Chronic non-communicable diseases in Cameroon - burden, determinants and current policies

Cameroon is experiencing an increase in the burden of chronic non-communicable diseases (NCDs), which accounted for 43% of all deaths in 2002. This article reviews the published literature to critically evaluate the evidence on the frequency, determinants and consequences of NCDs in Cameroon, and to identify research, intervention and policy gaps. The rising trends in NCDs have been documented for hypertension and diabetes, with a 2-5 and a 10-fold increase in their respective prevalence between 1994 and 2003. Magnitudes are much higher in urban settings, where increasing prevalence of overweight/obesity (by 54-82%) was observed over the same period. These changes largely result from the adoption of unfavorable eating habits, physical inactivity, and a probable increasing tobacco use. These behavioral changes are driven by the economic development and social mobility, which are part of the epidemiologic transition. There is still a dearth of information on chronic respiratory diseases and cancers, as well as on all NDCs and related risk factors in children and adolescents. More nationally representative data is needed to tract risk factors and consequences of NCDs. These conditions are increasingly been recognized as a priority, mainly through locally generated evidence. Thus, national-level prevention and control programs for chronic diseases (mainly diabetes and hypertension) have been established. However, the monitoring and evaluation of these programs is necessary. Budgetary allocations data by the ministry of health would be helpful, to evaluate the investment in NCDs prevention and control. Establishing more effective national-level tobacco control measures and food policies, as well as campaigns to promote healthy diets, physical activity and tobacco cessation would probably contribute to reducing the burden of NCDs

Estimation of Absolute Cardiovascular Risk in Individuals with Diabetes Mellitus: Rationale and Approaches

Purpose. To examine the usefulness of cardiovascular risk estimation models in people with diabetes. Methods. Review of published studies that compare the discriminative power of major cardiovascular risk factors single or in combination in individuals with and without diabetes, for major cardiovascular outcomes. Results. In individuals with and without diabetes, major risk factors affect cardiovascular risk similarly, with no evidence of any significant interaction. Accounting for diabetes-specific parameters, cardiopreventative therapies can significantly improve risk estimation in diabetes. General and diabetes-specific cardiovascular risk models have a useful discriminative power, but tend to overestimate risk in individuals with diabetes. Their impact on care delivery, adherence to therapies, and patients' outcome remain poorly understood. Conclusions. The high-risk status conferred by diabetes does not preclude the estimation of absolute cardiovascular risk estimation using global risk tools in individuals with diabetes, as this is useful for the initiation and intensification of preventive measures

Risk models to predict chronic kidney disease and its progression: a systematic review

A systematic review of risk prediction models conducted by Justin Echouffo-Tcheugui and Andre Kengne examines the evidence base for prediction of chronic kidney disease risk and its progression, and suitability of such models for clinical use

Validation of two prediction models of undiagnosed chronic kidney disease in mixed-ancestry South Africans

BACKGROUND: Chronic kidney disease (CKD) is a global challenge. Risk models to predict prevalent undiagnosed CKD have been published. However, none was developed or validated in an African population. We validated the Korean and Thai CKD prediction model in mixed-ancestry South Africans. METHODS: Discrimination and calibration were assessed overall and by major subgroups. CKD was defined as 'estimated glomerular filtration rate (eGFR) <60ml/min/1.73m 2 ' or 'any nephropathy'. eGFR was based on the 4-variable Modification of Diet in Renal Disease (MDRD) formula. RESULTS: In all 902 participants (mean age 55years) included, 259 (28.7%) had prevalent undiagnosed CKD. C-statistics were 0.76 (95 % CI: 0.73-0.79) for 'eGFR <60ml/min/1.73m 2 ' and 0.81 (0.78-0.84) for 'any nephropathy' for the Korean model; corresponding values for the Thai model were 0.80 (0.77-0.83) and 0.77 (0.74-0.81). Discrimination was better in men, older and normal weight individuals. The model underestimated CKD risk by 10% to 13% for the Thai and 9% to 93% for the Korean model. Intercept adjustment significantly improved the calibration with an expected/observed risk of 'eGFR <60ml/min/1.73m 2 ' and 'any nephropathy' respectively of 0.98 (0.87-1.10) and 0.97 (0.86-1.09) for the Thai model; but resulted in an underestimation by 24% with the Korean model. Results were broadly similar for CKD derived from the Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI) formula. CONCLUSION: Asian prevalent CKD risk models had acceptable performances in mixed-ancestry South Africans. This highlights the potential importance of using existing models for risk CKD screening in developing countries

Renin-Angiotensin-Aldosterone System, Glucose Metabolism and Incident Type 2 Diabetes Mellitus: MESA.

