Statistical Methods for Health Care Research

Table of contentsKEYNOTE PRESENTATIONSK1 Researching complex interventions: the need for robust approachesPeter CraigK2 Complex intervention studies: an important step in developing knowledge for practiceIngalill Rahm-HallbergK3 Public and patient involvement in research: what, why and how?Nicky BrittenK4 Mixed methods in health service research – where do we go from here?Gunilla BorglinSPEAKER PRESENTATIONSS1 Exploring complexity in systematic reviews of complex interventionsGabriele Meyer, Sascha Köpke, Jane Noyes, Jackie ChandlerS2 Can complex health interventions be optimised before moving to a definitive RCT? Strategies and methods currently in useSara LevatiS3 A systematic approach to develop theory based implementation interventionsAnne SalesS4 Pilot studies and feasibility studies for complex interventions: an introductionLehana Thabane, Lora GiangregorioS5 What can be done to pilot complex interventions?Nancy Feeley, Sylvie CossetteS6 Using feasibility and pilot trials to test alternative methodologies and methodological procedures prior to full scale trialsRod TaylorS7 A mixed methods feasibility study in practiceJacqueline Hill, David A Richards, Willem KuykenS8 Non-standard experimental designs and preference designsLouise von EssenS9 Evaluation gone wild: using natural experimental approaches to evaluate complex interventionsAndrew WilliamsS10 The stepped wedge cluster randomised trial: an opportunity to increase the quality of evaluations of service delivery and public policy interventionsKarla Hemming, Richard Lilford, Alan Girling, Monica TaljaardS11 Adaptive designs in confirmatory clinical trials: opportunities in investigating complex interventionsMunyaradzi DimairoS12 Processes, contexts and outcomes in complex interventions, and the implications for evaluationMark PetticrewS13 Processes, contexts and outcomes in complex interventions, and the implications for evaluationJanis Baird, Graham MooreS14 Qualitative evaluation alongside RCTs: what to consider to get relevant and valuable resultsWillem Odendaal, Salla Atkins, Elizabeth Lutge, Natalie Leon, Simon LewinS15 Using economic evaluations to understand the value of complex interventions: when maximising health status is not sufficientKatherine PayneS16 How to arrive at an implementation planTheo van AchterbergS17 Modelling process and outcomes in complex interventionsWalter SermeusS18 Systems modelling for improving health careMartin Pitt, Thomas Monks

Researching Complex Interventions in Health: The State of the Art (vol 16, 101, 2016)

Objective To investigate the relationship between job satisfaction, work stress, work–family conflict and turnover intention, and explore factors associated with turnover intention, among physicians in Guangdong Province, China. Methods From August to October 2013, physicians completed questionnaires and scales with regard to their job satisfaction, work stress, work–family conflict, and turnover intention. Binary logistic regression and structural equation modelling (SEM) were used in data analysis. Results A total of 3963 physicians were approached, with 3563 completing the questionnaire. The mean score of the overall perception of turnover intention of physicians who worked in Guangdong was 2.71 on a scale ranging from 1 to 6. Hours worked per week, working in an urban/rural area, type of institution, and age significantly impacted on turnover intention. Turnover intention was directly and negatively related to job satisfaction, and it was directly, indirectly and positively related to work stress and work–family conflict. Conclusion Job satisfaction, work stress, work–family conflict, hours worked per week, working in an urban/rural area, types of institution and age are influencing factors of turnover intention. Reducing working hours, raising salary, providing more opportunities for career development and training, supporting and encouraging physicians by senior managers could potentially contribute to the reduction in turnover intention.

https://bmjopen.bmj.com/content/bmjopen/7/5/e014894.full.pdf

The relationship between job satisfaction, work stress, work-family conflict, and turnover intention among physicians in Guangdong, China: a cross-sectional study.

