Difference in Gastrointestinal Cancer Risk and Mortality by Dietary Pattern Analysis: A Systematic Review and Meta-Analysis.

CONTEXT: Several studies have demonstrated that dietary patterns identified by a posteriori and hybrid methods are associated with gastrointestinal (GI) cancer risk and mortality. These studies applied different methods for analyzing dietary data and reported inconsistent findings. OBJECTIVE: This systematic review and meta-analysis were aimed to determine the association between dietary patterns, derived using principal component analysis (PCA) and reduced rank regression (RRR), and GI cancer risk and GI cancer-caused mortality. DATA SOURCE: Articles published up to June 2023 in English were eligible for inclusion. The Medline, SCOPUS, Cochrane Library, CINHAL, PsycINFO, ProQuest, and Web of Sciences databases were used to identify prospective studies. The Preferred Reporting Item for Systematic Review and Meta-analysis Protocol 2020 was used to report results. DATA EXTRACTION: A total of 28 studies were eligible for inclusion. Varied approaches to deriving dietary patterns were used, including PCA (n = 22), RRR (n = 2), combined PCA and RRR (n = 1), cluster analysis (CA; n = 2) and combined PCA and CA (n = 1). DATA ANALYSIS: Two dietary patterns, "healthy" and "unhealthy," were derived using PCA and RRR. The healthy dietary pattern was characterized by a higher intake of fruits, whole grains, legumes, vegetables, milk, and other dairy products, whereas the unhealthy dietary pattern was characterized by a higher intake of red and processed meat, alcohol, and both refined and sugar-sweetened beverages. The findings indicated that the PCA-derived healthy dietary pattern was associated with an 8% reduced risk (relative risk [RR], 0.92; 95% CI, 0.87-0.98), and the unhealthy dietary pattern was associated with a 14% increased risk (RR, 1.14; 95% CI, 1.07-1.22) of GI cancers. Similarly, the RRR-derived healthy dietary pattern (RR, 0.83; 95% CI, 0.61-1.12) may be associated with reduced risk of GI cancers. In contrast, the RRR-derived unhealthy dietary pattern (RR, 0.93; 95% CI, 0.57-1.52) had no association with a reduced risk of GI cancers. Similarly, evidence suggested that PCA-derived healthy dietary patterns may reduce the risk of death from GI cancers, whereas PCA-derived unhealthy dietary patterns may increase the risk. CONCLUSION: Findings from prospective studies on the association of PCA-derived dietary patterns and the risk of GI cancers support the evidence of healthy and unhealthy dietary patterns as either protective or risk-increasing factors for GI cancers and for survivorship, respectively. The findings also suggest that the RRR-derived healthy dietary pattern reduces the risk of GI cancers (albeit with low precision), but no association was found for the RRR-derived unhealthy dietary pattern. Prospective studies are required to further clarify disparities in the association between PCA- and RRR-derived dietary patterns and the risk of GI cancers. Systematic review registration: PROSPERO registration no. CRD42022321644.

Ultra-processed food consumption and risk of chronic respiratory diseases mortality among adults: evidence from a prospective cohort study.

PURPOSE: The purpose of the study was to determine the relationships between ultra-processed food (UPF) consumption and risk of mortality due to chronic respiratory diseases (CRDs) overall, chronic obstructive pulmonary disease (COPD), and lung cancer. METHODS: A total of 96,607 participants aged 55 years and over were included from the Prostate, Lung, Colorectal and Ovarian (PLCO) cancer trial. Dietary intake was measured using food frequency questionnaire. Cox regression was fitted to estimate the risk of all-cause mortality and mortality due to CRDs overall, COPD and lung cancer associated with UPF intake. Competing risk regression was used to account for deaths from other causes and censoring. RESULTS: During the follow-up of 1,379,655.5 person-years (median 16.8 years), 28,700 all-cause, 4092 CRDs, 2015 lung cancer and 1,536 COPD mortality occurred. A higher intake of UPF increased the risk of mortality from CRDs overall by 10% (HR 1.10; 95% CI 1.01, 1.22) and COPD by 26% (HR 1.26; 95% CI 1.06, 1.49) but not associated with lung cancer mortality risk (HR 0.97; 95% CI 0.84, 1.12). However, the risk of lung cancer increased by 16% (HR 1.16; 95% CI 1.01, 1.34) in the highest UPF intake after multiple imputation. Dose-response relationships existed for CRDs and COPD mortality but not lung cancer. CONCLUSION: UPF consumption was associated with an increased risk of CRD mortality. The association between UPF consumption and lung cancer mortality is inconclusive and only significant when multiple imputation was applied.

