Differences by sex in the prevalence of diabetes mellitus, impaired fasting glycaemia and impaired glucose tolerance in sub-Saharan Africa: a systematic review and meta-analysis

Bull World Health Organ 2013;91:671–682D | doi: http://dx.doi.org/10.2471/BLT.12.113415 Systematic reviews 671 Differences by sex in the prevalence of diabetes mellitus, impaired fasting glycaemia and impaired glucose tolerance in sub-Saharan Africa: a systematic review and meta-analysis Esayas Haregot Hilawe,a Hiroshi Yatsuya,b Leo Kawaguchia & Atsuko Aoyamaa Introduction Increasing urbanization and the accompanying changes in lifestyle are leading to a burgeoning epidemic of chronic non- communicable diseases in sub-Saharan Africa.1,2 At the same time, the prevalence of many acute communicable diseases is decreasing.1,2 In consequence, the inhabitants of sub-Saharan Africa are generally living longer and this increasing longevity will result in a rise in the future incidence of noncommunicable diseases in the region.1–3 Diabetes mellitus is one of the most prominent noncom- municable diseases that are undermining the health of the people in sub-Saharan Africa and placing additional burdens on health systems that are often already strained.4,5 In 2011, 14.7 million adults in the African Region of the World Health Organization (WHO) were estimated to be living with diabe- tes mellitus.6 Of all of WHO’s regions, the African Region is expected to have the largest proportional increase (90.5%) in the number of adult diabetics by 2030.6 Sex-related differences in lifestyle may lead to differences in the risk of developing diabetes mellitus and, in consequence, to differences in the prevalence of this condition in women and men.3 However, the relationship between a known risk factor for diabetes mellitus – such as obesity – and the development of symptomatic diabetes mellitus may not be simple. For ex- ample, in many countries of sub-Saharan Africa, women are more likely to be obese or overweight than men and might therefore be expected to have higher prevalences of diabetes mellitus.3,7 Compared with the corresponding men, women in Cameroon8, South Africa9 and Uganda10 were indeed found to have higher prevalences of diabetes mellitus. However, women in Ghana,11 Nigeria,12 Sierra Leone13 and rural areas of the United Republic of Tanzania14 were found to have lower prevalences of diabetes mellitus than the men in the same study areas. No significant differences between men and women in the prevalence of diabetes mellitus were detected in studies in Guinea,15 Mali,16 Sudan17 and urban areas of the United Republic of Tanzania,18 or in a meta-analysis of data collected in several studies in West Africa.19 Although wide variations in the distribution of diabetes mellitus by sex have been docu- mented in several review articles,3–5,7,20 the possible causes of this heterogeneity have never been examined in detail. Like obesity, impaired fasting glycaemia and impaired glucose tolerance appear to be risk factors in the develop- ment of diabetes mellitus.21,22 According to the International Diabetes Federation, the estimated age-adjusted prevalence of impaired fasting glycaemia in WHO’s African Region was substantially higher in 2011 than the corresponding global mean value – 9.7% versus 6.5%, respectively – and is expected to have risen further by 2030.23 Impaired fasting glycaemia and impaired glucose toler- ance are reported to be metabolically distinct entities that affect different subpopulations, albeit with some degree of overlap.22,24 In Mauritius, the prevalence of impaired fasting glycaemia was found to be significantly higher in men than in women, whereas the prevalence of impaired glucose tolerance was found to be higher in women than in men.24,25 Differences between men and women in the prevalence of diabetes mellitus, impaired fasting glycaemia and impaired glucose tolerance in much of sub-Saharan Africa have yet Objective To assess differences between men and women in the prevalence of diabetes mellitus, impaired fasting glycaemia and impaired glucose tolerance in sub-Saharan Africa. Methods In September 2011, the PubMed and Web of Science databases were searched for community-based, cross-sectional studies providing sex-specific prevalences of any of the three study conditions among adults living in parts of sub-Saharan Africa (i.e. in Eastern, Middle and Southern Africa according to the United Nations subregional classification for African countries). A random-effects model was then used to calculate and compare the odds of men and women having each condition. Findings In a meta-analysis of the 36 relevant, cross-sectional data sets that were identified, impaired fasting glycaemia was found to be more common in men than in women (OR: 1.56; 95% confidence interval, CI: 1.20–2.03), whereas impaired glucose tolerance was found to be less common in men than in women (OR: 0.84; 95% CI: 0.72–0.98). The prevalence of diabetes mellitus – which was generally similar in both sexes (OR: 1.01; 95% CI: 0.91–1.11) – was higher among the women in Southern Africa than among the men from the same subregion and lower among the women from Eastern and Middle Africa and from low-income countries of sub-Saharan Africa than among the corresponding men. Conclusion Compared with women in the same subregions, men in Eastern, Middle and Southern Africa were found to have a similar overall prevalence of diabetes mellitus but were more likely to have impaired fasting glycaemia and less likely to have impaired glucose tolerance. a Department of Public Health and Health Systems, Nagoya University School of Medicine, 65 Tsurumai-cho, Showa-ku, Nagoya, 466-8550, Japan. b Fujita Health University School of Medicine, Toyoake, Japan. Correspondence to Esayas Haregot Hilawe (e-mail: esayas@med.nagoya-u.ac.jp). (Submitted: 13 November 2012 – Revised version received: 21 February 2013 – Accepted: 25 March 2013 ) Systematic revi w Bull World Health Organ 2013;91:671–682D | doi: http://dx.doi.org/10.2471/BLT.12.113415672 Systematic reviews Sex differences in prevalence of glucose metabolism disorders Esayas Haregot Hilawe et al. to be reviewed. Given the variation in health care, culture, environment, hu- man behaviour and other determinants of health across sub-Saharan Africa,26 the conclusions drawn from a recent meta-analysis of data from West Africa19 should not be assumed to apply to the whole of sub-Saharan Africa. The sex- specific prevalence of at least one risk factor for diabetes mellitus – obesity – is known to differ across different parts of sub-Saharan Africa.7,27 The main aims of the present system- atic review were to examine differences be- tween men and women in the prevalence of three conditions – diabetes mellitus, impaired fasting glycaemia and impaired glucose tolerance – in Eastern, Middle and Southern Africa (i.e. all in sub-Saharan Africa according to the United Nations subregional classification for African countries),28 and to explore the possible causes of any variation observed. We followed the Meta-analysis of Observa- tional Studies in Epidemiology (MOOSE) group’s guidelines for the reporting of sys- tematic reviews of observational studies.29 Methods Data sources In September 2011, we searched PubMed and Web of Science for studies that pre- sented the sex-specific prevalences of diabetes mellitus, impaired fasting gly- caemia and/or impaired glucose toler- ance in Eastern, Middle and/or Southern Africa (Table 1). The medical subject