Changes in cancer incidence rates by stage during the COVID-19 pandemic in the US

The coronavirus disease 2019 (COVID-19) pandemic led to health care disruptions and declines in cancer diagnoses in the United States. However, the impact of the pandemic on cancer incidence rates by stage at diagnosis and race and ethnicity is unknown. This cross-sectional study calculated delay- and age-adjusted incidence rates, stratified by stage at diagnosis and race and ethnicity, and rate ratios (RRs) comparing changes in year-over-year incidence rates (eg, 2020 vs 2019) from 2016 to 2020 for 22 cancer types based on data obtained from the Surveillance, Epidemiology, and End Results 22-registry database. From 2019 to 2020, the incidence of local-stage disease statistically significantly declined for 19 of the 22 cancer types, ranging from 4% (RR = 0.96; 95%CI, 0.93-0.98) for urinary bladder cancer to 18% for colorectal (RR = 0.82; 95%CI, 0.81-0.84) and laryngeal (RR = 0.82; 95%CI, 0.78-0.88) cancers, deviating from pre-COVID stable year-over-year changes. Incidence during the corresponding period also declined for 16 cancer types for regional-stage and six cancer types for distant-stage disease. By race and ethnicity, the decline in local-stage incidence for screening-detectable cancers was generally greater in historically marginalized populations. The decline in cancer incidence rates during the first year of the COVID-19 pandemic occurred mainly for local- and regional-stage diseases across racial and ethnic groups. Whether these declines will lead to increases in advanced-stage disease and mortality rates remain to be investigated with additional data years. Nevertheless, the findings reinforce the importance of strengthening the return to preventive care campaigns and outreach for detecting cancers at early and more treatable stages.

Non-Pharmacological Preventive Measures Had an Impact on COVID-19 in Healthcare Workers before the Vaccination Effect: A Cohort Study

Healthcare workers have been and still are at the forefront of COVID-19 patient care. Their infection had direct implications and caused important challenges for healthcare performance. The aim of this study is to assess the impact of non-pharmacological preventive measures against COVID-19 among healthcare workers. This study is based on a dynamic cohort of healthcare workers (n = 5543) who had been hired by a Spanish hospital for at least one week during 2020. Negative binomial regression models were used to estimate the incidence rate and the rate ratio (RR) between the two waves (defined from 15 March to 21 June and from 22 June to 31 December), considering natural immunity during the first wave and contextual variables. All models were stratified by socio-occupational variables. The average COVID-19 incidence rate per 1000 worker-days showed a significant reduction between the two waves, dropping from 0.82 (CI95%: 0.73-0.91) to 0.39 (0.35-0.44). The adjusted RR was 0.54 (0.48-0.87) when natural immunity was acquired during the first wave, and contextual variables were considered. The significant reduction of the COVID-19 incidence rate could be explained mainly by improvement in the non-pharmacological preventive interventions. It is needed to identify which measures were more effective. Young workers and those with a replacement contract were identified as vulnerable groups that need greater preventive efforts. Future preparedness plans would benefit from these results.

Inference about ratios of age-standardized rates with sampling errors in the population denominators for estimating both rates

A rate ratio (RR) is an important metric for comparing cancer risks among different subpopulations. Inference for RR becomes complicated when populations used for calculating age-standardized cancer rates involve sampling errors, a situation that arises increasingly often when sample surveys must be used to obtain the population data. We compare a few strategies of estimating the standardized RR and propose bias-corrected ratio estimators as well as the corresponding variance estimators and confidence intervals that simultaneously consider the sampling error in estimating populations and the traditional Poisson error in the occurrence of cancer case or death. Performance of the proposed methods is evaluated empirically based on simulation studies. An application to immigration disparities in cancer mortality among Hispanic Americans is discussed. Our simulation studies show that a bias-corrected RR estimator performs the best in reducing the bias without increasing the coefficient of variation; the proposed variance estimators for the RR estimators and associated confidence intervals are fairly accurate. Finding of our application study are both interesting and consistent with the common sense as well as the results of our simulation studies.

Semiparametric modelling and estimation of covariate-adjusted dependence between bivariate recurrent events

A time-dependent measure, termed the rate ratio, was proposed to assess the local dependence between two types of recurrent event processes in one-sample settings. However, the one-sample work does not consider modeling the dependence by covariates such as subject characteristics and treatments received. The focus of this paper is to understand how and in what magnitude the covariates influence the dependence strength for bivariate recurrent events. We propose the covariate-adjusted rate ratio, a measure of covariate-adjusted dependence. We propose a semiparametric regression model for jointly modeling the frequency and dependence of bivariate recurrent events: the first level is a proportional rates model for the marginal rates and the second level is a proportional rate ratio model for the dependence structure. We develop a pseudo-partial likelihood to estimate the parameters in the proportional rate ratio model. We establish the asymptotic properties of the estimators and evaluate the finite sample performance via simulation studies. We illustrate the proposed models and methods using a soft tissue sarcoma study that examines the effects of initial treatments on the marginal frequencies of local/distant sarcoma recurrence and the dependence structure between the two types of cancer recurrence.

