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Rural and urban differences in diabetes prevalence in Tanzania: the role of obesity, physical inactivity and urban living
TRANSACTIONSOFTHEROYALSOCIETYOFTROPICALMEDICINEANDHYGIENE(2000)94,637-644
Rural and urban differences in diabetes physical inactivity and urban living
prevalence
in Tanzania:
the role of obesity,
Terence J. Aspray I*, Ferdinand Mugusi2, Seif Rashid’, David Whitin&, Richard Edwards3, Disease Health Intervention K. George Albert? and Nigel C. Unwin 1*3for the Essential Non-Communicable Project Departments of ‘Medicine and 3Epidemiology and Public Health, The Medical School, Giversi of Newcastle upon Tme, NE2 4HH, UK; ‘Adult Morbidity and Mortality Project and Muhimbili Medical Centre, I? 0. Box 65243, Dar es &la&n, Tanzania Abstract A population-based survey in 1996 and 1997 of 770 adults (aged 3 15 years) from an urban district of Dar es Salaam and 928 from a village in rural Kilimanjaro district (Tanzania) revealed that the prevalence of diabetes, impaired fasting glucose (IFG), overweight, obesity, and physical inactivity was higher in the urban area for men and women. The difference between urban and rural prevalence of diabetes was 3.8 [ l.l-6.51% for men and 2.9 [0.8-4.91% for women. For IFG, the difference was 2.8 [0.3-5.31% for men and 3.9 [ 1.4-6.41% for women; for overweight and obesity, the difference was 21.5 [ 15.8-27. l] % and 6.2 [3~5-8~9]%formenand17~4[11~5-23~3]%and12~7[8~5-16~8]%forwomen,respectively.Thedifference in prevalence of physical inactivity was 12.5 [7.0- 18.31% for men and 37.6 [3 1.9-43.31% for women. For men with diabetes, the odds for being overweight, obese and having a large waist:hip ratio were 14.1,5*3 and 12.5, respectively; for women the corresponding values were 9*0,10.5 and 2.4 (the last not significant) with an attributable fraction for overweight between 64% and 69%. We conclude that diabetes prevalence is higher in the urban Tanzanian community and that this can be explained by differences in the prevalence of overweight. The avoidance of obesity in the adult population is likely to prevent increases in diabetes incidence in this population. Keywords: diabetes, epidemiology, prevalence, rural population, urban population, risk factors, overweight, obesity, physical activity, Tanzania
Introduction Diabetes is of increasing concern in sub-Saharan Africa. A 2-3-fold increasein prevalence is projected for the next 20 vears LIMOS et al.. 1997). Models of epidemiological and health transition (OP&AN, 197 1) predict that demographic change will lead to an increase in health burden from chronic, non-communicable diseases such as diabetes and hypertension. Urbanization of the population may be associated with increased prevalence of diabetes in African populations (GILLUM, 1996). The impact of HIV and AIDS on diabetes prevalence may also be important, with a projected slowing in growth of the adult population, related to premature death of adults from AIDS. As the peak agesoecific urevalence of HIV is earlier than diabetes, fewer imectedrpeople may survive to middle age (PhZ & JOFFE, 1999). The net effect is still an absolute increase in total number of cases of diabetes but a reduction in projected numbers based on demographic change alone (AMOS et al., 1997). Diabetes is already an important cause of adult death in Tanzania, with a mortality comparable to that in the USA (MCLARTY et al., 1996). Previously reported values for diabetes prevalence in rural areas of subSaharan Africa have been around 1% (DAVIDSON et al., 1969; FISCH et al., 1987; MCLARTY et al., 1989). A prevalence of 4+2-8-O% has been reported in urban areas of South Africa (LEVITT et aZ., 1993; OMAR et al., 1993) but no significant difference was detected between urban and rural Cameroon (MBANYA et al., 1997). In Tanzania, selected urban populations have a higher prevalence of diabetes, with 4-10% of the economically privileged in Dar es Salaam having diabetes and up to 20% with imuaired nlucose tolerance CMCLARTY etal.. 1997). Few studies have used standard diagnostic criteria (MCCARTY et al., 1997) and none so far has presented prevalence values in urban and rural populations, using the latest epidemiological diagnostic criteria, proposed by the American Diabetes Association (ADA, 1998) and WHO (ALBERTI 81ZIMMET, 1998). Our aim in this study, involving surveys in November 1996 and May 1997, was to estimate the prevalences of ‘Author for correspondence; phone +44 (0) 191222 5407, fax +44 (0) 191 222 0723, e-mail [email protected]
diabetes mellitus and impaired fasting glycaemia (ADA alternative impaired fasting glucose, IFG), obesity and physical inactivity in men and women living in rural and urban Tanzania, and to determine the contribution of physical activity and indices of general and abdominal obesity to the prevalence of these conditions. Research design and Methods The context of the work is described in detail elsewhere (UNWIN et al., 1999). Shari village has a population of 4000 and is situated in the foothills of Mount Kilimanjaro. This is a Chagga farming community in one of the least-deprived rural areas, growing coffee and banana as cash crops. Ilala Ilala (population 10 500) is an inner-city area of Dar es Salaam, the commercial centre of Tanzania. The community is ‘middle-income’ relative to the city as a whole. Its residents are more diverse in their geographical origins, coming from all over the United Republic of Tanzania and Zanzibar, including Swahili and Arabic peoples. Residents’ livelihoods inelude small business, taxi-driving and employment as office workers. Both Shari and Ilala Ilala are monitored by a demographic surveillance system (UNWIN et al., 1999), which supplied the sampling frame for subjects. The study was designed to provide at least 90% power to estimate the prevalence of diabetes with a precision of 2%. Households were selected at random in both districts. Residents aged >15 years in each household were seen by trained field workers who administered a questionnaire about subjects’ health with respect to diabetes. Physical activity was assessed using 2 open questions about activity over the previous 2 weeks during work and leisure time. Responses were coded into 5 categories: Category 1 comprised those who did minimal strenuous activity at work or in the house and had no vigorous leisure activities; Category 5 consisted of people doing daily strenuous physical activity at work or in leisure time. A more quantitative approach was piloted in which the number of hours spent on a range of different activities was asked - an approach commonly used in North America and Europe (WILSON, et al. 1986). However, many found it difficult to quantify the time spent at different activities and so a more open approach was used pending our own development of a questionnaire more appropriate for these populations.
638
Anthropometric variables were measured, comprising weight to the nearest O-1kg, using a mechanical scale (Seca GmBh, Germany); height to the nearest 05 cm, using a portable stadiometer (‘Leicester’ Stadiometer, Seca GmBh); waist and hip circumference, using a rigid measuring tape to the nearest cm. Blood pressure was measured in the morning, with the subject sitting after 10 min of rest, to the nearest 2 mmHg, using a mercury sphygmomanometer (Accoson, London) and the measurement was repeated after a further 10 min. Capillary blood glucose was also measured in the morning after an overnight fast. Whole blood glucose was estimated using a glucose dehydrogenase method with a photometric alucometer (Hemocue AB, Am-relholm, Sweden). Calibration of the machine was confirmed using the’manufacturer’s control cuvette and high- and low-value glucose solutions were used. The diagnostic criterion for diabetes was a fasting capillary whole blood glucose (fcg) 26.1 mmol/L. The criterion for IFG was fcg ~5.6 mmol/L and <6-l mmol/ L in accordance with WHO recommendations (ALBERTI & ZIMMET, 1998). Hypertension was defined as mean systolic blood pressure 2 160 mmHg or diastolic blood pressure 295 mmHg (WHO, 1996). Self-reported diagnosis with current appropriate treatment was also diagnostic for both diabetes and hvnertension. The definitions of overweight and obesity w&e based on body massindex (BMI) 225 kg/m2 and 30 kg/m2, respectively (JAMES, 1996). Abdominal obesity was defined as a waist:hip ratio >0*80 for women and 0.95 for men (CRUIKSHANK et al., 1991). Statistical analyses were performed using Stata (version 5, Stata Corporation, TX. USA). All results are nresented with robust estimates of variance after adjustment for the design effect. Throughout the article, 95% confidence intervals are presented in brackets. Disease and risk factor prevalence is presented asboth crude rate and age-standardized rate, using the direct method, referred to the new World Population standard (WHO, 1992). Age adjustment was also performed using linear and logistic regression modelling, as appropriate. Logistic regression analysis was performed to estimate the relative contribution of overweight, general and abdominal obesity, and urban residence to risk of diabetes. Models were adjusted for age and interactions were explored. Using these models, the population attributable fraction (synonym aetiological fraction) for diabetes and IFG was estimated for urban residency, overweight, obesity, abdominal obesity and physical inactivity. The attributable fraction is the proportion by which the diseasein the entire population (for example, prevalence of diabetes) would be reduced in the absence of the ‘exposure’ (for example obesity) (LAST, 1988). This assumes that the relationship is causal, that removal of the exposure would reduce the risk to that in those unexposed and that there are no confounding or interacting risk factors. Thus, attributable fraction is an estimate of the maximum benefit to be gained if it were possible to remove the exposure. Confidence intervals for the attributable fraction are based on asymptotic approximations (GREENLAND & DRESCHER, 1993). The project received ethical approval from the Tanzanian Ministry of Health. Results
Response was higher in Shari village with 401 men (80% response) and 527 women (85% response) compared with 332 men (64.8% response) and 438 women (81.7%) in Ilala Ilala. The mean age for all adult respondents was greater in Shari at 42.1 [41 *Oto 43.31 years than in Ilala at 30.6 [29*6 to 31.51 years. Prevalence of diabetes, IFG and anthropometric risk factors
The nrevalences of diabetes. IFG. overweiaht. obesitv. and physical inactivity in Ilala and Shari f& men a& women are presented in Table 1. Prevalences are presented for each sex by agetertile with crude rates and age-
TERENCEJ.ASPRAYETAL.