headings (MeSH) and search terms we used are described in Box 1. We limited our search to human studies but placed no restrictions on the language of pub- lication. We also used Google, Google Scholar and WHO’s InfoBase to search the “grey” literature for relevant studies and reports. The citations in articles that appeared to be relevant were examined for other articles that might hold use- ful data. When it seemed possible that relevant data had been recorded but not published, the authors of published study reports were contacted via e-mail to see if they could provide such data. Inclusion and exclusion criteria Data were included in the meta-analysis if they came from studies that fulfilled all of the following criteria: • community-based; • cross-sectional; • reported prevalence of diabetes mellitus, impaired fasting glycaemia and/or impaired glucose tolerance; • reported either odds ratios (ORs) for differences between men and women in the prevalence of diabetes mellitus, impaired fasting glycaemia and/or impaired glucose tolerance or data that allowed the computation of such ORs; • conducted in apparently healthy, non-pregnant subjects; • most subjects are adults (i.e. aged ≥ 15 years) and residing in the UN-designated Eastern, Middle or Southern subregions of Africa; • both men and women investigated; • employed any of WHO’s diagnostic criteria – or the equivalent criteria of the American Diabetic Associa- tion – for diabetes mellitus, impaired fasting glycaemia and/or impaired glucose tolerance;30–38 • reported results either in English or in another language with an abstract in English. When multiple reports of the same study were retrieved, only the most in- formative report was selected. Clinic-, hospital- and laboratory-based studies, anonymous reports, letters, commen- taries, case studies and reviews were excluded. Data abstraction After reading each article that appeared relevant and met the inclusion criteria, one of the authors (EHH) made notes of the year of study and publication, sam- pling method, sample size, response rate, study design, diagnostic criteria, study area, mean age and/or age range of the subjects, mean blood glucose level, the recorded prevalences of diabetes mel- litus, impaired fasting glycaemia and/ Table 1. Countries comprising sub-Saharan Africa, by African subregiona Subregion Eastern Middle Southern Western Burundi Angola Botswana Benin Comoros Cameroon Lesotho Burkina Faso Djibouti Central African Republic Namibia Cape Verde Eritrea Chad South Africa Côte d’Ivoire Ethiopia Congo Swaziland Gambia Kenya Democratic Republic of the Congo Ghana Madagascar Equatorial Guinea Guinea-Bissau Malawi Gabon Liberia Mauritius Sao Tome and Principe Mali Mozambique Mauritania Rwanda Niger Seychelles Nigeria Somalia Senegal Sudan Sierra Leone Uganda Togo United Republic of Tanzania Zambia Zimbabwe a As designated by the United Nations.28 Box 1. Strategy followed in searching PubMed and the Web of Science Various medical subject headings (MeSH) and search terms, including “prevalence”, “incidence”, “epidemiology”, “proportion”, “rate”, “diabetes mellitus”, “hyperglycaemia”, “abnormal* blood glucose”, “glucose intolerance”, “dysglycaemia”, “insulin resistance”, “metabolic* syndrome”, “insulin resistance syndrome X”, “cardiovascular syndrome”, “hypertension”, “increase* blood pressure”, “obesity”, “overweight”, “hypercholesterolaemia”, “hyperlipidaemia”, “dyslipidaemia”, “physical inactivity”, “smoking”, “cardiovascular diseases risk factors” and “Africa South of the Sahara” – and alternative spellings such as “hyperglycemia” were used. Searches were combined with the names of each country in Eastern, Middle and Southern Africa (Table 1) – except Cameroon, which was included in a previous study on West Africa19 – by using the Boolean operators “OR” or “AND”. Bull World Health Organ 2013;91:671–682D | doi: http://dx.doi.org/10.2471/BLT.12.113415 673 Systematic reviews Sex differences in prevalence of glucose metabolism disordersEsayas Haregot Hilawe et al. or impaired glucose tolerance, and, if available, the OR and corresponding 95% confidence intervals (CIs) that in- dicated the type and significance of any differences in these prevalences by sex. When articles presented data separately for urban and rural subjects, informa- tion for these two groups of subjects was extracted separately. When articles pre- sented data stratified by subject age, only the data for subjects aged 15 years or older were included in the analysis. All of the extracted data were independently reviewed by a second author (HY). Quality appraisal A checklist – adopted from one created by the University of Wisconsin39 – was used to assess the quality of the included studies. The checklist had eight questions relating to the research question, selec- tion of study subjects, comparability of study groups, handling of withdrawals, measurement of outcomes, statistical analyses, results and conclusions, and funding or sponsorship. If the answers to five or more of these questions were positive, the study involved was catego- rized as “positive” and considered to be of good quality. If the answers to five or more of these questions were negative, the study involved was categorized as “negative” and considered to be of poor quality. All other studies were catego- rized as “neutral”. Statistical analysis ORs were used as “effect estimates” to quantify the relationship between sex and the prevalence of diabetes mellitus, impaired fasting glycaemia and im- paired glucose tolerance. If no OR had been reported, it was calculated from the raw data. Since the studies included in the meta-analysis used different standard populations, crude prevalences were preferred to the age-adjusted values when both were available. The DerSimo- nian and Laird random-effects model was used to estimate the mean OR for all of the studies included in the meta- analysis.40 Statistical heterogeneity across the studies was evaluated using both the Q and I2 statistics.40 In the Q-tests, a P- value of < 0.1 was considered indicative of statistically significant heterogene- ity. We performed subgroup analyses to assess the potential influence of the following study-level covariates on the OR for any sex-specific differences: area of residence (urban or rural), subregion of residence in sub-Saharan Africa (i.e. Eastern, Middle or Southern Africa), study year, ethnicity of the study sub- jects, and the World-Bank-determined income level of the study country.41 Random-effects univariate meta-regres- sion analysis40 was also performed as an extension of the subgroup analyses. The potential influence of each in- dividual study on the overall summary estimates was assessed by rerunning the meta-analysis while omitting one study at a time. Sensitivity analysis was performed to assess the impact of the quality of the studies on the overall ef- fect estimates. For those studies that reported both crude and age-adjusted prevalences, we also assessed if the effect estimates would have been substantially altered if the age-adjusted values had been used instead of the crude ones. Publication bias40 was assessed using a funnel plot to examine the re- lationship between the effect size and study precision. Begg and Mazumdar’s rank-correlation test40 was then used to test this relationship statistically. Finally, Duval and Tweedie’s “trim and fill” analysis was used to assess the pos- sible impact of publication bias on the effect size.40 Version 2 of