Burden of 30 cancers among men: Global statistics in 2022 and projections for 2050 using population-based estimates.

BACKGROUND: Men exhibit higher prevalence of modifiable risk factors, such as smoking and alcohol consumption, leading to greater cancer incidence and lower survival rates. Comprehensive evidence on global cancer burden among men, including disparities by age group and country, is sparse. To address this, the authors analyzed 30 cancer types among men in 2022, with projections estimated for 2050. METHODS: The 2022 GLOBOCAN estimates were used to describe cancer statistics for men in 185 countries/territories worldwide. Mortality-to-incidence ratios (MIRs) were calculated by dividing age-standardized mortality rates by incidence rates. RESULTS: In 2022, a high MIR (indicating poor survival) was observed among older men (aged 65 years and older; 61%) for rare cancer types (pancreatic cancer, 91%) and in countries with low a Human Development Index (HDI; 74%). Between 2022 and 2050, cancer cases are projected to increase from 10.3 million to 19 million (≥84%). Deaths are projected to increase from 5.4 million to 10.5 million (≥93%), with a greater than two-fold increase among men aged 65 years and older (≥117%) and for low-HDI and medium-HDI countries/territories (≥160%). Cancer cases and deaths are projected to increase among working-age groups (≥39%) and very-high-HDI countries/territories (≥50%). CONCLUSIONS: Substantial disparities in cancer cases and deaths were observed among men in 2022, and these are projected to widen by 2050. Strengthening health infrastructure, enhancing workforce quality and access, fostering national and international collaborations, and promoting universal health coverage are crucial to reducing cancer disparities and ensuring cancer equity among men globally.

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.

Dietary patterns derived by reduced rank regression, macronutrients as response variables, and variation by economic status: NHANES 1999-2018.

PURPOSE: Macronutrient intakes vary across people and economic status, leading to a disparity in diet-related metabolic diseases. This study aimed to provide insight into this by: (1) identifying dietary patterns in adults using reduced rank regression (RRR), with macronutrients as response variables, and (2) investigating the associations between economic status and macronutrient based dietary patterns, and between dietary patterns with central obesity (waist circumference) and systemic inflammation (C-reactive protein [CRP]). METHODS: 41,849 US participants from the National Health and Nutrition Examination Survey (NHANES), 1999-2018 were included. The percentages of energy from protein, carbohydrates, saturated fats, and unsaturated fats were used as response variables in RRR. Multivariable generalized linear models with Gaussian distribution were employed to investigate the associations. RESULTS: Four dietary patterns were identified. Economic status was positively associated with both the high fat, low carbohydrate [ßHighVsLow = 0.22; 95% CI: 0.16, 0.28] and high protein patterns [ßHighVsLow = 0.07; 95% CI: 0.03, 0.11], and negatively associated with both the high saturated fat [ßHighVsLow = -0.06; 95% CI: -0.08, -0.03] and the low alcohol patterns [ßHighVsLow = -0.08; 95% CI; -0.10, -0.06]. The high saturated fat pattern was positively associated with waist circumference [ßQ5VsQ1 = 1.71; 95% CI: 0.97, 2.44] and CRP [ßQ5VsQ1 = 0.37; 95% CI: 0.26, 0.47]. CONCLUSION: Macronutrient dietary patterns, which varied by economic status and were associated with metabolic health markers, may explain associations between economic status and health.

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.

Associations of baseline obstructive sleep apnea and sleep macroarchitecture with cognitive function after 8 years in middle-aged and older men from a community-based cohort study.