Background Resilience can be defined as the maintenance or quick recovery of mental health during or after periods of stressor exposure, which may result from a potentially traumatising event, challenging life circumstances, a critical life transition phase, or physical illness. Healthcare professionals, such as nurses, physicians, psychologists and social workers, are exposed to various work-related stressors (e.g. patient care, time pressure, administration) and are at increased risk of developing mental disorders. This population may benefit from resilience-promoting training programmes. Objectives To assess the effects of interventions to foster resilience in healthcare professionals, that is, healthcare staff delivering direct medical care (e.g. nurses, physicians, hospital personnel) and allied healthcare staff (e.g. social workers, psychologists). Search methods We searched CENTRAL, MEDLINE, Embase, 11 other databases and three trial registries from 1990 to June 2019. We checked reference lists and contacted researchers in the field. We updated this search in four key databases in June 2020, but we have not yet incorporated these results. Selection criteria Randomised controlled trials (RCTs) in adults aged 18 years and older who are employed as healthcare professionals, comparing any form of psychological intervention to foster resilience, hardiness or post-traumatic growth versus no intervention, wait-list, usual care, active or attention control. Primary outcomes were resilience, anxiety, depression, stress or stress perception and well-being or quality of life. Secondary outcomes were resilience factors. Data collection and analysis Two review authors independently selected studies, extracted data, assessed risks of bias, and rated the certainty of the evidence using the GRADE approach (at post-test only). Main results We included 44 RCTs (high-income countries: 36). Thirty-nine studies solely focused on healthcare professionals (6892 participants), including both healthcare staff delivering direct medical care and allied healthcare staff. Four studies investigated mixed samples (1000 participants) with healthcare professionals and participants working outside of the healthcare sector, and one study evaluated training for emergency personnel in general population volunteers (82 participants). The included studies were mainly conducted in a hospital setting and included physicians, nurses and different hospital personnel (37/44 studies). Participants mainly included women (68%) from young to middle adulthood (mean age range: 27 to 52.4 years). Most studies investigated group interventions (30 studies) of high training intensity (18 studies; > 12 hours/sessions), that were delivered face-to-face (29 studies). Of the included studies, 19 compared a resilience training based on combined theoretical foundation (e.g. mindfulness and cognitive-behavioural therapy) versus unspecific comparators (e.g. wait-list). The studies were funded by different sources (e.g. hospitals, universities), or a combination of different sources. Fifteen studies did not specify the source of their funding, and one study received no funding support. Risk of bias was high or unclear for most studies in performance, detection, and attrition bias domains. At post-intervention, very-low certainty evidence indicated that, compared to controls, healthcare professionals receiving resilience training may report higher levels of resilience (standardised mean difference (SMD) 0.45, 95% confidence interval (CI) 0.25 to 0.65; 12 studies, 690 participants), lower levels of depression (SMD -0.29, 95% CI -0.50 to -0.09; 14 studies, 788 participants), and lower levels of stress or stress perception (SMD -0.61, 95% CI -1.07 to -0.15; 17 studies, 997 participants). There was little or no evidence of any effect of resilience training on anxiety (SMD -0.06, 95% CI -0.35 to 0.23; 5 studies, 231 participants; very-low certainty evidence) or well-being or quality of life (SMD 0.14, 95% CI -0.01 to 0.30; 13 studies, 1494 participants; very-low certainty evidence). Effect sizes were small except for resilience and stress reduction (moderate). Data on adverse effects were available for three studies, with none reporting any adverse effects occurring during the study (very-low certainty evidence). Authors' conclusions For healthcare professionals, there is very-low certainty evidence that, compared to control, resilience training may result in higher levels of resilience, lower levels of depression, stress or stress perception, and higher levels of certain resilience factors at post-intervention. The paucity of medium- or long-term data, heterogeneous interventions and restricted geographical distribution limit the generalisability of our results. Conclusions should therefore be drawn cautiously. The findings suggest positive effects of resilience training for healthcare professionals, but the evidence is very uncertain. There is a clear need for high-quality replications and improved study designs.

Psychological interventions to foster resilience in healthcare professionals

Will we have the same employees in hospitality after all? The impact of COVID-19 on employees’ work attitudes and turnover intentions

Objectives This cross-sectional study aimed to explore job satisfaction among healthcare staff in Guangdong following the health system reforms in 2009, and to investigate the association between job satisfaction and work stress, work–family conflict and doctor–patient relationship. Design Cross-sectional survey. Setting The Fifth National Health Service Survey was carried out in Guangdong, China. Participants All participants in this study were healthcare staff including physicians, nurses and public health staff from hospitals, health service centres and health clinics. A total of 6583 questionnaires were distributed and collected. After excluding the incomplete questionnaires, 5845 questionnaires were included for the analysis. Outcome measures Sociodemographic information and scores for evaluating job satisfaction, work stress, work–family conflict and doctor–patient relationship were obtained using the questionnaire developed by the National Health and Family Planning Commission of the People9s Republic of China. To assess the significantly associated factors on job satisfaction of the healthcare staff in Guangdong, a binary logistic regression model was used. Results Based on the 5845 valid responses of the healthcare staff who worked in Guangdong, the mean score of overall perception of job satisfaction was 3.99 on a scale of 1–6. Among the sociodemographic variables, occupation, educational background, professional status, years of service, annual income and night shift frequency significantly influenced the level of job satisfaction. Work stress, work–family conflict and doctor–patient relationship also had significant effect on job satisfaction. Conclusions The overall job satisfaction exceeded slightly dissatisfied (score 3) and approached slightly satisfied (score 4). Measures to enhance job satisfaction include the reduction of workload, increase of welfare, maintaining moderate stress and balancing work–family conflict. Moreover, relevant laws should be issued to protect the healthcare staff from violent acts.

