WN3(G2)

RESEARCH ARTICLE Open Access

Urbanization, ethnicity and cardiovascular risk in a population in transition in Nakuru, Kenya: a population-based survey Wanjiku Mathenge, Allen Foster, Hannah Kuper*

Abstract

Background: Cardiovascular disease (CVD) is the leading cause of death among older people in Africa. This study aimed to investigate the relationship of urbanization and ethnicity with CVD risk markers in Kenya.

Methods: A cross-sectional population-based survey was carried out in Nakuru Kenya in 2007-2008. 100 clusters of 50 people aged ≥50 years were selected by probability proportionate to size sampling. Households within clusters were selected through compact segment sampling. Participants were interviewed by nurses to collect socio- demographic and lifestyle information. Nurses measured blood pressure, height, weight and waist and hip circumference. A random finger-prick blood sample was taken to measure glucose and cholesterol levels. Hypertension was defined as systolic blood pressure (SBP) ≥140 mm Hg, or diastolic blood pressure (DBP) ≥90 mm Hg or current use of antihypertensive medication; Diabetes as reported current medication or diet control for diabetes or random blood glucose level ≥11.1 mmol/L; High cholesterol as random blood cholesterol level ≥5.2 mmol/L; and Obesity as Body Mass Index (BMI)≥30 kg/m2.

Results: 5010 eligible subjects were selected, of whom 4396 (88%) were examined. There was a high prevalence of hypertension (50.1%, 47.5-52.6%), obesity (13.0%, 11.7-14.5%), diabetes (6.6%, 5.6-7.7%) and high cholesterol (21.1%, 18.6-23.9). Hypertension, diabetes and obesity were more common in urban compared to rural groups and the elevated prevalence generally persisted after adjustment for socio-demographic, lifestyle, obesity and cardiovascular risk markers. There was also a higher prevalence of hypertension, obesity, diabetes and high cholesterol among Kikuyus compared to Kalenjins, even after multivariate adjustment. CVD risk markers were clustered both across the district and within individuals. Few people received treatment for hypertension (15%), while the majority of cases with diabetes received treatment (68%).

Conclusions: CVD risk markers are common in Kenya, particularly in urban areas. Exploring differences in CVD risk markers between ethnic groups may help to elucidate the epidemiology of these conditions.

Background Infectious diseases are still the principal cause of death in Africa [1]. However, among older people coronary heart disease (CHD) and stroke are emerging as the leading cause, responsible for more than a quarter of deaths in people 60 years and over in Africa [2,3]. This represents a dramatic shift as CHD was virtually unknown in Africa until recently [4,5]. Stroke is particu- larly common in Africa in comparison to CHD [6], and

stroke mortality rates and prevalence of disabling stroke in most African countries are comparable to levels seen in high-income countries [7-9]. African countries are therefore experiencing a shift in the epidemiological transition [10], while retaining a high burden of infec- tious diseases. The rise in cardiovascular disease (CVD) is linked to the

increase in hypertension, diabetes, obesity, and high cho- lesterol observed in Africa in recent years. Obesity [11-14] and hypertension [11,15-17] are now common throughout Africa, particularly in urban areas [11,12,14,16,17]. The number of people with diabetes in Sub-Saharan Africa is expected to more than double between 2000 and 2030

* Correspondence: [email protected] London School of Hygiene & Tropical Medicine, Keppel Street, London WC1E 7HT, UK

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© 2010 Mathenge et al; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

[18], and diabetes is particularly common in urban areas [19]. Urbanisation is therefore a key feature in the rise of CVD. Currently 40% of Africans live in urban areas [20], and it is estimated that by 2030 half of Africans will live in urban areas. The Kenyan Luo Migration Study ele- gantly demonstrated the impact of rural-urban migration on CVD risk in Africa [21]. Rural migrants to Nairobi experienced a rise in systolic and diastolic blood pressure after only one month of migration. In contrast, the effect of rural-urban migration on blood pressure was not observed in a recent study in Tanzania, even though phy- sical activity levels fell and weight increased after migra- tion [22]. The discrepancy between the two studies may be because the rural-urban contrast in sodium intake was smaller in the Tanzanian compared to the Kenyan study (personal communication). The impact of rural- urban migration on health may therefore vary depending on setting. People of African origin may be particularly vulnerable

