- Research article
- Open Open Peer Review
A population-based survey of prevalence of diabetes and correlates in an urban slum community in Nairobi, Kenya
BMC Public Healthvolume 13, Article number: 371 (2013)
Urban slum populations in Africa continue to grow faster than national populations. Health strategies that focus on non-communicable diseases (NCD) in this segment of the population are generally lacking. We determined the prevalence of diabetes and associated cardiovascular disease (CVD) risk factors correlates in Kibera, Nairobi’s largest slum.
We conducted a population-based household survey utilising cluster sampling with probability proportional to size. Households were selected using a random walk method and consenting residents aged 18 years and above were recruited. The WHO STEPS instrument was administered. A random capillary blood sugar (RCBS) was obtained; known persons with diabetes and subjects with a RCBS >11.1 had an 8 hours fasting blood sugar (FBS) drawn. Diabetes was defined as a RCBS of ≥ 11.1 mmol/l and a FBS of ≥ 7.0 mmol/l, or a prior diagnosis or receiving diabetes drug treatment.
Out of 2061 enrolled; 50.9% were males, mean age was 33.4 years and 87% had a minimum of primary education. Only 10.6% had ever had a blood sugar measurement. Age adjusted prevalence of diabetes was 5.3% (95% CI 4.2-6.4) and prevalence increased with age peaking at 10.5% (95% CI 6.8-14.3%) in the 45–54 year age category. Diabetes mellitus (DM) correlates were: 13.1% smoking, 74.9% alcohol consumption, 75.7% high level of physical activity; 16.3% obese and 29% overweight with higher rates in women.
Among persons with diabetes the odds of obesity, elevated waist circumference and hypertension were three, two and three fold respectively compared to those without diabetes. Cardiovascular risk factors among subjects with diabetes were high and mirrored that of the entire sample; however they had a significantly higher use of tobacco.
This previously unstudied urban slum has a high prevalence of DM yet low screening rates. Key correlates include cigarette smoking and high alcohol consumption. However high levels of physical activity were also reported. Findings have important implications for NCD prevention and care. For this rapidly growing youthful urban slum population policy makers need to focus their attention on strategies that address not just communicable diseases but non communicable diseases as well.
The global prevalence of non-communicable diseases (NCD) dominated by diabetes  is increasing with the greatest burden occurring in developing countries, and it is projected that by the year 2020, NCD’s will surpass communicable diseases as a cause of death [2, 3]. Current disease estimates for Sub-Saharan Africa (SSA) are based on sparse data, but projections indicate increases in NCD’s caused by demographic and epidemiologic transitions.
The world prevalence of diabetes among adults was projected at 6.4% in 2010, affecting 285 million adults, and by 2030, it is estimated to be 7.7% with 439 million adults affected . Much of this burden will be in developing countries with an increase of 69% in the number of adults with diabetes . The urban poor are the population segment most likely to be affected. Low socio-economic status including low education levels has been associated with development of diabetes mellitus [5, 6]. An association between increasing poverty levels and increasing prevalence of diabetes has been shown among women but is not as strong for men. [7, 8]. A study in India among a poor urban slum population generally believed to have low risk for lifestyle diseases found a high prevalence of risk factors for NCD’s .
Currently the estimated prevalence of diabetes in Africa is 1–3% in rural areas and 5-6% in urban Sub Saharan Africa however country reports have varied widely . The International Diabetes Federation Atlas estimated that in 2006, 10.8 million people had diabetes in Sub Saharan Africa and that this would increase to 18.7 million by 2025, an increase of 80% exceeding the predicted worldwide increase of 55% . The epidemiology of diabetes in Kenya has not been studied to any great extent. Anecdotal evidence from health care services suggests that the incidence of diabetes is on the increase. The best estimate of diabetes is from an opportunity sample of an urban and rural population that reported a non-age adjusted prevalence of 4.2% .
Urbanisation has been identified as the key driver of the evolving NCD epidemic in developing countries . Identified lifestyle changes include reduced decreased physical activity; increase in smoking; increased alcohol intake. These are the behavioural risk factors for diabetes, which can lead to development of metabolic risk factors such as obesity. Since 2008, and for the first time in human history, the majority of the world’s population has lived in urban areas . In Kenya while 22.3% of the population is urban, the urban population growth rate is 4.2% almost double the national population growth rate of 2.4% . In Nairobi a majority (60%) of the population live in slums with much of the migrant population settling in slums. Further 75% of the urban population growth is absorbed by informal settlements. It is estimated that the number of urban population living in slums will double in the next 10 years . Yet three quarters of the urban slum dwellers are deprived poor, living under impoverished conditions.