standardized rates, using the direct method. The difference between Ilala Ilala and Shari in crude prevalence of diabetes for men was 3.8 [l*l-6.51% and 2.9 [0.84.91% for women. The difference in prevalences of IFG for men was 2.8 [0*3-5.31% and for women 3.9 [1.46.41%. For overweight (BMIa25 kg/m2) and obesity (BMIs30 kg/m’), the difference in prevalences was 21.5 [15*8-27-l]% and 6.2 [3*5-8-g]% for men and 17.4 [11.5-23.31% and 12.7 [8.5-16.81% for women, respectively. The difference in prevalences of physical inactivity was 12.5 [7*0-18.31% for men and 37.6 [31*9-43.31% for women. Abdominal obesity and diabetes
Table 2 summarizes the anthropometric measurements and fasting capillary glucose levels for men and women in Shari and Ilala Ilala. These data suggest that measurements of waist, hip and waist:hip ratio and fasting capillary glucose levels are higher for both genders in Ilala Ilala and in older age-groups compared with the younger. Comparisons between urban and rural populations are presented below as mean differences with ageadjustment, using linear regression. Men in Ilala Ilala were 7.7 15.7-9.61 ke heavier with BMI areater bv 3.2 [2*5-3*8]‘kg/m’, and’8lthough they were ihorter by 1.2 [-2.5-0.21 cm, this difference was not significant. Waist circumference was greater by 5.9 [4.4-7:4] cm, as were bin circumference. bv 4.3 12*7-5.91 cm. and waist:hio raso, by 0.021 [O*bO9-0.0321; fasting capillary glucose was 0.77 [0.56-0.971 mmol/L higher. Women in Dar es Salaam were shorter by 4.2 [3.35.11 cm and heavier by 3.4 [l-4-5.4] kg with a higher BMI by 2.65 [lag-3.4 kg/m*. Waist circumference was greater by 2.2.[0*4-4.0]>m, as was hip circumference, bv 4.0 12.3-5-71 cm. but waist:hio ratio was lower. bv 0:015 [&003-0:026j; fasting capiilary glucose was also significantly higher, by 0.46 [O-34-O-581mmol/L. There were significant interactions between age and area of residence for most anthropometric variables. Thus, BMI increased for men by 0.10 [0.06-0.141 kg per year of age in Ilala and by 0.03 [0.02-0*05] kg per year of agein Shari. For women, there was an increase by 0.12 [0*07-O* 171per year of agein Ilala with an apparent decreasein Shari of O-02 [ -0.0 l-0.031 kg per year of age (not significant). Interactions were also seen for men between age and area for height, weight, waist, waist:hip ratio and fasting blood glucose but not hip measurement, systolic or diastolic blood pressure. For women there were interactions between age and area of residence for weight, waist, hip, waist:hip ratio but not height, fasting blood glucose, or systolic or diastolic blood pressure. Diabetes had already been diagnosed in 1 of 6 men and none of 6 women in Shari and in 5 of 17 men and 2 of 17 women in Ilala. For men, diabetic subjects were on average 9.1 years older and 16.7 kg heavier. Waist circumference was greater by 17 cm and waist:hip ratio by 0.08. For women, diabetic subjects were on average 5.1 vears older and 14.8 ke heavier. Waist circumference was-greater by 17.1 cm&d waist:hip ratio by 0.05. Hypertension and physical inactivity were more prevalent amone subiects with diabetes. All these differences were sign&ant (P < 0.05) and were further explored, using logistic regression (Table 3). For both sexes,being overweight or obese and having a raised waist:hip ratio were more likely among diabetic than non-diabetic subjects. There were no significant associations between anthropometric variables& physical activity and IFG. However, urban residence was more likelv for IFG in both sexes. Age-adjustment made a remarkable difference only to the models for urban residence, which was associated with a higher age-adjusted odds ratio for diabetes of 6.4 [2.2-18.71 for men and 5.8 12.0-17.01 for women. For abnormal fasting glucose (IFG or diabetes), the odds ratio fell between that for IFG and diabetes’separately, as seen in Table 3. The population attributable fractions of general and
(0.5) (1.1) (2.6) (1.0) (1.7)
2.3 5.8 33.3 12.7 47.9
320
215 1.9 4.3 18.0 3.7 82.5
(0.8)“’ (1.4)NS (2.6) (1.9) (2.9)
(0.9)NS (1.4) (2.7) (1.2) (2.7)
Shari
1.5 1.0 32.5 7.5 91.4
201 (0.9) (0.7) (3.3) (1.9) (2.0)
138 1.4 (1.0) 2.2 (1.2) 8-O (2.3) 0.7 (0.7) 96.4 (1.6)
35-54
8.1 5.8 57.9 35.2 50.0
89
90 10.4 4.7 45.9 13.8 69.3
(3.0) (2.5) (5.2) (5.0) (5.4)
Shari
1.6 0.8 12.9 5.2 61.5
123 (1.1) (0.8) (3.1) (2.0) (4.4)
113 2.7 (1.5) 1.7 (1.2) 9.7 (2.8) 7z.7 (4.1)
55+ Ilala
l ii1 50.0 15.4 36.0
29 (5.9) (9.8) (7.1)NS (9.0)
42.3 (9.6) 3.8 (3.8) 65.4 (9.3)NS
27
y;. 22.0 4.8 85.2
527 y; . (1.8) (0.9) (1.6)
(0.6) (0.6) (1.2) (0.3) (1.5)
Shari 401 1.5 1.2 6.0 0.3 90.2
438 4.0 5.4 39.4 17.5 47.7
(0.9) (1.2) (2.3) (1.9) (25)
activity
527 1.1 1.6 20.6 4.2 87.4
401 1.7 0.8 4.4 0.2 92.7
(0.4) (0.6) (1.7) (0.8) (1.3)
(0.6) (0.4) (1.0) (0.2) (1.3)
Shari
(15) (1.0) (2.8) (1.6) (2.8) (1.3) (1-O) (2.8) (2.4) (2.7)
332 5.9 3.6 31.5 7.3 75.8 438 5.7 4.7 44.1 19.8 45.2
Ilala
All (adjusted)”
obesity and physical
(1.2) (1.2) (2.6) (1.4) (2.5)
Ilala 332 5.3 4.0 27.5 6.5 77.5
All (crude)
fasting glucose (IFG) , overweight,
(years)
impaired
Age-group
of diabetes,
(3.2) (2.3)NS (5.4) (3.7) (4.9)
Ilala
(Ilala) area of Tanzania,
All differences in prevalence between Shari and Ilala are significant (at least P < 0.05) except where markedNs. “Adjustment is to the age distribution to the New World Population. bBody mass index ~25 kg/m’ ‘Body mass index 230 kg/m’. ‘Vhysically active is presented as proportion who score l-3 out of 5 on physical activity questionnaire (see text for more details).
0.5 2.5 16.8 2.0 93.6
203
Women (n)
Diabetes IFG Overweight or obeseb Obese’ Physically active (%)d
150 0.7 (0.7) 0 1.3 (0.9) 9z.3 (1.7)
Men (n) Diabetes IFG Overweight or obeseb Obesec Physically active (%)d
Ilala
and an urban
15534
(Shari)
Shari
Table 1. Prevalence, in a rural presented as percentage (SE)
E s
2
z
E
Shari
203 22.4 (0.2) 160.1 (0.4) 56.8 (0.6) 73.7 (0.6’1 91.6 (ii
84:7 (0:5) 0.831 (0.003) 4.25 (0.05)
150 19.5 (0.2) 166.1 (0.8) 3;‘; g;;
Ilala
320 24-O (0.3) 156.3 (0.4).._ 58.9 (0.8)“> 74.0 (0.7F 94.9 (0.7) 0.777 (0.005) 4.98 (0.06)
215 22.1 (0.3) 166.6 (0.6)NS 61.5 (0.7) 74.4 (0.7) 90-O (0.9) 0.838 (0.006)NS 4.87 (0.05)
Shari
0.893 (0.010) 5.30 (0.22)
4z.O (0.6) 167.3 (0.7)
Ilala
201 9t.o (0.7) 23.6 (0.3) 160.2 (0.4) 155.8 (0.6) 60.8 (0.8) 68.2 (1.9) 79.3-\-(0.7), 86.1 (1.6‘) 96.0 (0.7) 103.1 (1.4) 0.827 (0.005) 0.834 (0.009)NS 4.69 (0.07) 5.00 (0.08)
89:5 (0.5) 0.865 (O-004) 4.39 (0.06)
138 21.1 (0.2) 170.5 (0.5) ;;.‘6 g;;
35-54
ratio and fasting
z.5 (0.7) 164.0 <1.5j 65.9 (2.2) fji.0” yj . . 0.838 (0.017) 5.6 (0.45)
Ilala
123 21.3 (0.4) z.2 (1.3) 156.6 (0.60) 152.2 (1.1) 52.3 (1.0) 57.4 (2.9) 77.2 (1.00) 80.2 (3.1)NS 91.5 (0.9) 95.4 (2.8)“’ 0.845 (0.005) 0.838 (0.017)NS 4.57 (0.06) 5-20 (0.22)
(0.3) (0.6) (0.9) (0.9) (0.7) (0.005) (0.10)
Shari
55-t
(years)
527 22.5 (0.2) 159.3 (0.3) 57.3 (0.5) 76.7 (0.5) 93.3 (0.4) 0.822 (0,003) 4.57 (0.03)
(0.1) (0.4) (0.5) (0.4) (0.3) (0.003) (0.04)
Shari 401 20.5 168.1 58.2 75.2 87.8 0.854 4.37
Ilala
(Shari)
438 24.9 (O-3) 156.0 (0.3) 60.7 (0.8) 76.9 (0.7)NS 96.6 (0.7) 0.793 (0.004) 5.00 (0.05)
332 23.1 (0.3) 166.6 (0.5) 64.1 (O-8) 78.3 (0.7) 90.7 (0.7) 0.861 (0.006)NS 5.05 (0.08)
All (crude)
glucose in men and women from a rural
Age-group
capillary
113 20.9 167.7 59.1 78.6 89.8 0.845 4.50
BMI, body mass index; WHR, waist:hip ratio; FCG, fasting capillary blood glucose. All differences between Shari and Ilala are significant (at least P < 0.05) except where marked r~‘. ‘Adjustment is to the age distribution to the New World Population.
