment (CRA) methodology is commonly used to estimate the share of the burden attributable to risk factors. The aim of this paper is to identify and address some selected important challenges associated with CRA, illustrated by examples, and to discuss ways to handle them. Further, the main challenges are addressed and finally, similarities and differences between CRA and health impact assessments (HIA) are discussed, as these concepts are sometimes referred to synonymously but have distinctly different applications.

Results

CRAs are very data demanding. One key element is the exposure-response relationship described e.g. by a mathematical function. Combining estimates to arrive at coherent functions is challenging due to the large variability in risk exposure definitions and data quality. Also, the uncertainty attached to this data is difficult to account for. Another key issue along the CRA-steps is to define a theoretical minimal risk exposure level for each risk factor. In some cases, this level is evident and self-explanatory (e.g., zero smoking), but often more difficult to define and justify (e.g., ideal consumption of whole grains). CRA combine all relevant information and allow to estimate population attributable fractions (PAFs) quantifying the proportion of disease burden attributable to exposure. Among many available formulae for PAFs, it is important to use the one that allows consistency between definitions, units of the exposure data, and the exposure response functions. When combined effects of different risk factors are of interest, the non-additive nature of PAFs and possible mediation effects need to be reflected. Further, as attributable burden is typically calculated based on current exposure and current health outcomes, the time dimensions of risk and outcomes may become inconsistent. Finally, the evidence of the association between exposure and outcome can be heterogeneous which needs to be considered when interpreting CRA results.

Conclusions

The methodological challenges make transparent reporting of input and process data in CRA a necessary prerequisite. The evidence for causality between included risk-outcome pairs has to be well established to inform public health practice.

Psychometric Evaluation of the Health State Description Questionnaire in Chile: A Proposal for a Latent Variable Approach for Valuating Health States

BACKGROUND

A few instruments that identify and valuate health states are based on the International Classification of Functioning, Disability and Health States of the World Health Organization. One of them is the Health State Description (HSD) questionnaire first used in the World Health Survey (WHS) initiative (HSD-WHS), whose psychometric properties have not been evaluated in Chile. Additionally, the use of latent variables for the valuation process of health states has been scarcely investigated in the context of population health metrics. We aim to evaluate the psychometric properties and factorial structure of the HSD-WHS for Chile and describe a latent variable method for valuating health states associated with diseases.

METHODS

We used data from the second Chilean National Health population-based survey from 2009 to 2010 (N = 5293). We explored the factorial structure of the HSD-WHS through exploratory and confirmatory factor analyses, the reliability, and the discriminant validity of the latent variable of disability. Disability weights for diseases were calculated using a linear regression model.

RESULTS

We found an adequate goodness of fit for a second-order model with 9 factors corresponding to disability domains (Tucker-Lewis index = 0.99, comparative fit index = 0.99, root mean square error of approximation = 0.060), and good reliability estimates (standardized α = 0.91). The rescaled (between 0 and 100) latent variable of disability showed significant difference according to the explored variables. We estimated disability weights for the following: (1) depressive episode, 13.6 (12.1-15.2), (2) hypertension, 1.6 (0.0-3.3), and (3) diabetes, 5.0 (2.5-7.4).

CONCLUSIONS

This study supports the use of the HSD-WHS questionnaire in the Chilean population and a latent variable approach for valuating health states associated with diseases.

Prevalence, burden of disease, and lost in health state utilities attributable to chronic musculoskeletal disorders and pain in Chile

Background

Musculoskeletal disorders are a leading cause of disability adjusted life years (DALY) in the world. We aim to describe the prevalence and to compare the DALYs and loss of health state utilities (LHSU) attributable to common musculoskeletal disorders in Chile.

Methods

We used data from the Chilean National Health Survey carried out in 2016–2017. Six musculoskeletal disorders were detected through the COPCOPRD questionnaire: chronic musculoskeletal pain, chronic low back pain, chronic shoulder pain, osteoarthritis of hip and knee, and fibromyalgia. We calculated the DALY for each disorder for 18 sex and age strata, and LHSU following an individual and population level approaches. We also calculated the fraction of LHSU attributable to pain.

