Attributable fraction functions for censored event times

Associations of polysocial risk score, lifestyle and genetic factors with incident psoriasis: a larger-scale prospective cohort study

OBJECTIVES

The impact of polysocial risk score (PsRS), a composite measure of multiple social risk factors, on the development of psoriasis remains unclear. Moreover, the potential modifying effects of lifestyle and genetic susceptibility on the relationship between PsRS and psoriasis risk require further exploration.

STUDY DESIGN

This was a prospective cohort study conducted among UK Biobank.

METHODS

In this study, we analyzed 331,631 participants enrolled in the UK Biobank cohort. To derive the PsRS, we utilized a summative strategy, amalgamating six social determinants of health derived from three domains: socio-economic status, psychosocial factors, and neighborhood and living environment consistently linked to incident psoriasis. Cox proportional hazard models were used to assess the associations between PsRS and psoriasis incidence. Furthermore, we constructed a lifestyle score and a genetic risk score to explore the potential modifying effects of these factors on the relationship between PsRS and psoriasis risk.

RESULTS

Compared with individuals with a low PsRS (≤1), those with intermediate PsRS (2-4) and high PsRS (≥5) had 1.20 (95% confidence interval [CI], 1.06-1.36) and 1.53 (95% CI, 1.31-1.78) times higher risks of developing psoriasis, respectively. Our findings revealed an additive interaction between PsRS and genetic susceptibility. Moreover, it was found that individuals with high PsRS and unhealthier lifestyles had a 2.60 times higher risk of developing psoriasis than those with lower PsRS and healthier ones.

CONCLUSIONS

Our study results imply that an elevated PsRS is linked to a heightened risk of psoriasis, which is further influenced by genetic factors. Our results also indicate that greater social vulnerability and unhealthier lifestyle may synergistically contribute to the additional risk of psoriasis.

Relationships of Residential Distance to Major Traffic Roads with Dementia Incidence and Brain Structure Measures: Mediation Role of Air Pollution

Background: Uncertainty exists regarding the operating pathways between near-roadway exposure and dementia incidence. We intend to examine relationships between proximity to major roadways with dementia incidence and brain MRI structure measures, and potential mediation roles of air and noise pollution. Methods: The cohort study was based on the UK Biobank. Baseline survey was conducted from 2006 to 2010, with linkage to electronic health records conducted for follow-up. Residential distance to major roadways was ascertained residential address postcode. A land use regression model was applied for estimating traffic-related air pollution at residence. Dementia incidence was ascertained using national administrative databases. Brain MRI measures were derived as image-derived phenotypes, including total brain, white matter, gray matter, and peripheral cortical gray matter. Results: We included 460,901 participants [mean (SD) age: 57.1 (8.1) years; men: 45.7%]. Compared with individuals living >1,000 m from major traffic roads, living ≤1,000 m was associated with a 13% to 14% higher dementia risk, accounting for 10% of dementia cases. Observed association between residential distance and dementia was substantially mediated by traffic-related air pollution, mainly nitrogen dioxide (proportion mediated: 63.6%; 95% CI, 27.0 to 89.2%) and PM2.5 (60.9%, 26.8 to 87.0%). The shorter residential distance was associated with smaller volumes of brain structures, which was also mediated by traffic-related air pollutants. No significant mediation role was observed of noise pollution. Conclusions: The shorter residential distance to major roads was associated with elevated dementia incidence and smaller brain structure volumes, which was mainly mediated by traffic-related air pollution.

Incident cancers attributable to using opium and smoking cigarettes in the Golestan cohort study

Background

Opium consumption has recently been identified as a carcinogen, but the impact of opium use on cancer burden is unknown. We aimed to evaluate the fraction of cancers that could be attributed to opium use alone and in combination with cigarette smoking in a region where opium is widely used.

Methods

50,045 Iranian adults were recruited to this prospective cohort study between 2004 and 2008 and were followed through January 2022. We assessed the association between using opium and/or cigarette smoking and various cancers using proportional hazards regression models. We then calculated population attributable fractions (PAFs) for all cancers and for groups of cancers causally linked to opium and cigarette smoking.

Findings

Of the total participants, 8% only used opium, 8.3% only smoked cigarettes, and 9% used both substances. During a median 14 years of follow-up, 2195 individuals were diagnosed with cancer, including 215 opium-related cancers (lung, larynx, and bladder) and 1609 tobacco-related cancers (20 types). Opium use alone was estimated to cause 35% (95% CI: 26%-45%) of opium-related cancers, while smoking cigarettes alone was estimated to cause 9% (6%-12%) of tobacco-related cancers in this population. Using opium and/or cigarettes was estimated to cause 13% (9%-16%) of all cancers, 58% (49%-66%) of opium-related cancers, and 15% (11%-18%) of tobacco-related cancers. Moreover, joint exposure to opium and cigarettes had the greatest impact on cancers of the larynx, pharynx, lung, and bladder, with PAFs ranging from 50% to 77%.

Interpretation

Using opium and smoking cigarettes account for a large proportion of cancers in this population. To reduce the cancer burden, prevention policies should aim to decrease the use of both substances through public awareness campaigns and interventional efforts.

Funding

The Golestan Cohort Study work was funded by the Tehran University of Medical Sciences, Cancer Research UK, U.S. National Cancer Institute, International Agency for Research on Cancer. The presented analysis was supported by the International HundredK+ Cohorts Consortium (IHCC).