Epidemiologic Factors in Relation to Colorectal Cancer Risk and Survival by Genotoxic Colibactin Mutational Signature.

BACKGROUND: The genotoxin colibactin causes a tumor single-base substitution (SBS) mutational signature, SBS88. It is unknown whether epidemiologic factors' association with colorectal cancer risk and survival differs by SBS88. METHODS: Within the Genetic Epidemiology of Colorectal Cancer Consortium and Colon Cancer Family Registry, we measured SBS88 in 4,308 microsatellite stable/microsatellite instability low tumors. Associations of epidemiologic factors with colorectal cancer risk by SBS88 were assessed using multinomial regression (N = 4,308 cases, 14,192 controls; cohort-only cases N = 1,911), and with colorectal cancer-specific survival using Cox proportional hazards regression (N = 3,465 cases). RESULTS: 392 (9%) tumors were SBS88 positive. Among all cases, the highest quartile of fruit intake was associated with lower risk of SBS88-positive colorectal cancer than SBS88-negative colorectal cancer [odds ratio (OR) = 0.53, 95% confidence interval (CI) 0.37-0.76; OR = 0.75, 95% CI 0.66-0.85, respectively, Pheterogeneity = 0.047]. Among cohort studies, associations of body mass index (BMI), alcohol, and fruit intake with colorectal cancer risk differed by SBS88. BMI ≥30 kg/m2 was associated with worse colorectal cancer-specific survival among those SBS88-positive [hazard ratio (HR) = 3.40, 95% CI 1.47-7.84], but not among those SBS88-negative (HR = 0.97, 95% CI 0.78-1.21, Pheterogeneity = 0.066). CONCLUSIONS: Most epidemiologic factors did not differ by SBS88 for colorectal cancer risk or survival. Higher BMI may be associated with worse colorectal cancer-specific survival among those SBS88-positive; however, validation is needed in samples with whole-genome or whole-exome sequencing available. IMPACT: This study highlights the importance of identification of tumor phenotypes related to colorectal cancer and understanding potential heterogeneity for risk and survival.

Adherence to 2018 WCRF/AICR Cancer Prevention Recommendations and Risk of Cancer: The Melbourne Collaborative Cohort Study.

BACKGROUND: We examined associations between adherence to adaptations of the 2018 World Cancer Research Fund/American Institute for Cancer Research (WCRF/AICR) cancer prevention recommendations and total, exposure-related and site-specific cancer risk. METHODS: A total of 20,001 participants ages 40 to 69 years at enrollment into the Melbourne Collaborative Cohort Study in 1990 to 1994, who had diet, body size, and lifestyle reassessed in 2003 to 2007 ("baseline"), were followed-up through June 2021. We constructed diet and standardized lifestyle scores based on core WCRF/AICR recommendations on diet, alcohol intake, body size and physical activity, and additional scores incorporating weight change, sedentary behavior, and smoking. Associations with cancer risk were estimated using Cox regression, adjusting for confounders. RESULTS: During follow-up (mean = 16 years), 4,710 incident cancers were diagnosed. For highest quintile ("most adherent") of the standardized lifestyle score, compared with lowest ("least adherent"), a HR of 0.82 [95% confidence interval (CI): 0.74-0.92] was observed for total cancer. This association was stronger with smoking included in the score (HR = 0.74; 95% CI: 0.67-0.81). A higher score was associated with lower breast and prostate cancer risk for the standardized score, and with lung, stomach, rectal, and pancreatic cancer risk when the score included smoking. Our analyses identified alcohol use, waist circumference and smoking as key drivers of associations with total cancer risk. CONCLUSIONS: Adherence to WCRF/AICR cancer prevention recommendations is associated with lower cancer risk. IMPACT: With <0.2% of our sample fully adherent to the recommendations, the study emphasizes the vast potential for preventing cancer through modulation of lifestyle habits.

Genome-Wide Gene-Environment Interaction Analyses to Understand the Relationship between Red Meat and Processed Meat Intake and Colorectal Cancer Risk.