WHR FCG (mmol/L)
Hip (cd
Women (n) BMI (kg/m*) Height (cm) Weight (kg) Waist fcmj
Men (n) BMI (kg/m’) Height (cm) Weight (kg) waist (cm) Hip(cm) WHR FCG (mrnol/L)
15-34
Table 2. Mean (SE) body mass index, waist, hip, waist-to-hip of Tanzania
527 22.0 (0.2) 156.3 (0.3) 56.2 (0.5) 74.6 to.45 6i.i {b.ij 0.803 (0.003) 4.45 (0.03)
(0.1) (0.4) (0.5) (0.4) (0.3) (0.002) (0.04)
Shari 401 20.3 168.1 57.7 74.3 87.3 0.850 4.34
area
438 25.0 (0.4) 152.2 (0.4j 60.4 (1.0) 77.5 (0.9) 95.7 (0.8) 0.791 (0.005) 4.94 (0.05)
332 23.4 (0.3) 166.5 (0.5) 65.1 (0.9) 79.9 (0.7) 91.2 (0.7) 0.87 1 (0.006) 5.11 (0.11)
Ilala
(Ilala)
All (adjusted)”
and an urban
9.0 10.5 2.4NS 1.6NS 3.6
Women Uvei%%l&t or obese” Obesityb Abdominal obesityc Physically inactived Living in city
13.3-24.53 [4.5-24.61 [0.9-6-21 [0.6-4.11 [ 1.4-9.21
[5.7-35.11 [1.5-19.51 [5.2-29-91 [0.9-5.91 [ 1.4-9.31 9.3 10.7 2.2NS 1.6NS 5.8
13.1 5.7 10.8 1.7NS 6.4 [3S--26.63 [4.4-25.91 [0.8-5.71 [0.6-3.91 [2.0- 17.01
l.Ows 0.7NS 0.5Ns 2.1NS 3.8
2.4NS 2.2NS 1.7= 2~4~~ 3.5
(IFG) in relation
[5.2-32.81 [1.6-20.81 [4.6-25.21 [0.6-4.61 [2.2- 18.71
Age-adjusted
glucose
[OS2.31 [O-2-2.8] [0.2-1.11 [l.O-4.41 [1.7-8.81
[0.9-6.71 [0.3-17.31 [O-4-7.9] [0.8-7.31 [1.2-10.01
Crude
IFG
to overweight,
obesity,
l.INS 0.7NS 0.6NS 2.2NS 3.4
2.6NS 2.2NS 1.8NS 2.6NS 3.7 f&+2.4] [0.2-2.91 [0.3- 1.31 [l.O-4.51 [l-5-7.9]
[0.9-7.11 [0.3- 17.41 [0.4-8.51 [0.9-7.91 [1.5-9.31
Age-adjusted
abdominal
Values are odds ratio [95% confidence interval]. Denominator for diabetes and ‘IFG or diabetes’ is the whole population and for IFG alone is the non-diabetic population. NSIndicates that the odds ratio was not significantly greater than one (F > 0.05). All other odds ratios were significant (P < 0.05). “Body mass index 325 kg/m’. ‘Body mass index 230 kg/m’. ‘Waist:hip ratio 2 0.95 (men) or 0.80 (women). dProportion who score >3 out of 5 on physical activity questionnaire (see text for more details).
14.1 5.3 12.5 2.3NS 3.7
Crude
fasting
Diabetes
and impaired
Men Overweight or obese” Obesityb Abdominal obesi@ Physical inactived Living in city
Table 3. Odds ratio for diabetes
2.5 3.3 l.ONS 1.9Ns 3.7
6.7 4.0 6.1 2.4 3.6
physical
and urban
[ 1.4-4.4] [1.7-6.31 [0.6-1.71 [l-O-3.5] [2.0-7.01
residence
2.5 3.3 l.ONS 1.9 4.2
6.5 4.0 5.7 2.1NS 5.0
[l-4-4-4] [1.7-6.31 [0.6-1.71 [l.l-3.61 [2.1-8.41
[3.5-12.11 [1.3-12.11 [2.9-l 1.61 [l.O-4.41 [2.4-10.41
Age-adjusted
IFG or diabetes
[3.6- 12.51 [1.3-12.21 [3.0-12.41 [l.l-4,8] [1.8-7.21
Crude
inactivity
z
E
c;
642
TERENCE J. ASPRAY ETAL.
abdominal obesity for diabetes and IFG are presented in Table 4. The contribution of being overweight or obese to the risk of diabetes is greater in women than men in this population. For example, 63% of diabetes in men and 69% of diabetes in women might not occur, if overweight were avoided. Obesity, rather than total overweight, does not have a significant effect for men. The attributable fraction for large waist:hip ratio was greater in men than women. Urban residence accounted for just over half of diabetes cases in both sexes. The impact of overweight and obesity on IFG was not significant, although over half of IFG cases might be avoided by not living in the city. The roles of overweight and physical inactivity as mechanisms by which urban residence may influence diabetes prevalence were explored. Thus, Table 5 shows, in both men and women, that age-adjusted differences in obesity (BMI or waist:hip measurement) explain much of the effect of urbanization. For men, the odds ratio for urban residence falls from 6.37 to a non-significant 2.42 with the inclusion of BMI, but for women BMI accounts for most but not all of the impact of urban residence. Similarly, for both sexes,
Table 4. Population abdominal obesity,
the odds ratio for urban residence decreased after adjustment for waist:hip ratio, but this did not explain the effects of urbanization completely. Physical activity level did not make any independent contribution to explaining the effects of urbanization. However, for both sexes the inclusion of physical activity with obesity variables lowers the odds ratio further for urban residence. In Table 6, age-adjusted logistic regression models are presented for the sexes between areas. For men in Shari, BMI is associated with an odds ratio of 183 for diabetes but there is no improvement with the incorporation of other variables. However, in urban men, BMI and waisthip ratio both predict diabetes separately, and the model incorporating these with physical activity score is the best (OR for BMI rises to 2.22 with a pseudo ti of 29%). For women in Shari, there is a very strong association of BMI with diabetes (odds ratio 12.16 and pseudo 12 50%) and adjustment for physical activity improves the model (OR for BMI 12.5, pseudo 1255%). For urban women, these variables do not predict diabetes well, although BMI is the best (odds ratio 1.22 but pseudo ? for the model is 8%).
attributable C-action for diabetes and impaired physical inactivity and urban residence
fasting
Diabetes
Men Overweight or obese” Obesityb Abdominal obesityc Physically inactived Living in city Women Overweight or obese” Obesityb Abdominal obesi@ Physically inactived Living in city
glucose
with
overweight,
Impaired fasting glucose
Crude
Age-adjusted
Crude
Age-adjusted
64 [42-861 10Ns [O-23] 44 [23-651 16Ns [O-38] 53 [22-851
63 [41-851 1ONS[O-23] 43 [22-641 11ns [O-35] 62 [35-891
16;; [O-39]
17;: [O-39]
;NS ;;I;;; 17ns [O-49] 51 [15-871
;NS [;--;;; 18Ns [O-49] 52 [20-851
69 [45-931 47 [25-691 42NS [O-82] 1gNS [O-48] 53 [21-851
69 [45-931 47 [25-691 3gNS [O-82] 1gNS [O-48] 61 [33-891
ONS ONS ONS 2P [O-50] 54 [27-811
ii 2;” 51
[O-50] [22-801
Values are percentage [95% confidence interval]. Denominator for diabetes is the whole population and for impaired fasting glucose alone is the non-diabetic population. NsIndicates that the attributable fraction was not significantly greater than zero (P> 0.05). All other attributable fractions were significant (P < 0.05). “Body mass index 225 kg/m*. bBody mass index ~30 kg/m’ ‘Waist:hip ratio 20.95 (men) or 20.80 (women). dProportion who score >3 out of 5 on physical activity questionnaire (see text for more details).
Table 5. Logistic urban residence
regression
models of diabetes
Sex
Urban area
Men
6.37 [2*17-18.701 2.42NS [0.72-8.071 4.93 [1*76-13.851 6.39 [2*00-20.431 1.96NS [0.59-6.571 4.35 [1*48-12.711 l.gNS [0.58-6.291 5.85 [2*01-17.051 3.32 [1.04-10.661 6.36 [2*25-17.981 3.86 [l*lO-13.561 2.35NS [0.56-9.911 4.19 [1.26-13.921 2.45NS [0.58- 10.331
Women
with overweight,
Body mass index
abdominal
Waste:hip ratio
obesity,
physical
inactivity
Physical inactivity
1.69 [l-23-2.31] 1.28
[l-07-1.531
1.96 [1.41-2.751 1.88 [1.33-2.651
1.34 [1*09-1.651 l.OSNS [0*89-1.311
l*llNS [0*41-3.001 l.OINS [0.37-2.761 1*15NS [0*43-3.091 1*02NS [0.37-2.851
1.49 [l.ll-1.991 1.23
[l-03-1.461
1.45 [1.07-l-961 1.39 [1*03-l-871
1.26 [1*03-1.561 1*13NS [0.93-1.381
0.94NS [0*31-2.841 0.94NS [0.30-2.941 l.OONS [0.33-3.021 0.98NS [0.31-3.081
and
Pseudo 13 10% 21% 14% 11% 25% 16% 26% 7% 16% 10% 4% 12% 8% 13%
NSIndicates that the odds ratio was not significantly greater than one (P > 0.05). All other odds ratios were significant (P < 0.05). All models are age-adjusted. Body mass index and waist:hip ratio effects are presented as per decile (see text for details), and physical inactivity is a binary variable (active versus inactive).
643
DIABETES IN URBAN AND RURAL TANZANIA
Table 6. Logistic regression activity in men and women Area Men Shari
Ilala
models of diabetes
Body mass index 1.83
in association Waist:hip ratio
abdominal
[1.17-2.841
1.88
[1*16-3.061
1.65
[1.06-2.551
1.18NS [0.86-1.621 0.93NS [0.71-1.231
2.41
[1.59-3.631
2.22
[1.56-3.161
1~08~~ [O.ll-ll*ll] 1.06NS [0.09-13.101 l*lONS [0~10-11*05] l.lONS [O-09-12.741
17% 3% 2% 17% 3% 17%
1.26NS [0.39-4.071 1.27NS [0.40-4.081 1.28NS [O-40-4.091 1.31NS [0*40-4.281
19% 14% 13% 28% 19% 29%
12.16
[2.12-69.721 6.05NS [0.77-47.71 5*31NS [0.77-36.491 5.65NS [0.77-41.541 4.70NS [0.67-33.141
50% 7% 6% 55% 12% 56%
0.54NS [0*18-l-671 0.53NS [0.17-1.621 0.56NS [0.18-1.751 0.55NS [0.17-1.711
8% 7% 2% 4% 6% 6%
[1.04-1.651
1.41 [1.03-1.931 1.13NS [0.86-1.501
1.42NS [0.91-2,231 12.50
[2.02-77.431
11.86
[1.88-74.771
1~42~~ [0.89-2.261 Ilala
1.17NS [0.75-1.821
1.22NS [0.98-1.511 1.18NS [0.97-1.431 1.17NS [0.94-1.461 1.09NS [0.89-1.351
1.23NS [0.97-1.571 1.19= [0.93-1.511
physical
Pseudo $
1*18NS [0.86-1.621 1.82
obesity,
Physical inactivity
[1.18-2.831
1.31
Women Shari
with overweight,
NSIndicates that the odds ratio was not significantly greater than one (P > 0.05). All other odds ratios were significant (P < 0.05). All models are age-adjusted. Body mass index and waist:hip ratio effects are presented as per decile (see text for details), and physical inactivity is a binary variable (active versus inactive).