The effect of surgical complications on long-term prognosis following oesophagectomy

INTRODUCTION

Complications are frequent after oesophagectomy, and there is evidence these adversely impact long-term prognosis. However, the effect of multiple complications, and the absolute magnitude of effect on survival is unclear. This study aimed to examine these effects in a single high-volume UK unit.

METHODS

Patients undergoing oesophagectomy for cancer and who survived to 90 days post-oesophagectomy were analysed. Complications were graded according to the Clavien-Dindo (CD) classification and the Comprehensive Complication Index (CCI). The effect and magnitude of effect of complications on survival were assessed using multivariable cox regression and the risk-adjusted population attributable fraction.

RESULTS

In total, 380 patients were included. Complications occurred in 251 (66.1%). Suffering ≥3 complications (HR 1.89, 95%CI 1.13-3.16, p = 0.015) or an unplanned escalation in care (HR 2.22, 95%CI 1.43-3.45, p < 0.001) significantly reduced survival whereas pulmonary complications and anastomotic leak did not. Patients with a CCI>30 had worse overall survival (HR 1.91, 95%CI 1.32-2.76, p < 0.001) and CCI>30 due to multiple minor complications gave a worse prognosis compared to CCI>30 due to major complications (HR 2.44, 95%CI 1.14-5.20, p = 0.022). An estimated 9.1% (95%CI 3.4-14.4%) of deaths at 5 years were attributable to a CCI>30.

CONCLUSION

Long-term survival following oesophagectomy for cancer is significantly affected by complications and the cumulative effect of multiple complications. Interestingly, multiple minor complications had a worse effect on survival than major complications. The absolute magnitude of effect is substantial: minimising all types of postoperative complications could have significant benefit to overall outcomes.

Interethnic Differences in Bladder Cancer Incidence and the Association between Type 2 Diabetes and Bladder Cancer in the Multiethnic Cohort Study

Background

Research on the association between type 2 diabetes (T2D) and bladder cancer (BCA) risk among non-European ancestry populations is sparse to nonexistent, and most prior studies rely on a single baseline assessment of T2D status.

Methods

We estimated the T2D-BCA association using the Multiethnic Cohort Study of 185,059 men and women in California and Hawaii. Participants were African American, European American, Japanese American, Latin American, and Native Hawaiian, ages 45-75 years at enrollment (1993-1996). T2D was assessed by self-report at baseline, follow-up surveys, and Medicare claims. Cases were identified using Surveillance, Epidemiology and End Results Program cancer registries through 2016. Associations were estimated by race/ethnicity using Cox proportional hazards regression. Adjusted attributable fractions (AAF) and cumulative absolute risk of bladder cancer were estimated across groups.

Results

Over an average 19.7 years of follow-up 1,890 incident bladder cancer cases were diagnosed. Time-varying T2D was associated with bladder cancer in the multiethnic sample (HR = 1.17; 95% confidence interval, 1.05-1.30); however, the HR did not differ by race/ethnicity (P = 0.85). The AAF was 4.2% in the multiethnic sample and largest among Native Hawaiians (9.8%). Absolute risk of bladder cancer among European Americans without T2D was higher than all other groups with T2D.

Conclusion

T2D is significantly associated with bladder cancer risk in a multiethnic sample.

Significance

Those with T2D have higher incidence of bladder cancer, regardless of racial/ethnic group. Reducing T2D prevalence could substantially lower bladder cancer incidence among Native Hawaiians due to T2D being more common in this group. High absolute risk of bladder cancer among European Americans, regardless of T2D status, indicates that elevated bladder cancer risk in this group may be due to factors other than T2D. Future studies must explore reasons for this difference in incidence.

Estimation of separable direct and indirect effects in continuous time

Many research questions involve time-to-event outcomes that can be prevented from occurring due to competing events. In these settings, we must be careful about the causal interpretation of classical statistical estimands. In particular, estimands on the hazard scale, such as ratios of cause-specific or subdistribution hazards, are fundamentally hard to interpret causally. Estimands on the risk scale, such as contrasts of cumulative incidence functions, do have a clear causal interpretation, but they only capture the total effect of the treatment on the event of interest; that is, effects both through and outside of the competing event. To disentangle causal treatment effects on the event of interest and competing events, the separable direct and indirect effects were recently introduced. Here we provide new results on the estimation of direct and indirect separable effects in continuous time. In particular, we derive the nonparametric influence function in continuous time and use it to construct an estimator that has certain robustness properties. We also propose a simple estimator based on semiparametric models for the two cause-specific hazard functions. We describe the asymptotic properties of these estimators and present results from simulation studies, suggesting that the estimators behave satisfactorily in finite samples. Finally, we reanalyze the prostate cancer trial from Stensrud et al. (2020).

Oral anticoagulant management of patients with mechanical heart valves at the Salam Centre of Khartoum: Observations on quality of anticoagulation and thrombotic risk

INTRODUCTION

Rheumatic heart disease with mechanical heart valve (MHV) replacement is common in Africa. However, MHV requires long-life anticoagulation and managing this can be challenging.

METHODS AND RESULTS

We report data of a prospective observational study conducted between August 2018 and September 2019 in MHV patients in the Salam Centre for Cardiac Surgery built in Khartoum, by Emergency, an Italian Non-Governmental Organization, to evaluate the quality of anticoagulation control and the risk of thrombotic complications.