BACKGROUND: High red meat and/or processed meat consumption are established colorectal cancer risk factors. We conducted a genome-wide gene-environment (GxE) interaction analysis to identify genetic variants that may modify these associations. METHODS: A pooled sample of 29,842 colorectal cancer cases and 39,635 controls of European ancestry from 27 studies were included. Quantiles for red meat and processed meat intake were constructed from harmonized questionnaire data. Genotyping arrays were imputed to the Haplotype Reference Consortium. Two-step EDGE and joint tests of GxE interaction were utilized in our genome-wide scan. RESULTS: Meta-analyses confirmed positive associations between increased consumption of red meat and processed meat with colorectal cancer risk [per quartile red meat OR = 1.30; 95% confidence interval (CI) = 1.21-1.41; processed meat OR = 1.40; 95% CI = 1.20-1.63]. Two significant genome-wide GxE interactions for red meat consumption were found. Joint GxE tests revealed the rs4871179 SNP in chromosome 8 (downstream of HAS2); greater than median of consumption ORs = 1.38 (95% CI = 1.29-1.46), 1.20 (95% CI = 1.12-1.27), and 1.07 (95% CI = 0.95-1.19) for CC, CG, and GG, respectively. The two-step EDGE method identified the rs35352860 SNP in chromosome 18 (SMAD7 intron); greater than median of consumption ORs = 1.18 (95% CI = 1.11-1.24), 1.35 (95% CI = 1.26-1.44), and 1.46 (95% CI = 1.26-1.69) for CC, CT, and TT, respectively. CONCLUSIONS: We propose two novel biomarkers that support the role of meat consumption with an increased risk of colorectal cancer. IMPACT: The reported GxE interactions may explain the increased risk of colorectal cancer in certain population subgroups.

Healthy lifestyle is associated with reduced cardiovascular disease, depression and mortality in people at elevated risk of sleep apnea.

We assessed: (1) the independent and joint association of obstructive sleep apnea risk and healthy lifestyle with common consequences (excessive daytime sleepiness, depression, cardiovascular disease and stroke) of obstructive sleep apnea; and (2) the effect of healthy lifestyle on survival in people with increased obstructive sleep apnea risk. Data from 13,694 adults (median age 46 years; 50% men) were used for cross-sectional and survival analyses (mortality over 15 years). A healthy lifestyle score with values from 0 (most unhealthy) to 5 (most healthy) was determined based on diet, alcohol intake, physical activity, smoking and body mass index. In the cross-sectional analysis, obstructive sleep apnea risk was positively associated with all chronic conditions and excessive daytime sleepiness in a dose-response manner (p for trend < 0.001). The healthy lifestyle was inversely associated with all chronic conditions (p for trend < 0.001) but not with excessive daytime sleepiness (p for trend = 0.379). Higher healthy lifestyle score was also associated with reduced odds of depression and cardiovascular disease. We found an inverse relationship between healthy lifestyle score with depression (p for trend < 0.001), cardiovascular disease (p for trend = 0.003) and stroke (p for trend = 0.025) among those who had high obstructive sleep apnea risk. In the survival analysis, we found an inverse association between healthy lifestyle and all-cause mortality for all categories of obstructive sleep apnea risk (moderate/high- and high-risk groups [p for trend < 0.001]). This study emphasises the crucial role of a healthy lifestyle in mitigating the effects of obstructive sleep apnea risk in individuals with an elevated obstructive sleep apnea risk.

Socio-demographic correlates of unhealthy lifestyle in Ethiopia: a secondary analysis of a national survey.

BACKGROUND: Multiple lifestyle risk factors exhibit a stronger association with non-communicable diseases (NCDs) compared to a single factor, emphasizing the necessity of considering them collectively. By integrating these major lifestyle risk factors, we can identify individuals with an overall unhealthy lifestyle, which facilitates the provision of targeted interventions for those at significant risk of NCDs. The aim of this study was to evaluate the socio-demographic correlates of unhealthy lifestyles among adolescents and adults in Ethiopia. METHODS: A national cross-sectional survey, based on the World Health Organization's NCD STEPS instruments, was conducted in Ethiopia. The survey, carried out in 2015, involved a total of 9,800 participants aged between 15 and 69 years. Lifestyle health scores, ranging from 0 (most healthy) to 5 (most unhealthy), were derived considering factors such as daily fruit and vegetable consumption, smoking status, prevalence of overweight/obesity, alcohol intake, and levels of physical activity. An unhealthy lifestyle was defined as the co-occurrence of three or more unhealthy behaviors. To determine the association of socio-demographic factors with unhealthy lifestyles, multivariable logistic regression models were utilized, adjusting for metabolic factors, specifically diabetes and high blood pressure. RESULTS: Approximately one in eight participants (16.7%) exhibited three or more unhealthy lifestyle behaviors, which included low fruit/vegetable consumption (98.2%), tobacco use (5.4%), excessive alcohol intake (15%), inadequate physical activity (66%), and obesity (2.3%). Factors such as male sex, urban residency, older age, being married or in a common-law relationship, and a higher income were associated with these unhealthy lifestyles. On the other hand, a higher educational status was associated with lower odds of these behaviors. CONCLUSION: In our analysis, we observed a higher prevalence of concurrent unhealthy lifestyles. Socio-demographic characteristics, such as sex, age, marital status, residence, income, and education, were found to correlate with individuals' lifestyles. Consequently, tailored interventions are imperative to mitigate the burden of unhealthy lifestyles in Ethiopia.