Interval Estimation for Age-Adjusted Rate Ratios Using Bayesian Convolution Model

Spatial correlation raises challenges in estimating confidence intervals for region specific event rates and rate ratios between geographic units that are nested. Methods have been proposed to incorporate spatial correlation by assuming various distributions for the structure of autocorrelation patterns. However, the derivation of these statistics based on approximation may have to condition on the distributional assumption underlying the data generating process, which may not hold for certain situations. This paper explores the feasibility of utilizing a Bayesian convolution model (BCM), which includes an uncorrelated heterogeneity (UH) and a conditional autoregression (CAR) component to accommodate both uncorrelated and correlated spatial heterogeneity, to estimate the 95% confidence intervals for age-adjusted rate ratios among geographic regions with existing spatial correlations. A simulation study is conducted and a BCM method is applied to two cancer incidence datasets to calculate age-adjusted rate/ratio for the counties in the State of Kentucky relative to the entire state. In comparison to three existing methods, without and with spatial correlation, the Bayesian convolution model-based estimation provides moderate shrinkage effect for the point estimates based on the neighbor structure across regions and produces a wider interval due to the inclusion of uncertainty in the spatial autocorrelation parameters. The overall spatial pattern of region incidence rate from BCM approach appears to be like the direct estimates and other methods for both datasets, even though "smoothing" occurs in some local regions. The Bayesian Convolution Model allows flexibility in the specification of risk components and can improve the accuracy of interval estimates of age-adjusted rate ratios among geographical regions as it considers spatial correlation.

Significant Impacts of Work-Related Cerebrovascular and Cardiovascular Diseases among Young Workers: A Nationwide Analysis

Background: While occupational factors linked to the onset of cerebrovascular and cardiovascular diseases (CVDs) have been reported among workers, much remains unknown about the impacts that occupation has on the onset of CVDs in various age groups. We attempted to describe temporal trends in total and work-related CVDs (WRCVDs) rates stratified by age and year and explore the relative contributions of work to the CVD risk. Methods: This study was conducted using two populations from the Labor Insurance Database as the working population and the National Health Insurance Research Database as the general population. We included all people aged 15⁻75 years from 2006 to 2013. All CVD events and WRCVD events were identified. A Poisson regression was used to estimate the morbidity rate ratio (RR) stratified by age and period, and an RR adjusted for residual confounding was also used. Results: Incident CVD rates increased with aging in the general population (from 1113.55 to 1853.32 per 100,000 persons), and WRCVD rates increased in the working population over time (from 2.10 in 2006 to 8.60 in 2013 per 100,000 persons). In the age and period analysis, CVD attacks showed disparities in different populations. The RR of the WRCVD risk was mainly in the working population aged >45 years, and the RR of the CVD risk occurred in the oldest group (aged 55⁻64 years) of the general population. The population-attributable risk of working exposure was 13.5%. After eliminating residual confounding factors, higher population attributed risk (PAR) work-related excessive CVD risk mainly occurred in workers aged 25⁻34 and 35⁻44 years. A decreasing PAR trend was found in the age groups as follows: 15⁻24, 25⁻34, 35⁻44, 45⁻54, and 55⁻64 years, with percentages of 17.64%, 16.89%, 16.46%, 10.6%, and 0.65%, respectively. Conclusions: There is evidence that period and age trends of CVD rates differed between the working population and general population. Relative effects attributed to work were more severe in younger workers, particularly in workers aged <55 years.

Clustering of malignant pleural mesothelioma in asbestos factories: a subgroup analysis in a 29-year follow-up study to identify high-risk industries in Taiwan

OBJECTIVE

Exposure to asbestos is the major cause for malignant pleural mesothelioma (MPM), but the causal link of individual cases is difficult to establish for lack of exposure information and long disease latency.

METHODS

We established a retrospective cohort of workers employed in asbestos industries during the period of 1950-1989 and the occurrence of MPM during the period of 1980-2009 was examined with the Taiwan Cancer Registry. Estimated rate ratios (eRRs) were computed for each factory where any case of MPM was diagnosed by assuming Poisson distribution with a minimal latency of 20 years.