Previous prospective studies examining associations of obstructive sleep apnea and sleep macroarchitecture with future cognitive function recruited older participants, many demonstrating baseline cognitive impairment. This study examined obstructive sleep apnea and sleep macroarchitecture predictors of visual attention, processing speed, and executive function after 8 years among younger community-dwelling men. Florey Adelaide Male Ageing Study participants (n = 477) underwent home-based polysomnography, with 157 completing Trail-Making Tests A and B and the Mini-Mental State Examination. Associations of obstructive sleep apnea (apnea-hypopnea index, oxygen desaturation index, and hypoxic burden index) and sleep macroarchitecture (sleep stage percentages and total sleep time) parameters with future cognitive function were examined using regression models adjusted for baseline demographic, biomedical, and behavioural factors, and cognitive task performance. The mean (standard deviation) age of the men at baseline was 58.9 (8.9) years, with severe obstructive sleep apnea (apnea-hypopnea index ≥30 events/h) in 9.6%. The median (interquartile range) follow-up was 8.3 (7.9-8.6) years. A minority of men (14.6%) were cognitively impaired at baseline (Mini-Mental State Examination score <28/30). A higher percentage of light sleep was associated with better Trail-Making Test A performance (B = -0.04, 95% confidence interval [CI] -0.06, -0.01; p = 0.003), whereas higher mean oxygen saturation was associated with worse performance (B = 0.11, 95% CI 0.02, 0.19; p = 0.012). While obstructive sleep apnea and sleep macroarchitecture might predict cognitive decline, future studies should consider arousal events and non-routine hypoxaemia measures, which may show associations with cognitive decline.

The Good Sleeper Scale-15 items: a questionnaire for the standardised assessment of good sleepers.

Research with 'good sleepers' is ubiquitous, yet there are no standardised criteria to identify a 'good sleeper'. The present study aimed to create and validate a questionnaire for identifying good sleepers for use in research studies known as the Good Sleeper Scale-15 items (GSS-15). Data were derived from a population-based survey of Australian adults (n = 2,044). A total of 23 items were chosen for possible inclusion. An exploratory factor analysis (EFA) was conducted on ~10% of the survey dataset (n = 191) for factor identification and item reduction. A confirmatory factor analysis (CFA) was conducted on the remaining data (n = 1,853) to test model fit. Receiver operating characteristic curves and correlations were conducted to derive cut-off scores and test associations with sleep, daytime functioning, health, and quality-of-life. The EFA identified six factors: 'Sleep Difficulties', 'Timing', 'Duration', 'Regularity', 'Adequacy', and 'Perceived Sleep Problem'. The CFA showed that model fit was high and comparable to other sleep instruments, χ2 (63) = 378.22, p < 0.001, root mean square error of approximation = 0.05, with acceptable internal consistency (α = 0.76). Strong correlations were consistently found between GSS-15 global scores and outcomes, including 'a good night's sleep' (r = 0.7), 'feeling un-refreshed' (r = -0.59), and 'experienced sleepiness' (r = -0.51), p < 0.001. Cut-off scores were derived to categorise individuals likely to be a good sleeper (GSS-15 score ≥40) and those very likely to be a good sleeper (GSS-15 score ≥45). The GSS-15 is a freely available, robust questionnaire that will assist in identifying good sleepers for the purpose of sleep research. Future work will test relationships with other sleep measures in community and clinical samples.

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.

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.

Comorbid insomnia and sleep apnoea is associated with all-cause mortality.

BACKGROUND: Increased mortality has been reported in people with insomnia and in those with obstructive sleep apnoea (OSA). However, these conditions commonly co-occur and the combined effect of comorbid insomnia and sleep apnoea (COMISA) on mortality risk is unknown. This study used Sleep Heart Health Study (SHHS) data to assess associations between COMISA and all-cause mortality risk. METHODS: Insomnia was defined as difficulties falling asleep, maintaining sleep and/or early morning awakenings from sleep ≥16 times per month, and daytime impairments. OSA was defined as an apnoea-hypopnoea index ≥15 events·h-1. COMISA was defined if both conditions were present. Multivariable adjusted Cox proportional hazards models were used to determine the association between COMISA and all-cause mortality (n=1210) over 15 years of follow-up. RESULTS: 5236 participants were included. 2708 (52%) did not have insomnia/OSA (reference group), 170 (3%) had insomnia-alone, 2221 (42%) had OSA-alone and 137 (3%) had COMISA. COMISA participants had a higher prevalence of hypertension (OR 2.00, 95% CI 1.39-2.90) and cardiovascular disease (CVD) (OR 1.70, 95% CI 1.11-2.61) compared with the reference group. Insomnia-alone and OSA-alone were associated with higher risk of hypertension but not CVD compared with the reference group. Compared with the reference group, COMISA was associated with a 47% (hazard ratio 1.47, 95% CI 1.06-2.07) increased risk of mortality. The association between COMISA and mortality was consistent across multiple definitions of OSA and insomnia. CONCLUSIONS: COMISA was associated with higher rates of hypertension and CVD at baseline, and an increased risk of all-cause mortality compared with no insomnia/OSA.