Conclusion Our study clearly shows a higher prevalence of diabetes and IFG in men and women living in an urban area compared with a rural area of Tanzania. Differences in prevalence of risk factors for type 2 diabetes (overweight and physical inactivity) are also evident. Our diabetic populations in both areas were older, heavier, with a higher BMI and larger waist:hip ratio. They were also less active. Although there are limitations in the use of crosssectional survey data in the identification of aetiological factors, 2 strong patterns appear. Being overweight is associated with an odds ratio of 14.1 for diabetes in men and 9.0 in women, corresponding to an attributable fraction of at least 4 1% in men and 45% in women, after adjusting for age. Abdominal obesity shows similar effects, although the threshold set for waisthip ratio may not be as sensitive as for BMI and may differ between the sexes and areas in its performance. The second finding is that the attributable fraction for living in the citv for diabetes is at least 35% in men and 33% in women. Physical inactivity was not an independent risk factor for diabetes. although for men from Ilala Ilala and women in Shari adjustment for physical activity improved the regression model. The performance of the exercise questionnaire was disappointing. The best evidence for physical activity as a determinant of diabetes risk is from longitudinal studies and those eliciting et al., 1991; historical physical activity (HELMRICH KRISKA et al., 1993). Thus, current reported activity levels (as we used) are likely to be insensitive compared with anthropometric variables which represent cumulative effects of overweight achieved over probable decades. In assessing abdominal obesity, we did not include waist measurement in our analyses, in the absence of internationally accepted standards (WHO, 1998). However, the development of locally validated waist measurement cut-offs would be of considerable value and this work is currently underway (ASPRAY et al., 1999).
For men, the effect on diabetes prevalence of living in the city was explained mostly by differences in BMI. For women, an interesting finding was the low explanatory Dower of BMI for diabetes in the urban residents (see Table 6). This observation may suggest that other factors associated with urban residence but not measured in our study may be very important as risk factors for diabetes in this urban population. These data present strong evidence of a major difference in diabetes prevalence between urban and rural communities in Tanzania. The urban populations also have a higher prevalence of risk factors for diabetes, including overweight, obesity and physical inactivity. Our findings suggest that primary prevention initiatives, particularly the avoidance of overweight, are priorities for adult health in this population. References ADA (1998). American Diabetic Association Consensus development conference report: consensus development conference on insulin resistance. Diabetes Cure, 21,310-314. Alberti, K. & Zimmet, P. (1998). Definition, diagnosis and classification of diabetes mellitus and its comnlications. Part 1: diagnosis and classification of diabetes mell
644
Fisch, A., Pichard, E., Prazuck, T., Leblanc, H., Sibide, Y. & Brucker, G. (1987). Prevalence and risk factors of diabetes mellitus in the rural region of Mali (West Africa): a practical approach. Diabetologia, 30,859-862. Gillum, R. F. (1996). The epidemiology of cardiovascular disease in black Americans. New EnglandJournal of Medicine, 335,1597-1598. Greenland, S. & Drescher, K. (1993). Maximum likelihood estimation of the attributable fraction from logistic models. Biometrics, 49,865-872. Helmrich, S. P., Ragland, D. R., Leung, R. W. & Paffenbarger, R. S. Jr (199 1). Physical activity and reduced occurrence of non-insulin-dependent diabetes mellitus. New EngZandJournal of Medicine, 325, 147-152. James, W. P. T. (1996). The epidemiology of obesity. Ciba Foundation Symposium 20 1: The Origins and Consequencesof Obesity. Chichester: John Wiley and Sons, pp. 1- 16. Kriska, A. M., LaPorte, R. E., Pettitt, D. J., Charles, M. A., Nelson, R. G., Kuller, L. H., Bennett, P. H. & Knowler, W. C. (1993). The association of physical activity with obesity, fat distribution and glucose intolerance in Pima Indians. Diabetologia, 36, 863-869. Last, J. M. (1988). Attributable fraction (exnosedj. In: A D&Gonay if Epidemiology, 2nd edn, Last, j. M. ‘(editor). Oxford: Oxford University Press, pp. 9-10. Levitt, N. S., Katzenellenbogen, J. M., Bradshaw,D., Hoffman, M. N. & Bonnici, F. (1993). The prevalence and identification of risk factors for NIDDM in urban Africans in Cape Town, South Africa. Diabetes Care, 16,601-607. Mbanya, J. C., Ngogang, J., Salah, J. N., Minkoulou, E. & Balkau, B. (1997). Prevalence of NIDDM and impaired glucose tolerance in a rural and an urban population in Cameroon. Diabetologib, 40, 824-829. McLarty, D. G., Swai, A. B., Kitange, H. M., Masuki, G., Mtinangi, B. L., Kilima, P. M., Makene, W. J., Chuwa, L. M. & Alberti, K. G. (1989). Prevalence of diabetes and impaired glucose tolerance in rural Tanzania. Lancet, i, 87 l-875. McLarty, D, Unwin, N., Kitange, H. & Alberti, K. G. M. M.
Book Review Microbial Pathogenesis: a Principles-Orientated Approach Bruce A. McClane and Timothy A. Mietzner (editors) and John N. Dowling and Bruce A. Phillips (coeditors). Madison: Fence Creek Publishing, 2000. xii + 485~~. Price E15.95. ISBN l-889325-27-9. Microbial Pathogenesis is designed as a course supplement for first- and second-year medical students. The paper-backed volume is divided into 29 chapters which provide a basic introduction to the principles ofmicrobial growth, physiology and antimicrobial strategies, modes of transmission, colonization and subversion of host defences, mechanisms of host damage, the genetics of virulence, the outcome of infection and the principles of vaccines. As with other titles in this Integrated Medical Sciences Series from Fence Creek, the content is expressly designed to explain the basic science principles that underlie a &en clinical condition. This is achieved by dividing ea& chapter into 3 distinct sections. First a clinical case relevant to the subject matter of the chapter is introduced, next the fundamental microbiological principles and details are given, and finally the clinical case resolution is described which draws upon the basic science content of the chapter in order to explain the outcome. At the conclusion of each chapter there are a number of multiple-choice questions which review the reader’s understanding of the subject matter followed by the answers and helpful explanatory summaries. In general, this approach works well and helps to divide this very broad subject matter into a digestible set ofbitesized chunks. Perhaps inevitably, given the scope of the topics, the coverage of some areas is rather superficial. For example
TERENCE J. ASPRAY ETAL. (1996). Diabetes as a cause of death in sub-Saharan Africa: results of a community-based study in Tanzania. Diabetic Medicine, 13, 990-994. M&arty, D., Pollitt, C., Swai, A., & Alberti, K. G. M. M. (1997). Epidemiology of diabetes in Africa. In: Diabetes in Africa, Gill, G., Mbanya, J.-C. & Alberti, K. (editors). Cambridge, UK: FSG Communications, pp. 1- 18. Omar, M. A., Seedat, M. A., Motala, A. A., Dyer, R. B. & Becker, P. (1993). The prevalence of diabetes mellitus and impaired glucose tolerance in a group of urban South African blacks. South African Medical Yournal, 83, 641-643. Omran, A. (197 1j. The epidem&logic t&&ition: a theory of the epidemiology of population change. Millbank Memorial Fund Quarterly, 49, 509-538. Panz, V. R. & Joffe, B. I. (1999). Impact of HIV infection and AIDS on prevalence of tvne 2 diabetes in South Africa in 2010. Bri&hMedicalJou&& 318, 1351. Unwin,N.,Alberti,G.,Aspray,T., Edwards,R.,Mbanya, J. C., Sobngwi, E., Mugusi, F., Rashid, S., Setel, P. &Whiting, D. (1999). Tackling the emerging pandemic of non-communicable diseases in sub-Saharan Africa: the essential NCD health intervention project. Public Health, 113, 141-146. WHO (1992). World Health Statistics Annual. Geneva: World Health Organization. WHO (1996). Hypertension Control. Report of a WHO Expert Committee. Geneva: World Health Organization. Technical Report Series, no. 862. WHO (1998). Obesity: preventing and managing the global epidemic. Report of a WHO consultation on obesity. Geneva: World Health Organization. WHO/NUT/NCD/98.1. Wilson, P., Paffenbarger, R., Morris, J. N. & Havlik, R. J. (1986). Assessment methods for physical activity and physical fitness in population studies: a report of a NHLBI workshop. American HeanJournal, 111, 1177-1192.