to hypertension. The prevalence of hypertension is high among people of African origin compared to Whites independent of BMI [23-25], and there is a younger age at onset in Africa [15] and among people of African des- cent [26]. Although the same risk markers are largely responsible for myocardial infarction (MI) across the globe [27], hypertension was associated with a higher MI risk in the Black African group than in the overall INTERHEART group [28]. There may also be greater difficulty in achieving control [26], and more aggressive presentation [26,29] and progress [28] of hypertension among people of African origin. This vulnerability may be due to lifestyle factors, but may also be influenced by ethnicity which varies widely within Africa and is linked to substantial heterogeneity in body composition [14], which may exert important metabolic effects [30]. The aim of this study was to investigate the relation-

ship of urbanization and ethnicity with the prevalence of obesity, hypertension, diabetes and high cholesterol in a study of elderly people in Nakuru district, Kenya.

Methods Settings and population Nakuru district has a population of 1.2 million, one third of which is urban. Nakuru is broadly representa- tive of Kenya in terms of ethnic diversity and economic activities. The two dominant ethnic groups are Kikuyus and Kalenjins. The Kikuyu are related to other Bantu- speaking peoples of East Africa while Kalenjins are of Nilotic origin. During Jan 2007- Dec 2008, a sample of 100 clusters

of 50 people aged ≥50 years were selected across Nakuru district through probability proportionate to size sampling, using the electoral role as the sampling frame. Clusters were classed as “rural” or “urban” using

the classification of the district statistical office. House- holds were selected within clusters through modified compact segment sampling [31]. The village leaders pro- duced a sketch map of the polling area. The polling area was divided into segments each including approximately 50 people aged ≥ 50 years. One segment was chosen at random by drawing lots and all households in the seg- ment were included in the sample sequentially, until 50 people aged ≥50 years were identified. If the segment did not include 50 people aged ≥50 years then another segment was chosen at random and sampling continued. The enumeration team visited households, assisted by

a village guide, and invited all eligible participants aged ≥50 years to the examination clinic which would be held at a convenient place in the cluster over the subse- quent two days. Eligible participants were defined as those aged ≥50 years resident in the cluster (i.e. living there at least 6 months per year) who had slept in the house either the night before or were planning on sleep- ing in the house that night. If an eligible person was absent then the survey team revisited the household at least two times.

Examination clinic Interviews Participants were interviewed by trained nurses. Infor- mation was collected on demographic data, education and assets (building materials of the house - type of walls, roof, floor and toilet; ownership of household assets - radio, TV, fridge, phone, cupboard, sofa set, sewing machine, table, bicycle and vehicle; animal own- ership - cows, sheep/goats). People were asked whether their mother tongue was “Kikuyu”, “Kalenjin” or other. For simplicity, people will be classified as “Kikuyu” and “Kalenjin” in the text. Information was also collected on health behaviour (smoking, alcohol use) and health sta- tus (diagnosis of diabetes or hypertension, family history and their treatment). Physical examination A nurse recorded the blood pressure of participants three times on the right arm of the participant, at least five minutes apart after an initial period of five minutes of rest using the Omron digital automatic monitor (model HEM907). A medium cuff size was used (to fit arms 22 to 32 cm). The average of the last two readings was used as the measures of systolic and diastolic blood pressure (to nearest 1 mm Hg). A random finger-prick blood sample was taken to measure glucose (Accutrend GC system) and cholesterol levels (Accutrend GC sys- tem). The technical data from the company asserts that precision is <3% for glucose and <5% for cholesterol [32]. Weight was measured to the nearest kg using stan- dard scales to the nearest 0.1 kg (Seca 761 scales) after the participant had removed all heavy clothing and

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shoes. Height was measured to the nearest cm while the participant stood without shoes using a standardized stadiometer (Leicester Height Measure). For weight and height the average of two readings was recorded. Waist and hip circumferences were measured with a tape. The waist circumference was measured at umbilicus level in mid-expiration to the nearest 0.1 cm. The hip circum- ference was measured at the point of largest gluteal cir- cumference to the nearest 0.1 cm.

Training The examination team received three weeks of training. Inter observer variation (IOV) was assessed during the training weeks at the Nakuru Provincial General Hospi- tal. IOV on anthropometric variables were done by repeat measuring of 50 subjects by the two nurses. The level of agreement required was to the nearest 1 cm for circumferences and height, and to the nearest 0.5 kg for weight. The staff were retrained or replaced if IOV scores indicated poor comparability (kappa < 0.5).