the Comprehen- sive Meta-Analysis software package (Biostat, Englewood, United States of America) was used for all of the statis- tical analyses. All statistical tests were two-sided. A P-value of < 0.05 was gen- erally considered indicative of statistical significance. Results Literature search Although the PubMed and Web of Sci- ence searches revealed 5129 potentially useful reports, only 25 of these reports were found to satisfy all of the inclusion criteria (Fig. 1). Four additional reports that met all of the inclusion criteria were identified via a Google search (n = 2), a search of the WHO InfoBase (n = 1) or contact with authors (n = 1). The meta- analysis therefore included data from 29 reports that, together, covered 36 studies in which cross-sectional data were col- lected.14,17,42–68 Study characteristics Table 2 (available at: http://www.who. int/bulletin/volumes/91/9/12-113415) provides detailed descriptive informa- tion for the 36 studies included in the meta-analysis. These studies involved 75 928 subjects and were conducted between 1983 and 2009 in Angola, the Democratic Republic of the Congo, Ke- nya, Malawi, Mauritius, Mozambique, Seychelles, South Africa, Sudan, Uganda, the United Republic of Tanzania, Zambia or Zimbabwe. Most (92%) of the stud- ies included in the meta-analysis em- ployed probability- or census-sampling techniques and had response rates of 62–99%. Sex-specific prevalences of diabetes mellitus, impaired fasting gly- caemia and impaired glucose tolerance were included in the reports of 35, 21 and 11 of the studies, respectively. Almost half (45%) of the studies were conducted in both urban and rural areas. The other studies were conducted exclusively in urban (26%), rural (23%) or periurban (6%) areas. In terms of quality, the stud- ies were categorized as either “positive” (n = 31) or “neutral” (n = 5)42,49,58,61,63 (Appendix A, available at: http://www. med.nagoya-u.ac.jp/intnl-h/swfu/d/ auto-UZzMJC.pdf). Sex-specific prevalences The prevalence of diabetes mellitus was 5.7% (95% CI: 4.8–6.8) overall, with a slight difference between the men (5.5%; 95% CI: 4.1–7.2) and women (5.9%; 95% CI: 4.6–7.6) included in the meta- analysis. The prevalence of impaired fasting glycaemia was 4.5% (95% CI: 3.3–6.1) overall – 5.7% (95% CI: 3.7–8.6) among the men and 3.5% (95% CI: 2.1–5.8) among the women – whereas the prevalence of impaired glucose tolerance was 7.9% (95% CI: 6.7–9.2) overall – 7.3% (95% CI: 6.0–8.8) among the men and 8.5% (95% CI: 6.7–10.7) among the women. Odds ratios The prevalence of diabetes mellitus among men was not significantly differ- ent from that among women (OR: 1.01; 95% CI: 0.91–1.11). However, impaired fasting glycaemia appeared to be sig- nificantly more common among men than among women (OR: 1.56; 95% CI: 1.20–2.03), whereas impaired glucose tolerance appeared to be significantly less common among men than among women (OR: 0.84; 95% CI: 0.72–0.98) (Fig. 2). These significant differences between the sexes were still observed when the analysis was restricted to those studies in which the prevalences of both impaired fasting glycaemia and impaired glucose tolerance were Bull World Health Organ 2013;91:671–682D | doi: http://dx.doi.org/10.2471/BLT.12.113415674 Systematic reviews Sex differences in prevalence of glucose metabolism disorders Esayas Haregot Hilawe et al. determined in the same study cohorts (data not shown). A moderate to sub- stantial level of heterogeneity between studies was detected in the data for diabetes mellitus (I2 = 54.62%; P < 0.001 in Q-test), impaired fasting glycaemia (I2 = 85.38%; P < 0.001 in Q-test) and impaired glucose tolerance (I2 = 74.13%; P < 0.001 in Q-test). Subgroup analyses Table 3 summarizes the results of the subgroup analyses. Significant hetero- geneity in the OR for diabetes mellitus was observed by area of residence (i.e. urban or rural), subregion of residence in Africa, ethnicity of the study sub- jects, and country income level – each of which gave a P- value of < 0.05 in a Q-test. The prevalence of diabetes mellitus was found to be significantly higher in men than in women in studies conducted in a mix of urban and rural areas, in Middle or Eastern Africa or in low-income countries. However, in studies conducted in Southern Africa or among subjects of Indian ethnicity, the prevalence of diabetes mellitus was significantly higher among women than among the corresponding men. Significant heterogeneity in the OR for impaired fasting glycaemia was observed by subregion of residence in Africa (P = 0.02) and country income level (P = 0.006). In studies conducted in Eastern Africa or upper-middle- income countries, impaired fasting glycaemia appeared to be significantly more common among men than among women. With impaired glucose tolerance, significant heterogeneity in the OR was observed by area of residence (P < 0.001), subregion of residence in Africa (P = 0.001), ethnicity (P = 0.002), and country income level (P = 0.03). The odds of impaired glucose tolerance were found to be higher in men than in women in studies conducted on urban residents or subjects of Indian ethnicity. Meta-regression In general, the univariate random- effects meta-regression revealed similar associations – between the OR and study-level covariates – as seen in the subgroup analyses (Appendix A). For example, the OR for the sex-specific prevalences of diabetes mellitus ap- peared to be significantly affected by area of residence (rural versus urban; P = 0.018), subregion of residence in Af- rica (Southern and Middle Africa versus Eastern Africa; P < 0.001), ethnicity of the study subjects (multi-ethnic versus Indian; P = 0.013), study year (1990s versus 2000s; P = 0.039), and country income level (low versus upper middle; P < 0.001). Subregion of residence (East- ern versus Southern Africa; P = 0.047) and country income level (low versus upper-middle; P = 0.006) also had a significant effect on the OR for impaired fasting glycaemia, whereas subregion of residence (Eastern versus Southern Afri- ca; P < 0.001), ethnicity of study subjects (multi-ethnic versus Indian; P < 0.001), country income level (low versus upper- middle; P < 0.001), and area of residence – both rural versus urban (P < 0.001) and rural versus urban and rural combined (P = 0.003) – had significant effects on the OR for impaired glucose tolerance. Sensitivity and influence analyses No meaningful change in the OR was evident when the meta-analysis was rerun either with the data from the five studies of “neutral” quality omitted or using age-adjusted prevalences instead of the crude values (data not shown). The results of the influence analysis indicated that the omission of the data from any of seven studies – described in five reports43,44,47,48,57 – could eliminate the statistical significance of the overall differences between men and women in the prevalence of impaired glucose tolerance. However, even when the data from one of these studies were omitted, women still showed a higher prevalence of impaired glucose tolerance than the corresponding men, with a P-value of > 0.05 but < 0.1. The pooled results for diabetes or impaired fasting glycaemia were not substantially affected by the omission of the data from any one study. Publication bias The funnel plots for diabetes mellitus and impaired fasting glycaemia were asymmetric, indicating possible publi- Fig. 1. Flow diagram of the study selection procedure References excluded after screening titles and/or