https://bmjopen.bmj.com/content/bmjopen/6/7/e011388.full.pdf

Job satisfaction and associated factors among healthcare staff: a cross-sectional study in Guangdong Province, China

Objectives In epidemiological studies, it is important to identify independent associations between collective exposures and a health outcome. The current stepwise selection technique ignores stochastic errors and suffers from a lack of stability. The alternative LASSO-penalized regression model can be applied to detect significant predictors from a pool of candidate variables. However, this technique is prone to false positives and tends to create excessive biases. It remains challenging to develop robust variable selection methods and enhance predictability. Material and methods Two improved algorithms denoted the two-stage hybrid and bootstrap ranking procedures, both using a LASSO-type penalty, were developed for epidemiological association analysis. The performance of the proposed procedures and other methods including conventional LASSO, Bolasso, stepwise and stability selection models were evaluated using intensive simulation. In addition, methods were compared by using an empirical analysis based on large-scale survey data of hepatitis B infection-relevant factors among Guangdong residents. Results The proposed procedures produced comparable or less biased selection results when compared to conventional variable selection models. In total, the two newly proposed procedures were stable with respect to various scenarios of simulation, demonstrating a higher power and a lower false positive rate during variable selection than the compared methods. In empirical analysis, the proposed procedures yielding a sparse set of hepatitis B infection-relevant factors gave the best predictive performance and showed that the procedures were able to select a more stringent set of factors. The individual history of hepatitis B vaccination, family and individual history of hepatitis B infection were associated with hepatitis B infection in the studied residents according to the proposed procedures. Conclusions The newly proposed procedures improve the identification of significant variables and enable us to derive a new insight into epidemiological association analysis.

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Improved Variable Selection Algorithm Using a LASSO-Type Penalty, with an Application to Assessing Hepatitis B Infection Relevant Factors in Community Residents.

Objectives: To evaluate the performance of clustering methods used in the prognostic assessment of categorical clinical data for hepatocellular carcinoma (HCC) patients in China, and establish a predictable prognostic nomogram for clinical decisions. Materials and Methods: A total of 332 newly diagnosed HCC patients treated with hepatic resection during 2006-2009 were enrolled. Patients were regularly followed up at outpatient clinics. Clustering methods including the Average linkage, k-modes, fuzzy k-modes, PAM, CLARA, protocluster, and ROCK were compared by Monte Carlo simulation, and the optimal method was applied to investigate the clustering pattern of the indices including platelet count, platelet/lymphocyte ratio (PLR) and serum aspartate aminotransferase activity/platelet count ratio index (APRI). Then the clustering variable, age group, tumor size, number of tumor and vascular invasion were studied in a multivariable Cox regression model. A prognostic nomogram was constructed for clinical decisions. Results: The ROCK was best in both the overlapping and nonoverlapping cases performed to assess the prognostic value of platelet-based indices. Patients with categorical platelet-based indices significantly split across two clusters, and those with high values, had a high risk of HCC recurrence (hazard ratio [HR] 1.42, 95% CI 1.09-1.86; p<0·01). Tumor size, number of tumor and blood vessel invasion were also associated with high risk of HCC recurrence (all p<0·01). The nomogram well predicted HCC patient survival at 3 and 5 years. Conclusions: A cluster of platelet-based indices combined with other clinical covariates could be used for prognosis evaluation in HCC.

http://koreascience.or.kr:80/article/JAKO201429765167363.pdf

Xiao-Min Hu

Papers

The relationship between job satisfaction, work stress, work-family conflict, and turnover intention among physicians in Guangdong, China: a cross-sectional study.

Job satisfaction and associated factors among healthcare staff: a cross-sectional study in Guangdong Province, China

Improved Variable Selection Algorithm Using a LASSO-Type Penalty, with an Application to Assessing Hepatitis B Infection Relevant Factors in Community Residents.

Prognostic Evaluation of Categorical Platelet-based Indices Using Clustering Methods Based on the Monte Carlo Comparison for Hepatocellular Carcinoma