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      Effectiveness of the population-based ‘check your health preventive programme’ conducted in a primary care setting: a pragmatic randomised controlled trial

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          Abstract

          Background

          Health checks have been suggested as an early detection approach aiming at lowering the risk of chronic disease development. This study aimed to evaluate the effectiveness of a health check programme offered to the general population, aged 30–49 years.

          Methods

          The entire population aged 30–49 years (N=26 216) living in the municipality of Randers, Denmark, was invited to a health check during 5 years. A pragmatic household cluster-randomised controlled trial was conducted in 10 505 citizens. The intervention group (IG, N=5250) included citizens randomised to the second year and reinvited in the 5th year. The comparison group (CG, N=5255) included citizens randomised to the 5th year. Outcomes were modelled cardiovascular disease (CVD) risk; self-reported physical activity (PA) and objectively measured cardio respiratory fitness (CRF); self-rated health (short-form 12 (SF-12)), self-rated mental health (SF-12_Mental Component Score (MCS)) and, registry information on sick-leave and employment. Due to low participation, we compared groups matched on propensity scores for participation when reinvited.

          Results

          Participation in the first health check was 51% (N=2698) in the IG and 40% (N=2120) in the CG. In the IG 26% (N=1340) participated in both the first and second health checks. No intervention effects were found comparing IG and CG. Mean differences were (95% CI): modelled CVD risk: −0.052 (95% CI −0.107 to 0.003)%, PA: −0.156 (−0.331 to 0.019) days/week with 30 min moderate PA, CRF: 0.133 (−0.560 to 0.826) mL O 2/min/kg, SF-12: −0.003 (−0.032 to 0.026), SF-12_MCS: 0.355 (-0.423 to 1.132), sick leave periods ≥3 weeks: −0.004 (−0.025 to 0.017), employment: −0.004 (−0.032 to 0.024).

          Conclusions

          Preventive health checks offered to the general population, aged 30–49 years, had no effects on a wide range of indicators of chronic disease risk.

          Trial registration number

          NCT02028195.

          Related collections

          Most cited references29

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          A 12-Item Short-Form Health Survey: construction of scales and preliminary tests of reliability and validity.

          Regression methods were used to select and score 12 items from the Medical Outcomes Study 36-Item Short-Form Health Survey (SF-36) to reproduce the Physical Component Summary and Mental Component Summary scales in the general US population (n=2,333). The resulting 12-item short-form (SF-12) achieved multiple R squares of 0.911 and 0.918 in predictions of the SF-36 Physical Component Summary and SF-36 Mental Component Summary scores, respectively. Scoring algorithms from the general population used to score 12-item versions of the two components (Physical Components Summary and Mental Component Summary) achieved R squares of 0.905 with the SF-36 Physical Component Summary and 0.938 with SF-36 Mental Component Summary when cross-validated in the Medical Outcomes Study. Test-retest (2-week)correlations of 0.89 and 0.76 were observed for the 12-item Physical Component Summary and the 12-item Mental Component Summary, respectively, in the general US population (n=232). Twenty cross-sectional and longitudinal tests of empirical validity previously published for the 36-item short-form scales and summary measures were replicated for the 12-item Physical Component Summary and the 12-item Mental Component Summary, including comparisons between patient groups known to differ or to change in terms of the presence and seriousness of physical and mental conditions, acute symptoms, age and aging, self-reported 1-year changes in health, and recovery for depression. In 14 validity tests involving physical criteria, relative validity estimates for the 12-item Physical Component Summary ranged from 0.43 to 0.93 (median=0.67) in comparison with the best 36-item short-form scale. Relative validity estimates for the 12-item Mental Component Summary in 6 tests involving mental criteria ranged from 0.60 to 107 (median=0.97) in relation to the best 36-item short-form scale. Average scores for the 2 summary measures, and those for most scales in the 8-scale profile based on the 12-item short-form, closely mirrored those for the 36-item short-form, although standard errors were nearly always larger for the 12-item short-form.
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            Multiple imputation for missing data in epidemiological and clinical research: potential and pitfalls

            Most studies have some missing data. Jonathan Sterne and colleagues describe the appropriate use and reporting of the multiple imputation approach to dealing with them
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              Optimal caliper widths for propensity-score matching when estimating differences in means and differences in proportions in observational studies

              In a study comparing the effects of two treatments, the propensity score is the probability of assignment to one treatment conditional on a subject's measured baseline covariates. Propensity-score matching is increasingly being used to estimate the effects of exposures using observational data. In the most common implementation of propensity-score matching, pairs of treated and untreated subjects are formed whose propensity scores differ by at most a pre-specified amount (the caliper width). There has been a little research into the optimal caliper width. We conducted an extensive series of Monte Carlo simulations to determine the optimal caliper width for estimating differences in means (for continuous outcomes) and risk differences (for binary outcomes). When estimating differences in means or risk differences, we recommend that researchers match on the logit of the propensity score using calipers of width equal to 0.2 of the standard deviation of the logit of the propensity score. When at least some of the covariates were continuous, then either this value, or one close to it, minimized the mean square error of the resultant estimated treatment effect. It also eliminated at least 98% of the bias in the crude estimator, and it resulted in confidence intervals with approximately the correct coverage rates. Furthermore, the empirical type I error rate was approximately correct. When all of the covariates were binary, then the choice of caliper width had a much smaller impact on the performance of estimation of risk differences and differences in means. Copyright © 2010 John Wiley & Sons, Ltd.
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                Author and article information

                Contributors
                (View ORCID Profile)
                Journal
                Journal of Epidemiology and Community Health
                J Epidemiol Community Health
                BMJ
                0143-005X
                1470-2738
                June 18 2021
                : jech-2021-216581
                Article
                10.1136/jech-2021-216581
                931b36ad-85af-482d-b1b8-bb5b7858fe0a
                © 2021

                Free to read

                http://creativecommons.org/licenses/by-nc/4.0/

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