The association between diet quality, plant-based diets, systemic inflammation, and mortality risk: findings from NHANES.

PURPOSE: To our knowledge, no studies have examined the association of diet quality and plant-based diets (PBD) with inflammatory-related mortality in obesity. Therefore, this study aimed to determine the joint associations of Healthy Eating Index-2015 (HEI-2015), plant-based dietary index (PDI), healthy PDI (hPDI), unhealthy PDI (uPDI), pro-vegetarian dietary index (PVD), and systemic inflammation with all-cause, cardiovascular disease (CVD), and cancer mortality risks by obesity status. METHODS: Participants from NHANES were included in cross-sectional (N = 27,915, cycle 1999-2010, 2015-2018) and longitudinal analysis (N = 11,939, cycle 1999-2008). HEI-2015, PDI, hPDI, uPDI, and PVD were constructed based on the 24-h recall dietary interview. The grade of inflammation (low, moderate, and high) was determined based on C-reactive protein (CRP) values and multivariable ordinal logistic regression was used to determine the association. Cox proportional hazard models were used to determine the joint associations of diet and inflammation with mortality. RESULTS: In the fully adjusted model, HEI-2015 (ORT3vsT1 = 0.76, 95% CI 0.69-0.84; p-trend = < 0.001), PDI (ORT3vsT1 = 0.83, 95% CI 0.75-0.91; p trend = < 0.001), hPDI (ORT3vsT1 = 0.79, 95% CI 0.71-0.88; p trend = < 0.001), and PVD (ORT3vsT1 = 0.85, 95% CI 0.75-0.97; p trend = 0.02) were associated with lower systemic inflammation. In contrast, uPDI was associated with higher systemic inflammation (ORT3vsT1 = 1.18, 95% CI 1.06-1.31; p-trend = 0.03). Severe inflammation was associated with a 25% increase in all-cause mortality (ORT3vsT1 = 1.25, 95% CI 1.03-1.53, p trend = 0.02). No association was found between PDI, hPDI, uPDI, and PVD with mortality. The joint association, between HEI-2015, levels of systemic inflammation, and all-cause, CVD and cancer mortality, was not significant. However, a greater reduction in mortality risk with an increase in HEI-2015 scores was observed in individuals with low and moderate inflammation, especially those with obesity. CONCLUSION: Higher scores of HEI-2015 and increased intake of a healthy plant-based diet were associated with lower inflammation, while an unhealthy plant-based diet was associated with higher inflammation. A greater adherence to the 2015 dietary guidelines may reduce the risk of mortality associated with inflammation and may also benefit individuals with obesity who had low and moderate inflammation.

Cancer signs and risk factors awareness in Addis Ababa, Ethiopia: a population-based survey.

BACKGROUND: There is a paucity of data on public awareness of cancer in Ethiopia. This study assessed cancer signs, symptoms, and risk factors awareness among individuals aged 18 and older in Addis Ababa, Ethiopia. METHOD: A population-based face-to-face interview was conducted applying a validated cancer awareness measure (CAM) tool. A total of 600 adults (315 males and 285 females) were recruited using a multistage sampling technique. One open-ended and ten closed-ended questions were used to assess awareness of cancer signs and symptoms. To assess awareness of cancer risk factors, one open-ended and twelve closed-ended questions were used. Logistic regression analysis was used to test the association between sociodemographic status and awareness of cancer signs, symptoms, and risk factors. RESULTS: Based on the responses for the open-ended questions, unexplained bleeding (23.16%) and smoking (24.17%) were the most frequently recalled cancer sign and risk factor, respectively. Based on the responses for the closed questions, the majority of respondents identified tiredness all the time (80.7%) as a cancer symptom and alcohol use (82.5%) as a cancer risk factor.  The odds of cancer signs and symptoms awareness was higher in those with primary (AOR = 4.50, 95% CI, 1.72-11.79, p = 0.02), secondary (AOR = 4.62; 95% CI 1.86-11.43; p = 0.001), and tertiary (AOR = 7.51; 95% CI 3.04-18.56; p < 0.001) education than those who were illiterate. The odds of awareness about cancer signs and risk factors was 0.28 (95% CI 0.12-0.65; p = 0.003) and 0.22 (95% CI 0.83-0.58; p = 0.002) times lower, respectively, among individuals aged 60 and older than those aged 18 to 29. CONCLUSIONS: Young adults who attended formal education of primary or higher level may have a better cancer signs and symptoms awareness. Future education interventions to increase awareness of the society in Addis Ababa may target illiterate and the elderly.