Statistical analysis Statistical analyses were undertaken using the SAS sta- tistical package (version 9.2). The four CVD risk mar- kers considered were hypertension, diabetes, high cholesterol and obesity. Hypertension was defined as systolic blood pressure (SBP) was ≥140 mm Hg, or dia- stolic blood pressure (DBP) ≥90 mm Hg or current use of antihypertensive medication [33]. Diabetes was defined as reported current medication (tablets or insu- lin) or diet control for diabetes or random blood glucose level ≥11.1 mmol/L [34]. People were categorized as having high cholesterol if their random blood choles- terol level was ≥5.2 mmol/L [35]. Obesity was defined as Body Mass Index (BMI)≥30 kg/m2 [36]. Prevalence esti- mates for the four CVD risk markers were calculated taking account of the design effect (DEFF) in estimating the confidence intervals. The DEFF was not taken into account for other analyses. A relative index of socio- economic status (SES) was calculated based on building materials of the house, ownership of ten household assets and education status using principal components analysis [37]. The derived index was divided into quar- tiles from poorest to least poor. We assessed the association between rural-urban sta-

tus and, in turn, hypertension, diabetes, high cholesterol and obesity through logistic regression models. The models were adjusted in turn for, a) for age (50-59, 60- 69, 70-79, ≥80) and sex, b) age, sex and socio-demo- graphic factors (SES score in quartiles), c) age, sex, BMI (<20, 20-25, >25-30, ≥30), waist hip ratio (WHR - in quartiles), smoking (current, former, never) and alcohol (current, former, never), d) age, sex, diabetes and high cholesterol (as appropriate) and e) fully adjusted model.

These models were repeated assessing the association between the four CVD markers and Kikuyu or Kalenjin ethnicity, adjusting for urban status in models b and e. We included an interaction factor in the logistic regres- sion models for ethnicity to assess whether there was an interaction between ethnicity and urban status in the relationship with CVD risk markers. We assessed the proportion of people receiving medical treatment among people who were defined as “hypertensive”, and attempted to identify predictors of treatment status through logistic regression models. This was repeated for people with diabetes. We assessed whether the CVD risk markers were clus-

tered geographically by calculating the DEFF for each of the variables. We assessed whether there was clustering of the CVD risk markers within individuals. To do this, we derived expected frequencies of co-occurrence of risk markers (hypertension, diabetes, high cholesterol and obesity) from none through to four risk markers by combining probabilities, assuming a binomial distribu- tion and independence between them [38]. We esti- mated observed to expected ratios for all participants and then separately for urban and rural groups and for Kikuyu and Kalenjin groups. We considered that there was clustering if the observed:expected ratios were high for no risk markers, low for one risk marker and high for three or more risk markers. We calculated chi- square statistics with 3 degrees of freedom to test the significance of the overall distribution of expected and observed counts within each group.

Ethical approval Ethical approval for this work was granted by the Lon- don School of Hygiene & Tropical Medicine and The Kenya Medical Research Institute Ethical Committee and Nakuru District Health Management Team. Infor- med consent was obtained from the subjects. All people with other treatable conditions were referred for appro- priate treatment.

Results We examined 4,396 (88%) of the 5,010 people invited. Among those examined 1,437 (33%) lived in urban and 2,959 (67%) in rural areas (Table 1). Urban dwellers were younger, and had higher education levels and asset scores than rural dwellers. They were also more likely to be smokers and obese, than rural participants. Kikuyus made up 63% of the sample and Kalenjins 23% while the remaining 15% consisted of other language speakers. Kikuyus were more likely than Kalenjins to live in urban areas or to be female, and they had higher levels of edu- cation and higher SES scores. Kikuyus were less likely to be current smokers or consumers of alcohol. Kikuyus were significantly shorter yet heavier than Kalenjins

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among both men (166 cm vs 168 cm, 64 kg vs 60 kg) and women (156 cm vs 158 cm, 62 kg vs 58 kg) and consequently had higher BMIs. There was a high prevalence of hypertension (50.1%,

47.5-52.6%), obesity (13.0%, 11.7-14.5%), diabetes (6.6%, 5.6-7.7%) and high cholesterol (21.1%, 18.6-23.9%). Mean SBP and DBP were higher in urban than rural areas among both men and women (Table 2). Similarly, mean glucose and cholesterol levels and markers of obesity were higher in urban than rural men and this pat- tern was generally repeated among women. The preva- lence of obesity generally fell sharply with age (Figure 1). The prevalence of hypertension increased steadily with age, and was consistently higher in urban than rural areas

