Search

OBJECTIVE: To identify, assess, and summarize the measures to assess burden of treatment in patients with multimorbidity (BoT-MMs) and their measurement properties. STUDY DESIGN AND SETTING: MEDLINE via PubMed was searched from inception until May 2021. Independent reviewers extracted data from studies in which BoT-MMs were developed, validated, or reported as used, including an assessment of their measurement properties (e.g., validity and reliability) using the COnsensus-based Standards for the selection of health Measurement INstruments (COSMIN). RESULTS: Eight BoT-MMs were identified across 72 studies. Most studies were performed in English (68%), in high-income countries (90%), without noting urban-rural setting (90%). No BoT-MMs had both sufficient content validity and internal consistency; BoT-MMs had some measurement properties either insufficient or uncertain (e.g., responsiveness). Other frequent limitations of BoT-MMs included absent recall time, presence of floor effects, and unclear rationale for categorizing and interpreting raw scores. CONCLUSION: The evidence needed for use of extant BoT-MMs in patients with multimorbidity remains insufficiently developed, including that of suitability on their development, measurement properties, interpretability of scores, and use in low-resource settings. This review summarizes this evidence and identifies issues needing attention for using BoT-MMs in research and clinical practice.

Juan Carlos Bazo-Alvarez,1,2 Tim P Morris,3 Tra My Pham,3 James R Carpenter,3,4 Irene Petersen1,5 1Research Department of Primary Care and Population Health, University College London (UCL), London, UK; 2Instituto de Investigación, Universidad Católica Los Ángeles de Chimbote, Chimbote, Peru; 3MRC Clinical Trials Unit at UCL, London, UK; 4Department of Medical Statistics, London School of Hygiene and Tropical Medicine, London, UK; 5Department of Clinical Epidemiology, Aarhus University, Aarhus, DenmarkCorrespondence: Juan Carlos Bazo-AlvarezResearch Department of Primary Care and Population Health, University College London (UCL), Rowland Hill Street, London NW3 2PF, UKTel +44 7376076260Email juan.alvarez.16@ucl.ac.ukBackground: In the interrupted time series (ITS) approach, it is common to average the outcome of interest at each time point and then perform a segmented regression (SR) analysis. In this study, we illustrate that such ‘aggregate-level’ analysis is biased when data are missing at random (MAR) and provide alternative analysis methods.Methods: Using electronic health records from the UK, we evaluated weight change over time induced by the initiation of antipsychotic treatment. We contrasted estimates from aggregate-level SR analysis against estimates from mixed models with and without multiple imputation of missing covariates, using individual-level data. Then, we conducted a simulation study for insight about the different results in a controlled environment.Results: Aggregate-level SR analysis suggested a substantial weight gain after initiation of treatment (average short-term weight change: 0.799kg/week) compared to mixed models (0.412kg/week). Simulation studies confirmed that aggregate-level SR analysis was biased when data were MAR. In simulations, mixed models gave less biased estimates than SR analysis and, in combination with multilevel multiple imputation, provided unbiased estimates. Mixed models with multiple imputation can be used with other types of ITS outcomes (eg, proportions). Other standard methods applied in ITS do not help to correct this bias problem.Conclusion: Aggregate-level SR analysis can bias the ITS estimates when individual-level data are MAR, because taking averages of individual-level data before SR means that data at the cluster level are missing not at random. Avoiding the averaging-step and using mixed models with or without multilevel multiple imputation of covariates is recommended.Keywords: interrupted time series analysis, segmented regression, missing data, multiple imputation, mixed effects models, electronic health records, big data

INTRODUCTION:Instrumental activities of daily living (IADL) are cognitively complex activities related to independent living in the community. Robust IADL scales are needed, however the psychometric properties of instruments have been little evaluated. There is no validated instrument for Sri Lankan older populations. Sri Lanka has the highest proportion of older people in South Asia with rapid population ageing. Therefore, it is essential to have standard instruments to assess activity limitations. We aimed to cross-culturally adapt the original Lawton Instrumental Activities of Daily Living Scale from English to Sinhala and evaluate the psychometric properties of the Sinhala version. METHODS:Cross-cultural adaptation of the instrument was performed. The instrument was validated in a sample of 702 community-dwelling older adults aged 60 years and above in Sri Lanka. Reliability (internal consistency and inter-rater reliability) was assessed. Construct validity of the scale was evaluated by performing exploratory and confirmatory factor analysis and testing convergent and divergent validity. RESULTS:The Lawton IADL scale was successfully adapted to Sri Lankan context. Internal consistency of the scale was very high (Cronbach's alpha = 0.91). Very good inter-rater reliability was observed with very good agreement for all items. Inter-class correlations for overall IADL score ranged from 0.57 to 0.91. Results of the exploratory and confirmatory factor analyses supported the unidimensionality of the scale. Goodness of fit indices in confirmatory factor analysis were in acceptable range (CFI = 0.98, SRMR = 0.06, NNFI = 0.97). Strength of associations were significant and in the expected direction. Results of the known group validity were also significant, confirming the convergent and divergent validity. CONCLUSION:The Lawton IADL scale was successfully translated and culturally adapted to Sinhala language. The Sinhala version demonstrated excellent reliability and construct validity. Given good psychometric properties, this scale would be recommended for use in future research.

BACKGROUND: Variations in the distribution of cardiovascular disease and risk factors by socioeconomic status (SES) have been described in affluent societies, yet a better understanding of these patterns is needed for most low- and middle-income countries. OBJECTIVE: This study sought to describe the relationship between cardiovascular risk factors and SES using monthly family income, educational attainment, and assets index, in 4 Peruvian sites. METHODS: Baseline data from an age- and sex-stratified random sample of participants, ages ≥35 years, from 4 Peruvian sites (CRONICAS Cohort Study, 2010) were used. The SES indicators considered were monthly family income (n = 3,220), educational attainment (n = 3,598), and assets index (n = 3,601). Behavioral risk factors included current tobacco use, alcohol drinking, physical activity, daily intake of fruits and vegetables, and no control of salt intake. Cardiometabolic risk factors included obesity, elevated waist circumference, hypertension, insulin resistance, diabetes mellitus, low high-density lipoprotein cholesterol, and high triglyceride levels. RESULTS: In the overall population, 41.6% reported a monthly family income

Background Variations in the distribution of cardiovascular disease and risk factors by socioeconomic status (SES) have been described in affluent societies, yet a better understanding of these patterns is needed for most low- and middle-income countries. Objective This study sought to describe the relationship between cardiovascular risk factors and SES using monthly family income, educational attainment, and assets index, in 4 Peruvian sites. Methods Baseline data from an age- and sex-stratified random sample of participants, ages ≥35 years, from 4 Peruvian sites (CRONICAS Cohort Study, 2010) were used. The SES indicators considered were monthly family income (n = 3,220), educational attainment (n = 3,598), and assets index (n = 3,601). Behavioral risk factors included current tobacco use, alcohol drinking, physical activity, daily intake of fruits and vegetables, and no control of salt intake. Cardiometabolic risk factors included obesity, elevated waist circumference, hypertension, insulin resistance, diabetes mellitus, low high-density lipoprotein cholesterol, and high triglyceride levels. Results In the overall population, 41.6% reported a monthly family income