Received 10 April 2000; revised 19 May 2000; accepted for publication 24 May 2000
metabolism and nutrient acquisition, although fundamental to bacterial survival, are barely mentioned. However, throughout the book the authors do encourage the reader to visit more specialized texts to address each area in more detail. In this context it would have been helpful to have included specific suggestions for further reading or to have highlighted some of the extensive Internet resources that are now available. Another shortcoming is the omission of any references to the current frenzied research activity in the fields of microbial genomics and the interplay between bacteria and the pattern recognition systems of the innate host defences. Whilst not absolutely essential to an initial understanding of pathogenesis, an attempt to convey the level of excitement which currently surrounds advances in these areas may have helped to persuade the reader to delve into this burgeoning literature and even to consider a future research career in the molecular basis of infectious disease. Nonetheless, MicrobiaZ Pathogenesis is a very useful addition to medical-undergraduate learning resources and would not even be out of place on the bookshelf of basic-microbiology students who would benefit from a text which neatly illustrates the clinical relevance and outcomes of the interaction between micro-organisms and their host. Mike Curtis Medical Microbiology St Bartholomew’s and the Royal London School of Medicine and Dent&y 32, Newark Street London El 2AA, UK The book is available from Blackwell Science, +44 (0) 1865 206233, fax +44 (0) 1865 206026, [email protected]
phone e-mail
Rural and urban differences in diabetes physical inactivity and urban living
prevalence
in Tanzania:
the role of obesity,
Terence J. Aspray I*, Ferdinand Mugusi2, Seif Rashid’, David Whitin&, Richard Edwards3, Disease Health Intervention K. George Albert? and Nigel C. Unwin 1*3for the Essential Non-Communicable Project Departments of ‘Medicine and 3Epidemiology and Public Health, The Medical School, Giversi of Newcastle upon Tme, NE2 4HH, UK; ‘Adult Morbidity and Mortality Project and Muhimbili Medical Centre, I? 0. Box 65243, Dar es &la&n, Tanzania Abstract A population-based survey in 1996 and 1997 of 770 adults (aged 3 15 years) from an urban district of Dar es Salaam and 928 from a village in rural Kilimanjaro district (Tanzania) revealed that the prevalence of diabetes, impaired fasting glucose (IFG), overweight, obesity, and physical inactivity was higher in the urban area for men and women. The difference between urban and rural prevalence of diabetes was 3.8 [ l.l-6.51% for men and 2.9 [0.8-4.91% for women. For IFG, the difference was 2.8 [0.3-5.31% for men and 3.9 [ 1.4-6.41% for women; for overweight and obesity, the difference was 21.5 [ 15.8-27. l] % and 6.2 [3~5-8~9]%formenand17~4[11~5-23~3]%and12~7[8~5-16~8]%forwomen,respectively.Thedifference in prevalence of physical inactivity was 12.5 [7.0- 18.31% for men and 37.6 [3 1.9-43.31% for women. For men with diabetes, the odds for being overweight, obese and having a large waist:hip ratio were 14.1,5*3 and 12.5, respectively; for women the corresponding values were 9*0,10.5 and 2.4 (the last not significant) with an attributable fraction for overweight between 64% and 69%. We conclude that diabetes prevalence is higher in the urban Tanzanian community and that this can be explained by differences in the prevalence of overweight. The avoidance of obesity in the adult population is likely to prevent increases in diabetes incidence in this population. Keywords: diabetes, epidemiology, prevalence, rural population, urban population, risk factors, overweight, obesity, physical activity, Tanzania
Introduction Diabetes is of increasing concern in sub-Saharan Africa. A 2-3-fold increasein prevalence is projected for the next 20 vears LIMOS et al.. 1997). Models of epidemiological and health transition (OP&AN, 197 1) predict that demographic change will lead to an increase in health burden from chronic, non-communicable diseases such as diabetes and hypertension. Urbanization of the population may be associated with increased prevalence of diabetes in African populations (GILLUM, 1996). The impact of HIV and AIDS on diabetes prevalence may also be important, with a projected slowing in growth of the adult population, related to premature death of adults from AIDS. As the peak agesoecific urevalence of HIV is earlier than diabetes, fewer imectedrpeople may survive to middle age (PhZ & JOFFE, 1999). The net effect is still an absolute increase in total number of cases of diabetes but a reduction in projected numbers based on demographic change alone (AMOS et al., 1997). Diabetes is already an important cause of adult death in Tanzania, with a mortality comparable to that in the USA (MCLARTY et al., 1996). Previously reported values for diabetes prevalence in rural areas of subSaharan Africa have been around 1% (DAVIDSON et al., 1969; FISCH et al., 1987; MCLARTY et al., 1989). A prevalence of 4+2-8-O% has been reported in urban areas of South Africa (LEVITT et aZ., 1993; OMAR et al., 1993) but no significant difference was detected between urban and rural Cameroon (MBANYA et al., 1997). In Tanzania, selected urban populations have a higher prevalence of diabetes, with 4-10% of the economically privileged in Dar es Salaam having diabetes and up to 20% with imuaired nlucose tolerance CMCLARTY etal.. 1997). Few studies have used standard diagnostic criteria (MCCARTY et al., 1997) and none so far has presented prevalence values in urban and rural populations, using the latest epidemiological diagnostic criteria, proposed by the American Diabetes Association (ADA, 1998) and WHO (ALBERTI 81ZIMMET, 1998). Our aim in this study, involving surveys in November 1996 and May 1997, was to estimate the prevalences of ‘Author for correspondence; phone +44 (0) 191222 5407, fax +44 (0) 191 222 0723, e-mail [email protected]
diabetes mellitus and impaired fasting glycaemia (ADA alternative impaired fasting glucose, IFG), obesity and physical inactivity in men and women living in rural and urban Tanzania, and to determine the contribution of physical activity and indices of general and abdominal obesity to the prevalence of these conditions. Research design and Methods The context of the work is described in detail elsewhere (UNWIN et al., 1999). Shari village has a population of 4000 and is situated in the foothills of Mount Kilimanjaro. This is a Chagga farming community in one of the least-deprived rural areas, growing coffee and banana as cash crops. Ilala Ilala (population 10 500) is an inner-city area of Dar es Salaam, the commercial centre of Tanzania. The community is ‘middle-income’ relative to the city as a whole. Its residents are more diverse in their geographical origins, coming from all over the United Republic of Tanzania and Zanzibar, including Swahili and Arabic peoples. Residents’ livelihoods inelude small business, taxi-driving and employment as office workers. Both Shari and Ilala Ilala are monitored by a demographic surveillance system (UNWIN et al., 1999), which supplied the sampling frame for subjects. The study was designed to provide at least 90% power to estimate the prevalence of diabetes with a precision of 2%. Households were selected at random in both districts. Residents aged >15 years in each household were seen by trained field workers who administered a questionnaire about subjects’ health with respect to diabetes. Physical activity was assessed using 2 open questions about activity over the previous 2 weeks during work and leisure time. Responses were coded into 5 categories: Category 1 comprised those who did minimal strenuous activity at work or in the house and had no vigorous leisure activities; Category 5 consisted of people doing daily strenuous physical activity at work or in leisure time. A more quantitative approach was piloted in which the number of hours spent on a range of different activities was asked - an approach commonly used in North America and Europe (WILSON, et al. 1986). However, many found it difficult to quantify the time spent at different activities and so a more open approach was used pending our own development of a questionnaire more appropriate for these populations.
638
Anthropometric variables were measured, comprising weight to the nearest O-1kg, using a mechanical scale (Seca GmBh, Germany); height to the nearest 05 cm, using a portable stadiometer (‘Leicester’ Stadiometer, Seca GmBh); waist and hip circumference, using a rigid measuring tape to the nearest cm. Blood pressure was measured in the morning, with the subject sitting after 10 min of rest, to the nearest 2 mmHg, using a mercury sphygmomanometer (Accoson, London) and the measurement was repeated after a further 10 min. Capillary blood glucose was also measured in the morning after an overnight fast. Whole blood glucose was estimated using a glucose dehydrogenase method with a photometric alucometer (Hemocue AB, Am-relholm, Sweden). Calibration of the machine was confirmed using the’manufacturer’s control cuvette and high- and low-value glucose solutions were used. The diagnostic criterion for diabetes was a fasting capillary whole blood glucose (fcg) 26.1 mmol/L. The criterion for IFG was fcg ~5.6 mmol/L and <6-l mmol/ L in accordance with WHO recommendations (ALBERTI & ZIMMET, 1998). Hypertension was defined as mean systolic blood pressure 2 160 mmHg or diastolic blood pressure 295 mmHg (WHO, 1996). Self-reported diagnosis with current appropriate treatment was also diagnostic for both diabetes and hvnertension. The definitions of overweight and obesity w&e based on body massindex (BMI) 225 kg/m2 and 30 kg/m2, respectively (JAMES, 1996). Abdominal obesity was defined as a waist:hip ratio >0*80 for women and 0.95 for men (CRUIKSHANK et al., 1991). Statistical analyses were performed using Stata (version 5, Stata Corporation, TX. USA). All results are nresented with robust estimates of variance after adjustment for the design effect. Throughout the article, 95% confidence intervals are presented in brackets. Disease and risk factor prevalence is presented asboth crude rate and age-standardized rate, using the direct method, referred to the new World Population standard (WHO, 1992). Age adjustment was also performed using linear and logistic regression modelling, as appropriate. Logistic regression analysis was performed to estimate the relative contribution of overweight, general and abdominal obesity, and urban residence to risk of diabetes. Models were adjusted for age and interactions were explored. Using these models, the population attributable fraction (synonym aetiological fraction) for diabetes and IFG was estimated for urban residency, overweight, obesity, abdominal obesity and physical inactivity. The attributable fraction is the proportion by which the diseasein the entire population (for example, prevalence of diabetes) would be reduced in the absence of the ‘exposure’ (for example obesity) (LAST, 1988). This assumes that the relationship is causal, that removal of the exposure would reduce the risk to that in those unexposed and that there are no confounding or interacting risk factors. Thus, attributable fraction is an estimate of the maximum benefit to be gained if it were possible to remove the exposure. Confidence intervals for the attributable fraction are based on asymptotic approximations (GREENLAND & DRESCHER, 1993). The project received ethical approval from the Tanzanian Ministry of Health. Results
Response was higher in Shari village with 401 men (80% response) and 527 women (85% response) compared with 332 men (64.8% response) and 438 women (81.7%) in Ilala Ilala. The mean age for all adult respondents was greater in Shari at 42.1 [41 *Oto 43.31 years than in Ilala at 30.6 [29*6 to 31.51 years. Prevalence of diabetes, IFG and anthropometric risk factors
The nrevalences of diabetes. IFG. overweiaht. obesitv. and physical inactivity in Ilala and Shari f& men a& women are presented in Table 1. Prevalences are presented for each sex by agetertile with crude rates and age-
TERENCEJ.ASPRAYETAL.