(Figure 2). The association between prevalence and age were less clear for the diabetes (Figure 3) and high cho- lesterol (data not shown). Meanwhile, SBP increased with age while DBP decreased, and both remained higher for people from urban than rural areas across the age groups (Figure 4). Kikuyus had higher SBP than Kalenjins, but the differences were less clear for DBP (Table 3). Kikuyus also had higher levels of glucose, cholesterol, BMI and waist circumference but not of WHR. Kikuyus were more likely to be hypertensive, obese or diabetic in all age groups (Figure 5, 6, 7, 8). The odds of hypertension was higher in urban than

rural groups after adjustment for age and sex (Odds ratio - OR = 1.7, 95% CI = 1.5-1.9) (Table 4). The

Table 1 Demographic characteristics and health behavior comparing urban and rural participants, and Kikuyus and Kalenjins

Urban (n = 1437)

Rural (n = 2959)

Age and sex adjusted OR (95% CI)

Kikuyu (n = 2760)

Kalenjin (n = 1015)

Age and sex adjusted OR (95% CI)

Age

50-59 57% 38% Baseline 40% 40% Baseline

60-69 26% 31% 0.6 (0.5-0.7) 32% 28% 1.2 (1.0-1.4)

70-79 11% 20% 0.4 (0.3-0.5) 17% 20% 0.9 (0.7-1.1)

≥80 6% 11% 0.4 (0.3-0.5) 10% 11% 0.9 (0.7-1.2)

Sex

Men 49% 47% Baseline 44% 53% Baseline

Women 51% 53% 0.9 (0.8-1.0) 56% 47% 1.4 (1.2-1.6)

Language

Kikuyu 65% 62% Baseline

Kalenjin 6% 31% 0.2 (0.1-0.2)

Other 29% 7% 3.3 (2.7-4.0)

Urban 931 (34%) 84 (8%) Baseline

Rural 1812 (66%) 924 (92%) 5.7 (4.5-7.2)

Education

Any 79% 61% 1.4 (1.3-1.6) 71% 51% 1.9 (1.7-2.1)

None 21% 39% Baseline 29% 49% Baseline

SES score

1 (poorest) 8% 33% Baseline 20% 44% Baseline

2 14% 31% 1.6 (1.2-2.0) 26% 27% 2.4 (2.0-2.9)

3 24% 26% 3.1 (2.5-3.9) 27% 20% 3.4 (2.8-4.2)

4 (richest) 54% 11% 16.7 (13.3-21.1) 26% 9% 7.8 (6.0-10.2)

Smoking

Never 70% 70% Baseline 69% 73% Baseline

Current 7% 8% 1.4 (1.1-1.8) 8% 6% 0.5 (0.3-0.6)

Former 23% 22% 1.1 (0.9-1.3) 24% 20% 2.0 (1.6-2.5)

Alcohol

Never 41% 38% Baseline 45% 23% Baseline

Former 41% 46% 0.9 (0.7-1.0) 44% 49% 0.4 (0.3-0.5)

Current 18% 17% 0.9 (0.7-1.1) 11% 28% 0.2 (0.1-0.2)

BMI cat

Underweight 8% 17% 0.7 (0.5-0.9) 12% 21% 0.7 (0.6-0.9)

Normal 41% 54% Baseline 48% 56% Baseline

Overweight 30% 19% 2.1 (1.7-2.4) 25% 15% 1.9 (1.5-2.3)

Obese 20% 10% 2.8 (2.3-3.5) 14% 8% 2.0 (1.5-2.6)

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association was weakened after adjustment for SES mar- kers and for obesity, smoking and alcohol, but not after adjustment for diabetes and cholesterol. In the fully adjusted model the odds of hypertension remained higher among urban compared to rural dwellers (1.3, 1.1-1.5). People in urban areas were also more likely to have diabetes (2.3, 1.8-2.9). The increased odds was reduced after adjustment for socio-demographic vari- ables, though adjustment for other CVD risk markers had less effect. In the fully adjusted model the odds of diabetes was no longer elevated in urban compared to rural participants (1.3, 0.9-1.7). No clear association was apparent between the odds of high cholesterol and urban status. The pattern for the association between obesity and urban residence was similar to that for diabetes. The odds of hypertension was higher among Kikuyus

