6,009 research outputs found

    Foreword

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    Urbanization, ethnicity and cardiovascular risk in a population in transition in Nakuru, Kenya: a population-based survey.

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    BACKGROUND: Cardiovascular disease (CVD) is the leading cause of death among older people in Africa. This study aimed to investigate the relationship of urbanization and ethnicity with CVD risk markers in Kenya. METHODS: A cross-sectional population-based survey was carried out in Nakuru Kenya in 2007-2008. 100 clusters of 50 people aged ≥ 50 years were selected by probability proportionate to size sampling. Households within clusters were selected through compact segment sampling. Participants were interviewed by nurses to collect socio-demographic and lifestyle information. Nurses measured blood pressure, height, weight and waist and hip circumference. A random finger-prick blood sample was taken to measure glucose and cholesterol levels.Hypertension was defined as systolic blood pressure (SBP) ≥ 140 mm Hg, or diastolic blood pressure (DBP) ≥ 90 mm Hg or current use of antihypertensive medication; Diabetes as reported current medication or diet control for diabetes or random blood glucose level ≥ 11.1 mmol/L; High cholesterol as random blood cholesterol level ≥ 5.2 mmol/L; and Obesity as Body Mass Index (BMI)≥ 30 kg/m2. RESULTS: 5010 eligible subjects were selected, of whom 4396 (88%) were examined. There was a high prevalence of hypertension (50.1%, 47.5-52.6%), obesity (13.0%, 11.7-14.5%), diabetes (6.6%, 5.6-7.7%) and high cholesterol (21.1%, 18.6-23.9). Hypertension, diabetes and obesity were more common in urban compared to rural groups and the elevated prevalence generally persisted after adjustment for socio-demographic, lifestyle, obesity and cardiovascular risk markers. There was also a higher prevalence of hypertension, obesity, diabetes and high cholesterol among Kikuyus compared to Kalenjins, even after multivariate adjustment. CVD risk markers were clustered both across the district and within individuals. Few people received treatment for hypertension (15%), while the majority of cases with diabetes received treatment (68%). CONCLUSIONS: CVD risk markers are common in Kenya, particularly in urban areas. Exploring differences in CVD risk markers between ethnic groups may help to elucidate the epidemiology of these conditions

    Prevalence and predictors of refractive error and spectacle coverage in Nakuru, Kenya: a cross-sectional, population-based study.

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    A cross-sectional study was undertaken in Nakuru, Kenya to assess the prevalence of refractive error and the spectacle coverage in a population aged ≥50 years. Of the 5,010 subjects who were eligible, 4,414 underwent examination (response rate 88.1 %). LogMAR visual acuity was assessed in all participants and refractive error was measured in both eyes using a Topcon auto refractor RM8800. Detailed interviews were undertaken and ownership of spectacles was assessed. Refractive error was responsible for 51.7 % of overall visual impairment (VI), 85.3 % (n = 191) of subjects with mild VI, 42.7 % (n = 152) of subjects with moderate VI, 16.7 % (n = 3) of subjects with severe VI and no cases of blindness. Myopia was more common than hyperopia affecting 59.5 % of those with refractive error compared to 27.4 % for hyperopia. High myopia (+5.0 DS). Of those who needed distance spectacles (spectacle coverage), 25.5 % owned spectacles. In conclusion, the oldest, most poor and least educated are most likely to have no spectacles and they should be specifically targeted when refractive services are put in place

    Balancing the books.

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    A nonlinear model of information seeking behaviour

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    Synplutonic dikes of the Idaho batholith Idaho and western Montana and their relationship to the generation of the batholith

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    Qualitative Code Book:For Foster’s Nonlinear Model of Information Behaviour

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    Is quality affordable?

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    The question “Is quality affordable?” is loaded with dynamite!Can a person who lives on less than US $1 per day afford a high-quality cataract operation? If the answer is ‘No’, then do we offer that person poor or low-quality services? Do people living in poverty have a ‘right’ to high-quality eye or health care? If the answer is ‘Yes’, then at what price and who should pay? Should we ignore quality and focus on affordability? Or should we provide high-quality services in the hope that someone else will pay?These are difficult questions, which policy makers, managers, and clinicians must face and try to answer
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