RESULTS

A total of 18 MPM (17 males, 1 female) in eight factories were found. The incidence rate of MPM for the eight factories was 18.0 per million, ranging from 6.2 per million (military factory) to 268.2 per million (asbestos cement). We observed significantly increased risks for MPM in asbestos cement, thermal insulation and shipbuilding industries, with eRR (genders combined) of 113.6, 87.5 and 15.8, respectively. The sensitivity analyses considering latency showed similar findings in latency ≥30 years, and the shipbuilding industry presented a significant eRR given a latency ≥40 years. The gender-specific eRR showed similar results in men, but high eRR of 729.6 was observed in an asbestos cement factory where a female MPM was diagnosed.

CONCLUSIONS

This nationwide study in Taiwan comprehensively shows that different asbestos manufacturing processes, including asbestos cement, thermal insulation and shipbuilding industries, were at significantly increased risks for MPM. We recommend to establish a medical screening programme for workers previously exposed to asbestos to identify MPM and other asbestos-related diseases at an earlier stage.

Invited Commentary: When Case-Control Studies Came of Age

In his 1976 paper "Estimability and Estimation in Case-Referent Studies" (Am J Epidemiol. 1976;103(2):226-235), Miettinen weaved together a patchwork of new ideas into a coherent view of case-control studies. His article spurred theoretical development in epidemiologic methods and became a platform for teaching about some key concepts in epidemiologic study design.

Biostatistics Series Module 8: Assessing Risk

In observational studies, as well as in interventional ones, it is frequently necessary to estimate risk that is the association between an observed outcome or event and exposure to one or more factors that may be contributing to the event. Understanding incidence and prevalence are the starting point in any discussion of risk assessment. Incidence rate uses person-time as the denominator rather than a simple count. Ideally, rates and ratios estimated from samples should be presented with their corresponding 95% confidence intervals (CIs). To assess the importance of an individual risk factor, it is necessary to compare the risk of the outcome in the exposed group with that in the nonexposed group. A comparison between risks in different groups can be made by examining either their ratio or the difference between them. The 2 × 2 contingency table comes in handy in the calculation of ratios. Odds ratio (OR) is the ratio of the odds of an event in the exposed group, to the odds of the same event in the nonexposed group. It can range from zero to infinity. When the odds of an outcome in the two groups are identical, then the OR equals one. OR >1 indicates exposure increases risk while OR <1 indicates that exposure is protecting against risk. The OR should be presented with its 95% CI to enable more meaningful interpretation – if this interval includes 1, then even a relatively large OR will not carry much weight. The relative risk (RR) denotes the ratio of risk (probability) of event in exposed group to risk of same event in the nonexposed group. Its interpretation is similar (but not identical) to the OR. If the event in question is relatively uncommon, values of OR and RR tend to be similar. Absolute risk reduction (ARR) is a measure of the effectiveness of an intervention with respect to a dichotomous event. It is calculated as proportion experiencing the event in control group minus the proportion experiencing the event in treated group. It is often used to denote the benefit to the individual. The reciprocal of ARR is the number needed to treat (NNT), and it denotes the number of subjects who would need to be treated to obtain one more success than that obtained with a control treatment. Alternatively, this could also denote the number that would need to be treated to prevent one additional adverse outcome as compared to control treatment. Extended to toxicity, the NNT becomes a measure of harm and is then known as the number needed to harm (NNH). NNT and NNH are important concepts from the policy makers perspective and ideally should be calculated in all trials of therapeutic or prophylactic intervention.

Estimating the ratio of multivariate recurrent event rates with application to a blood transfusion study

In comparative effectiveness studies of multicomponent, sequential interventions like blood product transfusion (plasma, platelets, red blood cells) for trauma and critical care patients, the timing and dynamics of treatment relative to the fragility of a patient's condition is often overlooked and underappreciated. While many hospitals have established massive transfusion protocols to ensure that physiologically optimal combinations of blood products are rapidly available, the period of time required to achieve a specified massive transfusion standard (e.g. a 1:1 or 1:2 ratio of plasma or platelets:red blood cells) has been ignored. To account for the time-varying characteristics of transfusions, we use semiparametric rate models for multivariate recurrent events to estimate blood product ratios. We use latent variables to account for multiple sources of informative censoring (early surgical or endovascular hemorrhage control procedures or death). The major advantage is that the distributions of latent variables and the dependence structure between the multivariate recurrent events and informative censoring need not be specified. Thus, our approach is robust to complex model assumptions. We establish asymptotic properties and evaluate finite sample performance through simulations, and apply the method to data from the PRospective Observational Multicenter Major Trauma Transfusion study.