compared to Kalenjins (1.6, 1.4-1.8) (Table 5). The asso- ciation persisted after adjustment for socio-demographic variables and other CVD risk markers (1.4, 1.2-1.7). Similarly, Kikuyus were more likely to have diabetes and

high cholesterol, and these associations were not fully explained by adjustment for potential confounders. Kikuyus were more likely to be obese compared to Kalenjin, but not in models adjustment for SES and urban status. For all four risk markers, the biggest change in the association occurred after adjustment for SES and urban status. There was no interaction between urban status and

ethnicity for these conditions after adjustment for age and sex (data not shown). There was substantial variation in the prevalence of

hypertension (range 17-77% (DEFF = 2.9) diabetes (range = 0-26%; DEFF = 1.8), high cholesterol (range 0-51.2%, DEFF = 4.3) and obesity (range 0-40%, DEFF = 1.8) between clusters. The variation was similar in rural and urban areas and among Kikuyus and Kalenjins. Few people had 3-4 (4.5%) or 2 (18.5%) risk markers

and the vast majority of the population had no (37.0%) or one (40.1%) risk marker (Table 6). Generally rural dwellers and Kalenjins had fewer risk factors than urban dwellers and Kikuyus. There was a greater than expected

Table 2 Means (and standard error) of cardiovascular risk markers, by gender and urban-rural status

Urban-rural comparison

Men Women

No. Urban/Rural Urban Rural Age adjusted p-value No. Urban/Rural Urban Rural Age adjusted p-value

Mean SBP 705/1395 143 (24) 140 (24) <0.0001 726/1550 143 (26) 140 (25) <0.0001

Mean DBP 705/1395 84 (14) 81 (13) <0.0001 726/1550 86 (14) 83 (13) <0.0001

Glucose 692/1373 5.8 (2.9) 4.8 (2.0) <0.0001 704/1527 5.7 (2.8) 5.1 (2.3) <0.0001

Cholesterol 698/1342 4.5 (0.9) 4.3 (0.9) 0.0004 718/1514 4.8 (0.9) 4.7 (1.0) 0.08

BMI 700/1385 24 (6) 22 (4) <0.0001 718/1543 27 (6) 24 (6) <0.0001

Waist 703/1390 92 (13) 86 (11) <0.0001 719/1546 96 (13) 89 (13) <0.0001

WHR 703/1390 0.92 (0.07) 0.92 (0.06) 0.0009 718/1546 0.89 (0.06) 0.89 (0.08) 0.34

Figure 1 Age trends in obesity by urban status.

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frequency of people with 3-4 risk markers than would be expected by chance, and to a lesser extent a higher fre- quency of people with no risk markers, except among Kalenijn. Together, this provides evidence for clustering of risk markers within individuals, which was more apparent among rural versus urban dwellers, and among Kalenjins compared to Kikuyus. Among people with hypertension, only 323 (15%)

received drug treatment, 38 (2%) received diet treatment and 7 traditional medicine treatment (0.3%) (Table 7). Among those on drug treatment, only 98 (29%) had controlled hypertension. A far higher proportion of peo- ple with diabetes were receiving treatment (68%), which included insulin (n = 32), tablets (n = 143) and/or diet

control (n = 43). A further 10 received traditional treat- ment. For both hypertension and diabetes, treatment was more among people living in urban areas, women, older people and those with a higher SES score. Kalen- jins were less likely to receive treatment for hyperten- sion than Kikuyus (0.5, 0.3-0.8), but there was no difference for diabetes.

Sensitivity analysis We assessed the impact of lowering the threshold ran- dom blood glucose level for the classification of “dia- betes”. Lowering the threshold to ≥10 mmol/L added an additional 13 cases to the original 283 cases (revised prevalence 6.9%), for ≥9 mmol/L this was 41 cases

Figure 2 Age trends in hypertension by urban status.

Figure 3 Age trends in diabetes by urban status.

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Figure 4 Age trends in systolic and diastolic blood pressure by urban status.