RESULTS

We studied 3647 patients (median age 25.1 years; 53.9 % female). Median Time in Therapeutic Range (TTR) was 53 % (interquartile range 37 % to 67 %) and 70 thrombotic events (rate 1.8 × 100 pt-years [95 % CI 1.38-2.23]) were recorded. Among patients in the first quartile of TTR (≤37 %), we recorded 34/70 (48.6 %) of all thrombotic events (rate 3.7 × 100 pt-years [95 % CI 2.5-5.1]), with a high mortality rate (2.2 × 100 pt-years [95 % CI 1.3-3.3]). In patients with guideline-recommended TTR (≥65 %) the event rate was 0.8 × 100 pt-years for thrombotic events [95 % CI 0.3-1.5] and 0.4 × 100 pt-years for mortality [95 % CI 0.1-0.9]. Multivariable analysis showed that having a TTR in the lowest quartile (≤37 %) and being noncompliant are significantly associated with increased thrombotic risk. Aspirin use or different valve type did not influence the thrombotic risk. Almost 40 % of all thromboembolic complications could have been potentially prevented by further improving VKA management to obtain a TTR > 37 %.

CONCLUSION

The thrombotic risk of MHV patients on VKAs living in a low-income country like Sudan is associated with low quality of anticoagulation control. Efforts should be made to decrease the number of non-compliant patients and to reach a guideline-recommended TTR of ≥65 %.

Could greenness modify the effects of physical activity and air pollutants on overweight and obesity among children and adolescents?

Greenness could theoretically increase the impact of physical activity (PA) and reduce the adverse effects of air pollution on overweight/obesity. However, no evidence systematically compares these two pathways, especially in longitudinal studies of children and adolescent's cohort. Greenness, PA, and air pollution were assessed by Normalized Difference Vegetation Index (NDVI), International Physical Activity Short Form, and 7 pollutants (PM₁, PM_2.5, PM₁₀, SO₂, NO₂, CO, and O₃). Each exposure was divided into low-/high-level groups based on the 50% quantile. Proportional hazards and logistic regression model were used to assess the associations of greenness, PA, pollutants with overweight/obesity. The incidence of overweight/obesity was 1.98% in the national survey, and the cumulative incidence and incidence density were 12.76% and 3.43 per 100 person-year in the dynamic cohort, separately. An increase of 0.1 units in NDVI was associated with a 12% lower risk of overweight/obesity, but no significant link between PA and incidence was observed. The HRs of the high-level of PM₁, PM_2.5, PM₁₀, SO₂, NO₂, CO, and O₃ on the risk of overweight/obesity were 2.21, 2.63, 1.88, 2.38, 1.33, 2.43, and 1.33 in the low-level of greenness, which was higher than those in the high-level of greenness. The AFs of PM₁, PM_2.5, PM₁₀, SO₂, NO₂, CO, and O₃ were 25.58%, 44.37%, 22.96%, 29.15%, 11.55%, 29.50%, and 10.92% in the low-level of greenness, which simultaneously was higher than those in the high-level of greenness. Moreover, the risk of overweight/obesity associated with high-level of greenness in the high-level of PM₁₀, SO₂, CO were 0.83, 0.81, and 0.83 respectively. Our findings confirmed that greenness has a moderating effect on the effects of air pollutants on childhood overweight/obesity especially in heavy-industry areas where PM₁₀, SO₂, and CO are the major pollutants, although it did not influence the association between PA and overweight/obesity risks.

Risk factors for development of long-term mood and anxiety disorder after pediatric traumatic brain injury: a population-based, birth cohort analysis

OBJECTIVES

The objective of this study was to identify characteristics associated with an increased risk of anxiety and mood disorder prior to 25 years of age, in children who sustained a traumatic brain injury (TBI) prior to age 10.

METHODS

This population-based study identified 562 TBI cases from a 1976-1982 birth cohort in Olmsted County, Minnesota. TBI cases were manually confirmed and classified by injury severity. Separate Cox proportional hazards regression models were fit to estimate the association of TBI and secondary non-TBI related characteristics with the risk of a subsequent clinically determined anxiety or mood disorder. Multivariable-adjusted population attributable risk (PAR) estimates were calculated for TBI characteristics.

RESULTS

Older age at initial TBI and extracranial injury at time of initial TBI were significantly associated with an increased risk of anxiety (adjusted HR [95% CI]: 1.33 [1.16, 1.52] per 1-year increase and 2.41 [1.26, 4.59]), respectively. Older age at initial TBI was significantly associated with an increased risk of a mood disorder (adjusted HR 1.17 [1.08-1.27]).

CONCLUSION

In individuals sustaining a TBI prior to age 10, age at injury greater than 5 years old was the largest contributor to development of a mood or anxiety disorder.

Greenness alleviates the effects of ambient particulate matter on the risks of high blood pressure in children and adolescents

Both ambient particulate matter (PM) and decrease of greenness have been suggested as risk factors for high blood pressure (HBP) in children and adolescents. But most evidence were from cross-sectional studies with limited data from prospective cohorts. In this cohort study, we included 588,004 children and adolescents aged 7 to 18 years without HBP from 2005 to 2018 in Beijing (240,081) and Zhongshan (347,923) city of China. The cumulative incidence of HBP was 32.04%, and incidence rate was 14.86 per 100 person-year. After adjustment for confounders, the ten-unit increase in PM₁, PM_2.5, and PM₁₀ exposure was significantly associated with 43%, 70%, and 43%- higher risks of HBP, respectively, but the 0.1-unit increase in NDVI exposure was significantly associated with a 25% lower risk of HBP. The HRs of PM₁ on the HBP risk were 1.486 and 1.150 in the low and the high-level of greenness, and they were 2.635 and 2.507 for PM_2.5, and for PM₁₀ 1.367 and 1.702 in the two groups. The attributable fraction (AFs) of PM₁, PM_2.5, and PM₁₀ on HBP incidents were 13.74%, 40.08%, and 15.47% in the low-level of greenness, which simultaneously was higher than those in the high-level of greenness (AF = 4.62%, 17.28%, and 9.96%). The exposure to higher ambient PM air pollution and lower greenness around schools were associated with a higher risk of HBP in children and adolescents, but higher greenness alleviated the adverse effects of ambient PM₁ and PM_2.5 on the HBP risks. Our findings highlighted a synergic strategy in preventing childhood HBP by decreasing air pollution reduction and improving greenness concurrently.