standardized rates, using the direct method. The difference between Ilala Ilala and Shari in crude prevalence of diabetes for men was 3.8 [l*l-6.51% and 2.9 [0.84.91% for women. The difference in prevalences of IFG for men was 2.8 [0*3-5.31% and for women 3.9 [1.46.41%. For overweight (BMIa25 kg/m2) and obesity (BMIs30 kg/m’), the difference in prevalences was 21.5 [15*8-27-l]% and 6.2 [3*5-8-g]% for men and 17.4 [11.5-23.31% and 12.7 [8.5-16.81% for women, respectively. The difference in prevalences of physical inactivity was 12.5 [7*0-18.31% for men and 37.6 [31*9-43.31% for women. Abdominal obesity and diabetes
Table 2 summarizes the anthropometric measurements and fasting capillary glucose levels for men and women in Shari and Ilala Ilala. These data suggest that measurements of waist, hip and waist:hip ratio and fasting capillary glucose levels are higher for both genders in Ilala Ilala and in older age-groups compared with the younger. Comparisons between urban and rural populations are presented below as mean differences with ageadjustment, using linear regression. Men in Ilala Ilala were 7.7 15.7-9.61 ke heavier with BMI areater bv 3.2 [2*5-3*8]‘kg/m’, and’8lthough they were ihorter by 1.2 [-2.5-0.21 cm, this difference was not significant. Waist circumference was greater by 5.9 [4.4-7:4] cm, as were bin circumference. bv 4.3 12*7-5.91 cm. and waist:hio raso, by 0.021 [O*bO9-0.0321; fasting capillary glucose was 0.77 [0.56-0.971 mmol/L higher. Women in Dar es Salaam were shorter by 4.2 [3.35.11 cm and heavier by 3.4 [l-4-5.4] kg with a higher BMI by 2.65 [lag-3.4 kg/m*. Waist circumference was greater by 2.2.[0*4-4.0]>m, as was hip circumference, bv 4.0 12.3-5-71 cm. but waist:hio ratio was lower. bv 0:015 [&003-0:026j; fasting capiilary glucose was also significantly higher, by 0.46 [O-34-O-581mmol/L. There were significant interactions between age and area of residence for most anthropometric variables. Thus, BMI increased for men by 0.10 [0.06-0.141 kg per year of age in Ilala and by 0.03 [0.02-0*05] kg per year of agein Shari. For women, there was an increase by 0.12 [0*07-O* 171per year of agein Ilala with an apparent decreasein Shari of O-02 [ -0.0 l-0.031 kg per year of age (not significant). Interactions were also seen for men between age and area for height, weight, waist, waist:hip ratio and fasting blood glucose but not hip measurement, systolic or diastolic blood pressure. For women there were interactions between age and area of residence for weight, waist, hip, waist:hip ratio but not height, fasting blood glucose, or systolic or diastolic blood pressure. Diabetes had already been diagnosed in 1 of 6 men and none of 6 women in Shari and in 5 of 17 men and 2 of 17 women in Ilala. For men, diabetic subjects were on average 9.1 years older and 16.7 kg heavier. Waist circumference was greater by 17 cm and waist:hip ratio by 0.08. For women, diabetic subjects were on average 5.1 vears older and 14.8 ke heavier. Waist circumference was-greater by 17.1 cm&d waist:hip ratio by 0.05. Hypertension and physical inactivity were more prevalent amone subiects with diabetes. All these differences were sign&ant (P < 0.05) and were further explored, using logistic regression (Table 3). For both sexes,being overweight or obese and having a raised waist:hip ratio were more likely among diabetic than non-diabetic subjects. There were no significant associations between anthropometric variables& physical activity and IFG. However, urban residence was more likelv for IFG in both sexes. Age-adjustment made a remarkable difference only to the models for urban residence, which was associated with a higher age-adjusted odds ratio for diabetes of 6.4 [2.2-18.71 for men and 5.8 12.0-17.01 for women. For abnormal fasting glucose (IFG or diabetes), the odds ratio fell between that for IFG and diabetes’separately, as seen in Table 3. The population attributable fractions of general and
(0.5) (1.1) (2.6) (1.0) (1.7)
2.3 5.8 33.3 12.7 47.9
320
215 1.9 4.3 18.0 3.7 82.5
(0.8)“’ (1.4)NS (2.6) (1.9) (2.9)
(0.9)NS (1.4) (2.7) (1.2) (2.7)
Shari
1.5 1.0 32.5 7.5 91.4
201 (0.9) (0.7) (3.3) (1.9) (2.0)
138 1.4 (1.0) 2.2 (1.2) 8-O (2.3) 0.7 (0.7) 96.4 (1.6)
35-54
8.1 5.8 57.9 35.2 50.0
89
90 10.4 4.7 45.9 13.8 69.3
(3.0) (2.5) (5.2) (5.0) (5.4)
Shari
1.6 0.8 12.9 5.2 61.5
123 (1.1) (0.8) (3.1) (2.0) (4.4)
113 2.7 (1.5) 1.7 (1.2) 9.7 (2.8) 7z.7 (4.1)
55+ Ilala
l ii1 50.0 15.4 36.0
29 (5.9) (9.8) (7.1)NS (9.0)
42.3 (9.6) 3.8 (3.8) 65.4 (9.3)NS
27
y;. 22.0 4.8 85.2
527 y; . (1.8) (0.9) (1.6)
(0.6) (0.6) (1.2) (0.3) (1.5)
Shari 401 1.5 1.2 6.0 0.3 90.2
438 4.0 5.4 39.4 17.5 47.7
(0.9) (1.2) (2.3) (1.9) (25)
activity
527 1.1 1.6 20.6 4.2 87.4
401 1.7 0.8 4.4 0.2 92.7
(0.4) (0.6) (1.7) (0.8) (1.3)
(0.6) (0.4) (1.0) (0.2) (1.3)
Shari
(15) (1.0) (2.8) (1.6) (2.8) (1.3) (1-O) (2.8) (2.4) (2.7)
332 5.9 3.6 31.5 7.3 75.8 438 5.7 4.7 44.1 19.8 45.2
Ilala
All (adjusted)”
obesity and physical
(1.2) (1.2) (2.6) (1.4) (2.5)
Ilala 332 5.3 4.0 27.5 6.5 77.5
All (crude)
fasting glucose (IFG) , overweight,
(years)
impaired
Age-group
of diabetes,
(3.2) (2.3)NS (5.4) (3.7) (4.9)
Ilala
(Ilala) area of Tanzania,
All differences in prevalence between Shari and Ilala are significant (at least P < 0.05) except where markedNs. “Adjustment is to the age distribution to the New World Population. bBody mass index ~25 kg/m’ ‘Body mass index 230 kg/m’. ‘Vhysically active is presented as proportion who score l-3 out of 5 on physical activity questionnaire (see text for more details).
0.5 2.5 16.8 2.0 93.6
203
Women (n)
Diabetes IFG Overweight or obeseb Obese’ Physically active (%)d
150 0.7 (0.7) 0 1.3 (0.9) 9z.3 (1.7)
Men (n) Diabetes IFG Overweight or obeseb Obesec Physically active (%)d
Ilala
and an urban
15534
(Shari)
Shari
Table 1. Prevalence, in a rural presented as percentage (SE)
E s
2
z
E
Shari
203 22.4 (0.2) 160.1 (0.4) 56.8 (0.6) 73.7 (0.6’1 91.6 (ii
84:7 (0:5) 0.831 (0.003) 4.25 (0.05)
150 19.5 (0.2) 166.1 (0.8) 3;‘; g;;
Ilala
320 24-O (0.3) 156.3 (0.4).._ 58.9 (0.8)“> 74.0 (0.7F 94.9 (0.7) 0.777 (0.005) 4.98 (0.06)
215 22.1 (0.3) 166.6 (0.6)NS 61.5 (0.7) 74.4 (0.7) 90-O (0.9) 0.838 (0.006)NS 4.87 (0.05)
Shari
0.893 (0.010) 5.30 (0.22)
4z.O (0.6) 167.3 (0.7)
Ilala
201 9t.o (0.7) 23.6 (0.3) 160.2 (0.4) 155.8 (0.6) 60.8 (0.8) 68.2 (1.9) 79.3-\-(0.7), 86.1 (1.6‘) 96.0 (0.7) 103.1 (1.4) 0.827 (0.005) 0.834 (0.009)NS 4.69 (0.07) 5.00 (0.08)
89:5 (0.5) 0.865 (O-004) 4.39 (0.06)
138 21.1 (0.2) 170.5 (0.5) ;;.‘6 g;;
35-54
ratio and fasting
z.5 (0.7) 164.0 <1.5j 65.9 (2.2) fji.0” yj . . 0.838 (0.017) 5.6 (0.45)
Ilala
123 21.3 (0.4) z.2 (1.3) 156.6 (0.60) 152.2 (1.1) 52.3 (1.0) 57.4 (2.9) 77.2 (1.00) 80.2 (3.1)NS 91.5 (0.9) 95.4 (2.8)“’ 0.845 (0.005) 0.838 (0.017)NS 4.57 (0.06) 5-20 (0.22)
(0.3) (0.6) (0.9) (0.9) (0.7) (0.005) (0.10)
Shari
55-t
(years)
527 22.5 (0.2) 159.3 (0.3) 57.3 (0.5) 76.7 (0.5) 93.3 (0.4) 0.822 (0,003) 4.57 (0.03)
(0.1) (0.4) (0.5) (0.4) (0.3) (0.003) (0.04)
Shari 401 20.5 168.1 58.2 75.2 87.8 0.854 4.37
Ilala
(Shari)
438 24.9 (O-3) 156.0 (0.3) 60.7 (0.8) 76.9 (0.7)NS 96.6 (0.7) 0.793 (0.004) 5.00 (0.05)
332 23.1 (0.3) 166.6 (0.5) 64.1 (O-8) 78.3 (0.7) 90.7 (0.7) 0.861 (0.006)NS 5.05 (0.08)
All (crude)
glucose in men and women from a rural
Age-group
capillary
113 20.9 167.7 59.1 78.6 89.8 0.845 4.50
BMI, body mass index; WHR, waist:hip ratio; FCG, fasting capillary blood glucose. All differences between Shari and Ilala are significant (at least P < 0.05) except where marked r~‘. ‘Adjustment is to the age distribution to the New World Population.
