Tobacco smoking and all-cause mortality in a large Australian cohort study: findings from a mature epidemic with current low smoking prevalence

This study finds that up to two-thirds of deaths in current smokers  in Australia can be attributed to smoking. Abstract Background The smoking epidemic in Australia is characterised by historic levels of prolonged smoking, heavy smoking, very high levels of long-term cessation, and low current smoking prevalence, with 13% of adults reporting that they smoked daily in 2013. Large-scale quantitative evidence on the relationship of tobacco smoking to mortality in Australia is not available despite the potential to provide independent international evidence about the contemporary risks of smoking. Methods This is a prospective study of 204,953 individuals aged ≥45 years sampled from the general population of New South Wales, Australia, who joined the 45 and Up Study from 2006–2009, with linked questionnaire, hospitalisation, and mortality data to mid-2012 and with no history of cancer (other than melanoma and non-melanoma skin cancer), heart disease, stroke, or thrombosis. Hazard ratios (described here as relative risks, RRs) for all-cause mortality among current and past smokers compared to never-smokers were estimated, adjusting for age, education, income, region of residence, alcohol, and body mass index. Results Overall, 5,593 deaths accrued during follow-up (874,120 person-years; mean: 4.26 years); 7.7% of participants were current smokers and 34.1% past smokers at baseline. Compared to never-smokers, the adjusted RR (95% CI) of mortality was 2.96 (2.69–3.25) in current smokers and was similar in men (2.82 (2.49–3.19)) and women (3.08 (2.63–3.60)) and according to birth cohort. Mortality RRs increased with increasing smoking intensity, with around two- and four-fold increases in mortality in current smokers of ≤14 (mean 10/day) and ≥25 cigarettes/day, respectively, compared to never-smokers. Among past smokers, mortality diminished gradually with increasing time since cessation and did not differ significantly from never-smokers in those quitting prior to age 45. Current smokers are estimated to die an average of 10 years earlier than non-smokers. Conclusions In Australia, up to two-thirds of deaths in current smokers can be attributed to smoking. Cessation reduces mortality compared with continuing to smoke, with cessation earlier in life resulting in greater reductions

Systematic review and meta-analysis of residential radon and lung cancer in never-smokers

Background: Globally, radon is the leading risk factor for lung cancer in never-smokers (LCINS). In this study, we systematically reviewed and meta-analysed the evidence of the risk of LCINS associated with residential radon exposure. Methods: Medline and Embase databases were searched using predefined inclusion and exclusion criteria to identify relevant studies published from 1 January 1990 to 5 March 2020 focused on never-smokers. We identified four pooled collaborative studies (incorporating data from 24 case–control studies), one case–control study and one cohort study for systematic review. Meta-analysis was performed on the results of the four pooled studies due to different measures of effect and outcome reported in the cohort study and insufficient information reported for the case–control study. In a post hoc analysis, the corresponding risk for ever-smokers was also examined. Results: Risk estimates of lung cancer from residential radon exposure were pooled in the meta-analysis for 2341 never-smoker cases, 8967 never-smoker controls, 9937 ever-smoker cases and 12 463 ever-smoker controls. Adjusted excess relative risks (aERRs) per 100 Bq·m−3 of radon level were 0.15 (95% CI 0.06–0.25) for never-smokers and 0.09 (95% CI 0.03–0.16) for ever-smokers, and the difference between them was statistically insignificant (p=0.32). The aERR per 100 Bq·m−3was higher for men (0.46; 95% CI 0.15–0.76) than for women (0.09; 95% CI −0.02–0.20) among never-smokers (p=0.027). Conclusion: This study provided quantified risk estimates for lung cancer from residential radon exposure among both never-smokers and ever-smokers. Among never-smokers in radon-prone areas, men were at higher risk of lung cancer than womenS

Quality of life three years after diagnosis of localised prostate cancer: population based cohort study

Objective To quantify the risk and severity of negative effects of treatment for localised prostate cancer on long term quality of life