Despite this large and growing proportion of the population, there is a paucity of documentation on their burden of NCD. Our objective in this survey of the largest Nairobi slum was to determine the prevalence of diabetes mellitus and correlates such as physical activity, tobacco consumption; alcohol intake and metabolic risk factors such as hypertension and obesity.
The urban slum of Kibera in Nairobi, the capital of Kenya was chosen as the study area. Kibera is located 5 km southwest of Nairobi city centre and is approximately 2.5 square kilometres, with an estimated 300,000 inhabitants and density of 49,228 persons per square kilometre . This study was conducted using UN recommendation for cluster sampling with probability proportional to size adjusted to achieve our sample size . A design effect of 2.0 was used to account for the clustering of the study participants. A sampling frame with the list of villages and the projected populations of each was obtained. As a result a total of 80 clusters each containing 10 households and thus 25 participants per cluster were created within the eight villages with the number of clusters proportional to the population size within the particular village. The households in each cluster were visited in a random walk method from the nearest health centre, church or school as the focal point in each cluster. Within each household, all consenting adults 18 years and above who had been residents of the area for more than 3 months were enrolled. Pregnant women were excluded on account of the transient nature of gestational diabetes mellitus. The next household was taken as the one nearest to that previously visited until the sample size for the given cluster was achieved. The WHO STEPS instrument for collecting surveillance data for non-communicable diseases (NCD’s) was adopted to collect data.The WHO STEP wise approach to surveillance of non-communicable diseases is a validated instrument developed by WHO for collection of surveillance data on NCDs in resource poor settings. It is a sequential process made up of three main sections, which are the risk stratification questionnaire (step1), anthropometric measurements (step 2) and biochemical measurements (step3) [18, 19]. The WHO steps instrument is attached as Additional file 1.
The survey was conducted between June and August 2010 with research teams visiting selected household between 8 am and 4 pm daily on weekdays. We minimized missing respondents by making a maximum of two revisits on subsequent Sundays. The questionnaire was administered by study trained registered medical assistants teams supervised by a study medical doctor. The questionnaire covered the smoking habits, alcohol use and physical activity pattern, history of prior evaluation for diabetes, and if any, the medication and lifestyle counselling that had been given. Anthropometric measures were recorded. Height was measured to the nearest 0.5 cm using a metal measuring tape against a wall and a flat headboard at right angles to the wall. Weight was determined using a good quality bathroom scale with the subject in light clothing and without shoes. Mid upper arm circumference was measured to the nearest 0.5 cm. Waist circumference was taken with a flexible tape measure placed on a horizontal plane at the level midpoint of the superior border of the iliac crest and the inferior margin of the last rib mid-axillary plane and the recording was at the point of normal expiration. Body mass index (BMI), calculated as weight (kg)/height (m2), was used as a measure of total body obesity while waist circumference and waist: hip ratio was used as measures of abdominal obesity. BMI <18.5 was recorded as underweight, 18.5-24.9 as normal, 25–29.9 as overweight and BMI more than 30 was recorded as obesity. Significant waist circumference was recorded as more than 102 cm (40 inches) for males and more than 88 cm (35 inches) in females. Significant waist to hip ratio was considered abnormal in females with a ratio of >0.8 and males >0.9.
Blood pressure was recorded using a mercury sphygmomanometer after the risk factor questionnaire had been filled to ensure that subjects had been seated for at least fifteen minutes. American Heart Association guidelines for measuring blood pressure were used. Three intermittent readings were taken and an average obtained. A random capillary blood sugar (RCBS) was recorded using a glucometer. Participants who had a RCBS >11.1 were invited to present themselves to the nearest health facility at a later designated date after a minimal 8 hours overnight fast. A fasting specimen of blood (five millilitres (5 mls) was drawn from a peripheral vein preferably the ante-cubital fossa and the serum was assayed for fasting venous blood sugar.
