Global, regional, and national burden of chronic kidney disease, 1990–2017 : a systematic analysis for the Global Burden of Disease Study 2017

Background Health system planning requires careful assessment of chronic kidney disease (CKD) epidemiology, but data for morbidity and mortality of this disease are scarce or non-existent in many countries. We estimated the global, regional, and national burden of CKD, as well as the burden of cardiovascular disease and gout attributable to impaired kidney function, for the Global Burden of Diseases, Injuries, and Risk Factors Study 2017. We use the term CKD to refer to the morbidity and mortality that can be directly attributed to all stages of CKD, and we use the term impaired kidney function to refer to the additional risk of CKD from cardiovascular disease and gout. Methods The main data sources we used were published literature, vital registration systems, end-stage kidney disease registries, and household surveys. Estimates of CKD burden were produced using a Cause of Death Ensemble model and a Bayesian meta-regression analytical tool, and included incidence, prevalence, years lived with disability, mortality, years of life lost, and disability-adjusted life-years (DALYs). A comparative risk assessment approach was used to estimate the proportion of cardiovascular diseases and gout burden attributable to impaired kidney function. Findings Globally, in 2017, 1·2 million (95% uncertainty interval [UI] 1·2 to 1·3) people died from CKD. The global all-age mortality rate from CKD increased 41·5% (95% UI 35·2 to 46·5) between 1990 and 2017, although there was no significant change in the age-standardised mortality rate (2·8%, −1·5 to 6·3). In 2017, 697·5 million (95% UI 649·2 to 752·0) cases of all-stage CKD were recorded, for a global prevalence of 9·1% (8·5 to 9·8). The global all-age prevalence of CKD increased 29·3% (95% UI 26·4 to 32·6) since 1990, whereas the age-standardised prevalence remained stable (1·2%, −1·1 to 3·5). CKD resulted in 35·8 million (95% UI 33·7 to 38·0) DALYs in 2017, with diabetic nephropathy accounting for almost a third of DALYs. Most of the burden of CKD was concentrated in the three lowest quintiles of Socio-demographic Index (SDI). In several regions, particularly Oceania, sub-Saharan Africa, and Latin America, the burden of CKD was much higher than expected for the level of development, whereas the disease burden in western, eastern, and central sub-Saharan Africa, east Asia, south Asia, central and eastern Europe, Australasia, and western Europe was lower than expected. 1·4 million (95% UI 1·2 to 1·6) cardiovascular disease-related deaths and 25·3 million (22·2 to 28·9) cardiovascular disease DALYs were attributable to impaired kidney function. Interpretation Kidney disease has a major effect on global health, both as a direct cause of global morbidity and mortality and as an important risk factor for cardiovascular disease. CKD is largely preventable and treatable and deserves greater attention in global health policy decision making, particularly in locations with low and middle SDI

Association between depression, anxiety and weight change in young adults

Background To investigate whether there are bi-directional associations between anxiety and mood disorders and body mass index (BMI) in a cohort of young adults. Methods We analysed data from the 2004–2006 (baseline) and 2009–2011 (follow-up) waves of the Childhood Determinants of Adult Health study. Lifetime DSM-IV anxiety and mood disorders were retrospectively diagnosed with the Composite International Diagnostic Interview. Potential mediators were individually added to the base models to assess their potential role as a mediator of the associations. Results In males, presence of mood disorder history at baseline was positively associated with BMI gain (β = 0.77, 95% CI: 0.14–1.40), but baseline BMI was not associated with subsequent risk of mood disorder. Further adjustment for covariates, including dietary pattern, physical activity, and smoking reduced the coefficient (β) to 0.70 (95% CI: 0.01–1.39), suggesting that the increase in BMI was partly mediated by these factors. In females, presence of mood disorder history at baseline was not associated with subsequent weight gain, however, BMI at baseline was associated with higher risk of episode of mood disorder (RR per kg/m2: 1.04, 95% CI: 1.01–1.08), which was strengthened (RR per kg/m2 = 1.07, 95% CI: 1.00–1.15) after additional adjustment in the full model. There was no significant association between anxiety and change in BMI and vice-versa. Conclusion The results do not suggest bidirectional associations between anxiety and mood disorders, and change in BMI. Interventions promoting healthy lifestyle could contribute to reducing increase in BMI associated with mood disorder in males, and excess risk of mood disorder associated with BMI in females

