Model-based estimation of measures of association for time-to-event outcomes

Loddon Mallee healthcare worker COVID-19 study-protocol for a prospective cohort study examining the health and well-being of rural Australian healthcare workers during the COVID-19 pandemic

INTRODUCTION

The COVID-19 pandemic is creating immense psychosocial disturbance. While global, broad-based research is being conducted, little is known about the effects of the COVID-19 pandemic on health and well-being or how protective and resilience factors influence the human response in Australian rural and regional communities. Rural and regional communities often have less resources to deal with such public health emergencies and face additional environmental adversity. Healthcare workers, including those in rural and regional areas, have felt the immediate impacts of COVID-19 in a multitude of ways and these impacts will continue for years to come. Therefore, this study aims to describe and understand the impacts of the COVID-19 pandemic on the rural and regional healthcare workforce within the Loddon Mallee region, Victoria, Australia.

METHODS AND ANALYSIS

This prospective cohort of rural and regional healthcare workers will be recruited and followed over 3 years to examine the effects of the COVID-19 pandemic on their health and well-being. Self-administered online questionnaires will be administered every 6 months for a 36-month period. Multiple outcomes will be assessed; however, the primary outcomes are emotional health and well-being and psychological resilience. Emotional health and well-being will be measured using validated instruments that will assess multiple domains of the emotional health and well-being continuum.Linear and logistic regression and latent growth curve modelling will be used to examine the association between baseline and follow-up participant emotional health, well-being and resilience while adjusting for potentially time-varying confounding variables. Participant characteristics measured at baseline will also be tested for association with incident health, morbidity, mortality and health service utilisation outcomes at follow-up.

ETHICS AND DISSEMINATION

Ethical approval has been obtained through the Bendigo Health Human Research Ethics Committee. The study findings will be disseminated through international conferences, international peer-reviewed journals and social media.

TRIAL REGISTRATION NUMBER

ACTRN12620001269921.

Combined associations of body mass index and adherence to a Mediterranean-like diet with all-cause and cardiovascular mortality: A cohort study

BACKGROUND

It is unclear whether the effect on mortality of a higher body mass index (BMI) can be compensated for by adherence to a healthy diet and whether the effect on mortality by a low adherence to a healthy diet can be compensated for by a normal weight. We aimed to evaluate the associations of BMI combined with adherence to a Mediterranean-like diet on all-cause and cardiovascular disease (CVD) mortality.

METHODS AND FINDINGS

Our longitudinal cohort design included the Swedish Mammography Cohort (SMC) and the Cohort of Swedish Men (COSM) (1997-2017), with a total of 79,003 women (44%) and men (56%) and a mean baseline age of 61 years. BMI was categorized into normal weight (20-24.9 kg/m2), overweight (25-29.9 kg/m2), and obesity (30+ kg/m2). Adherence to a Mediterranean-like diet was assessed by means of the modified Mediterranean-like diet (mMED) score, ranging from 0 to 8; mMED was classified into 3 categories (0 to <4, 4 to <6, and 6-8 score points), forming a total of 9 BMI × mMED combinations. We identified mortality by use of national Swedish registers. Cox proportional hazard models with time-updated information on exposure and covariates were used to calculate the adjusted hazard ratios (HRs) of mortality with their 95% confidence intervals (CIs). Our HRs were adjusted for age, baseline educational level, marital status, leisure time physical exercise, walking/cycling, height, energy intake, smoking habits, baseline Charlson's weighted comorbidity index, and baseline diabetes mellitus. During up to 21 years of follow-up, 30,389 (38%) participants died, corresponding to 22 deaths per 1,000 person-years. We found the lowest HR of all-cause mortality among overweight individuals with high mMED (HR 0.94; 95% CI 0.90, 0.98) compared with those with normal weight and high mMED. Using the same reference, obese individuals with high mMED did not experience significantly higher all-cause mortality (HR 1.03; 95% CI 0.96-1.11). In contrast, compared with those with normal weight and high mMED, individuals with a low mMED had a high mortality despite a normal BMI (HR 1.60; 95% CI 1.48-1.74). We found similar estimates among women and men. For CVD mortality (12,064 deaths) the findings were broadly similar, though obese individuals with high mMED retained a modestly increased risk of CVD death (HR 1.29; 95% CI 1.16-1.44) compared with those with normal weight and high mMED. A main limitation of the present study is the observational design with self-reported lifestyle information with risk of residual or unmeasured confounding (e.g., genetic liability), and no causal inferences can be made based on this study alone.