High-quality and anti-inflammatory diets and a healthy lifestyle are associated with lower sleep apnea risk.

STUDY OBJECTIVES: Most studies on diet and sleep apnea focus on calorie restriction. Here we investigate potential associations between dietary quality (Healthy Eating Index [HEI], Dietary Inflammatory Index [DII]) and overall healthy lifestyle with sleep apnea risk. METHODS: National Health and Nutrition Examination Survey data (waves 2005-2008 and 2015-2018; n = 14,210) were used to determine HEI, DII, and their quintiles, with the fifth quintile indicating highest adherence to each dietary construct. A healthy lifestyle score was determined using diet, smoking, alcohol intake, and physical activity level. The STOP-BANG questionnaire was used to define sleep apnea risk. Generalized linear regression models with binomial family and logit link were used to investigate potential associations. The models were adjusted for socioeconomic status, lifestyle factors, and chronic conditions. RESULTS: The prevalence of high sleep apnea risk was 25.1%. Higher DII was positively associated with sleep apnea (odds ratioQuintile 5 vs Quintile 1 = 1.55; 95% confidence interval, 1.24-1.94; P for trend < .001), whereas higher HEI was associated with reduced sleep apnea risk (odds ratioQuintile 5 vs Quintile 1 = 0.72; 95% confidence interval, 0.59-0.88; P for trend = .007). Higher healthy lifestyle score was also associated with decreased odds of sleep apnea (P for trend < .001). There was a significant interaction between healthy lifestyle and sex with sleep apnea risk (P for interaction = .049) whereby females with higher healthy lifestyle scores had a lower risk of sleep apnea compared to males. CONCLUSIONS: Higher-quality and anti-inflammatory diets and a healthier overall lifestyle are associated with lower sleep apnea risk. These findings underline the importance of strategies to improve overall diet quality and promote healthy behavior, not just calorie restriction, to reduce sleep apnea risk. CITATION: Melaku YA, Reynolds AC, Appleton S, et al. High-quality and anti-inflammatory diets and a healthy lifestyle are associated with lower sleep apnea risk. J Clin Sleep Med. 2022;18(6):1667-1679.

Risk factors for COVID-19 infection, disease severity and related deaths in Africa: a systematic review.

OBJECTIVE: The aim of this study was to provide a comprehensive evidence on risk factors for transmission, disease severity and COVID-19 related deaths in Africa. DESIGN: A systematic review has been conducted to synthesise existing evidence on risk factors affecting COVID-19 outcomes across Africa. DATA SOURCES: Data were systematically searched from MEDLINE, Scopus, MedRxiv and BioRxiv. ELIGIBILITY CRITERIA: Studies for review were included if they were published in English and reported at least one risk factor and/or one health outcome. We included all relevant literature published up until 11 August 2020. DATA EXTRACTION AND SYNTHESIS: We performed a systematic narrative synthesis to describe the available studies for each outcome. Data were extracted using a standardised Joanna Briggs Institute data extraction form. RESULTS: Fifteen articles met the inclusion criteria of which four were exclusively on Africa and the remaining 11 papers had a global focus with some data from Africa. Higher rates of infection in Africa are associated with high population density, urbanisation, transport connectivity, high volume of tourism and international trade, and high level of economic and political openness. Limited or poor access to healthcare are also associated with higher COVID-19 infection rates. Older people and individuals with chronic conditions such as HIV, tuberculosis and anaemia experience severe forms COVID-19 leading to hospitalisation and death. Similarly, high burden of chronic obstructive pulmonary disease, high prevalence of tobacco consumption and low levels of expenditure on health and low levels of global health security score contribute to COVID-19 related deaths. CONCLUSIONS: Demographic, institutional, ecological, health system and politico-economic factors influenced the spectrum of COVID-19 infection, severity and death. We recommend multidisciplinary and integrated approaches to mitigate the identified factors and strengthen effective prevention strategies.