Heart failure in older Australians: Epidemiology and risk prediction modelling

The population burden and costs of heart failure (HF) are predicted to increase, unless those at high-risk of HF are identified and targeted for risk reduction. This thesis used data from older hypertensive cohort managed in a community to investigate the epidemiology of HF, and develop HF risk prediction model. HF is a frequent long-term outcome in older hypertensives, and survival after HF diagnosis remains poor. Despite the abundance of HF prediction models, their application remains unclear due to methodological limitations. Multivariable model consisting of readily accessible variables that allows reliable prediction of 10-year risk of incident HF was developed

Low birth weight and macrosomia in Tigray, Northern Ethiopia: who are the mothers at risk?

Abstract Background Infant birth weight, which is classified into low birth weight, normal birth weight and macrosomia, is associated with short and long-term health consequences, such as neonatal mortality and chronic disease in life. Macrosomia and low birth weight are double burden problems in developing counties, such as Ethiopia, but the paucity of evidence has made it difficult to assess the extent of this situation. As a result there has been inconsistency in the reported prevalence of low birth weight and macrosomia in Ethiopia. This study aimed to determine the incidence and predictors of low birth weight and macrosomia in Tigray, Northern Ethiopia. Method We conducted a cross-sectional survey among a cohort of 1152 neonates delivered in Tigray Region at randomly selected hospitals between April and July 2014. We used the birth weight category described previously as an outcome variable. Data were collected using structured questionnaire by midwives. We entered and analyzed data using STATA™ Version 11.0. Data were described using a frequency, percentage, relative risk ratio, and 95% confidence interval. Multinomial logistic regression was conducted to identify independent predictors of low birth weight and macrosomia. Result In this study, we found a 10.5% and 6.68% incidence of low birth weight and macrosomia, respectively. Seventy (57.8%) of all low birth weight neonates were term births. The predictors for low birth weight were: early marriage (<18 year) (RRR: 0.59, CI: 0.35–0.97); rural residence (RRR: 0.53, CI: 0.32–0.9); prematurity (RRR: 15.4, CI: 9.18–25.9); no antenatal follow-up (RRR: 6.78, CI: 2.39–19.25); and female sex (RRR: 1.77, CI: 1.13–2.77). Predictors for macrosomia were: female gender (RRR: 0.58, CI: 0.35–0.9); high body mass index (RRR: 5.0, CI: 1.56–16); post-maturity (RRR: 2.23, CI: 1.06–4.6); and no maternal complication (RRR: 0.46, CI: 0.27–0.8). Conclusion In this study, we found gestational age and gender of the neonate to be common risk factors for both low birth weight and macrosomia. Strengthening antenatal follow up, prevention of pre and post maturity, controlling body mass index, and improving socioeconomic status of mothers are recommendations to prevent the double burden (low birth weight and macrosomia) and associated short and long-term consequences

Trend in standardized mortality rate, the KA-HDSS cohort, September 11, 2009–September 10, 2012.

<p>* P-value represents test for linear trend in stand.ardized rates.</p><p>Year I: September 11, 2009–september 10, 2010.</p><p>Year II: September 11, 2010–september 10, 2011.</p><p>Year III: September 11, 2011–september 10, 2012.</p

Mortality rates by background and geographic variables, the KA-HDSS cohort, September 11, 2009-September 10, 2012.

<p>All rates are per 1000 person-years.</p><p>* adults who are never married before.</p

Person-years of follow-up and number of deaths to the KA-HDSS cohort, September 11, 2009–September 10, 2012.

<p>*under 10 years old.</p