CONCLUSIONS

These findings suggest that diet quality modifies the association between BMI and all-cause mortality in women and men. A healthy diet may, however, not completely counter higher CVD mortality related to obesity.

CHA2DS2VASc score and adverse outcomes in middle-aged individuals without atrial fibrillation

Aims

The CHA2DS2VASc score is used to evaluate the risk of thromboembolic events in patients with non-valvular atrial fibrillation. We assessed the prognostic yield of CHA2DS2VASc for new-onset atrial fibrillation, cardiovascular morbidity and mortality in a non-atrial fibrillation population.

Methods

We analysed a population-based cohort of 22,179 middle-aged individuals with ( n = 3542) and without ( n = 18,367) a history of atrial fibrillation; we grouped the population into five CHA2DS2VASc strata (0–1–2–3–≥4), and compared the risk of major adverse cerebro-cardiovascular events and mortality. Furthermore, we analysed the annual incidence of atrial fibrillation across different CHA2DS2VASc strata.

Results

Over a median follow-up of 15 years, 1572 patients (6.9%) had ischaemic strokes, 2162 (9.5%) coronary events and 5899 (26%) died. The cumulative incidence of ischaemic stroke in CHA2DS2VASc ≥ 4 subjects without atrial fibrillation was similar to patients with atrial fibrillation and CHA2DS2VASc 2, with a 10-year crude incidence rate of 0.91 (95% confidence interval (CI) 0.68–1.19) and 1.13 (95% CI 0.93–1.36) ischaemic strokes per 100 patient-years, respectively. CHA2DS2VASc in a non-atrial fibrillation population showed higher predictive accuracy for ischaemic stroke compared with an atrial fibrillation population (area under the curve 0.60 vs. 0.56; P = 0.001). In multivariable Cox regression analysis, CHA2DS2VASc ≥ 2 was an independent predictor of all-cause death (adjusted hazard ratio (aHR) 2.58; 95% CI 2.42–2.76), cardiovascular death (aHR 3.40; 95% CI 2.98–3.89), ischaemic stroke (aHR 2.20; 95% CI 1.92–2.53) and coronary events (aHR 1.83; 95% CI 1.63–2.04). The cumulative incidence of atrial fibrillation was greater with increasing CHA2DS2VASc strata, with an absolute annual incidence of more than 2% per year if CHA2DS2VASc ≥ 4.

Conclusion

The CHA2DS2VASc score is a sensitive tool for predicting new-onset atrial fibrillation and adverse outcomes in subjects both with and without atrial fibrillation.

Estimate risk difference and number needed to treat in survival analysis

The hazard ratio (HR) is a measure of instantaneous relative risk of an increase in one unit of the covariate of interest, which is widely reported in clinical researches involving time-to-event data. However, the measure fails to capture absolute risk reduction. Other measures such as number needed to treat (NNT) and risk difference (RD) provide another perspective on the effectiveness of an intervention, and can facilitate clinical decision making. The article aims to provide a step-by-step tutorial on how to compute RD and NNT in survival analysis with R. For simplicity, only one measure (RD or NNT) needs to be illustrated, because the other measure is a reverse of the illustrated one (NNT=1/RD). An artificial dataset is composed by using the survsim package. RD and NNT are estimated with Austin method after fitting a Cox-proportional hazard regression model. The confidence intervals can be estimated using bootstrap method. Alternatively, if the standard errors (SEs) of the survival probabilities of the treated and control group are given, confidence intervals can be estimated using algebraic calculations. The pseudo-value model provides another method to estimate RD and NNT. Details of R code and its output are shown and explained in the main text.