Results

Chronic musculoskeletal pain disorder affects a fifth of the adult population, with a significant difference between sexes. Among specific musculoskeletal disorders highlights chronic low back pain with the highest prevalence. Musculoskeletal disorders are a significant cause of LHSU at the individual level, especially in the case of fibromyalgia. Chronic musculoskeletal pain caused 503,919 [283,940 - 815,132] DALYs in 2017, and roughly two hundred thousand LSHU at population level, which represents 9.7% [8.8–10.6] of the total LSHU occurred in that year. Discrepancy in the burden of musculoskeletal disorders was observed according to DALY or LSHU estimation. The pain and discomfort domain of LHSU accounted for around half of total LHSU in people with musculoskeletal disorders.

Conclusion

Chronic musculoskeletal pain is a major source of burden and LHSU. Fibromyalgia should deserve more attention in future studies. Using the attributable fraction offers a straightforward and flexible way to explore the burden of musculoskeletal disorders.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12889-021-10953-z.

The Mortality Effect of Apparent Temperature: A Multi-City Study in Asia

(1) Background: The health effect of temperature has become a rising public health topic. The objective of this study is to assess the association between apparent temperature and non-accidental deaths, and the mortality burden attributed to cold and heat temperature; (2) Methods: The daily data on temperature and deaths were collected from 10 cities in Thailand, Korea and China. We fitted a time-series regression with a distributed lag nonlinear model (DLNM) to derive the health risk of temperature for each city and then pooled them to get the overall cumulative risk by multivariate meta-analysis. Additionally, we calculated the attributable fraction of deaths for heat and cold, which was defined as temperatures above and below minimum-mortality temperature (MMT); (3) Results: There are regional heterogeneities in the minimum mortality percentiles (MMP) and attributable fractions for different countries. The MMP varied from about the 5-10th percentile in Thailand to 63-93rd percentile in China and Korea. The attributable fractions of the total deaths due to short-term exposure to temperature in Asia is 7.62%, of which the cold effect (6.44%) is much higher than the heat effect (1.18%); (4) Conclusions: Our study suggested that apparent temperature was associated with an increase in non-accidental mortality. Most of the temperature-related mortality burden was attributable to cold, except for Thailand.

Different Alternatives to Assess the Burden of Disease Using Attributable Fraction on a Disability Variable: The Case of Pain and Chronic Musculoskeletal Disorders in Chile

OBJECTIVES

To estimate the burden of disease through 4 complementary procedures to years lived with disability (YLDs) using the concept of attributable fraction and including analysis of subdomains of disability.

METHODS

We explored the burden on disability for 7 common musculoskeletal disorders (CMD) using the 2009 to 2010 Chilean National Health Survey, which included the Community Oriented Programme for the Control of Rheumatic Disease Core Questionnaire to identify cases with CMD, and an 8-domain questionnaire for health state descriptions. We calculated the proportion of disability attributable to pain in the general population and people with CMD. We also estimated the burden of CMD expressed as YLD and as the proportion of the disability in the general population attributable to people with CMD, with a particular focus in the pain domain of disability. Second order of uncertainty around point estimations was also characterized.

RESULTS

Pain domain of disability accounted for 23.4% of the total disability in the general population, and between 20% (fibromyalgia) to 27.1% (osteoarthritis of the hip) in people with some of the selected CMD. People with chronic musculoskeletal pain accounted for 21.2% of total disability from general population, which generated 1.2 million of YLD (6679 YLD/100 000 inhabitants). Chronic low back pain and osteoarthritis of the knee were in the top position of specific CMDs, explaining the highest national burden.

CONCLUSION

Pain is an essential component of disability in people with CMD and also in the general population. The approach used can be easily applied to other health conditions and other domains of disability.