HHV-8 seroprevalence: a global view

BACKGROUND: Human herpes virus 8 (HHV-8) is the underlying infectious cause of Kaposi sarcoma (KS) and other proliferative diseases; that is, primary effusion lymphoma and multicentric Castleman disease. In regions with high HHV-8 seroprevalence in the general population, KS accounts for a major burden of disease. Outside these endemic regions, HHV-8 prevalence is high in men who have sex with men (MSM) and in migrants from endemic regions. We aim to conduct a systematic literature review and meta-analysis in order 1) to define the global distribution of HHV-8 seroprevalence (primary objective) and 2) to identify risk factors for HHV-8 infection, with a focus on HIV status (secondary objective).METHODS/DESIGN:We will include observational studies reporting data on seroprevalence of HHV-8 in children and/or adults from any region in the world. Case reports and case series as well as any studies with fewer than 50 participants will be excluded. We will search MEDLINE, EMBASE, and relevant conference proceedings without language restriction. Two reviewers will independently screen the identified studies and extract data on study characteristics and quality, study population, risk factors, and reported outcomes, using a standardized form. For the primary objective we will pool the data using a fully bayesian approach for meta-analysis, with random effects at the study level. For the secondary objective (association of HIV and HHV-8) we aim to pool odds ratios for the association of HIV and HHV-8 using a fully bayesian approach for meta-analysis, with random effects at the study level. Sub-group analyses and meta-regression analyses will be used to explore sources of heterogeneity, including factors such as geographical region, calendar years of recruitment, age, gender, ethnicity, socioeconomic status, different risk groups for sexually and parenterally transmitted infections (MSM, sex workers, hemophiliacs, intravenous drug users), comorbidities such as organ transplantation and malaria, test(s) used to measure HHV-8 infection, study design, and study quality.DISCUSSION:Using the proposed systematic review and meta-analysis, we aim to better define the global seroprevalence of HHV-8 and its associated risk factors. This will improve the current understanding of HHV-8 epidemiology, and could suggest measures to prevent HHV-8 infection and to reduce its associated cancer burden

Tobacco smoking changes during the first pre-vaccination phases of the COVID-19 pandemic: A systematic review and meta-analysis

Background: Globally, tobacco smoking remains the largest preventable cause of premature death. The COVID-19 pandemic has forced nations to take unprecedented measures, including ‘lockdowns’ that might impact tobacco smoking behaviour. We performed a systematic review and meta-analyses to assess smoking behaviour changes during the early pre-vaccination phases of the COVID-19 pandemic in 2020. Methods: We searched Medline/Embase/PsycINFO/BioRxiv/MedRxiv/SSRN databases (January–November 2020) for published and pre-print articles that reported specific smoking behaviour changes or intentions after the onset of the COVID-19 pandemic. We used random-effects models to pool prevalence ratios comparing the prevalence of smoking during and before the pandemic, and the prevalence of smoking behaviour changes during the pandemic. The PROSPERO registration number for this systematic review was CRD42020206383. Findings: 31 studies were included in meta-analyses, with smoking data for 269,164 participants across 24 countries. The proportion of people smoking during the pandemic was lower than that before, with a pooled prevalence ratio of 0·87 (95%CI:0·79–0·97). Among people who smoke, 21% (95%CI:14–30%) smoked less, 27% (95%CI:22–32%) smoked more, 50% (95%CI:41%-58%) had unchanged smoking and 4% (95%CI:1–9%) reported quitting smoking. Among people who did not smoke, 2% (95%CI:1–3%) started smoking during the pandemic. Heterogeneity was high in all meta-analyses and so the pooled estimates should be interpreted with caution (I2\u3e91% and p-heterogeneity\u3c0·001). Almost all studies were at high risk of bias due to use of non-representative samples, non-response bias, and utilisation of non-validated questions. Interpretation: Smoking behaviour changes during the first phases of the COVID-19 pandemic in 2020 were highly mixed. Meta-analyses indicated that there was a relative reduction in overall smoking prevalence during the pandemic, while similar proportions of people who smoke smoked more or smoked less, although heterogeneity was high. Implementation of evidence-based tobacco control policies and programs, including tobacco cessation services, have an important role in ensuring that the COVID-19 pandemic does not exacerbate the smoking pandemic and associated adverse health outcomes

In-memory Realization of In-situ Few-shot Continual Learning with a Dynamically Evolving Explicit Memory

Continually learning new classes from a few training examples without forgetting previous old classes demands a flexible architecture with an inevitably growing portion of storage, in which new examples and classes can be incrementally stored and efficiently retrieved. One viable architectural solution is to tightly couple a stationary deep neural network to a dynamically evolving explicit memory (EM). As the centerpiece of this architecture, we propose an EM unit that leverages energy-efficient in-memory compute (IMC) cores during the course of continual learning operations. We demonstrate for the first time how the EM unit can physically superpose multiple training examples, expand to accommodate unseen classes, and perform similarity search during inference, using operations on an IMC core based on phase-change memory (PCM). Specifically, the physical superposition of a few encoded training examples is realized via in-situ progressive crystallization of PCM devices. The classification accuracy achieved on the IMC core remains within a range of 1.28%--2.5% compared to that of the state-of-the-art full-precision baseline software model on both the CIFAR-100 and miniImageNet datasets when continually learning 40 novel classes (from only five examples per class) on top of 60 old classes.Comment: Accepted at the European Solid-state Devices and Circuits Conference (ESSDERC), September 202

High unreported mortality in children and youth (<25 years) living with HIV who were lost to care from antiretroviral therapy programs in Southern Africa: results from a multi-country tracing study.