WHR FCG (mmol/L)
Hip (cd
Women (n) BMI (kg/m*) Height (cm) Weight (kg) Waist fcmj
Men (n) BMI (kg/m’) Height (cm) Weight (kg) waist (cm) Hip(cm) WHR FCG (mrnol/L)
15-34
Table 2. Mean (SE) body mass index, waist, hip, waist-to-hip of Tanzania
527 22.0 (0.2) 156.3 (0.3) 56.2 (0.5) 74.6 to.45 6i.i {b.ij 0.803 (0.003) 4.45 (0.03)
(0.1) (0.4) (0.5) (0.4) (0.3) (0.002) (0.04)
Shari 401 20.3 168.1 57.7 74.3 87.3 0.850 4.34
area
438 25.0 (0.4) 152.2 (0.4j 60.4 (1.0) 77.5 (0.9) 95.7 (0.8) 0.791 (0.005) 4.94 (0.05)
332 23.4 (0.3) 166.5 (0.5) 65.1 (0.9) 79.9 (0.7) 91.2 (0.7) 0.87 1 (0.006) 5.11 (0.11)
Ilala
(Ilala)
All (adjusted)”
and an urban
9.0 10.5 2.4NS 1.6NS 3.6
Women Uvei%%l&t or obese” Obesityb Abdominal obesityc Physically inactived Living in city
13.3-24.53 [4.5-24.61 [0.9-6-21 [0.6-4.11 [ 1.4-9.21
[5.7-35.11 [1.5-19.51 [5.2-29-91 [0.9-5.91 [ 1.4-9.31 9.3 10.7 2.2NS 1.6NS 5.8
13.1 5.7 10.8 1.7NS 6.4 [3S--26.63 [4.4-25.91 [0.8-5.71 [0.6-3.91 [2.0- 17.01
l.Ows 0.7NS 0.5Ns 2.1NS 3.8
2.4NS 2.2NS 1.7= 2~4~~ 3.5
(IFG) in relation
[5.2-32.81 [1.6-20.81 [4.6-25.21 [0.6-4.61 [2.2- 18.71
Age-adjusted
glucose
[OS2.31 [O-2-2.8] [0.2-1.11 [l.O-4.41 [1.7-8.81
[0.9-6.71 [0.3-17.31 [O-4-7.9] [0.8-7.31 [1.2-10.01
Crude
IFG
to overweight,
obesity,
l.INS 0.7NS 0.6NS 2.2NS 3.4
2.6NS 2.2NS 1.8NS 2.6NS 3.7 f&+2.4] [0.2-2.91 [0.3- 1.31 [l.O-4.51 [l-5-7.9]
[0.9-7.11 [0.3- 17.41 [0.4-8.51 [0.9-7.91 [1.5-9.31
Age-adjusted
abdominal
Values are odds ratio [95% confidence interval]. Denominator for diabetes and ‘IFG or diabetes’ is the whole population and for IFG alone is the non-diabetic population. NSIndicates that the odds ratio was not significantly greater than one (F > 0.05). All other odds ratios were significant (P < 0.05). “Body mass index 325 kg/m’. ‘Body mass index 230 kg/m’. ‘Waist:hip ratio 2 0.95 (men) or 0.80 (women). dProportion who score >3 out of 5 on physical activity questionnaire (see text for more details).
14.1 5.3 12.5 2.3NS 3.7
Crude
fasting
Diabetes
and impaired
Men Overweight or obese” Obesityb Abdominal obesi@ Physical inactived Living in city
Table 3. Odds ratio for diabetes
2.5 3.3 l.ONS 1.9Ns 3.7
6.7 4.0 6.1 2.4 3.6
physical
and urban
[ 1.4-4.4] [1.7-6.31 [0.6-1.71 [l-O-3.5] [2.0-7.01
residence
2.5 3.3 l.ONS 1.9 4.2
6.5 4.0 5.7 2.1NS 5.0
[l-4-4-4] [1.7-6.31 [0.6-1.71 [l.l-3.61 [2.1-8.41
[3.5-12.11 [1.3-12.11 [2.9-l 1.61 [l.O-4.41 [2.4-10.41
Age-adjusted
IFG or diabetes
[3.6- 12.51 [1.3-12.21 [3.0-12.41 [l.l-4,8] [1.8-7.21
Crude
inactivity
z
E
c;
642
TERENCE J. ASPRAY ETAL.
abdominal obesity for diabetes and IFG are presented in Table 4. The contribution of being overweight or obese to the risk of diabetes is greater in women than men in this population. For example, 63% of diabetes in men and 69% of diabetes in women might not occur, if overweight were avoided. Obesity, rather than total overweight, does not have a significant effect for men. The attributable fraction for large waist:hip ratio was greater in men than women. Urban residence accounted for just over half of diabetes cases in both sexes. The impact of overweight and obesity on IFG was not significant, although over half of IFG cases might be avoided by not living in the city. The roles of overweight and physical inactivity as mechanisms by which urban residence may influence diabetes prevalence were explored. Thus, Table 5 shows, in both men and women, that age-adjusted differences in obesity (BMI or waist:hip measurement) explain much of the effect of urbanization. For men, the odds ratio for urban residence falls from 6.37 to a non-significant 2.42 with the inclusion of BMI, but for women BMI accounts for most but not all of the impact of urban residence. Similarly, for both sexes,
Table 4. Population abdominal obesity,
the odds ratio for urban residence decreased after adjustment for waist:hip ratio, but this did not explain the effects of urbanization completely. Physical activity level did not make any independent contribution to explaining the effects of urbanization. However, for both sexes the inclusion of physical activity with obesity variables lowers the odds ratio further for urban residence. In Table 6, age-adjusted logistic regression models are presented for the sexes between areas. For men in Shari, BMI is associated with an odds ratio of 183 for diabetes but there is no improvement with the incorporation of other variables. However, in urban men, BMI and waisthip ratio both predict diabetes separately, and the model incorporating these with physical activity score is the best (OR for BMI rises to 2.22 with a pseudo ti of 29%). For women in Shari, there is a very strong association of BMI with diabetes (odds ratio 12.16 and pseudo 12 50%) and adjustment for physical activity improves the model (OR for BMI 12.5, pseudo 1255%). For urban women, these variables do not predict diabetes well, although BMI is the best (odds ratio 1.22 but pseudo ? for the model is 8%).
attributable C-action for diabetes and impaired physical inactivity and urban residence
fasting
Diabetes
Men Overweight or obese” Obesityb Abdominal obesityc Physically inactived Living in city Women Overweight or obese” Obesityb Abdominal obesi@ Physically inactived Living in city
glucose
with
overweight,
Impaired fasting glucose
Crude
Age-adjusted
Crude
Age-adjusted
64 [42-861 10Ns [O-23] 44 [23-651 16Ns [O-38] 53 [22-851
63 [41-851 1ONS[O-23] 43 [22-641 11ns [O-35] 62 [35-891
16;; [O-39]
17;: [O-39]
;NS ;;I;;; 17ns [O-49] 51 [15-871
;NS [;--;;; 18Ns [O-49] 52 [20-851
69 [45-931 47 [25-691 42NS [O-82] 1gNS [O-48] 53 [21-851
69 [45-931 47 [25-691 3gNS [O-82] 1gNS [O-48] 61 [33-891
ONS ONS ONS 2P [O-50] 54 [27-811
ii 2;” 51
[O-50] [22-801
Values are percentage [95% confidence interval]. Denominator for diabetes is the whole population and for impaired fasting glucose alone is the non-diabetic population. NsIndicates that the attributable fraction was not significantly greater than zero (P> 0.05). All other attributable fractions were significant (P < 0.05). “Body mass index 225 kg/m*. bBody mass index ~30 kg/m’ ‘Waist:hip ratio 20.95 (men) or 20.80 (women). dProportion who score >3 out of 5 on physical activity questionnaire (see text for more details).
Table 5. Logistic urban residence
regression
models of diabetes
Sex
Urban area
Men
6.37 [2*17-18.701 2.42NS [0.72-8.071 4.93 [1*76-13.851 6.39 [2*00-20.431 1.96NS [0.59-6.571 4.35 [1*48-12.711 l.gNS [0.58-6.291 5.85 [2*01-17.051 3.32 [1.04-10.661 6.36 [2*25-17.981 3.86 [l*lO-13.561 2.35NS [0.56-9.911 4.19 [1.26-13.921 2.45NS [0.58- 10.331
Women
with overweight,
Body mass index
abdominal
Waste:hip ratio
obesity,
physical
inactivity
Physical inactivity
1.69 [l-23-2.31] 1.28
[l-07-1.531
1.96 [1.41-2.751 1.88 [1.33-2.651
1.34 [1*09-1.651 l.OSNS [0*89-1.311
l*llNS [0*41-3.001 l.OINS [0.37-2.761 1*15NS [0*43-3.091 1*02NS [0.37-2.851
1.49 [l.ll-1.991 1.23
[l-03-1.461
1.45 [1.07-l-961 1.39 [1*03-l-871
1.26 [1*03-1.561 1*13NS [0.93-1.381
0.94NS [0*31-2.841 0.94NS [0.30-2.941 l.OONS [0.33-3.021 0.98NS [0.31-3.081
and
Pseudo 13 10% 21% 14% 11% 25% 16% 26% 7% 16% 10% 4% 12% 8% 13%
NSIndicates that the odds ratio was not significantly greater than one (P > 0.05). All other odds ratios were significant (P < 0.05). All models are age-adjusted. Body mass index and waist:hip ratio effects are presented as per decile (see text for details), and physical inactivity is a binary variable (active versus inactive).
643
DIABETES IN URBAN AND RURAL TANZANIA
Table 6. Logistic regression activity in men and women Area Men Shari
Ilala
models of diabetes
Body mass index 1.83
in association Waist:hip ratio
abdominal
[1.17-2.841
1.88
[1*16-3.061
1.65
[1.06-2.551
1.18NS [0.86-1.621 0.93NS [0.71-1.231
2.41
[1.59-3.631
2.22
[1.56-3.161
1~08~~ [O.ll-ll*ll] 1.06NS [0.09-13.101 l*lONS [0~10-11*05] l.lONS [O-09-12.741
17% 3% 2% 17% 3% 17%
1.26NS [0.39-4.071 1.27NS [0.40-4.081 1.28NS [O-40-4.091 1.31NS [0*40-4.281
19% 14% 13% 28% 19% 29%
12.16
[2.12-69.721 6.05NS [0.77-47.71 5*31NS [0.77-36.491 5.65NS [0.77-41.541 4.70NS [0.67-33.141
50% 7% 6% 55% 12% 56%
0.54NS [0*18-l-671 0.53NS [0.17-1.621 0.56NS [0.18-1.751 0.55NS [0.17-1.711
8% 7% 2% 4% 6% 6%
[1.04-1.651
1.41 [1.03-1.931 1.13NS [0.86-1.501
1.42NS [0.91-2,231 12.50
[2.02-77.431
11.86
[1.88-74.771
1~42~~ [0.89-2.261 Ilala
1.17NS [0.75-1.821
1.22NS [0.98-1.511 1.18NS [0.97-1.431 1.17NS [0.94-1.461 1.09NS [0.89-1.351
1.23NS [0.97-1.571 1.19= [0.93-1.511
physical
Pseudo $
1*18NS [0.86-1.621 1.82
obesity,
Physical inactivity
[1.18-2.831
1.31
Women Shari
with overweight,
NSIndicates that the odds ratio was not significantly greater than one (P > 0.05). All other odds ratios were significant (P < 0.05). All models are age-adjusted. Body mass index and waist:hip ratio effects are presented as per decile (see text for details), and physical inactivity is a binary variable (active versus inactive).