Multidimensional sleep health is associated with mental well-being in Australian adults.

The few studies assessing the relationship between mental health and a multidimensional measure of sleep have been conducted in samples of almost exclusively women. In the present study, we therefore assessed associations of multidimensional sleep health with mental well-being in Australian adults. A cross-sectional, national online survey of sleep health was conducted in 2019 in 2,044 community dwelling adults aged 18-90 years. Composite scores of self-reported healthy sleep were based on Ru-SATED (R-SATED) dimensions of satisfaction, alertness, timing, efficiency and duration scored 0 (never/rarely), 1 (sometimes) or 2 (usually/always), and regularity (social jet lag) was scored 0 (≥1 hr) or 1 (<1 hr). Mental well-being was identified by a report of no problem on the EuroQol (European quality of life) five dimension five level scale (EQ-5D-5L) anxiety/depression scale, as well as absence of six chronic depressive symptoms. Ordinal logistic regression analyses determined associations of mental well-being with sleep dimensions, and total R-SATED (range 0-11) and SATED scores (0-10), adjusted for sociodemographic and health factors. In males and females, healthy categories of SATED dimensions (but not regularity) were associated with mental well-being. A 1 unit increase in the total SATED score (mean [SD] 6.6 [2.1]) was associated with less problems on the EQ-5D-5L (odds ratio [OR] 1.11, 95% confidence interval [CI] 1.08-1.14) and fewer chronic depressive symptoms (OR 1.20, 95% CI 1.17-1.23). These estimates did not differ when the total R-SATED score was considered. Sleep health promotion focussing solely on traditional messaging around healthy sleep duration may limit mental health gains. Rather, a more holistic approach to public health messaging to improve sleep health literacy may be beneficial for mental well-being.

The association of co-morbid insomnia and sleep apnea with prevalent cardiovascular disease and incident cardiovascular events.

Insomnia and obstructive sleep apnea commonly co-occur (co-morbid insomnia and sleep apnea), and their co-occurrence has been associated with worse cardiometabolic and mental health. However, it remains unknown if people with co-morbid insomnia and sleep apnea are at a heightened risk of incident cardiovascular events. This study used longitudinal data from the Sleep Heart Health Study (N = 5803) to investigate potential associations between co-morbid insomnia and sleep apnea and cardiovascular disease prevalence at baseline and cardiovascular event incidence over ~11 years follow-up. Insomnia was defined as self-reported difficulties initiating and/or maintaining sleep AND daytime impairment. Obstructive sleep apnea was defined as an apnea-hypopnea index ≥ 15 events per hr sleep. Co-morbid insomnia and sleep apnea was defined if both conditions were present. Data from 4160 participants were used for this analysis. The prevalence of no insomnia/obstructive sleep apnea, insomnia only, obstructive sleep apnea only and co-morbid insomnia and sleep apnea was 53.2%, 3.1%, 39.9% and 1.9%, respectively. Co-morbid insomnia and sleep apnea was associated with a 75% (odd ratios [95% confidence interval]; 1.75 [1.14, 2.67]) increase in likelihood of having cardiovascular disease at baseline after adjusting for pre-specified confounders. In the unadjusted model, co-morbid insomnia and sleep apnea was associated with a twofold increase (hazard ratio, 95% confidence interval: 2.00 [1.33, 2.99]) in risk of cardiovascular event incidence. However, after adjusting for pre-specified covariates, co-morbid insomnia and sleep apnea was not significantly associated with incident cardiovascular events (hazard ratio 1.38 [0.92, 2.07]). Comparable findings were obtained when an alternative definition of insomnia (difficulties initiating and/or maintaining sleep without daytime impairment) was used.

Eating architecture in adults at increased risk of type 2 diabetes: associations with body fat and glycaemic control.