Research assistants underwent training on completion of the STEPS questionnaire and on anthropometric measurements to ensure standardization. The sphygmomanometers, weighing scales and tape measures were assessed weekly by taking measurements of one person on each of the instruments to ensure they were standardized. Recommended procedures for specimen collection, preparation and storage were followed to minimize pre-analytical errors. Before analysis, all the assays were calibrated according to the manufacturer’s specifications. Commercial controls used to validate the calibrations. Results were transcribed onto data sheets, which were checked by two people to minimize post analytical transcriptional errors.
Participant were diagnosed with diabetes if they had a RCBS level of >/= 11.1 mmol/l and a venous FBS of >/= 7.0 mmol/l, or had been diagnosed with diabetes or were receiving treatment for diabetes with insulin or oral hypoglycaemic agents. Hypertension was defined and classified as per the Seventh Report of the Joint National Committee  on prevention, detection and treatment of high blood pressure as being systolic BP ≥ 140 mmHg and/or diastolic BP ≥90 mmHg or use of prescribed antihypertensive medication. Hypertension was classified into pre-hypertension, stage 1 or 2 hypertension as per JNC VII. If the systolic and diastolic pressure readings belonged to different categories, the higher of the two readings was used to assign the blood-pressure stage.
Statistical analysis was undertaken using Statistical Package for the Social Sciences (SPSS) version 16.0. Prevalence was age standardised utilising the new WHO world standard population . Associations between the subjects socio-demographic, clinical and laboratory characteristics were examined using chi-square test for the categorical data while for continuous variables the Student t-test was used to determine statistical significance and Mann Whitney U test used in the analysis where such continuous data were skewed. Adjusted odds ratios for the association between diabetes mellitus and body mass indices and hypertension were computed, with 95% CIs. Associations were considered significant at p value less than or equal to 0.05.
Study approval was obtained from the Kenyatta National Hospital (KNH) Ethics and Research Committee and the Ministry of Science and Technology. Administrative permission was obtained from the Nairobi City Council and the provincial administration and Kibera community leaders and elders were informed of the study. Follow up care for clinical conditions detected were facilitated by referral to nearby Mbagathi district Hospital or the national referral hospital as appropriate.
From a total of 936 households in eight villages, 2200 individuals were screened for eligibility, 2061 were enrolled and 139 excluded (38 pregnant, 50 declined consent and 51 below 18 years of age). We thus sampled on average 2.4 adults per household, however complete data was available in 2045 participants (99%). On account of insecurity incident during the survey one village (Laini Saba) was not visited.
Males comprised 50.9% and age ranged from 18 to 90 years with a mean age of 33.4 years (SD 11.6 years). Respondents were generally young with 53.9% aged between 25–44 years, 28.3% were under 24 years and only 5.2% were 55 years or older. Literacy level was high with 87% having either a primary or secondary level of educational attainment and the mean duration of years spent in school was 9.3 years.
Behavioural risk factors and demographic characteristics of the study sample are depicted in Table 1. Current cigarette smokers comprised 13.1% of whom 84.8% were daily smokers. The mean age of smoking commencement and duration of smoking was 19.7 years and 16.5 years respectively with a median of six-pack years. A larger proportion of males were smokers.
The study demonstrated a high level of alcohol consumption with 30% reported to have ever consumed alcohol; 74.9% of whom consumed alcohol in the previous twelve months and 62.2% in the previous 30 days of study. The frequency of consumption in the preceding twelve months was 19.7% on a daily basis and 43.4% between 1–6 days per week; with the average and largest number of drinks per sitting being four and six drinks respectively. Males demonstrated a higher consumption level of alcohol than their female counter parts.
As anticipated study subjects demonstrated a high level of physical activity (PA) that was predominantly work and travel related; with 75.7% (male 79.1%, female 73%) undertaking vigorous or moderate work related activity, for a median duration of six hours per day; and 77.4% (male 80.3%, female 74.3%) walking or cycling as mode of transport; the latter at a median frequency of 6 days per week. This is also demonstrated by METS minutes per week data. Vigorous work related PA was more common in males and the converse for moderate work related PA (Table 1).
