International journal of epidemiology最新文献

Background: The healthy worker effect may distort the association between exposure and health effects in workers. However, few studies have investigated both the healthy worker hire and survival effects simultaneously, and they are limited to mortality studies in male workers.

Methods: We utilized a data set comprising South Korean diagnostic medical radiation workers registered in the National Dose Registry between 1996 and 2011, and merged it with mortality and cancer incidence data. Standardized mortality ratios (SMRs) and standardized incidence ratios (SIRs) were computed for comparison with the general population. To account for time-varying confounders influenced by prior occupational radiation exposure, we applied g-estimation using structural nested accelerated failure time models and compared the outcomes with those from Weibull regression.

Results: A total of 1831 deaths and 3759 first primary cancer cases were identified among 93 918 workers. Both male (SMR = 0.44; 95% CI: 0.42, 0.46) and female workers (SMR = 0.53; 95% CI: 0.46, 0.60) showed lower mortality rates compared with national rates. In the SIR analysis, male workers exhibited reduced risks of solid cancer whereas female workers had increased risks. The g-estimation-derived hazard ratios (HRs) from radiation exposure exceeded those from Weibull regression estimates for all-cause death (HR = 2.55; 95% CI: 1.97, 3.23) and all-cancer incidence (HR = 1.96; 95% CI: 1.52, 2.55) in male workers whereas female workers showed the opposite results.

Conclusions: Comprehensive consideration of the healthy worker effect by sex is essential for estimating the unbiased impact of occupational exposure on health outcomes, notably in studies focusing on male mortality.

Background: This study aimed to estimate population-level and state-level lead-attributable mortality burdens stratified by socioeconomic status (SES) class in the USA.

Methods: Based on the National Health and Nutrition Examination Survey (NHANES), we constructed individual-level SES scores from income, employment, education and insurance data. We assessed the association between the blood lead levels (BLL) and all-cause mortality by Cox regression in the NHANES cohort (n = 31 311, 4467 deaths). With estimated hazard ratios (HR) and prevalences of medium (2-5 μg/dL) and high (≥ 5 μg/dL) BLL, we computed SES-stratified population-attributable fractions (PAFs) of all-cause mortality from lead exposure across 1999-2019. We additionally conducted a systematic review to estimate the lead-attributable mortality burden at state-level.

Results: The HR for every 2-fold increase in the BLL decreased from 1.23 (1.10-1.38) for the lowest SES class to 1.05 (0.90-1.23) for the highest SES class. Across all SES quintiles, medium BLL exhibited a greater mortality burden. Individuals with lower SES had higher lead-attributable burdens, and such disparities haver persisted over the past two decades. In 2017-19, annually 67 000 (32 000-112 000) deaths in the USA were attributable to lead exposure, with 18 000 (2000-41 000) of these deaths occurring in the lowest SES class. Substantial disparities in the state-level mortality burden attributable to lead exposure were also highlighted.

Conclusions: These findings suggested that disparities in lead-attributable mortality burden persisted within US adults, due to heterogeneities in the effect sizes of lead exposure as well as in the BLL among different SES classes.

Background: Empirical evaluation of inverse probability weighting (IPW) for self-selection bias correction is inaccessible without the full source population. We aimed to: (i) investigate how self-selection biases frequency and association measures and (ii) assess self-selection bias correction using IPW in a cohort with register linkage.

Methods: The source population included 17 936 individuals invited to the Copenhagen Aging and Midlife Biobank during 2009-11 (ages 49-63 years). Participants counted 7185 (40.1%). Register data were obtained for every invited person from 7 years before invitation to the end of 2020. The association between education and mortality was estimated using Cox regression models among participants, IPW participants and the source population.

Results: Participants had higher socioeconomic position and fewer hospital contacts before baseline than the source population. Frequency measures of participants approached those of the source population after IPW. Compared with primary/lower secondary education, upper secondary, short tertiary, bachelor and master/doctoral were associated with reduced risk of death among participants (adjusted hazard ratio [95% CI]: 0.60 [0.46; 0.77], 0.68 [0.42; 1.11], 0.37 [0.25; 0.54], 0.28 [0.18; 0.46], respectively). IPW changed the estimates marginally (0.59 [0.45; 0.77], 0.57 [0.34; 0.93], 0.34 [0.23; 0.50], 0.24 [0.15; 0.39]) but not only towards those of the source population (0.57 [0.51; 0.64], 0.43 [0.32; 0.60], 0.38 [0.32; 0.47], 0.22 [0.16; 0.29]).

Conclusions: Frequency measures of study participants may not reflect the source population in the presence of self-selection, but the impact on association measures can be limited. IPW may be useful for (self-)selection bias correction, but the returned results can still reflect residual or other biases and random errors.

Background: Observational studies are frequently used to estimate the comparative effectiveness of different colorectal cancer (CRC) screening methods due to the practical limitations and time needed to conduct large clinical trials. However, time-varying confounders, e.g. polyp detection in the last screening, can bias statistical results. Recently, generalized methods, or G-methods, have been used for the analysis of observational studies of CRC screening, given their ability to account for such time-varying confounders. Discretization, or the process of converting continuous functions into discrete counterparts, is required for G-methods when the treatment and outcomes are assessed at a continuous scale.