Eating architecture is a term that describes meal frequency, meal timing and meal size and the daily variation in each of these. The aim of this study was to determine the relationship between components of eating architecture on body fat and markers of glycaemic control in healthy adults at increased risk of type 2 diabetes (T2DM). Participants (n 73, 39 males, age 58·8 (8·1) years, BMI 33·4 (4·4) kg/m2) recorded food intake and wore accelerometers and continuous glucose monitors (CGM) for 7-14 d under free-living conditions. Body fat and glycated Hb (HbA1c) were also measured. The mean and day-to-day variation (calculated as the standard deviation during the monitoring period) of each component of eating architecture were calculated. Multivariable linear regression models were constructed for three separate outcome variables (body fat mass, mean CGM glucose and HbA1c) for each component of eating architecture before and after adjustment for confounders. Higher variability in the time of first meal consumption was associated with increased body fat mass after adjusting for confounders (ß = 0·227, 95 % CI: 0·019, 0·434, P = 0·033). Increased variability in the time lag from waking to first meal consumption was also positively associated with increased HbA1c after adjustment (ß = 0·285, 95 % CI: 0·040, 0·530, P = 0·023). Low day-to-day variability in first meal consumption was associated with lower body fat and improved glucose control in adults at increased risk of T2DM. Routine consumption of meals may optimise temporal regulation to anticipate and respond appropriately to a glucose challenge.

A bi-directional association between weight change and health-related quality of life: evidence from the 11-year follow-up of 9916 community-dwelling adults.

PURPOSE: To examine the prospective associations between body mass index (BMI) and health-related quality of life (HRQoL). METHODS: Data were extracted from a longitudinal, nationally representative sample of 9916 men and women aged 18 years and over who were followed annually between 2006 and 2016 in the Household, Income and Labour Dynamics in Australia (HILDA) survey. HRQoL was assessed using the self-administered SF-36 questionnaire annually between 2006 (baseline) and 2016. BMI was calculated from self-reported height and weight and was classified into the following four categories of baseline BMI: underweight (< 18.5 kg/m2), normal weight (18.5-24.9 kg/m2), overweight (25-29.9 kg/m2) and obese (≥ 30 kg/m2). We used linear mixed-effects regression models to investigate the associations between change in BMI (kg/m2) and concurrent changes in HRQoL scores over 11 years. RESULTS: BMI gain was associated with deterioration of Physical Component Summary (PCS) (P < 0.001), but not with change in Mental component summary (MCS) over the 11-year period. BMI gain was inversely associated (P < 0.001) with five of the eight HRQoL domains (physical functioning, role physical, bodily pain, general health and vitality) with a significant graded association according to baseline BMI category. Over the 11-year study period, every unit increase in PCS was associated with a decrease of 0.02 (P < 0.001), 0.03 (P < 0.001) and 0.04 (P < 0.001) BMI units per year among participants who were normal, overweight and obese at baseline, respectively. Five of the eight domains of HRQoL (physical functioning, role physical, bodily pain, general health and vitality) were inversely associated with BMI (P < 0.001) with a significant graded association according to baseline BMI category. CONCLUSIONS: Weight gain was not only associated with deterioration of HRQoL, and vice versa. The bi-directional association was stronger for physical than mental domains of HRQoL.

Burden and trend of diet-related non-communicable diseases in Australia and comparison with 34 OECD countries, 1990-2015: findings from the Global Burden of Disease Study 2015.

BACKGROUND: Diet is a major determining factor for many non-communicable chronic diseases (NCDs). However, evidence on diet-related NCD burden remains limited. We assessed the trends in diet-related NCDs in Australia from 1990 to 2015 and compared the results with other countries of the Organization for Economic Co-operation and Development (OECD). METHODS: We used data and methods from the Global Burden of Disease (GBD) 2015 study to estimate the NCD mortality and disability-adjusted life years (DALYs) attributable to 14 dietary risk factors in Australia and 34 OECD nations. Countries were further ranked from the lowest (first) to highest (35th) burden using an age-standardized population attributable fraction (PAF). RESULTS: In 2015, the estimated number of deaths attributable to dietary risks was 29,414 deaths [95% uncertainty interval (UI) 24,697 - 34,058 or 19.7% of NCD deaths] and 443,385 DALYs (95% UI 377,680-511,388 or 9.5% of NCD DALYs) in Australia. Young (25-49 years) and middle-age (50-69 years) male adults had a higher PAF of diet-related NCD deaths and DALYs than their female counterparts. Diets low in fruits, vegetables, nuts and seeds and whole grains, but high in sodium, were the major contributors to both NCD deaths and DALYs. Overall, 42.3% of cardiovascular deaths were attributable to dietary risk factors. The age-standardized PAF of diet-related NCD mortality and DALYs decreased over the study period by 28.2% (from 27.0% in 1990 to 19.4% in 2015) and 41.0% (from 14.3% in 1990 to 8.4% in 2015), respectively. In 2015, Australia ranked 12th of 35 examined countries in diet-related mortality. A small improvement of rank was recorded compared to the previous 25 years. CONCLUSIONS: Despite a reduction in diet-related NCD burden over 25 years, dietary risks are still the major contributors to a high burden of NCDs in Australia. Interventions targeting NCDs should focus on dietary behaviours of individuals and population groups.