Patterns and trends in eczema management in UK primary care (2009-2018): A population-based cohort study

Background

Despite the high disease burden of eczema, a contemporary overview of the patterns and trends in primary care healthcare utilization and treatment is lacking.

Objective

To quantify primary care consultations, specialist referrals, prescribing, and treatment escalation, in children and adults with eczema in England.

Methods

A large primary care research database was used to examine healthcare and treatment utilization in people with active eczema (n = 411,931). Management trends and variations by age, sex, socioeconomic status, and ethnicity were described from 2009 to 2018 inclusive.

Results

Primary care consultation rates increased from 87.8 (95% confidence interval [95% CI] 87.3–88.3) to 112.0 (95% CI 111.5–112.6) per 100 person‐years over 2009 to 2018. Specialist referral rates also increased from 3.8 (95% CI 3.7–3.9) to 5.0 (95% CI 4.9–5.1) per 100 person‐years over the same period. Consultation rates were highest in infants. Specialist referrals were greatest in the over 50s and lowest in people of lower socioeconomic status, despite a higher rate of primary care consultations. There were small changes in prescribing over time; emollients increased (prescribed to 48.5% of people with active eczema in 2009 compared to 51.4% in 2018) and topical corticosteroids decreased (57.3%–52.0%). Prescribing disparities were observed, including less prescribing of potent and very potent topical corticosteroids in non‐white ethnicities and people of lower socioeconomic status. Treatment escalation was more common with increasing age and in children of non‐white ethnicity.

Conclusion and clinical relevance

The management of eczema varies by sociodemographic status in England, with lower rates of specialist referral in people from more‐deprived backgrounds. There are different patterns of healthcare utilization, treatment, and treatment escalation in people of non‐white ethnicity and of more‐deprived backgrounds.

Risk Factors for Incident Stroke and Its Subtypes in China: A Prospective Study

Background

Managing risk factors is crucial to prevent stroke. However, few cohort studies have evaluated socioeconomic factors together with conventional factors affecting incident stroke and its subtypes in China.

Methods and Results

A 2014 to 2016 prospective study from the China National Stroke Screening and Intervention Program comprised 437 318 adults aged ≥40 years without stroke at baseline. There were 2429 cases of first‐ever stroke during a median follow‐up period of 2.1 years, including 2206 ischemic strokes and 237 hemorrhagic strokes. The multivariable Cox regression analysis indicated that age 50 to 59 years (versus 40–49 years), primary school or no formal education (versus middle school), having >1 child (versus 1 child), living in Northeast, Central, East, or North China (versus Southwest China), physical inactivity, hypertension, diabetes mellitus, and obesity were positively associated with the risk of total and ischemic stroke, whereas age 60 to 69 years and living with spouse or children (versus living alone) were negatively associated with the risk of total and ischemic stroke. Men, vegetable‐based diet, underweight, physical inactivity, hypertension, living in a high‐income region, having Urban Resident Basic Medical Insurance, and New Rural Cooperative Medical System were positively associated with the risk of hemorrhagic stroke, whereas age 60 to 69 years was negatively associated with the risk of hemorrhagic stroke.

Conclusions

We identified socioeconomic factors that complement traditional risk factors for incident stroke and its subtypes, allowing targeting these factors to reduce stroke burden.

Adjusted time-varying population attributable hazard in case-control studies

Population attributable fraction is a widely used measure for quantifying the disease burden associated with a modifiable exposure of interest at the population level. It has been extended to a time-varying measure, population attributable hazard function, to provide additional information on when and how the exposure's impact varies over time. However, like the classic population attributable fraction, the population attributable hazard is generally biased if confounders are present. In this article, we provide a natural definition of adjusted population attributable hazard to take into account the effects of confounders, and its alternative that is identifiable from case-control studies under the rare disease assumption. We propose a novel estimator, which combines the odds ratio estimator from logistic regression model, and the conditional density function estimator of the exposure and confounding variables distribution given the failure times of cases or the current times of controls from a kernel smoother. We show that the proposed estimators are consistent and asymptotically normal with variance that can be estimated empirically from the data. Simulation studies demonstrate that the proposed estimators perform well in finite sample sizes. Finally, we illustrate the method by an analysis of a case-control study of colorectal cancer. Supplementary materials for this article are available online.

Using evidence to direct quality improvement efforts: Defining the highest impact complications after complex cytoreductive surgery for ovarian cancer

OBJECTIVE

We sought to identify postoperative complications with the greatest impact on patient-centric outcomes to serve as high yield QI targets in ovarian cancer (OC) surgery.