Background Mechanistic studies suggest that aldosterone impairs glucose metabolism. We investigated the cross-sectional associations of aldosterone and plasma renin activity with fasting plasma glucose, insulin resistance ( IR ), β-cell function, and longitudinal association with incident diabetes mellitus among adults in MESA (the multiethnic study of atherosclerosis) prospective cohort study. Methods and Results Homeostatic model assessment of IR ( HOMA 2- IR ) and HOMA 2-β were used to estimate IR and β-cell function, respectively. Incident diabetes mellitus was defined as fasting plasma glucose ≥126 mg/dL or anti-diabetic medication use at follow-up. Linear regression was used to examine cross-sectional associations of aldosterone with fasting plasma glucose, HOMA 2- IR and HOMA 2-β; Cox regression was used to estimate hazard ratios ( HR ) for incident diabetes mellitus with multivariable adjustment. There were 116 cases of incident diabetes mellitus over 10.5 years among 1570 adults (44% non-Hispanic white, 13% Chinese American, 19% Black, 24% Hispanic American, mean age 64±10 years, 51% female). A 100% increase in log-aldosterone was associated with a 2.6 mg/dL higher fasting plasma glucose, 15% higher HOMA 2- IR and 6% higher HOMA 2-β ( P&lt;0.01). A 1- SD increase in log-aldosterone was associated with a 44% higher risk of incident diabetes mellitus ( P&lt;0.01) with the greatest increase of 142% ( P&lt;0.01) observed in Chinese Americans ( P for interaction=0.09 versus other ethnicities). Similar cross-sectional findings for log-plasma renin activity existed, but log-plasma renin activity was not associated with incident diabetes mellitus after full adjustment. Conclusions Aldosterone is associated with glucose homeostasis and diabetes mellitus risk with graded associations among Chinese Americans and blacks, suggesting that pleiotropic effects of aldosterone may represent a modifiable mechanism in diabetes mellitus pathogenesis with potential racial/ethnic variation

Risk models to predict hypertension: a systematic review

BACKGROUND: As well as being a risk factor for cardiovascular disease, hypertension is also a health condition in its own right. Risk prediction models may be of value in identifying those individuals at risk of developing hypertension who are likely to benefit most from interventions. Methods and FINDINGS: To synthesize existing evidence on the performance of these models, we searched MEDLINE and EMBASE; examined bibliographies of retrieved articles; contacted experts in the field; and searched our own files. Dual review of identified studies was conducted. Included studies had to report on the development, validation, or impact analysis of a hypertension risk prediction model. For each publication, information was extracted on study design and characteristics, predictors, model discrimination, calibration and reclassification ability, validation and impact analysis. Eleven studies reporting on 15 different hypertension prediction risk models were identified. Age, sex, body mass index, diabetes status, and blood pressure variables were the most common predictor variables included in models. Most risk models had acceptable-to-good discriminatory ability (C-statistic>0.70) in the derivation sample. Calibration was less commonly assessed, but overall acceptable. Two hypertension risk models, the Framingham and Hopkins, have been externally validated, displaying acceptable-to-good discrimination, and C-statistic ranging from 0.71 to 0.81. Lack of individual-level data precluded analyses of the risk models in subgroups. CONCLUSIONS: The discrimination ability of existing hypertension risk prediction tools is acceptable, but the impact of using these tools on prescriptions and outcomes of hypertension prevention is unclear

Reporting and handling of missing data in predictive research for prevalent undiagnosed type 2 diabetes mellitus: a systematic review

Missing values are common in health research and omitting participants with missing data often leads to loss of statistical power, biased estimates and, consequently, inaccurate inferences. We critically reviewed the challenges posed by missing data in medical research and approaches to address them. To achieve this more efficiently, these issues were analyzed and illustrated through a systematic review on the reporting of missing data and imputation methods (prediction of missing values through relationships within and between variables) undertaken in risk prediction studies of undiagnosed diabetes. Prevalent diabetes risk models were selected based on a recent comprehensive systematic review, supplemented by an updated search of English-language studies published between 1997 and 2014. Reporting of missing data has been limited in studies of prevalent diabetes prediction. Of the 48 articles identified, 62.5% (n=30) did not report any information on missing data or handling techniques. In 21 (43.8%) studies, researchers opted out of imputation, completing case-wise deletion of participants missing any predictor values. Although imputation methods are encouraged to handle missing data and ensure the accuracy of inferences, this has seldom been the case in studies of diabetes risk prediction. Hence, we elaborated on the various types and patterns of missing data, the limitations of case-wise deletion and state-of the-art methods of imputations and their challenges. This review highlights the inexperience or disregard of investigators of the effect of missing data in risk prediction research. Formal guidelines may enhance the reporting and appropriate handling of missing data in scientific journals

Cumulative social risk and type 2 diabetes in US adults: The National Health and Nutrition Examination Survey (NHANES) 1999–2006

Background: The cumulative effects of adverse social factors on the diabetes risk remains to be clarified. Design: Cross-sectional analysis of the US National Health and Nutrition Examination Survey (NHANES) 1999–2006. Methods: We included 10,276 adults aged ≥20 years. Diabetes mellitus was defined by physician diagnosis or fasting plasma glucose (≥126 mg/dl) or glycated hemoglobin (≥6.5%). Social risk factors (low family income, low education level, minority racial/ethnic group status, and single-living status) and health-related behaviors (physical activity and dietary intake) were self-reported. Social risk factors were combined in a cumulative social risk index (range 0 to ≥3) and logistic regression used to assess the association of cumulative social risk and diabetes, taking into account complex survey design and sampling weights. Results: Of 10,276 participants, 1515 (weighted proportion – 10%) had diabetes, 3295 (32.3%) and 1830 (9.0%) were exposed to ≥1 adverse social risk factor and ≥3 social risk factors, respectively. Diabetes was associated with increasing cumulative social risk in a graded manner (p for trend <0.001). Compared with a cumulative social risk score of 0, the age- and sex-adjusted diabetes odds for a cumulative social risk score of ≥3 was 2.84 (95% confidence interval: 2.23–3.62), and 2.72 (95% confidence interval: 2.05–3.60) after further adjustment for family history of diabetes, body mass index, smoking, dietary intake and leisure time physical activity. Health behaviors and adiposity only partially influenced the cumulative social risk and diabetes relationship. Conclusions: Simultaneous exposure to several adverse social risk factors significantly influences the odds of diabetes. Better prevention and control of diabetes needs accounting for all aspects of social disadvantage