Trends of mortality attributable to child and maternal undernutrition, overweight/obesity and dietary risk factors of non-communicable diseases in sub-Saharan Africa, 1990-2015: findings from the Global Burden of Disease Study 2015.

OBJECTIVE: To assess trends of mortality attributable to child and maternal undernutrition (CMU), overweight/obesity and dietary risks of non-communicable diseases (NCD) in sub-Saharan Africa (SSA) using data from the Global Burden of Disease (GBD) Study 2015. DESIGN: For each risk factor, a systematic review of data was used to compute the exposure level and the effect size. A Bayesian hierarchical meta-regression analysis was used to estimate the exposure level of the risk factors by age, sex, geography and year. The burden of all-cause mortality attributable to CMU, fourteen dietary risk factors (eight diets, five nutrients and fibre intake) and overweight/obesity was estimated. SETTING: Sub-Saharan Africa.ParticipantsAll age groups and both sexes. RESULTS: In 2015, CMU, overweight/obesity and dietary risks of NCD accounted for 826204 (95 % uncertainty interval (UI) 737346, 923789), 266768 (95 % UI 189051, 353096) and 558578 (95 % UI 453433, 680197) deaths, respectively, representing 10·3 % (95 % UI 9·1, 11·6 %), 3·3 % (95 % UI 2·4, 4·4 %) and 7·0 % (95 % UI 5·8, 8·3 %) of all-cause mortality. While the age-standardized proportion of all-cause mortality accounted for by CMU decreased by 55·2 % between 1990 and 2015 in SSA, it increased by 63·3 and 17·2 % for overweight/obesity and dietary risks of NCD, respectively. CONCLUSIONS: The increasing burden of diet- and obesity-related diseases and the reduction of mortality attributable to CMU indicate that SSA is undergoing a rapid nutritional transition. To tackle the impact in SSA, interventions and international development agendas should also target dietary risks associated with NCD and overweight/obesity.

A comparison of principal component analysis, partial least-squares and reduced-rank regressions in the identification of dietary patterns associated with bone mass in ageing Australians.

PURPOSE: The relative advantages of dietary analysis methods, particularly in identifying dietary patterns associated with bone mass, have not been investigated. We evaluated principal component analysis (PCA), partial least-squares (PLS) and reduced-rank regressions (RRR) in determining dietary patterns associated with bone mass. METHODS: Data from 1182 study participants (45.9% males; aged 50 years and above) from the North West Adelaide Health Study (NWAHS) were used. Dietary data were collected using a food frequency questionnaire (FFQ). Dietary patterns were constructed using PCA, PLS and RRR and compared based on the performance to identify plausible patterns associated with bone mineral density (BMD) and content (BMC). RESULTS: PCA, PLS and RRR identified two, four and four dietary patterns, respectively. All methods identified similar patterns for the first two factors (factor 1, "prudent" and factor 2, "western" patterns). Three, one and none of the patterns derived by RRR, PLS and PCA were significantly associated with bone mass, respectively. The "prudent" and dairy (factor 3) patterns determined by RRR were positively and significantly associated with BMD and BMC. Vegetables and fruit pattern (factor 4) of PLS and RRR was negatively and significantly associated with BMD and BMC, respectively. CONCLUSIONS: RRR was found to be more appropriate in identifying more (plausible) dietary patterns that are associated with bone mass than PCA and PLS. Nevertheless, the advantage of RRR over the other two methods (PCA and PLS) should be confirmed in future studies.

Associations of childhood, maternal and household dietary patterns with childhood stunting in Ethiopia: proposing an alternative and plausible dietary analysis method to dietary diversity scores.