Burden of disease attributable to suboptimal diet, metabolic risks and low physical activity in Ethiopia and comparison with Eastern sub-Saharan African countries, 1990-2015: findings from the Global Burden of Disease Study 2015.

BACKGROUND: Twelve of the 17 Sustainable Development Goals (SDGs) are related to malnutrition (both under- and overnutrition), other behavioral, and metabolic risk factors. However, comparative evidence on the impact of behavioral and metabolic risk factors on disease burden is limited in sub-Saharan Africa (SSA), including Ethiopia. Using data from the Global Burden of Disease (GBD) Study, we assessed mortality and disability-adjusted life years (DALYs) attributable to child and maternal undernutrition (CMU), dietary risks, metabolic risks and low physical activity for Ethiopia. The results were compared with 14 other Eastern SSA countries. METHODS: Databases from GBD 2015, that consist of data from 1990 to 2015, were used. A comparative risk assessment approach was utilized to estimate the burden of disease attributable to CMU, dietary risks, metabolic risks and low physical activity. Exposure levels of the risk factors were estimated using spatiotemporal Gaussian process regression (ST-GPR) and Bayesian meta-regression models. RESULTS: In 2015, there were 58,783 [95% uncertainty interval (UI): 43,653-76,020] or 8.9% [95% UI: 6.1-12.5] estimated all-cause deaths attributable to CMU, 66,269 [95% UI: 39,367-106,512] or 9.7% [95% UI: 7.4-12.3] to dietary risks, 105,057 [95% UI: 66,167-157,071] or 15.4% [95% UI: 12.8-17.6] to metabolic risks and 5808 [95% UI: 3449-9359] or 0.9% [95% UI: 0.6-1.1] to low physical activity in Ethiopia. While the age-adjusted proportion of all-cause mortality attributable to CMU decreased significantly between 1990 and 2015, it increased from 10.8% [95% UI: 8.8-13.3] to 14.5% [95% UI: 11.7-18.0] for dietary risks and from 17.0% [95% UI: 15.4-18.7] to 24.2% [95% UI: 22.2-26.1] for metabolic risks. In 2015, Ethiopia ranked among the top four countries (of 15 Eastern SSA countries) in terms of mortality and DALYs based on the age-standardized proportion of disease attributable to dietary and metabolic risks. CONCLUSIONS: In Ethiopia, while there was a decline in mortality and DALYs attributable to CMU over the last two and half decades, the burden attributable to dietary and metabolic risks have increased during the same period. Lifestyle and metabolic risks of NCDs require more attention by the primary health care system of the country.

Incidence, prevalence, mortality, disability-adjusted life years and risk factors of cancer in Australia and comparison with OECD countries, 1990-2015: findings from the Global Burden of Disease Study 2015.

BACKGROUND: Comparative evidence on the burden, trend, and risk factors of cancer is limited. Using data from the Global Burden of Disease (GBD) study, we aimed to assess cancer burden - incidence, prevalence, mortality, disability-adjusted life years (DALYs) - and attributable risk factors for Australia between 1990 and 2015, and to compare them with those of 34 members of the Organisation for Economic Co-operation and Development (OECD). METHODS: The general GBD cancer estimation methods were used with data input from vital registration systems and cancer registries. A comparative risk assessment approach was used to estimate the population-attributable fractions due to risk factors. RESULTS: In 2015 there were 198,880 (95% uncertainty interval [UI]: 183,908-217,365) estimated incident cancer cases and 47,562 (95% UI: 46,061-49,004) cancer deaths in Australia. Twenty-nine percent (95% UI: 28.2-29.8) of total deaths and 17.0% (95% UI: 15.0-19.1) of DALYs were caused by cancer in Australia in 2015. Cancers of the trachea, bronchus and lung, colon and rectum, and prostate were the most common causes of cancer deaths. Thirty-six percent (95% UI: 33.1-37.9) of all cancer deaths were attributable to behavioral risks. The age-standardized cancer incidence rate (ASIR) increased between 1990 and 2015, while the age-standardized cancer death rate (ASDR) decreased over the same period. In 2015, compared to 34 other OECD countries Australia ranked first (highest) and 24th based on ASIR and ASDR, respectively. CONCLUSION: The incidence of cancer has increased over 25 years, and behavioral risks are responsible for a large proportion of cancer deaths. Scaling up of prevention (using strategies targeting cancer risk factors), early detection, and treatment of cancer is required to effectively address this growing health challenge.

National mortality burden due to communicable, non-communicable, and other diseases in Ethiopia, 1990-2015: findings from the Global Burden of Disease Study 2015.