Conclusion Our study clearly shows a higher prevalence of diabetes and IFG in men and women living in an urban area compared with a rural area of Tanzania. Differences in prevalence of risk factors for type 2 diabetes (overweight and physical inactivity) are also evident. Our diabetic populations in both areas were older, heavier, with a higher BMI and larger waist:hip ratio. They were also less active. Although there are limitations in the use of crosssectional survey data in the identification of aetiological factors, 2 strong patterns appear. Being overweight is associated with an odds ratio of 14.1 for diabetes in men and 9.0 in women, corresponding to an attributable fraction of at least 4 1% in men and 45% in women, after adjusting for age. Abdominal obesity shows similar effects, although the threshold set for waisthip ratio may not be as sensitive as for BMI and may differ between the sexes and areas in its performance. The second finding is that the attributable fraction for living in the citv for diabetes is at least 35% in men and 33% in women. Physical inactivity was not an independent risk factor for diabetes. although for men from Ilala Ilala and women in Shari adjustment for physical activity improved the regression model. The performance of the exercise questionnaire was disappointing. The best evidence for physical activity as a determinant of diabetes risk is from longitudinal studies and those eliciting et al., 1991; historical physical activity (HELMRICH KRISKA et al., 1993). Thus, current reported activity levels (as we used) are likely to be insensitive compared with anthropometric variables which represent cumulative effects of overweight achieved over probable decades. In assessing abdominal obesity, we did not include waist measurement in our analyses, in the absence of internationally accepted standards (WHO, 1998). However, the development of locally validated waist measurement cut-offs would be of considerable value and this work is currently underway (ASPRAY et al., 1999).
For men, the effect on diabetes prevalence of living in the city was explained mostly by differences in BMI. For women, an interesting finding was the low explanatory Dower of BMI for diabetes in the urban residents (see Table 6). This observation may suggest that other factors associated with urban residence but not measured in our study may be very important as risk factors for diabetes in this urban population. These data present strong evidence of a major difference in diabetes prevalence between urban and rural communities in Tanzania. The urban populations also have a higher prevalence of risk factors for diabetes, including overweight, obesity and physical inactivity. Our findings suggest that primary prevention initiatives, particularly the avoidance of overweight, are priorities for adult health in this population. References ADA (1998). American Diabetic Association Consensus development conference report: consensus development conference on insulin resistance. Diabetes Cure, 21,310-314. Alberti, K. & Zimmet, P. (1998). Definition, diagnosis and classification of diabetes mellitus and its comnlications. Part 1: diagnosis and classification of diabetes mell
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Fisch, A., Pichard, E., Prazuck, T., Leblanc, H., Sibide, Y. & Brucker, G. (1987). Prevalence and risk factors of diabetes mellitus in the rural region of Mali (West Africa): a practical approach. Diabetologia, 30,859-862. Gillum, R. F. (1996). The epidemiology of cardiovascular disease in black Americans. New EnglandJournal of Medicine, 335,1597-1598. Greenland, S. & Drescher, K. (1993). Maximum likelihood estimation of the attributable fraction from logistic models. Biometrics, 49,865-872. Helmrich, S. P., Ragland, D. R., Leung, R. W. & Paffenbarger, R. S. Jr (199 1). Physical activity and reduced occurrence of non-insulin-dependent diabetes mellitus. New EngZandJournal of Medicine, 325, 147-152. James, W. P. T. (1996). The epidemiology of obesity. Ciba Foundation Symposium 20 1: The Origins and Consequencesof Obesity. Chichester: John Wiley and Sons, pp. 1- 16. Kriska, A. M., LaPorte, R. E., Pettitt, D. J., Charles, M. A., Nelson, R. G., Kuller, L. H., Bennett, P. H. & Knowler, W. C. (1993). The association of physical activity with obesity, fat distribution and glucose intolerance in Pima Indians. Diabetologia, 36, 863-869. Last, J. M. (1988). Attributable fraction (exnosedj. In: A D&Gonay if Epidemiology, 2nd edn, Last, j. M. ‘(editor). Oxford: Oxford University Press, pp. 9-10. Levitt, N. S., Katzenellenbogen, J. M., Bradshaw,D., Hoffman, M. N. & Bonnici, F. (1993). The prevalence and identification of risk factors for NIDDM in urban Africans in Cape Town, South Africa. Diabetes Care, 16,601-607. Mbanya, J. C., Ngogang, J., Salah, J. N., Minkoulou, E. & Balkau, B. (1997). Prevalence of NIDDM and impaired glucose tolerance in a rural and an urban population in Cameroon. Diabetologib, 40, 824-829. McLarty, D. G., Swai, A. B., Kitange, H. M., Masuki, G., Mtinangi, B. L., Kilima, P. M., Makene, W. J., Chuwa, L. M. & Alberti, K. G. (1989). Prevalence of diabetes and impaired glucose tolerance in rural Tanzania. Lancet, i, 87 l-875. McLarty, D, Unwin, N., Kitange, H. & Alberti, K. G. M. M.
Book Review Microbial Pathogenesis: a Principles-Orientated Approach Bruce A. McClane and Timothy A. Mietzner (editors) and John N. Dowling and Bruce A. Phillips (coeditors). Madison: Fence Creek Publishing, 2000. xii + 485~~. Price E15.95. ISBN l-889325-27-9. Microbial Pathogenesis is designed as a course supplement for first- and second-year medical students. The paper-backed volume is divided into 29 chapters which provide a basic introduction to the principles ofmicrobial growth, physiology and antimicrobial strategies, modes of transmission, colonization and subversion of host defences, mechanisms of host damage, the genetics of virulence, the outcome of infection and the principles of vaccines. As with other titles in this Integrated Medical Sciences Series from Fence Creek, the content is expressly designed to explain the basic science principles that underlie a &en clinical condition. This is achieved by dividing ea& chapter into 3 distinct sections. First a clinical case relevant to the subject matter of the chapter is introduced, next the fundamental microbiological principles and details are given, and finally the clinical case resolution is described which draws upon the basic science content of the chapter in order to explain the outcome. At the conclusion of each chapter there are a number of multiple-choice questions which review the reader’s understanding of the subject matter followed by the answers and helpful explanatory summaries. In general, this approach works well and helps to divide this very broad subject matter into a digestible set ofbitesized chunks. Perhaps inevitably, given the scope of the topics, the coverage of some areas is rather superficial. For example
TERENCE J. ASPRAY ETAL. (1996). Diabetes as a cause of death in sub-Saharan Africa: results of a community-based study in Tanzania. Diabetic Medicine, 13, 990-994. M&arty, D., Pollitt, C., Swai, A., & Alberti, K. G. M. M. (1997). Epidemiology of diabetes in Africa. In: Diabetes in Africa, Gill, G., Mbanya, J.-C. & Alberti, K. (editors). Cambridge, UK: FSG Communications, pp. 1- 18. Omar, M. A., Seedat, M. A., Motala, A. A., Dyer, R. B. & Becker, P. (1993). The prevalence of diabetes mellitus and impaired glucose tolerance in a group of urban South African blacks. South African Medical Yournal, 83, 641-643. Omran, A. (197 1j. The epidem&logic t&&ition: a theory of the epidemiology of population change. Millbank Memorial Fund Quarterly, 49, 509-538. Panz, V. R. & Joffe, B. I. (1999). Impact of HIV infection and AIDS on prevalence of tvne 2 diabetes in South Africa in 2010. Bri&hMedicalJou&& 318, 1351. Unwin,N.,Alberti,G.,Aspray,T., Edwards,R.,Mbanya, J. C., Sobngwi, E., Mugusi, F., Rashid, S., Setel, P. &Whiting, D. (1999). Tackling the emerging pandemic of non-communicable diseases in sub-Saharan Africa: the essential NCD health intervention project. Public Health, 113, 141-146. WHO (1992). World Health Statistics Annual. Geneva: World Health Organization. WHO (1996). Hypertension Control. Report of a WHO Expert Committee. Geneva: World Health Organization. Technical Report Series, no. 862. WHO (1998). Obesity: preventing and managing the global epidemic. Report of a WHO consultation on obesity. Geneva: World Health Organization. WHO/NUT/NCD/98.1. Wilson, P., Paffenbarger, R., Morris, J. N. & Havlik, R. J. (1986). Assessment methods for physical activity and physical fitness in population studies: a report of a NHLBI workshop. American HeanJournal, 111, 1177-1192.
Received 10 April 2000; revised 19 May 2000; accepted for publication 24 May 2000
metabolism and nutrient acquisition, although fundamental to bacterial survival, are barely mentioned. However, throughout the book the authors do encourage the reader to visit more specialized texts to address each area in more detail. In this context it would have been helpful to have included specific suggestions for further reading or to have highlighted some of the extensive Internet resources that are now available. Another shortcoming is the omission of any references to the current frenzied research activity in the fields of microbial genomics and the interplay between bacteria and the pattern recognition systems of the innate host defences. Whilst not absolutely essential to an initial understanding of pathogenesis, an attempt to convey the level of excitement which currently surrounds advances in these areas may have helped to persuade the reader to delve into this burgeoning literature and even to consider a future research career in the molecular basis of infectious disease. Nonetheless, MicrobiaZ Pathogenesis is a very useful addition to medical-undergraduate learning resources and would not even be out of place on the bookshelf of basic-microbiology students who would benefit from a text which neatly illustrates the clinical relevance and outcomes of the interaction between micro-organisms and their host. Mike Curtis Medical Microbiology St Bartholomew’s and the Royal London School of Medicine and Dent&y 32, Newark Street London El 2AA, UK The book is available from Blackwell Science, +44 (0) 1865 206233, fax +44 (0) 1865 206026, [email protected]
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