BACKGROUND: Identifying dietary patterns that consider the overall eating habits, rather than focusing on individual foods or simple counts of consumed foods, better helps to understand the combined effects of dietary components. Therefore, this study aimed to use dietary patterns, as an alternative method to dietary diversity scores (DDSs), and investigate their associations with childhood stunting in Ethiopia. METHODS: Mothers and their children aged under 5 years (n = 3788) were recruited using a two-stage random cluster sampling technique in two regions of Ethiopia. Socio-demographic, dietary and anthropometric data were collected. Dietary intake was assessed using standardized dietary diversity tools. Household, maternal and child DDSs were calculated and dietary patterns were identified by tetrachoric (factor) analysis. Multilevel linear and Poisson regression analyses were applied to assess the association of DDSs and dietary patterns with height-for-age z score (HAZ) and stunting, respectively. RESULTS: The overall prevalence of stunting among children under-five was 38.5% (n = 1459). We identified three dietary patterns each, for households ("fish, meat and miscellaneous", "egg, meat, poultry and legume" and "dairy, vegetable and fruit"), mothers ("plant-based", "egg, meat, poultry and legume" and "dairy, vegetable and fruit" and children ("grain based", "egg, meat, poultry and legume" and "dairy, vegetable and fruit"). Children in the third tertile of the household "dairy, vegetable and fruit" pattern had a 0.16 (ß = 0.16; 95% CI: 0.02, 0.30) increase in HAZ compared to those in the first tertile. A 0.22 (ß = 0.22; 95% CI: 0.06, 0.39) and 0.19 (ß = 0.19; 0.04, 0.33) increase in HAZ was found for those in the third tertiles of "dairy, vegetable and fruit" patterns of children 24-59 months and 6-59 months, respectively. Those children in the second (ß = -0.17; 95% CI: -0.31, -0.04) and third (ß = -0.16; 95% CI: -0.30, -0.02) tertiles of maternal "egg, meat, poultry and legume" pattern had a significantly lower HAZ compared to those in the first tertile. No significant associations between the household and child "egg, meat, poultry and legume" dietary patterns with HAZ and stunting were found. Statistically non-significant associations were found between household, maternal and child DDSs, and HAZ and stunting. CONCLUSION: A higher adherence to a "dairy, vegetable and fruit" dietary pattern is associated with increased HAZ and reduced risk of stunting. Dietary pattern analysis methods, using routinely collected dietary data, can be an alternative approach to DDSs in low resource settings, to measure dietary quality and in determining associations of overall dietary intake with stunting.

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 and mortality rates of malaria in Ethiopia from 1990 to 2015: analysis of the global burden of diseases 2015.

BACKGROUND: In Ethiopia there is no complete registration system to measure disease burden and risk factors accurately. In this study, the 2015 global burden of diseases, injuries and risk factors (GBD) data were used to analyse the incidence, prevalence and mortality rates of malaria in Ethiopia over the last 25 years. METHODS: GBD 2015 used verbal autopsy surveys, reports, and published scientific articles to estimate the burden of malaria in Ethiopia. Age and gender-specific causes of death for malaria were estimated using cause of death ensemble modelling. RESULTS: The number of new cases of malaria declined from 2.8 million [95% uncertainty interval (UI) 1.4-4.5 million] in 1990 to 621,345 (95% UI 462,230-797,442) in 2015. Malaria caused an estimated 30,323 deaths (95% UI 11,533.3-61,215.3) in 1990 and 1561 deaths (95% UI 752.8-2660.5) in 2015, a 94.8% reduction over the 25 years. Age-standardized mortality rate of malaria has declined by 96.5% between 1990 and 2015 with an annual rate of change of 13.4%. Age-standardized malaria incidence rate among all ages and gender declined by 88.7% between 1990 and 2015. The number of disability-adjusted life years lost (DALY) due to malaria decreased from 2.2 million (95% UI 0.76-4.7 million) in 1990 to 0.18 million (95% UI 0.12-0.26 million) in 2015, with a total reduction 91.7%. Similarly, age-standardized DALY rate declined by 94.8% during the same period. CONCLUSIONS: Ethiopia has achieved a 50% reduction target of malaria of the millennium development goals. The country should strengthen its malaria control and treatment strategies to achieve the sustainable development goals.

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.