BACKGROUND Antiretroviral therapy (ART) program mortality maybe underestimated if deceased patients are misclassified as lost. METHODS We used two-stage inverse probability weighting to account for probability of being: sampled for tracing and found by the tracer. RESULTS Among 680 children and youth aged <25 years on ART who were lost and traced in Southern Africa between October 2017-November 2019, estimated mortality was high at 9.1% (62/680). After adjusting for measured covariates and within-site clustering, mortality remained lower for young adults aged 20-24 years compared to infants aged <2years (adjusted Hazard ratio (aHR): 0.40 (95% confidence interval (CI): 0.31, 0.51)). CONCLUSIONS Our study confirms high unreported mortality in children and youth who are lost and the need for tracing to assess vital status among those who are lost to accurately report on program mortality

Updating vital status by tracking in the community among patients with epidemic Kaposi sarcoma who are lost to follow-up in sub-Saharan Africa.

BACKGROUND: Throughout most of sub-Saharan Africa (and, indeed, most resource-limited areas), lack of death registries prohibits linkage of cancer diagnoses and precludes the most expeditious approach to determining cancer survival. Instead, estimation of cancer survival often uses clinical records, which have some mortality data but are replete with patients who are lost to follow-up (LTFU), some of which may be caused by undocumented death. The end result is that accurate estimation of cancer survival is rarely performed. A prominent example of a common cancer in Africa for which survival data are needed but for which frequent LTFU has precluded accurate estimation is Kaposi sarcoma (KS). METHODS: Using electronic records, we identified all newly diagnosed KS among HIV-infected adults at 33 primary care clinics in Kenya, Uganda, Nigeria, and Malawi from 2009 to 2012. We determined those patients who were apparently LTFU, defined as absent from clinic for ≥90 days at database closure and unknown to be dead or transferred. Using standardized protocols which included manual chart review, telephone calls, and physical tracking in the community, we attempted to update vital status amongst patients who were LTFU. RESULTS: We identified 1222 patients with KS, of whom 440 were LTFU according to electronic records. Manual chart review revealed that 18 (4.1%) were classified as LFTU due to clerical error, leaving 422 as truly LTFU. Of these 422, we updated vital status in 78%; manual chart review was responsible for updating in 5.7%, telephone calls in 26%, and physical tracking in 46%. Among 378 patients who consented at clinic enrollment to be tracked if they became LTFU and who had sufficient geographic contact/locator information, we updated vital status in 88%. Duration of LTFU was not associated with success of tracking, but tracking success was better in Kenya than the other sites. CONCLUSION: It is feasible to update vital status in a large fraction of patients with HIV-associated KS in sub-Saharan Africa who have become LTFU from clinical care. This finding likely applies to other cancers as well. Updating vital status amongst lost patients paves the way towards accurate determination of cancer survival

Pitfalls of Practicing Cancer Epidemiology in Resource-limited Settings: the Case of Survival and Loss to Follow-up after a Diagnosis of Kaposi’s Sarcoma in Five Countries across Sub-Saharan Africa

Background: Survival after diagnosis is a fundamental concern in cancer epidemiology. In resource-rich settings, ambient clinical databases, municipal data and cancer registries make survival estimation in real-world populations relatively straightforward. In resource-poor settings, given the deficiencies in a variety of health-related data systems, it is less clear how well we can determine cancer survival from ambient data. Methods: We addressed this issue in sub-Saharan Africa for Kaposi’s sarcoma (KS), a cancer for which incidence has exploded with the HIV epidemic but for which survival in the region may be changing with the recent advent of antiretroviral therapy (ART). From 33 primary care HIV Clinics in Kenya, Uganda, Malawi, Nigeria and Cameroon participating in the International Epidemiologic Databases to Evaluate AIDS (IeDEA) Consortia in 2009–2012, we identified 1328 adults with newly diagnosed KS. Patients were evaluated from KS diagnosis until death, transfer to another facility or database closure. Results: Nominally, 22 % of patients were estimated to be dead by 2 years, but this estimate was clouded by 45 % cumulative lost to follow-up with unknown vital status by 2 years. After adjustment for site and CD4 count, agelost. Conclusions: In this community-based sample of patients diagnosed with KS in sub-Saharan Africa, almost half became lost to follow-up by 2 years. This precluded accurate estimation of survival. Until we either generally strengthen data systems or implement cancer-specific enhancements (e.g., tracking of the lost) in the region, insights from cancer epidemiology will be limited