Table 3 Means (and standard error) of cardiovascular risk markers, by gender and ethnicity

Kikuyu-Kalenjin comparison

Men Women

No. Kikuyu/Kalenjin Kikuyu Kalenjin Age adjusted p-value

No. Kikuyu/Kalenjin Kikuyu Kalenjin Age adjusted p-value

Mean SBP 1218/535 142 (24) 139 (23) 0.007 1531/473 142 (26) 136 (24) <0.0001

Mean DBP 1218/535 81 (13) 81 (13) 0.85 1531/473 84 (14) 82 (13) 0.02

Glucose 1209/515 5.5 (2.6) 4.4 (1.7) <0.0001 1516/449 5.5 (2.6) 4.9 (2.0) <0.0001

Cholesterol 1177/524 4.4 (1.0) 4.2 (0.7) <0.0001 1496/466 4.8 (1.0) 4.5 (1.0) <0.0001

BMI 1207/534 23 (5) 21 (4) <0.0001 1519/469 26 (6) 23 (5) <0.0001

Waist 1213/535 89 (12) 86 (11) 0.002 1522/470 91 (14) 88 (13) <0.0001

WHR 1213/535 0.92 (0.07) 0.92 (0.06) 0.24 1521/470 0.88(0.08) 0.90 (0.06) <0.0001

Figure 5 Age trends in obesity by ethnicity.

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(7.5%), for ≥8 mmol/L this was 84 cases (8.5%) and for ≥7 mmol/L this was 180 cases (10.8%). At the least con- servative threshold for diabetes (≥7 mmol/L) almost half of cases (42%) were receiving treatment.

Discussion This large survey in Kenya highlighted the high preva- lence of CVD risk markers, particularly in urban areas. SES was a more important mediator of the association between the individual CVD risk markers and urban sta- tus than health behavior or other CVD markers. How- ever, the urban-rural differences in hypertension and obesity were not explained fully after adjustment for SES, obesity, smoking, alcohol or other CVD risk

markers. The prevalence of CVD risk markers was higher among Kikuyus than among Kalenjins. Again, these associations were not fully explained after adjust- ment for the possible confounders, including urban sta- tus. A high degree of clustering of these risk markers was apparent, both geographically and within indivi- duals. The clustering within individuals was more marked among rural dwellers and Kalenjins, although they had fewer people with risk factors overall, poten- tially indicating that there were a few early adopters of these multiple risk factors compared to the more well established presence among urban dwellers and Kikuyus. Only 15% of people with hypertensive were receiving treatment, and this was particularly low among poorer

Figure 6 Age trends in hypertension by ethnicity.

Figure 7 Age trends in diabetes by ethnicity.

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Figure 8 Age trends in systolic and diastolic blood pressure by ethnicity.

Table 4 Rural-urban differences in the prevalence of CVD risk markers

Urban (n = 1431)

Rural (n = 2945)

Age and sex adjusted OR (95% CI)

Age, sex and socio- demographic adjusted OR (SES score)

Age, sex, BMI, WHR, smoking, alcohol

Age, sex, diabetes, cholesterol, hypertension

Fully adjusted model

Hypertension 812 (57%) 1379 (47%) 1.7 (1.5-1.9) 1.4 (1.2-1.6) 1.4 (1.2-1.6) 1.6 (1.4-1.8) 1.3 (1.1-1.5)

Normotensive 619 (43%) 1566 (53%) Baseline Baseline Baseline Baseline Baseline

Diabetic 139 (10.0%) 144 (5.0%) 2.3 (1.8-2.9) 1.3 (1.0-1.7) 2.0 (1.5-2.5) 2.2 (1.7-2.8) 1.3 (0.9-1.7)

Normal 1256 (90.0%) 2756 (95.0%) Baseline Baseline Baseline Baseline Baseline

High cholesterol

316 (22%) 588 (21%) 1.2 (1.0-1.4) 0.9 (0.8-1.1) 1.0 (0.9-1.2) 1.1 (0.9-1.3) 0.9 (0.7-1.1)

Normal cholesterol

1100 (78%) 2268 (79%) Baseline Baseline Baseline Baseline Baseline

Obese 287 (20%) 280 (10%) 2.3 (1.9-2.8) 1.3 (1.0-1.6) 2.9 (2.3-3.5) 2.2 (1.8-2.7) 1.5 (1.2-1.9)

Not obese 1131 (80%) 2648 (90%) Baseline Baseline Baseline Baseline Baseline

Table 5 Kikuyu-Kalenjin differences in the prevalence of CVD risk markers

Kikuyus Kalenjins Age and sex adjusted OR (95% CI)

Age, sex and socio- demographic adjusted OR (SES score and urban)

Age, sex, BMI, WHR, smoking, alcohol

Age, sex, diabetes, cholesterol, hypertension

Fully adjusted model

Hypertension 1445 (53%) 420 (42%) 1.6 (1.4-1.8) 1.3 (1.1-1.5) 1.5 (1.3-1.8) 1.5 (1.3-1.7) 1.4 (1.2-1.7)