Development: This paper evaluates the interplay between time-varying confounding and discretization, which can induce bias in assessing screening effectiveness. We investigate this bias in evaluating the effect of different CRC screening methods that differ from each other in typical screening frequency.

Application: First, using theory, we establish the direction of the bias. Then, we use simulations of hypothetical settings to study the bias magnitude for varying levels of discretization, frequency of screening and length of the study period. We develop a method to assess possible bias due to coarsening in simulated situations.

Conclusions: The proposed method can inform future studies of screening effectiveness, especially for CRC, by determining the choice of interval lengths where data are discretized to minimize bias due to coarsening while balancing computational costs.

Background: Numerous studies have linked fine particulate matter (PM2.5) to increased cardiovascular mortality. Less is known how the PM2.5-cardiovascular mortality association varies by use of cardiovascular medications. This study sought to quantify effect modification by statin use status on the associations between long-term exposure to PM2.5 and mortality from any cardiovascular cause, coronary heart disease (CHD), and stroke.

Methods: In this nested case-control study, we followed 1.2 million community-dwelling adults aged ≥66 years who lived in Ontario, Canada from 2000 through 2018. Cases were patients who died from the three causes. Each case was individually matched to up to 30 randomly selected controls using incidence density sampling. Conditional logistic regression models were used to estimate odds ratios (ORs) for the associations between PM2.5 and mortality. We evaluated the presence of effect modification considering both multiplicative (ratio of ORs) and additive scales (the relative excess risk due to interaction, RERI).

Results: Exposure to PM2.5 increased the risks for cardiovascular, CHD, and stroke mortality. For all three causes of death, compared with statin users, stronger PM2.5-mortality associations were observed among non-users [e.g. for cardiovascular mortality corresponding to each interquartile range increase in PM2.5, OR = 1.042 (95% CI, 1.032-1.053) vs OR = 1.009 (95% CI, 0.996-1.022) in users, ratio of ORs = 1.033 (95% CI, 1.019-1.047), RERI = 0.039 (95% CI, 0.025-0.050)]. Among users, partially adherent users exhibited a higher risk of PM2.5-associated mortality than fully adherent users.

Conclusions: The associations of chronic exposure to PM2.5 with cardiovascular and CHD mortality were stronger among statin non-users compared to users.

This paper presents causal loop diagrams (CLDs) as tools for studying complex public health problems like health inequality. These problems often involve feedback loops-a characteristic of complex systems not fully integrated into mainstream epidemiology. CLDs are conceptual models that visualize connections between system variables. They are commonly developed through literature reviews or participatory methods with stakeholder groups. These diagrams often uncover feedback loops among variables across scales (e.g. biological, psychological and social), facilitating cross-disciplinary insights. We illustrate their use through a case example involving the feedback loop between sleep problems and depressive symptoms. We outline a typical step-by-step process for developing CLDs in epidemiology. These steps are defining a specific problem, identifying the key system variables involved, mapping these variables and analysing the CLD to find new insights and possible intervention targets. Throughout this process, we suggest triangulating between diverse sources of evidence, including domain knowledge, scientific literature and empirical data. CLDs can also be evaluated to guide policy changes and future research by revealing knowledge gaps. Finally, CLDs may be iteratively refined as new evidence emerges. We advocate for more widespread use of complex systems tools, like CLDs, in epidemiology to better understand and address complex public health problems.

Background: The impact of societal factors on the occurrence of head and neck cancers (HNCs) remains understudied, especially in the Nordic countries.

Methods: To quantify the association between socio-economic status (SES) and the occurrence of HNCs, this cohort study uses data from the Nordic Occupational Cancer project that combine occupational and cancer registry data from 1961 to 2005 of 14.9 million individuals aged between 30 and 64 years. Occupational categories were combined into seven socio-economic categories. Standardized incidence ratio (SIR) analyses were conducted with the cancer incidence rates for the entire national study populations used as reference rates.

Results: Altogether, 83 997 HNCs-72% in men and 28% in women-were recorded. Among men, a gradient of risk associated with SES was observed for cancers of the tongue, other oral cavity subsites, pharynx, oropharynx and larynx in groups with lower SES. Managers showed decreased SIRs of 0.50 to -0.90 also for cancers of the lip, tongue, other oral cavity subsites, oropharynx, nasopharynx, nose and larynx. In contrast, excess risks of tongue, other oral cavity subsites, pharyngeal, oropharyngeal and laryngeal cancers were observed among clerical (SIRs 1.05-1.16), skilled workers (1.04-1.14), unskilled workers (1.16-1.26) and economically inactive men (1.38-1.87). Among women, no risk gradient similar to that in men was revealed.

Conclusions: The current study underscores the influence of SES on the incidence of HNCs and highlights the need for targeted interventions, including tobacco and alcohol control policies, and improved access to healthcare services, particularly for socio-economically disadvantaged populations.