abstracts (n = 3874) Reasons for exclusion: • No assessment of DM, IFG or IGT prevalence • Study conducted outside sub-Saharan Africa or in Western Africa • Study was hospital- or clinic-based • Study subjects were patients • Most study subjects aged < 15 years • Review, commentary or anonymous report References excluded after review of full text (n = 376) Reasons for exclusion: • Prevalences of DM, IFG and/or IGT not reported and impossible to determine from the data reported • Most study subjects aged < 15 years • Review article • Data already abstracted from another article • All subjects of one gender Additional studies from other sources (n = 4) • Google free search (n = 2) • WHO InfoBase (n = 1) • Contact with authors (n = 1) References initially identified by electronic search (n = 5129) • PubMed (n = 1204) • Web of Science (n = 3925) Duplicates removed (n = 854) References screened (n = 4275) Articles for data extraction (n = 29) Full texts retrieved for detailed evaluation (n = 401) Cross-sectional data sets included in meta-analysis (n = 36) DM, diabetes mellitus; IFG, impaired fasting glycaemia; IGT, impaired glucose tolerance. Esayas Haregot Hilawe et al. Sex differences in prevalence of glucose metabolism disorders Systematic reviews 675Bull World Health Organ 2013;91:671–682D | doi: http://dx.doi.org/10.2471/BLT.12.113415 cation bias. However, the correspond- ing results from Begg and Mazumdar’s rank-correlation tests – P-values of 0.93 and 0.64, respectively – were not statis- tically significant. Duval and Tweedie’s “trim and fill” analysis indicated that the meta-analysis would have benefitted from the inclusion of data from more studies – nine for diabetes mellitus and one for impaired fasting glycaemia – and that, if the asymmetry seen in the funnel plots was the result of publica- tion bias, the summary estimates of the sex-specific (i.e. men versus women) OR for diabetes mellitus and impaired fast- ing glycaemia should be 1.09 (95% CI: 0.98–1.20) and 1.65 (95% CI: 1.27–2.14), respectively (Appendix A). There were no indications of pub- lication bias in the data on impaired glucose tolerance. Discussion To our knowledge, this study is the first systematic review of possible associa- tions between sex and the prevalences of impairments in glucose tolerance and fasting glycaemia in Eastern, Middle and Southern Africa. Previous narrative reviews have reported on the prevalence of diabetes mellitus and, briefly, on the variation in the sex distribution of this illness in sub-Saharan Africa.3–5,7,20 How- ever, there appears to have been only one previous meta-analysis of data on the prevalence of diabetes mellitus in sub- Saharan Africa and that was limited to data collected in West Africa.19 The present results reveal consider- able between-country variation in the prevalence of diabetes mellitus among adults. However, the relatively high value recorded for all of the studies combined (5.7%) is a reflection of the rapid transition – from a predominance of communicable disease to one of non- communicable disease – that much of sub-Saharan Africa is facing. In this vast area of Africa, important risk factors for diabetes mellitus, such as impaired glucose tolerance, appear to be increas- ing in prevalence while humans are tending to live longer. The prevalence of diabetes mellitus in sub-Saharan Africa will therefore probably rise further un- less prevention efforts are intensified. 23 In the present meta-analysis – as in most22,24,69 – but not all70 – previous stud- ies on this risk factor for diabetes melli- tus – impaired fasting glucose was found to be significantly more common among Fig. 2. Forest plot of main meta-analysis results, showing sex-specific odds ratios for diabetes mellitus, impaired fasting glycaemia and impaired glucose tolerance in sub-Saharan Africa DM, diabetes mellitus; IFG, impaired fasting glycaemia; IGT, impaired glucose tolerance; MWMoH, Malawi Ministry of Health; OR, odds ratio; ZWMoH, Zimbabwe Ministry of Health and Child Welfare. Note: The ORs shown are for differences in prevalence between the sexes (i.e. odds in men versus odds in women). For each study, the plot indicates the mean OR (midpoint of the square), the corresponding 95% confidence interval (horizontal lines) and the weight given to the study (area of the square). Reference Type of area OR Diabetes mellitus Ahrén and Corrigan, 1984 Rural 0.61 Ahrén and Corrigan, 1984 Urban 1.00 Omar et al., 1985 Urban 0.53 Söderberg et al., 2005 Combined 1.05 McLarty et al., 1989 Rural 1.58 Tappy et al., 1991 Urban 0.73 Levitt et al., 1993 Urban 1.02 Mollentze et al., 1995 Rural 0.81 Mollentze et al., 1995 Urban 0.66 Söderberg et al., 2005 Combined 1.07 Omar et al., 1993 Urban 0.43 Omar et al., 1994 Urban 0.79 Elbagir et al., 1996 Combined 1.03 Levitt et al., 1999 Periurban 0.70 Erasmus et al., 2001 Periurban 0.72 Aspray et al., 2000 Rural 1.37 Aspray et al., 2000 Urban 1.34 Charlton et al., 2001 Rural 0.46 Alberts et al., 2005 Rural 0.99 Söderberg et al., 2005 Combined 1.07 Elbagir et al., 1998 Rural 0.32 Elbagir et al., 1998 Urban 1.57 Motala et al. , 2008 Rural 0.98 Faeh et al., 2007 Urban 0.90 ZWMoH, 2005 Combined 0.96 Kasiam Lasi On’Kin et al., 2008 Combined 1.44 Silva-Matos et al., 2011 Combined 1.45 Nsakashalo-Senkwe et al., 2011 Urban 0.69 Christensen et al., 2009 Combined 1.07 Tibazarwa et al., 2009 Urban 1.17 Mathenge et al., 2010 Rural 1.00 Mathenge et al., 2010 Urban 1.00 MWMoH, 2010 Combined 1.41 Evaristo-Neto et al., 2010 Rural 1.19 Maher et al., 2011 Rural 1.00 Overall summary estimate 1.01 Impaired fasting glycaemia Söderberg et al., 2005 Combined 1.94 Söderberg et al., 2005 Combined 2.16 Aspray et al., 2000 Rural 0.80 Aspray et al., 2000 Urban 0.73 Söderberg et al., 2005 Combined 1.69 Motala et al. , 2008 Rural 5.19 Faeh et al., 2007 Urban 1.95 Kasiam Lasi On’Kin et al., 2008 Combined 1.04 Silva-Matos et al., 2011 Combined 1.05 Nsakashalo-Senkwe et al., 2011 Urban 1.00 MWMoH, 2010 Combined 2.18 Overall summary estimate 1.56 Impaired glucose tolerance Omar et al., 1985 Urban 1.52 Söderberg et al., 2005 Combined 0.63 McLarty et al., 1989 Rural 0.91 Levitt et al., 1993 Urban 1.02 Söderberg et al., 2005 Combined 0.70 Omar et al., 1993 Urban 2.23 Omar et al., 1994 Urban 1.75 Elbagir et al., 1996 Combined 0.66 Levitt et al., 1999 Periurban 0.69 Erasmus et al., 2001 Periurban 2.31 Charlton et al., 2001 Rural 1.37 Söderberg et al., 2005 Combined 0.73 Elbagir et al., 1998 Rural 0.41 Elbagir et al., 1998 Urban 0.30 Motala et al., 2008 Rural 1.02 Faeh et al., 2007 Urban 1.19 ZW MoH, 2005 Combined 1.02 Kasiam Lasi On’Kin et al., 2008 Combined 0.77 Christensen et al., 2009 Combined 0.43 Wanjihia et al., 2009 Rural 0.28 Evaristo-Neto et al., 2010 Rural 0.59 Overall summary estimate 0.84 OR 0.01 0.1 1 10 100 Higher odds in women Higher odds in men Esayas Haregot Hilawe et al.Sex differences in prevalence of glucose metabolism disorders Systematic reviews 676 Bull World Health Organ 2013;91:671–682D | doi: http://dx.doi.org/10.2471/BLT.12.113415 men than among women, irrespective of the subgroup that was investigated. One possible explanation for this difference is that men tend to have lower hepatic sensitivity to insulin and may, in con- sequence, have generally higher fasting levels of plasma glucose.69 Another pos- sible explanation or contributing factor is that, within sub-Saharan Africa, men are more likely to smoke than women71 and smoking appears to increase the risk of impaired fasting glucose, by