METHODS

Women undergoing complex CRS (defined as cytoreductive surgery with colon resection) for OC between January 1, 2012 and 12/31/2016 were identified from the National Surgical Quality Improvement Program (NSQIP) database. We determined the population attributable fraction (PAF) to quantify the contribution of each major complication towards adverse outcomes. PAF represents the burden of adverse outcomes that could be eliminated if the corresponding complication was prevented. Organ space surgical site infection (SSI) was used as a surrogate for anastomotic leak (AL).

RESULTS

A total of 1434 women met inclusion criteria. Any adverse clinical outcome (composite of death, reoperation, or end organ dysfunction) occurred in 9.1% of women, and AL was the largest contributor to adverse clinical outcomes [PAF = 33.4% (95%CI: 22.3%-45.6%)]. The rates of increased resource utilization were as follows; prolonged hospitalization in 23.7%, non-home discharge in 10.7% and unplanned readmission in 14.8% of women. AL was the largest contributor to prolonged hospitalizations [PAF = 75.7% (95%CI: 51.4%-90.0%)] and readmissions [PAF = 17.1% (95%CI: 11.5%-22.6%)]; while transfusion was the largest contributor to non-home discharge [PAF = 22.8% (95%CI: 0.7%-42.4%)]. By comparison, the impact of other complications, including those targeted by the Surgical Care Improvement Project (SCIP), such as incisional SSI, venous thromboembolism, myocardial infarction, and urinary infection, was small.

CONCLUSIONS

Anastomotic leak is the largest contributor to adverse clinical outcomes and increased resource utilization after complex cytoreductive surgery. Quality improvement efforts to reduce AL and its impact should be of highest priority in OC surgery.

A causal proportional hazards estimator under homogeneous or heterogeneous selection in an IV setting

In this paper we present a framework to do estimation in a structural Cox model when there may be unobserved confounding. The model is phrased in terms of a selection bias function and a baseline model that describes how covariates affect the survival time in a scenario without exposure. In this way model congeniality is ensured. The method uses an instrumental variable. Interestingly, the formulated model turns out to have similarities to the so-called Cox-Aalen survival model for the observed data. We exploit this to enhance estimation of the unknown parameters. This also allows us to derive large sample properties of the proposed estimator.

The Causal Effect of Maternal Education on Child Mortality: Evidence From a Quasi-Experiment in Malawi and Uganda

Since the 1980s, the demographic literature has suggested that maternal schooling plays a key role in determining children's chances of survival in low- and middle-income countries; however, few studies have successfully identified a causal relationship between maternal education and under-5 mortality. To identify such a causal effect, we exploited exogenous variation in maternal education induced by schooling reforms introducing universal primary education in the second half of the 1990s in Malawi and Uganda. Using a two-stage residual inclusion approach and combining individual-level data from Demographic and Health Surveys with district-level data on the intensity of the reform, we tested whether increased maternal schooling reduced children's probability of dying before age 5. In Malawi, for each additional year of maternal education, children have a 10 % lower probability of dying; in Uganda, the odds of dying for children of women with one additional year of education are 16.6 % lower. We also explored which pathways might explain this effect of maternal education. The estimates suggest that financial barriers to medical care, attitudes toward modern health services, and rejection of domestic violence may play a role. Moreover, being more educated seems to confer enhanced proximity to a health facility and knowledge about the transmission of AIDS in Malawi, and wealth and improved personal illness control in Uganda.

The population-attributable fraction for time-dependent exposures and competing risks-A discussion on estimands

The population-attributable fraction (PAF) quantifies the public health impact of a harmful exposure. Despite being a measure of significant importance, an estimand accommodating complicated time-to-event data is not clearly defined. We discuss current estimands of the PAF used to quantify the public health impact of an internal time-dependent exposure for data subject to competing outcomes. To overcome some limitations, we proposed a novel estimand that is based on dynamic prediction by landmarking. In a profound simulation study, we discuss interpretation and performance of the various estimands and their estimators. The methods are applied to a large French database to estimate the health impact of ventilator-associated pneumonia for patients in intensive care.

The frailty index is a predictor of cause-specific mortality independent of familial effects from midlife onwards: a large cohort study

BACKGROUND

Frailty index (FI) is a well-established predictor of all-cause mortality, but less is known for cause-specific mortality and whether familial effects influence the associations. Middle-aged individuals are also understudied for the association between FI and mortality. Furthermore, the population mortality impact of frailty remains understudied.

METHODS

We estimated the predictive value of FI for all-cause and cause-specific mortality, taking into account familial factors, and tested whether the associations are time-dependent. We also assessed the proportion of all-cause and cause-specific deaths that are attributable to increased levels of frailty. We analyzed 42,953 participants from the Screening Across the Lifespan Twin Study (aged 41-95 years at baseline) with up to 20 years' mortality follow-up. The FI was constructed using 44 health-related items. Deaths due to cardiovascular disease (CVD), respiratory-related causes, and cancer were considered in the cause-specific analysis. Generalized survival models were used in the analysis.

RESULTS

Increased FI was associated with higher risks of all-cause, CVD, and respiratory-related mortality, with the corresponding hazard ratios of 1.28 (1.24, 1.32), 1.31 (1.23, 1.40), and 1.23 (1.11, 1.38) associated with a 10% increase in FI in male single responders, and 1.21 (1.18, 1.25), 1.27 (1.15, 1.34), and 1.26 (1.15, 1.39) in female single responders. No significant associations were observed for cancer mortality. No attenuation of the mortality associations in unrelated individuals was observed when adjusting for familial effects in twin pairs. The associations were time-dependent with relatively greater effects observed in younger ages. Before the age of 80, the proportions of deaths attributable to FI levels > 0.21 were 18.4% of all-cause deaths, 25.4% of CVD deaths, and 20.4% of respiratory-related deaths in men and 19.2% of all-cause deaths, 27.8% of CVD deaths, and 28.5% of respiratory-related deaths in women. After the age of 80, the attributable proportions decreased, most notably for all-cause and CVD mortality.