Anthropometric measures of body weight status, depicted in Table 2, was available for 98% of participants (Body Mass Index (BMI) n = 2036 Waist circumference (WC) n = 2031 and Waist Hip Ratio (WHR) n = 2026 Majority had a normal body weight measure of BMI, WC and WHR (50.1%, 78.5% and 86.7% respectively), however overall 16.3% were obese and 29% overweight by BMI and 21.5% and 13.3% had an elevated WC and WHR. An elevated body mass by all measures was more frequent in women, with that for obesity, WC and WHR reaching statistical significance. Remarkably the proportion of women with obesity was 32.2%, central obesity 41.5% and elevated WHR 24%. Overweight status did not show a sex differential.
A diagnosis of Diabetes was established in 3.2% (n 66; 95% CI 2.5-4.0) of screened subjects; 53% (n 35) screen detected and 47% (n 31) previously diagnosed. A meager 10.6% of those without-diabetes had ever in their lifetime had a blood sugar measurement. The age adjusted prevalence of diabetes was 5.3% (95% CI 4.2-6.4). Prevalence increased with age peaking at 10.5% (n 27; 95% CI 6.8-14.3%) in the 45–54 yr. age category, though the rates in those under 24 years and in the more advanced age categories were unstable on account of small numbers (Table 3). The female male ratio was 1.3: 1.0, however prevalence was not statistically different among sex in categories with stable rates. Therapy in use among those with diabetes was 58.8% dietary, 48.4% oral hypoglycaemic agents, 22.6% insulin and 12.9% herbal therapy.
Table 4 depicts the distribution of behavioural risk factors by diabetic and sex status. The proportion of persons with diabetes with the behavioural risk factors was high and mirrored that of the entire sample; at cigarette smoking 12.1% and alcohol consumption 21.2%, however persons with diabetes had a significantly 12 year longer duration of cigarette smoking (28.6 years; 16 years; p 0.0001) and double the pack years of persons without diabetes (12.8;6.0; p 0.049). Persons with diabetes demonstrated similarly high physical activity levels to persons without diabetes.
Body mass distribution by diabetes status and sex is depicted in Table 5. Compared to persons without, persons with diabetes were three fold (OR 3.2; 95% CI 1.7-6.2%) more likely to be obese, with no significant sex differences. Persons with diabetes were twice as likely to have an elevated WC (OR 2.3; 95% CI 1.2-4.6%) and WHR (OR 2.1; 95% 1.1-3.9%); with no significant sex differences detectable. This relationship between body mass and diabetes status was unchanged on adjusting for smoking, alcohol use and physical activity (MET). However the positive relationship between waist circumference and diabetes status was no longer significant on adjusting for smoking, alcohol use and physical activity (MET).
Overall 45.5% (n 30) of persons with diabetes were hypertensive; 43.9% (n 29) pre-hypertensive and only 10.6% (n 7) were normotensive; 53% (n 17/30) of hypertensives were classified as Stage I. The age, sex, smoking, alcohol and physical activity adjusted odds of a diabetic being hypertensive against being normotensive was 3.2 (OR 95% CI 1.6-6.1; 3.4 (OR 95% CI 1.1-10.0.) for Stage I hypertension and 6.0 (OR 95%CI 2.1-17.5) for Stage II hypertension (Table 6).
We set out in this first Kenyan urban-poor population survey to determine the prevalence of diabetes and associated behavioural and metabolic risk factors in the largest growing segment of our urban population, a population hitherto deemed to be burdened with infectious diseases and under nutrition. In this initial report we demonstrate a high age adjusted prevalence of predominantly survey detected, diabetes mellitus. The prevalence of diabetes increased with age and showed no sex differential. This is occurring in an unplanned high-density urban settlement community; of relative young age structure, with high levels of self-reported daily life related physical activity. However, these slum residents are exposed to high level of behavioural risk factors such as cigarette smoking and harmful alcohol consumption, the latter occurring in over two thirds of subjects. Close to half were either obese or overweight; a fifth had an elevated waistline and high body mass indices both of which were more frequent in females.
A higher prevalence of type 2 diabetes mellitus in deprived communities has been documented and is most striking in middle age (40–49 yr.) [6, 22, 23], which mirrors the highest age category specific prevalence in our data. The occurrence of DM in persons of low socio-economic status has been attributed to dietary patterns, low physical activity, cigarette smoking and low birth weight . Our data documents a high level of physical activity (PA) activity; moderate levels of obesity and overweight; moderate smoking; and heavy alcohol use thus suggesting that dietary factors are a significant contributor.