decreas- ing insulin sensitivity.72–74 In earlier research, impaired glu- cose tolerance has generally been found to be more common among women than among men.22,24,69 The same difference between the sexes was detected in most of the subgroups that were investigated in the present meta-analysis. In general, women have a smaller mass of muscle than men and therefore less muscle available for the uptake of the fixed glucose load (75 g) used in the oral glucose-tolerance test.69,75 Women also have relatively high levels of estrogen and progesterone, both of which can reduce whole-body insulin sensitivity.76 Physical inactivity77 and unhealthy diet78 have also both been associated with impaired glucose tolerance. In many countries in sub-Saharan Africa, women are more likely to be physically inactive than the corresponding men.79,80 The differences in the sex distribu- tion of both impaired fasting glycaemia and impaired glucose tolerance in sub- Saharan Africa need to be considered in evaluating the probability that indi- viduals will develop diabetes mellitus and in efforts to prevent the disease. Impairments in glucose tolerance and in fasting glycaemia are not metabolically equivalent, and the people classified as having each condition are different as well.22,81 If screening programmes were based only on the measurement of “fast- ing plasma glucose”, most individuals with impaired glucose tolerance would go undetected and the population iden- tified as being at risk would probably be biased towards males. The glycated haemoglobin (HbA1c) assay69 may offer a way of evaluating the risk of diabetes mellitus that is relatively sex-neutral, although this assay is currently too ex- pensive for routine use in Africa and it can also be affected by disorders such as malaria.82 Screening for both impaired fasting glycaemia and impaired glucose tolerance might eliminate most of the sex bias in the identification of those who are at risk of developing diabetes mellitus. Even then, the dose of glucose used in the oral glucose-tolerance test may have to be made lower for women than for men – or tailored to the height of the individual to be tested – to allow for the lower mean muscle mass in wom- en and so prevent the over-diagnosis of impaired glucose tolerance in women.72 In the present meta-analysis, de- spite the differences seen by sex in im- paired fasting glycaemia and impaired glucose tolerance, the overall prevalence of diabetes mellitus in men was found to be very similar to that in women. However, subgroup analyses revealed Table 3. Pooled odds ratios (ORs)a for diabetes mellitus and two associated risk factors Variable Diabetes mellitus Impaired fasting glycaemia Impaired glucose tolerance nb OR (95% CI) Pc nb OR (95% CI) Pc nb OR (95% CI) Pc All data sets 35 1.01 (0.91–1.11) 11 1.56 (1.20–2.03) 21 0.84 (0.72–0.98) Area of residence 0.009 0.56 < 0.001 Combined 9 1.17 (1.04–1.31) 6 1.61 (1.14–2.26) 7 0.69 (0.59–0.81) Periurban 2 0.70 (0.42–1.18) 2 0.79 (0.46–1.37) Rural 11 0.98 (0.80–1.20) 2 2.21 (0.87–5.64) 6 0.82 (0.61–1.09) Urban 13 0.86 (0.73–1.01) 3 1.24 (0.71–2.19) 6 1.33 (1.03–1.72) Subregion of residence < 0.001 0.019 0.001 Middle Africa 2 1.44 (1.31–1.59) 1 1.04 (0.65–1.65) 2 0.73 (0.49–1.09) Eastern Africa 21 1.08 (1.01–1.15) 9 1.65 (1.35–2.02) 11 0.71 (0.59–0.84) Southern Africa 12 0.80 (0.69–0.92) 1 5.19 (1.75–15.38) 8 1.30 (0.99–1.70) Ethnicity of subjects 0.012 0.066 0.002 African 24 1.12 (1.00–1.25) 7 1.30 (0.96–1.74) 11 0.81 (0.66–0.99) Indian 2 0.69 (0.52–0.94) 2 1.66 (1.10–2.50) Multi-ethnic 9 1.00 (0.87–1.14) 4 1.93 (1.42–2.62) 8 0.73 (0.60–0.89) Study year 0.125 0.81 0.61 Before 1991 9 0.90 (0.73–1.11) 1 1.94 (0.84–4.48) 4 0.90 (0.62–1.31) 1991–1999 13 0.96 (0.83–1.12) 4 1.41 (0.88–2.27) 10 0.90 (0.68–1.19) After 1999 13 1.13 (0.99–1.30) 6 1.58 (1.09–2.30) 7 0.74 (0.55–1.01) Country income level 0.008 0.006 0.028 Low 14 1.21 (1.06–1.37) 6 1.18 (0.89–1.57) 5 0.70 (0.52–0.95) Lower middle 4 1.16 (0.75–1.80) 4 0.50 (0.29–0.87) Upper middle 17 0.93 (0.83–1.03) 5 2.05 (1.56–2.69) 12 0.99 (0.80–1.23) CI, confidence interval. a ORs represent the odds in men versus the odds in women. b Number of data sets included in the analysis. c P-value for the category, estimated in a Q-test. Bull World Health Organ 2013;91:671–682D | doi: http://dx.doi.org/10.2471/BLT.12.113415 677 Systematic reviews Sex differences in prevalence of glucose metabolism disordersEsayas Haregot Hilawe et al. that diabetes mellitus was more com- mon in the men who lived in Middle and Eastern Africa than in the women who lived in the same African subre- gions, whereas the women who lived in Southern Africa were more likely to have diabetes mellitus than the correspond- ing men. Such differences between the sexes were not seen in the earlier study on diabetes mellitus in West Africa.19 Some of these differences may be related to differences between the sexes in the prevalence of central obesity, which, as a risk factor for diabetes mellitus, is more predictive than peripheral obesity.83 Central obesity has been found to be more common in men than in women in Eastern Africa84,85 and more common in women than men in Southern Africa.86 However, such obesity cannot be used to explain why the men of Middle Africa are more likely to have diabetes mellitus than the women, as central obesity is more common among the women in this area than among the men.87 Behavioural risk factors, such as smoking and alcohol use, which are more common among the men of sub-Saharan Africa than among the women,3,71 might contribute to the prevalence of diabetes mellitus among the men of Middle Africa. In the present meta-analysis, the income level of the country of residence – a proxy indicator of the economic status of the people in the country – ap- peared to contribute to the heterogene- ity seen in the association between sex and the prevalence of diabetes mellitus. Women of low socioeconomic status in Australia,88 Canada,89 Germany90 and the United States of America91 appear to be at markedly higher risk of diabetes mellitus than the corresponding men. In a recent meta-analysis, the incidence of Type 2 diabetes mellitus among adults with low socioeconomic status was found to be generally higher in women than in men; it was suggested that the women who lived in impoverished areas were more likely to be obese, physi- cally inactive and under high levels of psychosocial stress than the men in the same areas.92 In contrast, the results of the present meta-analysis indicated that men who lived in the low-income countries of sub-Saharan Africa were more likely to be diagnosed with dia- betes mellitus than the corresponding women. This difference between the sexes may be a consequence of differ- ences between men and women in the distribution of risk factors for diabetes mellitus (e.g. obesity, physical inactivity, poor diet and smoking, etc.) in low- income countries. Another possibility is that women in low-income countries have particularly poor access to health- care services and therefore little chance of being diagnosed with diabetes.88,89,91,92 In addition, as Africa is one of the most inequitable parts of the world in terms of income,93 the income level recorded for an African country might not cor- relate with the socioeconomic status of a study cohort in that country. There appear to be no published data sets that would allow sex-based differences in the relationship between individual socio- economic status and diabetes mellitus in sub-Saharan