CONCLUSIONS

Increased FI predicts higher risks of all-cause, CVD, and respiratory-related mortality independent of familial effects. Increased FI presents a relatively greater risk factor at midlife than in old age. Increased FI has a significant population mortality impact that is greatest through midlife until the age of 80.

Association of Dietary Niacin Intake With Incident Hip Fracture, BMD, and Body Composition: The Cardiovascular Health Study

Interest in niacin has increased in the setting of reports suggesting that niacin plays a role in diseases of aging. No study to date has examined the association of dietary niacin intake with multiple skeletal health parameters including bone mineral density (BMD), hip fractures, and body composition, and none have included both African American and white men and women. Participants included 5187 men and women ≥65 years from the Cardiovascular Health Study (CHS). Mean daily dietary niacin intake was 32.6 mg, with quartiles 1 through 4 defined as 3.6 to 21.8 mg/day, 21.9 to 30.2 mg/day, 30.3 to 40.9 mg/day, and 41.0 to 102.4 mg/day, respectively. Risk of incident hip fracture per 10 mg increment of daily dietary niacin intake was estimated using proportional hazards models. During a median follow-up of 13 years, 725 participants had an incident hip fracture. In models adjusted for demographic and clinical characteristics and diet, dietary niacin intake was significantly associated with an increased risk of hip fractures (hazard ratio [HR] 1.12; 95% CI, 1.01 to 1.24) with spline models suggesting a U-shaped association. In post hoc analyses, both the lowest (HR 1.31; 95% CI, 1.04 to 1.66) and highest (HR 1.53; 95% CI, 1.20 to 1.95) quartiles of niacin intake were associated with an increased risk of incident hip fracture versus quartiles 2 and 3. There was a trend for a significant inverse association of dietary niacin intake with hip BMD (p = 0.06), but no significant association with total body BMD or any body composition measures. In this cohort of elderly, community-dwelling African American and white men and women, both high and low dietary niacin intakes were associated with a significantly increased risk of subsequent hip fracture, suggesting a possible U-shaped association. By comparison, dietary niacin may have an inverse linear association with hip BMD. © 2018 American Society for Bone and Mineral Research.

Estimation of causal effect measures with the R-package stdReg

Measures of causal effects play a central role in epidemiology. A wide range of measures exist, which are designed to give relevant answers to substantive epidemiological research questions. However, due to mathematical convenience and software limitations most studies only report odds ratios for binary outcomes and hazard ratios for time-to-event outcomes. In this paper we show how logistic regression models and Cox proportional hazards regression models can be used to estimate a wide range of causal effect measures, with the R-package stdReg. For illustration we focus on the attributable fraction, the number needed to treat and the relative excess risk due to interaction. We use two publicly available data sets, so that the reader can easily replicate and elaborate on the analyses. The first dataset includes information on 487 births among 188 women, and the second dataset includes information on 2982 women diagnosed with primary breast cancer.

The effect of physical activity on mortality and cardiovascular disease in 130 000 people from 17 high-income, middle-income, and low-income countries: the PURE study

BACKGROUND

Physical activity has a protective effect against cardiovascular disease (CVD) in high-income countries, where physical activity is mainly recreational, but it is not known if this is also observed in lower-income countries, where physical activity is mainly non-recreational. We examined whether different amounts and types of physical activity are associated with lower mortality and CVD in countries at different economic levels.

METHODS

In this prospective cohort study, we recruited participants from 17 countries (Canada, Sweden, United Arab Emirates, Argentina, Brazil, Chile, Poland, Turkey, Malaysia, South Africa, China, Colombia, Iran, Bangladesh, India, Pakistan, and Zimbabwe). Within each country, urban and rural areas in and around selected cities and towns were identified to reflect the geographical diversity. Within these communities, we invited individuals aged between 35 and 70 years who intended to live at their current address for at least another 4 years. Total physical activity was assessed using the International Physical Activity Questionnaire (IPQA). Participants with pre-existing CVD were excluded from the analyses. Mortality and CVD were recorded during a mean of 6·9 years of follow-up. Primary clinical outcomes during follow-up were mortality plus major CVD (CVD mortality, incident myocardial infarction, stroke, or heart failure), either as a composite or separately. The effects of physical activity on mortality and CVD were adjusted for sociodemographic factors and other risk factors taking into account household, community, and country clustering.

FINDINGS

Between Jan 1, 2003, and Dec 31, 2010, 168 916 participants were enrolled, of whom 141 945 completed the IPAQ. Analyses were limited to the 130 843 participants without pre-existing CVD. Compared with low physical activity (<600 metabolic equivalents [MET] × minutes per week or <150 minutes per week of moderate intensity physical activity), moderate (600-3000 MET × minutes or 150-750 minutes per week) and high physical activity (>3000 MET × minutes or >750 minutes per week) were associated with graded reduction in mortality (hazard ratio 0·80, 95% CI 0·74-0·87 and 0·65, 0·60-0·71; p<0·0001 for trend), and major CVD (0·86, 0·78-0·93; p<0·001 for trend). Higher physical activity was associated with lower risk of CVD and mortality in high-income, middle-income, and low-income countries. The adjusted population attributable fraction for not meeting the physical activity guidelines was 8·0% for mortality and 4·6% for major CVD, and for not meeting high physical activity was 13·0% for mortality and 9·5% for major CVD. Both recreational and non-recreational physical activity were associated with benefits.