Screen detection rate of diabetes mellitus in communities has been shown to be a marker of availability of health care facilities , and is suggested in our sample by only 10% having ever had a blood sugar measured. High screen detection rates of 70-100% have been reported in SSA and 50% from urban South Africa [10, 26]. We report a similarly high rate, with 50% of our sample being undetected, in a population of relatively high literacy compared to the national adult literacy of 61.5%  implying that contributing factors are probably low access to primary health care facilities and low public awareness of diabetes mellitus. This raises the need for evaluation of these factors alongside preventive strategies of screening high-risk groups. Our data point to the high-risk groups of hypertension, that was significantly associated with the presence of DM, and general and central obesity. Family history and impaired glucose tolerance are other high-risk markers.
Behavioural risk factors among the diabetic subjects were similar to that of the entire sample, however persons with diabetes were three fold more likely to be have generalised obesity and hypertension. Central obesity as measured by waist circumference was not predictive of diabetes on adjusting for covariates of physical activity. This data suggests that our study population’s transition to higher cardio-metabolic risk is presently being driven predominantly by dietary factors and less so by physical inactivity and other behavioural determinants. Despite the absence of longitudinal data on the prevalence of diabetes in Kenya our documented high DM disease burden rate in slum dwellers is suggestive of a rapidly occurring reversal of the social class gradient of CVD risk factors in urban Kenya .
A further important potential consequence of a high prevalence of diabetes among the urban high density poor is the association with communicable diseases, particularly tuberculosis and therefore HIV . In other parts of the developing world diabetes has been shown to be associated with a threefold increase risk of tuberculosis  and one in five cases of new smear positive tuberculosis in India has been attributed to diabetes . The recognition of diabetes link with poverty is thus vital as it has important implications in the prevention and care in these settings.
The all age worldwide prevalence of diabetes mellitus in 2000 was estimated at 2.8% and is projected to rise to 4.4% by 2030 . In sub-Saharan Africa (SSA) type 2 diabetes mellitus is the most frequent type and although data is scarce prevalence has been shown to be rising over the last two decades with highest frequencies in urban areas. No comparative slum/urban poor data is available from Kenya. However utilising the 1998 WHO criteria, Mathenge in a 2010 survey of adults over the age of 50 years in an urban and rural parts of a cosmopolitan district in Kenya report a non-age adjusted prevalence of 6.6% . The only other contemporary data from Kenya is that of a mixed location opportunity sample in person over 18 years, using OGTT that reported an age standardised rate of 4.2% . Studies from other part of SSA report similar urban age adjusted rates of between 5.9-6.4% . These rates are consistent with our findings, which we believe to be an underestimate in the absence of oral glucose tolerance testing. The economic and social consequences of diabetes are expected to be greatest among the poor; reportedly utilising up to 25% of their income on care and predominantly affecting the breadwinners of these communities .
Strengths and limitations
To the best of our knowledge this is the first published urban slum NCD survey report from Kenya, with a stable age adjusted standardised prevalence rates, in a representative sample and utilising standard methods that allow for comparability of data across studies and regions. Relying predominantly on a fasting capillary blood sugar and exclusion of pregnant women may have contributed to a prevalence underestimate. Over representation of large families has potential to overestimate the prevalence, however we sampled 2.4 adults per household in an area with an average household size of 3.2 and thus we do not believe that family size significantly affects our results . We do not have evidence that the exclusion of one village due to insecurity would have had an effect on the estimate of prevalence.
The availability of dietary and socio-economic data would have strengthened our findings. Our physical activity data suffers from the limitation of being self-reported, even though the STEPS questionnaire has been validated in similar populations, and may represent a bi-directional measurement bias.
In conclusion we have demonstrated that this, previously unstudied urban slum community has a high prevalence of diabetes mellitus and associated risk factors signalling a lack of access to care and low public awareness in a highly literate but poor population. This sign of incomplete epidemiological transition has implications for transmission of infectious disease such as TB and consequences of increasing healthcare costs. These are the people suffering from the double burden of disease .