BACKGROUND: Ethiopia lacks a complete vital registration system that would assist in measuring disease burden and risk factors. We used the Global Burden of Diseases, Injuries, and Risk Factors Study 2015 (GBD 2015) estimates to describe the mortality burden from communicable, non-communicable, and other diseases in Ethiopia over the last 25 years. METHODS: GBD 2015 mainly used cause of death ensemble modeling to measure causes of death by age, sex, and year for 195 countries. We report numbers of deaths and rates of years of life lost (YLL) for communicable, maternal, neonatal, and nutritional (CMNN) disorders, non-communicable diseases (NCDs), and injuries with 95% uncertainty intervals (UI) for Ethiopia from 1990 to 2015. RESULTS: CMNN causes of death have declined by 65% in the last two-and-a-half decades. Injury-related causes of death have also decreased by 70%. Deaths due to NCDs declined by 37% during the same period. Ethiopia showed a faster decline in the burden of four out of the five leading causes of age-standardized premature mortality rates when compared to the overall sub-Saharan African region and the Eastern sub-Saharan African region: lower respiratory infections, tuberculosis, HIV/AIDS, and diarrheal diseases; however, the same could not be said for ischemic heart disease and other NCDs. Non-communicable diseases, together, were the leading causes of age-standardized mortality rates, whereas CMNN diseases were leading causes of premature mortality in 2015. Although lower respiratory infections, tuberculosis, and diarrheal disease were the leading causes of age-standardized death rates, they showed major declines from 1990 to 2015. Neonatal encephalopathy, iron-deficiency anemia, protein-energy malnutrition, and preterm birth complications also showed more than a 50% reduction in burden. HIV/AIDS-related deaths have also decreased by 70% since 2005. Ischemic heart disease, hemorrhagic stroke, and ischemic stroke were among the top causes of premature mortality and age-standardized death rates in Ethiopia in 2015. CONCLUSIONS: Ethiopia has been successful in reducing deaths related to communicable, maternal, neonatal, and nutritional deficiency diseases and injuries by 65%, despite unacceptably high maternal and neonatal mortality rates. However, the country's performance regarding non-communicable diseases, including cardiovascular disease, diabetes, cancer, and chronic respiratory disease, was minimal, causing these diseases to join the leading causes of premature mortality and death rates in 2015. While the country is progressing toward universal health coverage, prevention and control strategies in Ethiopia should consider the double burden of common infectious diseases and non-communicable diseases: lower respiratory infections, diarrhea, tuberculosis, HIV/AIDS, cardiovascular disease, cancer, and diabetes. Prevention and control strategies should also pay special attention to the leading causes of premature mortality and death rates caused by non-communicable diseases: cardiovascular disease, cancer, and diabetes. Measuring further progress requires a data revolution in generating, managing, analyzing, and using data for decision-making and the creation of a full vital registration system in the country.

National disability-adjusted life years (DALYs) for 257 diseases and injuries in Ethiopia, 1990-2015: findings from the global burden of disease study 2015.

