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    <title>Bonfrer, I.E.J. None</title>
    <link>http://repub.eur.nl/res/aut/48690/</link>
    <description>List of Publications</description>
    <language>en</language>
    <image>
      <url>http://repub.eur.nl/static-eur/img/logo.png</url>
      <title>RePub, Erasmus University Rotterdam</title>
      <link>http://repub.eur.nl</link>
    </image>
    <item>
      <title>Hypertension in Sub-Saharan Africa: Cross-sectional surveys in four rural and urban communities (Article)</title>
      <link>http://repub.eur.nl/res/pub/32915/</link>
      <pubDate>2012-03-12T00:00:00Z</pubDate>
      <description>Background: Cardiovascular disease (CVD) is the leading cause of adult mortality in low-income countries but data on the prevalence of cardiovascular risk factors such as hypertension are scarce, especially in sub-Saharan Africa (SSA). This study aims to assess the prevalence of hypertension and determinants of blood pressure in four SSA populations in rural Nigeria and Kenya, and urban Namibia and Tanzania. Methods and Findings: We performed four cross-sectional household surveys in Kwara State, Nigeria; Nandi district, Kenya; Dar es Salaam, Tanzania and Greater Windhoek, Namibia, between 2009-2011. Representative population-based samples were drawn in Nigeria and Namibia. The Kenya and Tanzania study populations consisted of specific target groups. Within a final sample size of 5,500 households, 9,857 non-pregnant adults were eligible for analysis on hypertension. Of those, 7,568 respondents ≥18 years were included. The primary outcome measure was the prevalence of hypertension in each of the populations under study. The age-standardized prevalence of hypertension was 19.3% (95%CI:17.3-21.3) in rural Nigeria, 21.4% (19.8-23.0) in rural Kenya, 23.7% (21.3-26.2) in urban Tanzania, and 38.0% (35.9-40.1) in urban Namibia. In individuals with hypertension, the proportion of grade 2 (≥160/100 mmHg) or grade 3 hypertension (≥180/110 mmHg) ranged from 29.2% (Namibia) to 43.3% (Nigeria). Control of hypertension ranged from 2.6% in Kenya to 17.8% in Namibia. Obesity prevalence (BMI ≥30) ranged from 6.1% (Nigeria) to 17.4% (Tanzania) and together with age and gender, BMI independently predicted blood pressure level in all study populations. Diabetes prevalence ranged from 2.1% (Namibia) to 3.7% (Tanzania). Conclusion: Hypertension was the most frequently observed risk factor for CVD in both urban and rural communities in SSA and will contribute to the growing burden of CVD in SSA. Low levels of control of hypertension are alarming. Strengthening of health care systems in SSA to contain the emerging epidemic of CVD is urgently needed. </description>
    </item> <item>
      <title>Does health care utilization match needs in Africa? Challenging conventional needs measurement (Research Paper)</title>
      <link>http://repub.eur.nl/res/pub/34820/</link>
      <pubDate>2012-01-01T00:00:00Z</pubDate>
      <description>Abstract. 
An equitable distribution of health care use, distributed according to people’s needs
instead of ability to pay, is an important goal featuring on many health policy agendas
worldwide. However, relatively little is known about the extent to which this principle
is violated across socio-economic groups in Sub Saharan Africa (SSA). We examine
cross-country comparative micro-data from eighteen SSA countries and find that (a)
considerable inequalities in health care use exist and vary across countries, but that
(b) identifying the extent to which these inequalities are unfair, i.e. do not correspond
to inequalities in need, is not straightforward to ascertain with the conventional tools.
These tools include rank-based measures such as the concentration index and the
index of inequity. The two main concerns when using conventional tools to measure
equity are (i) the reporting heterogeneity in self-reported health variables across
socio-economic groups and (ii) the weak relationship between need and use. We
show that the use of subjective self-reports of health leads to much lower measured
degrees of socio-economic inequalities than those obtained using more objective
indicators. This leads to an underestimation of the degree of inequity when using
self-reported health measures. The observed weak relationship between indicators of
ill-health and use of health care does not appear to provide an estimate of the
adequate response to needs, which further puts a downward bias on equity
measures. In all countries, apart from the more developed Mauritius, health care use
is distributed according to wealth rather than to need. A better match of needs and
use is realized in those countries with better governance and more physicians but,
perhaps surprisingly, not those with greater urbanization. Given the importance of
equity in many health policies worldwide, it is vital to develop more robust equity
measures relevant to low income settings.</description>
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