It is our recommendation that for this rapidly expanding segment of our population policy makers need to focus their attention on strategies that address not just communicable diseases but non-communicable diseases as well.
World Health Organization: Chronic diseases: a vital investment. 2005, Geneva: World Health Organization
The global burden of disease: a comprehensive assessment of mortality and disability from diseases, injuries, and risk factors in 1990 and projected to 2020. Edited by: Murray CJL, Lopez AD. 1996, Boston, Mass: Harvard, School of Public Health; (Global burden of disease and injuries Vol 1)
World Health Organization: World Health Report 2000: health systems: improving performance. 2000, Geneva: World Health Organization
Shaw JE, Sicree RA, Zimmet PZ: Global estimates for the prevalence of diabetes for 2010 and 2030. Diabetes Res Clin Pract. 2010, 87: 4-14. 10.1016/j.diabres.2009.10.007.
Millar WJ, Wigle DT: Socio-economic disparities in risk factors for cardiovascular diseases. CAN MED ASSOC J. 1986, 134: 127-132.
Ismail AA, Beeching GV, Gill GV, Bellis MA: Capture-recapture-adjusted prevalence rates of type 2 diabetes are related to social deprivation. Q J Med. 1999, 92: 707-710. 10.1093/qjmed/92.12.707.
Walker JJ, Walker JJ, Livingstone SJ, Colhoun HM, Lindsay RS, McKnight JA, Morris AD, Petrie JR: Effect of socioeconomic status on mortality among people with type 2 diabetes: a study from the Scottish diabetes research network epidemiology group. Diabetes Care. 2011, 34 (5): 1127-1132. 10.2337/dc10-1862.
Robbins JM, Vaccarino V, Zhang H, Kasl SV: Excess type 2 diabetes in African-American women and men aged 40–74 and socioeconomic status: evidence from the third national health and nutrition examination survey. J Epidemiol Community Health. 2000, 54 (11): 839-845. 10.1136/jech.54.11.839.
Anand K, Shah B, Yadav K, Singh R, Mathur P: Are the urban poor vulnerable to non-communicable diseases? A survey of risk factors for non-communicable diseases in urban slums of Faridabad. Natl Med J India. 2007, 28 (3): 115-120.
Mbanya NJC, Ayesha AM, Sobngwi E, Assah FK, Enoru S: Diabetes in sub-Saharan Africa. Lancet. 2010, 375: 2254-2266. 10.1016/S0140-6736(10)60550-8.
Levitt NS: Diabetes in Africa: epidemiology, management and health care challenges. Heart. 2008, 94: 1376-1382. 10.1136/hrt.2008.147306.
Christensen DI, Friss H, Mwaniki DL, Kilonzo B, Tetens I, Boit MK, Omondi B, Kaduka L, Borch-Johnsen K: Prevalence of glucose intolerance and associated risk factors in rural and urban populations of different ethnic groups in Kenya. Diabetes Res ClinPract. 2009, 84 (3): 303-310. 10.1016/j.diabres.2009.03.007.
Yusuf S, Reddy S, Ounpuu S, Anand S: Global burden of cardiovascular diseases. Part I: general considerations, the epidemiologic transition, risk factors, and impact of urbanization. Circulation. 2001, 104: 2746-2753. 10.1161/hc4601.099487.
UNFPA: UNFPA state of world population 2007: unleashing the potential of urban growth. 2007, New York: UNFPA
Kenya National Bureau of Statistics (KNBS): Kenya population and housing census. 2009, Nairobi: Kenya National Bureau of Statistics
United Nations Human Settlements Programme (UN-HABITAT): Nairobi urban sector profile. 2006, Nairobi: UN-HABITAT
Department of Economic and Social Affairs United Nations: Designing Household Survey Samples: Practical Guidelines (Series F) No.98. United Nations. Statistics Division, ed. 2008, New York: United Nations, Department of Economic and Social Affairs
World Health Organization (WHO): STEP wise approach to surveillance (STEPS). Geneva: World Health Organization, Available from: URL: http://www.who.int/chp/steps/en/ (accessed May 1 2010)
World Health Organization (WHO): Diabetes mellitus: report of a WHO study group (Technical Report Series no 727). 1985, Geneva: World Health Organization
Chobanian AV, Bakris GL, Black HR, Cushman WC, Green LA, Izzo JL: National high blood pressure education program coordinating committee. Seventh report of the joint national committee on prevention, detection, evaluation, and treatment of high blood pressure. Hypertension. 2003, 42: 1206-1252. 10.1161/01.HYP.0000107251.49515.c2.