Africa to be investigated. The present meta-analysis had several limitations. First, the studies that provided the data for the meta- analysis were conducted under different circumstances in different countries and the prevalences of diabetes mel- litus, impaired fasting glycaemia and/ or impaired glucose tolerance were not the primary outcomes of some of the studies. A random-effects model was therefore employed to embrace this con- siderable heterogeneity.40 Second, the studies had to be cross-sectional in de- sign to be included in the meta-analysis and may therefore have been affected by confounding and biases. However, we attempted to minimize selection bias by employing predefined study selection criteria and a quality appraisal checklist. Potential sources of heterogeneity were also assessed in subgroup and meta- regression analyses. Third, since our subgroup and meta-regression analyses were entirely observational in nature, the relationships recorded – across all of the studies – between some study-level characteristics and the effect estimate could be subject to confounding by other study-level characteristics. Un- fortunately, the studies included in the meta-analysis were too few to allow for a reasonable assessment of interactions between the study-level covariates. Fourth, we used the income levels of the countries of residence to stratify the studies because of a general lack of infor- mation on the socioeconomic status of study participants. The relationships that we observed between a country’s income level and the sex-specific prevalences of interest may therefore not reflect the re- lationships between the socioeconomic status of the subjects and their risks of impaired fasting glycaemia, impaired glucose tolerance or diabetes mellitus. Finally, our conclusions may have been affected by publication bias. The asym- metric funnel plots were indicative of possible publication bias in the data for diabetes mellitus and impaired fasting glucose. Furthermore, our study selec- tion criteria excluded reports that did not have an abstract in English and may have excluded some reports that were not recorded in the PubMed or Web of Science databases, although we did try to search the “grey” literature for relevant data. The results of the “trim and fill” analyses indicated that the impact of any publication bias on our conclusions was probably trivial. In summary, our meta-analysis demonstrated that, compared with the corresponding women, the men in Eastern, Middle and Southern Africa had a significantly higher prevalence of impaired fasting glycaemia and a lower prevalence of impaired glucose toler- ance. Although the overall prevalence of diabetes mellitus did not significantly differ by sex, the prevalence of diabetes mellitus was found to be lower or higher in women than in men when analysed by African subregion. Sex-based differences in the relationship between individual socioeconomic status and impaired fast- ing glycaemia, impaired glucose toler- ance and diabetes mellitus still need to be investigated in sub-Saharan Africa. Our observations may help in the targeting of appropriate – and perhaps sex-specific – interventions to prevent diabetes mel- litus in sub-Saharan Africa. ■ Acknowledgements We are grateful to the authors of the articles included in the meta-analysis, many of whom kindly provided us with additional information regarding their studies. Competing interests: None declared. Bull World Health Organ 2013;91:671–682D | doi: http://dx.doi.org/10.2471/BLT.12.113415678 Systematic reviews Sex differences in prevalence of glucose metabolism disorders Esayas Haregot Hilawe et al. صخلم في زوكولغلا لمتح للاتخاو مايصلا عم مدلا ركس للاتخاو ،يركسلا ءاد راشتنا لدعم في سنلجا بسح تافلاتخلاا رود يفصو ليلتحو يجهنم ضارعتسا :ىبركلا ءارحصلا بونج ايقيرفأ ءاد راشتنا لدعم في ءاسنلاو لاجرلا ينب تافلاتخلاا مميقت ضرغلا زوكولغلا لمتح للاتخاو مايصلا عم مدلا ركس للاتخاو ،يركسلا .ىبركلا ءارحصلا بونج ايقيرفأ في تانايب دعاوق في ثحبلا مت ،2011 برمتبس/لوليأ في ةقيرطلا ةيعمتجلما تاساردلا نع Web of Scienceو PubMed تلااح نم يلأ راشتنا تلادعم مدقت يتلا تاعاطقلا ةددعتم ايقيرفأ نم قطانم نونكسي نيذلا ينغلابلا ينب ثلاثلا ةساردلا ىطسولاو ةيقشرلا ايقيرفأ في يأ( ىبركلا ءارحصلا بونج ةيقيرفلأا نادلبلل ةيميلقلإا نود ةقطنلما فينصتل ًاقفو ةيبونلجاو ةيئاوشعلا تايرثأتلا جذومن مادختسا متو .)ةدحتلما مملأا بسح .ةلاح لك في ءاسنلاو لاجرلا ينب تلاماتحلاا باسلح تاذ تاعاطقلا ةددعتم تانايبلا تائفل يفصو ليلتح في جئاتنلا نأ لىإ لصوتلا مت ،ةئف 36 اهددع غلابلا اهديدتح مت يتلا ةلصلا ىدل هنع لاجرلا ىدل ًاعويش رثكأ مايصلا عم مدلا ركس للاتخا لىإ 1.20 نم :% 95 ةقثلا لصاف ؛1.56 :لماتحلاا ةبسن( ءاسنلا لقأ زوكولغلا لمتح للاتخا نأ لىإ لصوتلا مت ينح في ،)2.03 ؛0.84 :لماتحلاا ةبسن( ءاسنلا ىدل هنع لاجرلا ىدل ًاعويش ءاد راشتنا لدعم ناكو )0.72 لىإ 0.98 نم :% 95 ةقثلا لصاف :لماتحلاا ةبسن( ينسنلجا لاك في ًامومع هباشت يذلا – يركسلا ءاسنلا ينب لىعأ – )1.11 لىإ 0.91 نم :% 95 ةقثلا لصاف ؛1.01 ةيميلقلإا نود ةقطنلما سفن نم لاجرلا ينب هنع ةيبونلجا ايقيرفأ في ايقيرفأ نادلب نمو ىطسولاو ةيقشرلا ايقيرفأ نم ءاسنلا ينب لقأو لاجرلا ينب هنع لخدلا ةضفخنلما ىبركلا ءارحصلا بونج .مله ينلباقلما مت ،ةيميلقلإا نود قطانلما سفن نم ءاسنلاب ةنراقم جاتنتسلاا ةيبونلجاو ىطسولاو ةيقشرلا ايقيرفأ في لاجرلا نأ لىإ لصوتلا ميهدل تدادزا هنأ يرغ يركسلا ءادل هباشم ماع راشتنا لدعم ميهدل ميهدل تلق ينح في مايصلا عم مدلا ركس للاتخاب ةباصلإا ةيلماتحا .زوكولغلا لمتح للاتخاب ةباصلإا ةيلماتحا 摘要 撒哈拉以南非洲糖尿病、空腹血糖受损和糖耐量异常患病率的性别差异 : 系统回顾和元分析 目的 评估撒哈拉以南非洲糖尿病、空腹血糖受损和糖 耐量异常患病率的男女差异。 方法 在 2011 年 9 月 , 搜索 PubMed 和 Web of Science 数据库 , 查找基于社区、提供撒哈拉以南非洲区域 ( 即 根据联合国对非洲国家的亚区分类 : 东非、中非和南 非 ) 居住的成年人当中三种研究状况中任一种状况的 特定性别患病率的横断面研究。然后使用随机效果模 型计算和比较患有各种病情的男女差别。 结 果 在所识别的 36 个相关的横断面数据集的元 分析中 , 较之女性 , 在男性中空腹血糖受损更常见 (OR:1.56;95% 置信区间 ,CI:1.20–2.03), 而女性的糖耐量 受损比男性更常见 (OR:0.84;95% CI:0.72–0.98)。对于 两性之间大致差不多 (OR:1.01;95% CI:0.91–1.11) 的糖 尿病患病率 , 南非女性比同一亚区男性高 , 东非和中 非以及撒哈拉以南非洲低收入国家则是男高女低。 结论 与同一亚区女性比较 , 东非、中非和南非的男性 的糖尿病总体患病率相似 , 但是空腹血糖受损患病率 更高 , 糖耐量受损患病率更低。 Résumé Les différences entre les sexes dans la prévalence du diabète sucré, de la glycémie à jeun anormale et de l’intolérance au glucose en Afrique subsaharienne: examen systématique et méta-analyse Objectif Évaluer les différences entre hommes et femmes en termes de prévalence du diabète sucré, de la glycémie à jeun anormale et de l’intolérance au glucose en Afrique subsaharienne. Méthodes En septembre 2011, on a recherché dans les bases de données PubMed et Web of Science des études communautaires transversales fournissant les prévalences spécifiques au sexe des trois maladies faisant l’objet de l’étude, chez des adultes vivant dans certaines régions d’Afrique subsaharienne (par exemple en Afrique orientale, centrale et australe, selon la classification sous-régionale des Nations Unies pour les pays africains). Un modèle à effets aléatoires a ensuite été utilisé pour calculer et comparer les cotes des hommes et des femmes affectés par chacune de ces maladies. Résultats Dans une méta-analyse des 36 séries de données transversales pertinentes identifiées, on a découvert que la glycémie à jeun anormale était plus fréquente chez les hommes que chez les femmes (RC: 1,56, intervalle de confiance de 95%, IC: 1,20 à 2,03), tandis que la tolérance au glucose s’est révélée moins fréquente chez les hommes que chez les femmes (RC: 0,84, IC de 95%: 0,72 à 0,98). La prévalence du diabète sucré - généralement semblable chez les deux sexes (RC: 1,01, IC de 95%: 0,91 à 1,11) - était plus élevée chez les femmes d’Afrique australe que chez les hommes de la même sous-région, et plus