Performance of prior event rate ratio adjustment method in pharmacoepidemiology: a simulation study

PURPOSE

Prior event rate ratio (PERR) adjustment method has been proposed to control for unmeasured confounding. We aimed to assess the performance of the PERR method in realistic pharmacoepidemiological settings.

METHODS

Simulation studies were performed with varying effects of prior events on the probability of subsequent exposure and post-events, incidence rates, effects of confounders, and rate of mortality/dropout. Exposure effects were estimated using conventional rate ratio (RR) and PERR adjustment method (i.e. ratio of RR post-exposure initiation and RR prior to initiation of exposure).

RESULTS

In the presence of unmeasured confounding, both conventional and the PERR method may yield biased estimates, but PERR estimates appear generally less biased estimates than the conventional method. However, when prior events strongly influence the probability of subsequent exposure, the exposure effect from the PERR method was more biased than the conventional method. For instance, when the effect of prior events on the exposure was RR = 1.60, the effect estimate from the PERR method was RR = 1.13 and from the conventional method was RR = 2.48 (true exposure effect, RR = 2). In all settings, the variation of the estimates was larger for the PERR method than for the conventional method.

CONCLUSION

The PERR adjustment method can be applied to reduce bias as a result of unmeasured confounding. However, only in particular situations, it can completely remove the bias as a result of unmeasured confounding. When applying this method, theoretical justification using available clinical knowledge for assumptions of the PERR method should be provided.

Effectiveness of nationwide screening program for neuroblastoma in Japan

BACKGROUND

Neuroblastoma (NB) is one of the most malignant neoplasms in childhood. In Japan, while a nationwide screening program at six months of age was introduced in 1985, its efficacy has not been systematically evaluated before or after its introduction. The screening test was changed from a qualitative method to a quantitative method (i.e., high performance liquid chromatography, HPLC) with higher test precision around 1990. However, the Japanese government stopped the program in 2003, after reports which did not show a reduction in mortality from NB. To evaluate the effectiveness of the program, a systematic large-scale epidemiological study was conducted.

METHODS

A retrospective cohort study was carried out to evaluate the effectiveness of the NB screening with HPLC test at 6 months of age in Japan, in comparing mortality and incidence of NB after 6 months of age between screened children and concurrent non-screened children in the same area. The study cohort was defined retrospectively as those children who were born after the introduction of HPLC test, from its earliest introduction of January 1984 to December 31, 1997, in twenty-five prefectures of Japan, which cover approximately half of the newborn population of Japan.

RESULTS

The study cohort consisted of 4.31 million. We identified 66 NB deaths in the study cohort for the analysis after 6 months. Kaplan-Meier estimate of cumulative mortality of NB per million children at 6 years was 15.33 for the screened group and 32.63 for the non-screened group, respectively. The difference of hazard between the two groups was statistically significant. The age specific mortality rate ratio of NB (95% confidence interval (CI)) was statistically lower at 1 - 3 years [0.415 (0.212 - 0.810)]. The rate ratio of NB incidence (95% CI) at the early stage (i.e., 1, 2 and 4S) between them was statistically higher at 6 months - 1 year [9.56 (4.76 - 19.23)]. That of NB incidence at the advanced stage (i.e., 3 and 4) was statistically lower at 1 - 4 years [0.40 (0.26 - 0.62)].

CONCLUSION

The present study showed the reduction of mortality from NB, as well as the increase of the identification of early stage of NB and the decrease of advanced stage of NB. These findings strongly suggest the effectiveness of the NB screening with HPLC test in Japan. Although there could be several biases inherent to the study design, their possibilities are considered to be relatively low from observational information and theoretical consideration.

Left atrial appendage occlusion: opportunities and challenges

Stroke prevention in patients with atrial fibrillation is a growing clinical dilemma as the incidence of the arrhythmia increases and risk profiles worsen. Strategies in patients with nonvalvular atrial fibrillation have included anticoagulation with a variety of drugs. Knowledge that stroke in this setting typically results from thrombus in the left atrial appendage has led to the development of mechanical approaches, both catheter-based and surgical, to occlude that structure. Such a device, if it were safe and effective, might avoid the need for anticoagulation and prevent stroke in the large number of patients who are currently not treated with anticoagulants. Regulatory approval has been difficult due to trial design challenges, balance of the risk-benefit ratio, specific patient populations studied, selection of treatment in the control group, and specific endpoints and statistical analyses selected. Accumulating data from randomized trials and registries with longer-term follow-up continues to support a role for left atrial appendage exclusion from the central circulation as an alternative to anticoagulation in carefully-selected patient populations.