Ahmad OB, Boschi-Pinto C, Lopez AD, Murray CJL, Lozano R, Inoue M: Age Standardisation of rates: a New WHO standard. Global program on evidence for health policy discussion paper series: No 31. 2001, Geneva: World Health Organisation
Connolly V, Unwin N, Sherriff P, Bilous R, Kelly W: Diabetes prevalence and socioeconomic status: a population based study showing increased prevalence of type II diabetes in the deprived areas. J Epidemiol Community Health. 2000, 54: 173-177. 10.1136/jech.54.3.173.
Misra A, Pandy RM, Devi JR, Shama R, Vikram N: High prevalence of diabetes, obesity and dyslipidaemia in an urban slum population in Northern India. Int J Obes. 2001, 25: 1722-1729. 10.1038/sj.ijo.0801748.
Ezeamama AE, Viali S, Tuitele J, McGarvey ST: The influence of socioeconomic factors on cardiovascular disease risk factors in the context of economic development in the Samoan archipelago. Natl Med J India. 2006, 63 (10): 2533-2545.
International Diabetes Federation (IDF): Diabetes atlas. 2006, Brussels: International Diabetes Federation, 3
Hall V, Thomsen RW, Henriksen O, Lohse N: Diabetes in Sub Saharan Africa 1999–2011: epidemiology and public health implications. A systematic review. BMC Publ Health. 2011, 11: 564-10.1186/1471-2458-11-564.
Kilele AKM: Adult literacy in Kenya, Presentation on the results of the Kenya National Adult and Literacy Survey (KNALS). 2006, http://www.knbs.or.ke/downloads/pdf/KNALS2007.pdf, Accessed on 26th February 2013
Jeon CY, Murray MB: Diabetes mellitus increases the risk of active tuberculosis: a systematic review of 13 observational studies. PLoS Med. 2008, 5 (7): e152-10.1371/journal.pmed.0050152.
Stevenson CR, Forouhi NG, Roglic G, Williams BG, Lauer JA, Dye C, Unwin N: Diabetes and tuberculosis: the impact of the diabetes epidemic on tuberculosis incidence. BMC Publ Health. 2007, 7: 234-10.1186/1471-2458-7-234.
Wild S, Richard S, Gojka A, Aders G, Hilary K: Global prevalence of diabetes. Estimates for the year 2000 and projections for 2030. Diabetes Care. 2004, 27: 1047-1053. 10.2337/diacare.27.5.1047.
Mathenge W, Foster A, Kuper H: Urbanization, ethnicity and cardiovascular risk in a population in transit in Nakuru, Kenya: a population based survey. BMC Publ Health. 2010, 10: 569-581. 10.1186/1471-2458-10-569.
Sobhana R, Rao PR, Lavanya A, Williams R, Vijay V, Ramchandran A: Expenditure on health care incurred by diabetic subjects in a developing country-a study from southern India. Diabetes Res ClinPract. 2000, 48: 37-42. 10.1016/S0168-8227(99)00130-8.
Desgroppes A, Sophie T: Kibera: The Biggest Slum in Africa?. Les Cahiers de l'Afrique de l'Est. 2011, 44: 23-34.
Agyei-Mensah S, Aikins ADG: Epidemiological transition and the double burden of disease in Accra, Ghana. J Urban Health. 2010, 87: 879-897. 10.1007/s11524-010-9492-y.
The pre-publication history for this paper can be accessed here:http://www.biomedcentral.com/1471-2458/13/371/prepub
The authors declare that they have no competing interests.
RA conceived of the study, participated in the design, performed the statistical analysis and drafted the manuscript. MJ conceived the study, participated in the design of the study, performed the statistical analysis and drafted the manuscript. RW conceived of the study, participated in the design performed the statistical analysis and helped draft the manuscript. EKN participated in the design of the study and performed the statistical analysis. CFO participated in the design and helped draft the manuscript. KKM performed the statistical analysis and helped draft the manuscript. All authors read and approved the final manuscript.