faible chez les femmes d’Afrique orientale et centrale et des pays à faible revenu d’Afrique subsaharienne que chez les hommes des mêmes pays. Conclusion Par rapport aux femmes des mêmes sous-régions, on a découvert que la prévalence globale du diabète sucré était similaire chez les hommes d’Afrique orientale, mais que ceux-ci étaient plus susceptibles de souffrir de glycémie à jeun anormale et moins susceptibles d’être affectés par une intolérance au glucose. Bull World Health Organ 2013;91:671–682D | doi: http://dx.doi.org/10.2471/BLT.12.113415 679 Systematic reviews Sex differences in prevalence of glucose metabolism disordersEsayas Haregot Hilawe et al. Резюме Половые различия в распространенности сахарного диабета, нарушенной гликемии натощак и нарушенной переносимости глюкозы в Африке южнее Сахары: систематический обзор и мета-анализ Цель Оценить различия между мужчинами и женщинами в распространенности сахарного диабета, нарушенной гликемии натощак и нарушенной переносимости глюкозы в Африке южнее Сахары. Методы В сентябре 2011 года был осуществлен поиск в базах данных PubMed и Web of Science территориальных поперечных исследований, предоставляющих данные в половом разрезе о распространенности любого из трех исследуемых заболеваний среди взрослых, живущих в Африке южнее Сахары (то есть в Восточной, Средней и Южной Африке, согласно субрегиональной классификации африканских стран Организацией Объединенных Наций). Затем для расчета и сопоставления риска мужчин и женщин подвергнуться каждому из заболеваний была использована модель случайных эффектов. Результаты Мета-анализ идентифицированных 36 релевантных поперечных наборов данных показал, что нарушение гликемии натощак чаще встречается у мужчин, чем у женщин (соотношение риска, СР: 1,56; 95% доверительный интервал, ДИ: 1,20–2,03), в то время как нарушенная переносимость глюкозы у мужчин встречается реже, чем у женщин (СР: 0,84; 95% ДИ: 0.72–0.98). Распространенность сахарного диабета, которая в целом была аналогична у обоих полов (СР: 1,01; 95% ДИ: 0,91–1,11), в Южной Африке была выше среди женщин, чем среди мужчин из того же субрегиона, и ниже среди женщин из стран Восточной и Центральной Африки, а также из малообеспеченных стран Африки южнее Сахары, чем среди мужчин из той же выборки. Вывод У мужчин в Восточной, Средней и Южной Африке была обнаружена аналогичная с женщинами в тех же субрегионах общая распространенность сахарного диабета, но чаще встречались нарушения гликемии натощак и реже – нарушенная толерантность к глюкозе. Resumen Las diferencias entre sexos en la prevalencia de la diabetes mellitus, las alteraciones de la glucemia en ayunas y la intolerancia a la glucosa en África subsahariana: revisión sistemática y metaanálisis Objetivo Evaluar las diferencias entre hombres y mujeres respecto a la prevalencia de la diabetes mellitus, las alteraciones de la glucemia en ayunas y la intolerancia a la glucosa en África subsahariana. Métodos En septiembre de 2011, se realizaron búsquedas en las bases de datos de PubMed y Web of Science a fin de hallar estudios comunitarios transversales que proporcionaran datos sobre las prevalencias específicas de cada sexo de cualquiera de las tres enfermedades de estudio entre los adultos residentes en zonas de África subsahariana (es decir, en el Este, Centro y Sur de África, según la clasificación subregional de las Naciones Unidas para los países africanos). Se empleó un modelo de efectos aleatorios para calcular y comparar las probabilidades por parte de hombres y mujeres de padecer cada una de las enfermedades. Resultados En un metaanálisis de los 36 conjuntos de datos de carácter transversal pertinentes que se identificaron, se halló que las alteraciones de la glucemia en ayunas eran más comunes en hombres que en mujeres (OR: 1,56; intervalo de confianza del 95%, IC: 1,20 a 2,03), por el contrario, se descubrió que la intolerancia a la glucosa era menos común en los hombres que en las mujeres (OR: 0,84; IC del 95%: 0,72 a 0,98). La prevalencia de la diabetes mellitus (la cual fue, por lo general, similar en ambos sexos (OR: 1,01; IC 95%: 0,91 a 1,11) fue mayor entre las mujeres del Sur de África que entre los hombres de la misma subregión, y menor entre las mujeres del Este y Centro de África, así como en los países de ingresos bajos de África subsahariana, que entre los hombres correspondientes. 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De sc rip tio ns o f t he cr os s- se ct io na l d at a se ts in clu de d in th e m et a- an al ys is Au th or s Ye ar St ud y a re a Sa m pl in g m et ho d Re sp on se ra te (% ) Ta rg et po pu la tio n Ag e (y ea rs ) No . o f a du lts M ea n ag ea (y ea rs ) Di ag no sis Ou tc om es as se ss ed Pr ev al en ce (% )b Pu bl ica tio n St ud y Lo ca tio n Ty pe M en W om en Cr ite ria M et ho d Sp ec im en M en W om en Ah ré n an d Co rri ga n5 1 19 84 19 83 M w an za , U RT U rb an Cl us te r 95 Al l in ha bi ta nt s ≥ 2 0 16 1c 21 5c 35 .4 c W H O 19 80 FB G an d/ or O GT T cW B D M 1. 87 c 1. 86 c Ah ré n an d Co rri ga n5 1 19 84 19 83 Ka ha ng al a an d N do la ge , U RT Ru ra l Cl us te r 90 Al l in ha bi ta nt s ≥ 2 0 36 0c 48 9c 43 .3 c W H O 19 80 FB G an d/ or O GT T cW B D M 1. 1c 1. 84 c O m ar e t a l.4 6 19 85 N R D ur ba n, So ut h Af ric a U rb an Cl us te r 77 Ad ul ts ≥ 1 5 36 8 49 8 42 .5 W H O 19 85 FB G an d O GT T VP D M 7. 6 13 .5 IG T 7. 1 4. 8 Sö de rb er g et a l.4 3 20 05 19 87 M au rit iu s Co m bi ne d M ul tis ta ge cl us te r 86 Ad ul ts 25 –7 4 23 39 26 52 43 .3 W H O 19 99 FB G an d O GT T VP D M 14 .3 (1 3. 0) 13 .7 (1 2. 6) IF G 5. 1 (5 .1 ) 2. 7 (2 .6 ) IG T 13 .2 (1 2. 7) 19 .4 (1 9. 1) M cL ar ty e t al .14 19 89 19 88 d M or og or o an d Ki lim an ja ro , U RT Ru ra l Ra nd om 92 .6 Ad ul ts ≥ 1 5 26 23 34 60 37 W H O 19 85 FB G an d/ or O GT T vW B D M 1. 1 0. 7 IG T 7. 3 8. 0 Ta pp y et al .66 19 91 19 89 M ah e, Se yc he lle s U rb an St ra tifi ed ra nd om 86 .4 Ad ul ts 25 –6 4 51 1 56 7 N R AD A 19 88 FB G vW B D M N R (3 .4 ) N R (4 .6 ) Le vi tt e t a l.5 3 19 93 19 90 Ca pe To w n, So ut h Af ric a U rb an Cl us te r 79 Ad ul ts > 3 0 21 0 50 4 45 .1 W H O 19 85 FB G an d O GT T VP D M 6. 5 (6 .9 ) 6. 4 (7 .4 ) IG T 6. 0 5. 9 M ol le nt ze e t al .68 19 95 19 90 Q w aQ w a, So ut h Af ric a Ru ra l Ra nd om 68 Ad ul ts ≥ 2 5 27 9 57 4 52 .3 W H O 19 85 FB G an d O GT T VP D M 5. 4 6. 6 M ol le nt ze e t al .68 19 95 19 90 M an ga un g, So ut h Af ric a U rb an Ra nd om 62 Ad ul ts ≥ 2 5 29 0 46 8 48 .6 W H O 19 85 FB G an d O GT T VP D M 5. 8 8. 5 Sö de rb er g et a l.4 3 20 05 19 92 M au rit iu s Co m bi ne d M ul tis ta ge cl us te r 90 Ad ul ts ≥ 2 5 29 86 34 77 46 W H O 19 99 FB G an d O GT T VP D M 19 .3 (1 5. 5) 18 .3 (1 5. 0) IF G 8. 5 (8 .2 ) 4. 1 (3 .9 ) IG T 13 .0 (1 2. 0) 17 .7 (1 6. 3) O m ar e t a l.6 4 19 93 N R U m la zi , So ut h Af ric a U rb an Cl us te r 78 Ad ul ts ≥ 1 5 14 1 33 8 32 .9 W H O 19 85 FB G an d O GT T VP D M 2. 3 5. 2 IG T 11 .5 5. 5 (c on tin ue s. . . ) 682B Bull World Health Organ 2013;91:671–682D | doi: http://dx.doi.org/10.2471/BLT.12.113415 Systematic reviews Sex differences in prevalence of glucose metabolism disorders Esayas Haregot Hilawe et al. Au th or s Ye ar St ud y a re a Sa m pl in g m et ho d Re sp on se ra te (% ) Ta rg et po pu la tio n Ag e (y ea rs ) No . o f a du lts M ea n ag ea (y ea rs ) Di ag no sis Ou tc om es as se ss ed Pr ev al en ce (% )b Pu bl ica tio n St ud y Lo ca tio n Ty pe M en W om en Cr ite ria M et ho d Sp ec im en M en W om en O m ar e t a l.5 9 19 94 N R D ur ba n, So ut h Af ric a U rb an Cl us te r 92 Ad ul ts ≥ 1 5 10 38 14 41 N R W H O 19 85 FB G an d O GT T VP D M 8. 