INTERPRETATION

Higher recreational and non-recreational physical activity was associated with a lower risk of mortality and CVD events in individuals from low-income, middle-income, and high-income countries. Increasing physical activity is a simple, widely applicable, low cost global strategy that could reduce deaths and CVD in middle age.

FUNDING

Population Health Research Institute, the Canadian Institutes of Health Research, Heart and Stroke Foundation of Ontario, Ontario SPOR Support Unit, Ontario Ministry of Health and Long-Term Care, AstraZeneca, Sanofi-Aventis, Boehringer Ingelheim, Servier, GSK, Novartis, King Pharma, and national and local organisations in participating countries that are listed at the end of the Article.

Regression standardization and attributable fraction estimation with between-within frailty models for clustered survival data

The between-within frailty model has been proposed as a viable analysis tool for clustered survival time outcomes. Previous research has shown that this model gives consistent estimates of the exposure–outcome hazard ratio in the presence of unmeasured cluster-constant confounding, which the ordinary frailty model does not, and that estimates obtained from the between-within frailty model are often more efficient than estimates obtained from the stratified Cox proportional hazards model. In this paper, we derive novel estimation techniques for regression standardization with between-within frailty models. We also show how between-within frailty models can be used to estimate the attributable fraction function, which is a generalization of the attributable fraction for survival time outcomes. We illustrate the proposed methods by analyzing a large cohort on preterm birth and attention deficit hyperactivity disorder. To facilitate use of the proposed methods, we provide R code for all analyses.

On estimation of time-dependent attributable fraction from population-based case-control studies

Population attributable fraction (PAF) is widely used to quantify the disease burden associated with a modifiable exposure in a population. It has been extended to a time-varying measure that provides additional information on when and how the exposure's impact varies over time for cohort studies. However, there is no estimation procedure for PAF using data that are collected from population-based case-control studies, which, because of time and cost efficiency, are commonly used for studying genetic and environmental risk factors of disease incidences. In this article, we show that time-varying PAF is identifiable from a case-control study and develop a novel estimator of PAF. Our estimator combines odds ratio estimates from logistic regression models and density estimates of the risk factor distribution conditional on failure times in cases from a kernel smoother. The proposed estimator is shown to be consistent and asymptotically normal with asymptotic variance that can be estimated empirically from the data. Simulation studies demonstrate that the proposed estimator performs well in finite sample sizes. Finally, the method is illustrated by a population-based case-control study of colorectal cancer.

Estimation of exposure distribution adjusting for association between exposure level and detection limit

In environmental exposure studies, it is common to observe a portion of exposure measurements to fall below experimentally determined detection limits (DLs). The reverse Kaplan-Meier estimator, which mimics the well-known Kaplan-Meier estimator for right-censored survival data with the scale reversed, has been recommended for estimating the exposure distribution for the data subject to DLs because it does not require any distributional assumption. However, the reverse Kaplan-Meier estimator requires the independence assumption between the exposure level and DL and can lead to biased results when this assumption is violated. We propose a kernel-smoothed nonparametric estimator for the exposure distribution without imposing any independence assumption between the exposure level and DL. We show that the proposed estimator is consistent and asymptotically normal. Simulation studies demonstrate that the proposed estimator performs well in practical situations. A colon cancer study is provided for illustration. Copyright © 2017 John Wiley & Sons, Ltd.

Comparison of methods for estimating the attributable risk in the context of survival analysis

Background

The attributable risk (AR) measures the proportion of disease cases that can be attributed to an exposure in the population. Several definitions and estimation methods have been proposed for survival data.

Methods

Using simulations, we compared four methods for estimating AR defined in terms of survival functions: two nonparametric methods based on Kaplan-Meier’s estimator, one semiparametric based on Cox’s model, and one parametric based on the piecewise constant hazards model, as well as one simpler method based on estimated exposure prevalence at baseline and Cox’s model hazard ratio. We considered a fixed binary exposure with varying exposure probabilities and strengths of association, and generated event times from a proportional hazards model with constant or monotonic (decreasing or increasing) Weibull baseline hazard, as well as from a nonproportional hazards model. We simulated 1,000 independent samples of size 1,000 or 10,000. The methods were compared in terms of mean bias, mean estimated standard error, empirical standard deviation and 95% confidence interval coverage probability at four equally spaced time points.

Results

Under proportional hazards, all five methods yielded unbiased results regardless of sample size. Nonparametric methods displayed greater variability than other approaches. All methods showed satisfactory coverage except for nonparametric methods at the end of follow-up for a sample size of 1,000 especially. With nonproportional hazards, nonparametric methods yielded similar results to those under proportional hazards, whereas semiparametric and parametric approaches that both relied on the proportional hazards assumption performed poorly. These methods were applied to estimate the AR of breast cancer due to menopausal hormone therapy in 38,359 women of the E3N cohort.

Conclusion

In practice, our study suggests to use the semiparametric or parametric approaches to estimate AR as a function of time in cohort studies if the proportional hazards assumption appears appropriate.

Electronic supplementary material

The online version of this article (doi:10.1186/s12874-016-0285-1) contains supplementary material, which is available to authorized users.