Normotensive 1304 (47%) 588 (58%) Baseline Baseline Baseline Baseline Baseline

Diabetic 219 (8%) 24 (2%) 3.4 (2.2-5.3) 2.2 (1.4-3.4) 3.4 (2.2-5.4) 3.3 (2.2-5.2) 2.3 (1.5-3.8)

Normal 2506 (92%) 940 (98%) Baseline Baseline Baseline Baseline Baseline

High cholesterol

648 (24%) 142 (14%) 1.8 (1.5-2.2) 1.6 (1.3-2.0) 1.6 (1.3-2.0) 1.7 (1.4-2.1) 1.5 (1.2-1.9)

Normal cholesterol

2025 (76%) 848 (86%) Baseline Baseline Baseline Baseline Baseline

Obese 392 (14%) 80 (8%) 1.8 (1.4-2.3) 1.0 (0.8-1.3) 2.0 (1.5-2.7) 1.7 (1.3-2.2) 1.0 (0.7-1.4)

Not obese 2334 (86%) 923 (92%) Baseline Baseline Baseline Baseline Baseline

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people or rural dwellers. In contrast, more than two thirds of people with diabetes were receiving treatment, although this proportion fell if a lower blood glucose threshold was used for diagnosis of diabetes. The urban-rural differences in CVD risk markers are

likely to be explained by differences in health behavior, including diet and physical activity. These urban-rural differences in lifestyle may in turn explain some of the differences in CVD risk markers between Kikuyus and Kalenjins. However, Kikuyu and Kalenjin participants

clearly differed in physical characteristics, such as height, weight and waist and hip circumferences, and these may exert metabolic consequences [30], and explain some of the differences in CVD risk markers. The prevalence of hypertension in men in our survey

was generally high compared to other world regions, and exceeded the prevalence in the Established Market Economies and Latin American Countries for the oldest age group [15]. For women in our survey the pattern was more typical to that seen in the sub-Saharan Africa

Table 6 Clustering of risk markers by rural-urban status and ethnicity

No. of risk markers* Total group Urban Rural Kikuyu Kalenjin

Exp** Obs O:E Exp** Obs O:E Exp** Obs O:E Exp** Obs O:E Exp** Obs O:E

0 32.0 37.0 116 24.1 30.0 124 36.3 40.4 111 28.3 33.1 117 44.8 47.0 105

1 47.7 40.1 84 47.4 38.6 81 47.1 40.9 87 47.6 40.3 85 44.6 40.8 92

2 17.9 18.5 103 23.8 24.9 104 15.0 15.3 102 20.8 21.3 102 9.9 10.7 108

3-4 2.3 4.5 193 4.4 6.5 148 1.5 3.5 228 3.2 5.2 165 0.7 1.6 233

Χ 2 (3 df) 71.6 P < 0.0001 27.0 p < 0.0001 39.3 p < 0.0001 40.3 p < 0.0001 6.2 p = 0.10

*Hypertension, obesity, diabetes and high cholesterol.

**Based on random assortment of four risk marker.

Table 7 Treatment for hypertension and diabetes

Hypertension Diabetes

Drug treatment *

No treatment

% untreated

Multivariate adjusted OR (95% CI)***

Treatment** No treatment

% untreated

Multivariate adjusted OR (95% CI) ***

Number 323 1868 196 91

Rural 191 621 76% Baseline 86 59 41% Baseline

Urban 132 1247 90% 1.6 (1.2-2.1) 110 32 23% 2.0 (1.1-3.8)

Language

Kikuyu 232 1213 84% Baseline 152 69 31% Baseline

Kalenjin 20 400 95% 0.5 (0.3-0.8) 15 10 40% 0.9 (0.3-2.1)

Other 72 255 78% 1.3 (0.9-1.8) 29 12 29% 0.9 (0.4-2.0)

Age

50-59 128 726 85% Baseline 67 40 37% Baseline

60-69 107 561 84% 1.7 (1.2-2.3) 74 29 28% 1.9 (1.0-3.5)

70-79 57 357 86% 2.0 (1.4-3.0) 34 12 26% 3.3 (1.4-7.9)

≥80 31 224 88% 2.0 (1.2-3.3) 21 10 32% 2.6 (0.9-7.3)

Male 93 923 91% Baseline 92 47 34% Baseline

Female 230 945 80% 3.4 (2.5-4.5) 104 44 30% 1.8 (1.0-3.1)