BACKGROUND: Disability-adjusted life years (DALYs) provide a summary measure of health and can be a critical input to guide health systems, investments, and priority-setting in Ethiopia. We aimed to determine the leading causes of premature mortality and disability using DALYs and describe the relative burden of disease and injuries in Ethiopia. METHODS: We used results from the Global Burden of Diseases, Injuries, and Risk Factors Study 2015 (GBD 2015) for non-fatal disease burden, cause-specific mortality, and all-cause mortality to derive age-standardized DALYs by sex for Ethiopia for each year. We calculated DALYs by summing years of life lost due to premature mortality (YLLs) and years lived with disability (YLDs) for each age group and sex. Causes of death by age, sex, and year were measured mainly using Causes of Death Ensemble modeling. To estimate YLDs, a Bayesian meta-regression method was used. We reported DALY rates per 100,000 for communicable, maternal, neonatal, and nutritional (CMNN) disorders, non-communicable diseases, and injuries, with 95% uncertainty intervals (UI) for Ethiopia. RESULTS: Non-communicable diseases caused 23,118.1 (95% UI, 17,124.4-30,579.6), CMNN disorders resulted in 20,200.7 (95% UI, 16,532.2-24,917.9), and injuries caused 3781 (95% UI, 2642.9-5500.6) age-standardized DALYs per 100,000 in Ethiopia in 2015. Lower respiratory infections, diarrheal diseases, and tuberculosis were the top three leading causes of DALYs in 2015, accounting for 2998 (95% UI, 2173.7-4029), 2592.5 (95% UI, 1850.7-3495.1), and 2562.9 (95% UI, 1466.1-4220.7) DALYs per 100,000, respectively. Ischemic heart disease and cerebrovascular disease were the fourth and fifth leading causes of age-standardized DALYs, with rates of 2535.7 (95% UI, 1603.7-3843.2) and 2159.9 (95% UI, 1369.7-3216.3) per 100,000, respectively. The following causes showed a reduction of 60% or more over the last 25 years: lower respiratory infections, diarrheal diseases, tuberculosis, neonatal encephalopathy, preterm birth complications, meningitis, malaria, protein-energy malnutrition, iron-deficiency anemia, measles, war and legal intervention, and maternal hemorrhage. CONCLUSIONS: Ethiopia has been successful in reducing age-standardized DALYs related to most communicable, maternal, neonatal, and nutritional deficiency diseases in the last 25 years, causing a major ranking shift to types of non-communicable disease. Lower respiratory infections, diarrheal disease, and tuberculosis continue to be leading causes of premature death, despite major declines in burden. Non-communicable diseases also showed reductions as premature mortality declined; however, disability outcomes for these causes did not show declines. Recently developed non-communicable disease strategies may need to be amended to focus on cardiovascular diseases, cancer, diabetes, and major depressive disorders. Increasing trends of disabilities due to neonatal encephalopathy, preterm birth complications, and neonatal disorders should be emphasized in the national newborn survival strategy. Generating quality data should be a priority through the development of new initiatives such as vital events registration, surveillance programs, and surveys to address gaps in data. Measuring disease burden at subnational regional state levels and identifying variations with urban and rural population health should be conducted to support health policy in Ethiopia.

How much do the physician review and InterVA model agree in determining causes of death? A comparative analysis of deaths in rural Ethiopia.

BACKGROUND: Despite it is costly, slow and non-reproducible process, physician review (PR) is a commonly used method to interpret verbal autopsy data. However, there is a growing interest to adapt a new automated and internally consistent method called InterVA. This study evaluated the level of agreement in determining causes of death between PR and the InterVA model. METHODS: Verbal autopsy data for 434 cases collected between September 2009 and November 2012, were interpreted using both PR and the InterVA model. Cohen's kappa statistic (κ) was used to compare the level of chance corrected case-by-case agreement in the diagnosis reached by the PR and InterVA model. RESULTS: Both methods gave comparable cause specific mortality fractions of communicable diseases (36.6% by PR and 36.2% by the model), non-communicable diseases (31.1% by PR and 38.2% by the model) and accidents/injuries (12.9% by PR and 10.1% by the model). The level of case-by-case chance corrected concordance between the two methods was 0.33 (95% CI for κ = 0.29-0.34). The highest and lowest agreements were seen for accidents/injuries and non-communicable diseases; with κ = 0.75 and κ = 0.37, respectively. CONCLUSION: If the InterVA were used in place of the existing PR process, the overall diagnosis would be fairly similar. The methods had better agreement in important public health diseases like; TB, perinatal causes, and pneumonia/sepsis; and lower in cardiovascular diseases and neoplasms. Therefore, both methods need to be validated against a gold-standard diagnosis of death.

Applying the InterVA-4 model to determine causes of death in rural Ethiopia.

BACKGROUND: In Ethiopia, most deaths take place at home and routine certification of cause of death by physicians is lacking. As a result, reliable cause of death (CoD) data are often not available. Recently, a computerized method for interpretation of verbal autopsy (VA) data, called InterVA, has been developed and used. It calculates the probability of a set of CoD given the presence of circumstances, signs, and symptoms reported during VA interviews. We applied the InterVA model to describe CoD in a rural population of Ethiopia. OBJECTIVE: VA data for 436/599 (72.7%) deaths that occurred during 2010-2011 were included. InterVA-4 was used to interpret the VA data into probable cause of death. Cause-specific mortality fraction was used to describe frequency of occurrence of death from specific causes. RESULTS: InterVA-4 was able to give likely cause(s) of death for 401/436 of the cases (92.0%). Overall, 35.0% of the total deaths were attributed to communicable diseases, and 30.7% to chronic non-communicable diseases. Tuberculosis (12.5%) and acute respiratory tract infections (10.4%) were the most frequent causes followed by neoplasms (9.6%) and diseases of circulatory system (7.2%). CONCLUSION: InterVA-4 can produce plausible estimates of the major public health problems that can guide public health interventions. We encourage further validation studies, in local settings, so that InterVA can be integrated into national health surveys.