Semi-parametric regression model for survival data: graphical visualization with R

Cox proportional hazards model is a semi-parametric model that leaves its baseline hazard function unspecified. The rationale to use Cox proportional hazards model is that (I) the underlying form of hazard function is stringent and unrealistic, and (II) researchers are only interested in estimation of how the hazard changes with covariate (relative hazard). Cox regression model can be easily fit with coxph() function in survival package. Stratified Cox model may be used for covariate that violates the proportional hazards assumption. The relative importance of covariates in population can be examined with the rankhazard package in R. Hazard ratio curves for continuous covariates can be visualized using smoothHR package. This curve helps to better understand the effects that each continuous covariate has on the outcome. Population attributable fraction is a classic quantity in epidemiology to evaluate the impact of risk factor on the occurrence of event in the population. In survival analysis, the adjusted/unadjusted attributable fraction can be plotted against survival time to obtain attributable fraction function.

A cautionary note on the use of attributable fractions in cohort studies

The attributable fraction is a widely used measure to quantify the public health impact of an exposure on an outcome. It was originally proposed for binary outcomes, but attributable fraction estimators have also been proposed for time-to-event outcomes. In this note, we consider an estimator which was proposed by Benichou ( Stats Methods Med Res, 2001) and is supposed to estimate the cohort attributable fraction, i.e. the number of events that would have been prevented in the cohort during follow-up, if the exposure would hypothetically have been eliminated. We show that this estimator is only valid under certain assumptions, which are often likely to be violated in practice. We further argue that the cohort attributable fraction may not be of substantial scientific interest in the first place. We propose a potentially more relevant measure of attributable fraction in cohort studies; the baseline attributable fraction. We show how the baseline attributable fraction can be conveniently estimated in Cox proportional hazards models.

Doubly robust estimation of attributable fractions in survival analysis

The attributable fraction is a commonly used measure that quantifies the public health impact of an exposure on an outcome. It was originally defined for binary outcomes, but an extension has recently been proposed for right-censored survival time outcomes; the so-called attributable fraction function. A maximum likelihood estimator of the attributable fraction function has been developed, which requires a model for the outcome. In this paper, we derive a doubly robust estimator of the attributable fraction function. This estimator requires one model for the outcome, and one joint model for the exposure and censoring. The estimator is consistent if either model is correct, not necessarily both.

Percentage of deaths attributable to poor cardiovascular health lifestyle factors: Findings from the Aerobics Center Longitudinal Study

PURPOSE

We assessed the effects of the four newly defined American Heart Association (AHA) lifestyle factors on mortality by examining the associated population attributable fractions (PAFs) of these factors.

METHODS

Slightly modified AHA cardiovascular health factors (smoking, BMI, cardiorespiratory fitness, and diet) were measured among 11,240 (24% women) participants from the Aerobics Center Longitudinal Study between 1987 and 1999. The cohort was followed to December 31, 2003 or death. PAFs were calculated as the proportionate reduction in death attributable to identified risk factors.

RESULTS

During an average 12 years of follow-up, 268 deaths occurred. Low fitness had the highest PAFs at the 5^th, 10^th, and 15^th year of follow-up, respectively: 6.6%, 6.4%, and 5.5%. Current smokers had the second highest PAFs at the 5^th, 10^th, and 15^th year of follow-up, respectively: 5.4%, 5.2%, and 5.0%. Additional adjusting for other confounders in the model did not change the above associations. The PAFs for overweight or obesity and unhealthy diet were not significant in the current analyses.

CONCLUSIONS

Assuming a causal relationship between smoking, low fitness and mortality, avoidance of both would have prevented 13% of the deaths in the current population. Preventive interventions to increase physical activity and stop smoking would most likely promote longevity.

On collapsibility and confounding bias in Cox and Aalen regression models

We study the situation where it is of interest to estimate the effect of an exposure variable [Formula: see text] on a survival time response [Formula: see text] in the presence of confounding by measured variables [Formula: see text]. Quantifying the amount of confounding is complicated by the non-collapsibility or non-linearity of typical effect measures in survival analysis: survival analyses with or without adjustment for [Formula: see text] typically infer different effect estimands of a different magnitude, even when [Formula: see text] is not associated with the exposure, and henceforth not a confounder of the association between exposure and survival time. We show that, interestingly, the exposure coefficient indexing the Aalen additive hazards model is not subject to such non-collapsibility, unlike the corresponding coefficient indexing the Cox model, so that simple measures of the amount of confounding bias are obtainable for the Aalen hazards model, but not for the Cox model. We argue that various other desirable properties can be ascribed to the Aalen model as a result of this collapsibility. This work generalizes recent work by Janes et al. (Biostatistics 11:572-582, 2010).

Estimation of odds of concordance based on the Aalen additive model

The Cox regression model is often used when analyzing survival data as it provides a convenient way of summarizing covariate effects in terms of relative risks. The proportional hazards assumption may not hold, however. A typical violation of the assumption is time-changing covariate effects. Under such scenarios one may use more flexible models but the results from such models may be complicated to communicate and it is desirable to have simple measures of a treatment effect, say. In this paper we focus on the odds-of-concordance measure that was recently studied by Schemper et al. (Stat Med 28:2473-2489, 2009). They suggested to estimate this measure using weighted Cox regression (WCR). Although WCR may work in many scenarios no formal proof can be established. We suggest an alternative estimator of the odds-of-concordance measure based on the Aalen additive hazards model. In contrast to the WCR, one may derive the large sample properties for this estimator making formal inference possible. The estimator also allows for additional covariate effects.