6 (1 0. 4) 10 .6 (1 5. 0) IG T 7. 6 (8 .9 ) 4. 5 (5 .8 ) El ba gi r e t al .17 19 96 N R Su da n Co m bi ne d M ul tis ta ge N R Ad ul ts ≥ 2 5 46 1 82 3 46 .1 W H O 19 85 O GT T cW B D M 3. 5 3. 4 IG T 2. 2 3. 3 Le vi tt e t a l.5 2 19 99 19 96 M am re , So ut h Af ric a Pe riu rb an Cl us te r 64 .5 Ad ul ts ≥ 1 5 42 8 54 5 37 .6 W H O 19 85 O GT T VP D M 5. 8 8. 1 IG T 6. 5 9. 2 Er as m us e t al .67 20 01 19 97 d U m ta ta , So ut h Af ric a Pe riu rb an N R 73 Ad ul ts 20 –6 9 23 7 13 7 37 .9 W H O 19 85 FB G an d O GT T VP D M 2. 1 2. 9 IG T 3. 4 1. 5 As pr ay e t al .45 20 00 19 97 Ila la Il al a an d D ar e s Sa la am , U RT U rb an Ra nd om 73 .2 5 Ad ul ts ≥ 1 5 33 2 43 8 30 .6 W H O 19 98 FB G cW B D M 5. 3 (5 .9 ) 4. 0 (5 .7 ) IF G 4. 0 (3 .6 ) 5. 4 (4 .7 ) As pr ay e t al .45 20 00 19 97 Sh ar i, U RT Ru ra l Ra nd om 82 .5 Ad ul ts ≥ 1 5 40 1 52 7 42 .1 W H O 19 98 FB G cW B D M 1. 5 (1 .7 ) 1. 1 (1 .1 ) IF G 1. 2 (0 .8 ) 1. 5 (1 .6 ) Ch ar lto n et al .61 20 01 19 97 St H el en a Ba y an d Ve ld dr if, So ut h Af ric a Ru ra l Co nv en ie nc e N R Ad ul ts > 5 5 46 10 6 65 .4 W H O 19 85 ; AD A 19 97 FB G an d O GT T VP D M 15 .8 28 .9 IG T 13 .2 10 .0 Al be rt s e t al .56 20 05 19 97 d Li m po po , So ut h Af ric a Ru ra l Ce ns us 66 Ad ul ts > 3 0 49 8 16 08 57 .5 AD A 19 97 FB G VP D M 9. 9 (8 .5 ) 10 .0 (8 .8 ) Sö de rb er g et a l.4 3 20 05 19 98 M au rit iu s Co m bi ne d M ul tis ta ge cl us te r 87 Ad ul ts ≥ 2 0 23 92 30 00 48 .8 W H O 19 99 FB G an d O GT T VP D M 25 .2 (1 8. 3) 23 .8 (1 7. 6) IF G 5. 7 (6 .2 ) 3. 5 (2 .9 ) IG T 13 .2 (1 1. 2) 17 .2 (1 6. 2) El ba gi r e t al .48 19 98 N R N or th er n St at e, S ud an U rb an M ul tis ta ge N R Ad ul ts ≥ 2 5 11 8 19 7 38 W H O 19 85 O GT T cW B D M N R (1 5. 8) N R (1 0. 7) IG T N R (4 .5 ) N R (1 3. 5) (. . . co nt in ue d) (c on tin ue s. . . ) 682CBull World Health Organ 2013;91:671–682D | doi: http://dx.doi.org/10.2471/BLT.12.113415 Systematic reviews Sex differences in prevalence of glucose metabolism disordersEsayas Haregot Hilawe et al. Au th or s Ye ar St ud y a re a Sa m pl in g m et ho d Re sp on se ra te (% ) Ta rg et po pu la tio n Ag e (y ea rs ) No . o f a du lts M ea n ag ea (y ea rs ) Di ag no sis Ou tc om es as se ss ed Pr ev al en ce (% )b Pu bl ica tio n St ud y Lo ca tio n Ty pe M en W om en Cr ite ria M et ho d Sp ec im en M en W om en El ba gi r e t al .48 19 98 N R N or th er n St at e, S ud an Ru ra l M ul tis ta ge N R Ad ul ts ≥ 2 5 43 12 6 39 W H O 19 85 O GT T cW B D M N R (2 .8 ) N R (8 .3 ) IG T N R (4 .4 ) N R (1 0. 2) M ot al a et al .50 20 08 20 00 d U bo m bo di st ric t, So ut h Af ric a Ru ra l Cl us te r 78 .9 Ad ul ts ≥ 1 5 20 0 79 9 46 .9 W H O 19 98 FB G an d O GT T VP D M 4. 5 (3 .5 ) 4. 6 (3 .9 ) IF G 4. 5 (4 .0 ) 0. 9 (0 .8 ) IG T 6. 5 (4 .0 ) 6. 4 (4 .7 ) Fa eh e t a l.5 5 20 07 20 04 Se yc he lle s U rb an St ra tifi ed ra nd om 80 .2 Ad ul ts 25 –6 4 56 8 68 7 45 .2 AD A 20 04 FB G an d/ or O GT T VP D M N R (1 1. 0) N R (1 2. 1) IF G N R (3 0. 4) N R (1 8. 0) IG T N R (1 1. 2) N R (9 .6 ) ZW M oH 63 20 05 20 05 Zi m ba bw e Co m bi ne d M ul tis ta ge cl us te r 72 .1 Ad ul ts ≥ 2 5 40 2 12 64 48 W H O 19 99 FB G an d O GT T VP D M 2. 2 1. 3 IG T 5. 3 5. 2 Ka sia m L as i O n’ Ki n et al .57 20 08 20 05 Ki ns ha sa , D RC Co m bi ne d M ul tis ta ge cl us te r 90 .3 Al l in ha bi ta nt s > 1 2 45 80 51 90 46 W H O / AD A 20 03 FB G an d O GT T cW B D M N R (2 3. 7) N R (1 7. 7) IF G N R (9 .5 N R (9 .2 ) IG T N R (6 .4 ) N R (8 .2 ) Si lv a- M at os et a l.6 0 20 11 20 05 M oz am bi qu e U rb an e Cl us te r 70 .5 Ad ul ts 25 –6 4 N R N R 39 W H O 19 98 FB G cW B D M 5. 5 4. 9 IF G 3. 2 2. 0 Si lv a- M at os et a l.6 0 20 11 20 05 M oz am bi qu e Ru ra le Cl us te r 70 .5 Ad ul ts 25 –6 4 N R N R 39 W H O 19 98 FB G cW B D M 2. 4 1. 2 IF G 2. 3 2. 6 N sa ka sh al o- Se nk w e et al .49 20 11 20 05 Lu sa ka , Za m bi a U rb an M ul tis ta ge cl us te r N R Ad ul ts 25 –6 4 62 0 12 60 42 .1 W H O f FB G cW B D M 2. 1 3. 0 IF G 1. 3 1. 3 Ch ris te ns en et a l.4 7 20 09 20 06 Lu o, K am ba , M aa sa i a nd N ai ro bi , Ke ny a Co m bi ne d Ra nd om 98 .2 Al l in ha bi ta nt s ≥ 1 7 64 0 81 9 37 .5 W H O 19 99 FB G an d O GT T vW B D M N R (4 .5 ) N R (4 .2 ) IG T N R (6 .1 ) N R (1 3. 1) Ti ba za rw a et a l.4 2 20 09 20 07 So w et o, So ut h Af ric a U rb an Co nv en ie nc e 94 Ad ul ts N R 59 4 10 97 46 W H O 19 85 RB G cW B D M 3. 5 3. 0 (. . . co nt in ue d) (c on tin ue s. . . ) 682D Bull World Health Organ 2013;91:671–682D | doi: http://dx.doi.org/10.2471/BLT.12.113415 Systematic reviews Sex differences in prevalence of glucose metabolism disorders Esayas Haregot Hilawe et al. Au th or s Ye ar St ud y a re a Sa m pl in g m et ho d Re sp on se ra te (% ) Ta rg et po pu la tio n Ag e (y ea rs ) No . o f a du lts M ea n ag ea (y ea rs ) Di ag no sis Ou tc om es as se ss ed Pr ev al en ce (% )b Pu bl ica tio n St ud y Lo ca tio n Ty pe M en W om en Cr ite ria M et ho d Sp ec im en M en W om en W an jih ia e t al .44 20 09 20 08 d Bo nd o an d Ke ric ho , Ke ny a Ru ra l Ra nd om 99 .6 Al l in ha bi ta nt s ≥ 1 8 13 4 16 5 43 W H O 19 99 FB G an d O GT T cW B IG T 3. 7 11 .9 M at he ng e et a l.6 5 20 10 20 08 N ak ur u di st ric t, Ke ny a U rb an Cl us te r 88 Ad ul ts ≥ 5 0 70 7d 73 0d 60 .8 d W H O 19 85 RB G cW B D M 9. 9 9. 9 M at he ng e et a l.6 5 20 10 20 08 N ak ur u di st ric t, Ke ny a Ru ra l Cl us te r 88 Ad ul ts ≥ 5 0 13 99 d 15 60 d 64 .7 d W H O 19 85 RB G cW B D M 4. 9 4. 9 M W M oH 54 20 10 20 09 M al aw i Co m bi ne d M ul tis ta ge cl us te r 95 .5 Ad ul ts 25 –6 4 16 90 35 16 32 .9 W H O 19 99 FB G cW B D M 6. 5 4. 7 IF G 5. 7 2. 7 Ev ar ist o- N et o et a l.5 8 20 10 N R Be ng o, An go la Ru ra l M ul tis ta ge cl us te r 97 Ad ul ts 30 –6 9 12 6 29 5 49 .6 W H O 19 85 FB G an d O GT T cW B D M 3. 2 2. 7 IG T 5. 6 9. 1 M ah er e t al .62 20 11 20 09 So ut h- w es te rn U ga nd a Ru ra l Ce ns us 65 .6 Al l in ha bi ta nt s ≥ 1 3 27 19 39 59 32 .9 W H O 20 06 RB G VP D M N R (0 .4 ) N R (0 .4 ) AD A, A m er ic an D ia be te s A ss oc ia tio n; c W B, c ap ill ar y w ho le b lo od ; D M , d ia be te s m el lit us ; D RC , D em oc ra tic R ep ub lic o f t he C on go ; F BG , fa st in g bl oo d gl uc os e; IF G, im pa ire d fa st in g gl yc ae m ia ; IG T, im pa ire d gl uc os e to le ra nc e; M W M oH , M al aw i M in ist ry of H ea lth ; N R, n ot re po rte d; O GT T, or al g lu co se -to le ra nc e te st ; R BG , r an do m b lo od g lu co se ; U RT , U ni te d Re pu bl ic o f T an za ni a; V P, ve no us p la sm a; v W B, v en ou s w ho le b lo od ; W HO , W or ld H ea lth O rg an iza tio n; Z W M oH , Z im ba bw e M in ist ry o f H ea lth an d Ch ild W el fa re . a If ne ve r r ep or te d, e st im at ed fr om th e ag e di st rib ut io n of su bj ec ts . b Va lu es sh ow n ar e cr ud e pr ev al en ce s f ol lo w ed , in p ar en th es es , b y th e ag e- ad ju st ed v al ue s ( w he n re po rte d) . c D at a fo r s tu dy su bj ec ts a ge d ≥ 20 y ea rs . d Pr ev io us ly u np ub lis he d in fo rm at io n, su pp lie d by a n au th or o f t he c ite d re po rt. e Fo r t he m et a- an al ys is, p oo le d da ta fo r a ll of th e st ud y ar ea s i nv es tig at ed b y Si lv a- M at os e t a l.6 0 ( i.e . t ho se fo r u rb an a nd ru ra l a re as c om bi ne d) w er e us ed . f Ye ar n ot re po rte d. (. . . co nt in ue d)