SES score

1 (poorest)

23 456 95% Baseline 14 11 44% Baseline

2 39 456 92% 1.4 (0.8-2.3) 19 22 54% 0.7 (0.3-2.1)

3 87 493 85% 2.7 (1.7-4.4) 60 24 29% 2.3 (0.9-5.9)

4 (richest) 169 456 73% 4.7 (2.9-7.7) 102 33 24% 2.2 (0.8-5.8)

Schooling

Any 249 1217 83% 1.3 (1.1-1.5) 41 24 37% 1.2 (0.8-1.8)

None 74 651 90% Baseline 155 67 30% Baseline

*Treatment for hypertension included drug treatment only.

**Treatment for diabetes included insulin, tablets or diet control.

*** Adjusted for age, sex, urban/rural, ethnic group, SES score, schooling.

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region. Other studies from Africa confirm the higher pre- valence of hypertension, diabetes and obesity in urban compared to rural populations [11,12,14,16,17,19,39]. Previous surveys confirm our finding of a higher preva- lence of obesity among women compared to men in Africa [12-14], and suggest that BMI differences between rural and urban areas drive differences in diabetes preva- lence [39]. Few people with hypertension were currently treated in the Nakuru survey, which is consistent with the findings from other African settings [7,17,40], while the proportion of people with diabetics receiving treat- ment was higher than in other surveys [39]. The high prevalence of CVD markers in Nakuru, par-

ticularly hypertension, is likely to be of substantial pub- lic health importance, as untreated hypertension is an important modifiable risk factor for stroke in Africa [7,41]. This is compounded by the low proportion of hypertensives receiving treatment in this population, although the situation was better for diabetes. The rise of non communicable diseases are likely to put further pressure on an already overstretch primary health care system [9], and so prevention is important potentially through reducing obesity and salt intake [23,42,43]. Further studies investigating means of reducing hyper- tension in Africa are needed urgently [44]. There were a number of limitations to this study. The

study design was a cross-sectional survey, and so we could not take account of the temporal relationship between potential risk markers and outcomes. We mea- sured blood pressure on only one day and so regression to the mean was possible, and one cuff size was used for all participants. The blood glucose measures were obtained from non-fasting blood samples, rather than through use of the oral glucose tolerance test or fasting blood glucose, and this may have underestimated the prevalence of diabetes. Lowering the threshold blood glucose level cut-off for the definition of diabetes increased the prevalence, however, this was not substan- tial until the level was reduced to >7 mmol/L. We mea- sured smoking and alcohol status, but we did not assess physical activity or diet or other blood markers (e.g. renin), which were potentially important explanatory variables. Although we measured BMI and WHR, it would have been useful to include bioimpedance as a measure of body fat. Classifying people as “Kikuyu” or “Kalenjin” on the basis of their mother tongue may have been over-simplistic. This study also had important strengths. There was a high response rate, and the sam- ple was representative across Nakuru, limiting the impact of selection bias. We included a measure of SES which was previously validated for this area [37]. The measures of blood pressure and anthropometry were assessed by trained medical staff.

Conclusions The burden of CVD risk markers is high in Kenya, par- ticularly in urban areas. Exploring differences in CVD risk markers between ethnic groups may help us to elu- cidate the epidemiology of these conditions in Africa.

Abbreviations BMI: Body Mass Index; CHD: Coronary heart disease (CHD); CVD: Cardiovascular disease; DBP: Diastolic blood pressure; DEFF: Design effect; IOV: Inter observer variation; MI: Myocardial infarction; SBP: Systolic blood pressure; SES: Socio-economic status; WHR: Waist hip ratio.

Acknowledgements This study was funded by a grant from the British Council for Prevention of Blindness and through support from the Fred Hollows Foundation.

Authors’ contributions WM was responsible for carrying out the fieldwork and the cleaning and preparation of the database. HK and AF assisted with the design and supervision of the fieldwork. HK was primarily responsible for the data analysis and producing the first draft of the paper. All authors read and approved the final manuscript.

Competing interests The authors declare that they have no competing interests.

Received: 17 May 2010 Accepted: 22 September 2010 Published: 22 September 2010

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doi:10.1186/1471-2458-10-569 Cite this article as: Mathenge et al.: Urbanization, ethnicity and cardiovascular risk in a population in transition in Nakuru, Kenya: a population-based survey. BMC Public Health 2010 10:569.

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