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Prevalence and determinants of undiagnosed diabetes in an urban sub-Saharan African population
p r i m a r y c a r e d i a b e t e s 6 ( 2 0 1 2 ) 229–234
Contents lists available at SciVerse ScienceDirect
Primary Care Diabetes journal homepage: http://www.elsevier.com/locate/pcd
Original research
Prevalence and determinants of undiagnosed diabetes in an urban sub-Saharan African population Justin B. Echouffo-Tcheugui a , Anastase Dzudie b , Marielle E. Epacka b , Simeon P. Choukem b , Marie S. Doualla b , Henry Luma b , Andre P. Kengne c,d,e,∗ a
Hubert Department of Global Health, Rollins School of Public Health, Emory University, Atlanta, GA, USA Department of Internal Medicine, General Hospital of Douala, Douala, Cameroon c The Georges Institute for Global Health, Sydney, Australia d Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht, The Netherlands e South African Medical Research Council & University of Cape Town, Cape Town, South Africa b
a r t i c l e
i n f o
a b s t r a c t
Article history:
Aims: To report the prevalence of undiagnosed diabetes and its determinants among adults
Received 28 February 2012
Cameroonian urban dwellers.
Received in revised form
Methods: On May 17th 2011, a community-based combined screening for diabetes and hyper-
26 April 2012
tension was conducted simultaneously in four major Cameroonian cities. Adult participants
Accepted 14 May 2012
were invited through mass media. Fasting blood glucose was measured in capillary blood.
Available online 7 June 2012
Results: Of the 2120 respondents, 1591 (52% being men) received a fasting glucose test. The median age was 43.7 years, and 64.2% were overweight or obese. The sex-specific
Keywords:
age adjusted prevalence (for men and women) were 10.1% (95% confidence interval [CI]:
Diabetes mellitus
8.1–12.1%) and 11.2% (95%CI: 9.1–13.3%) for any diabetes, and 4.6% (95%CI: 2.6–6.6%) and
Screening
5.1% (95%CI: 3.0–7.2%) for screened-detected diabetes, respectively. The prevalence of dia-
Prevalence
betes increased with increasing age in men and women (all p ≤ 0.001 for linear trend). Older
Cameroon
age (p < 0.001), region of residence (p < 0.001), excessive alcohol intake (p = 0.02) were signif-
Sub-Saharan Africa
icantly associated with screened-detected diabetes, while physical inactivity, body mass index, and high waist girth were not significantly associated with the same outcome. Conclusions: Prevalence of undiagnosed diabetes is very high among Cameroonian urban dwellers, indicating a potentially huge impact of screening for diabetes, thus the need for more proactive policies of early detection of the disease. © 2012 Primary Care Diabetes Europe. Published by Elsevier Ltd. All rights reserved.
1.
Introduction
Since the 1980s data have indicated an escalating pattern of diabetes worldwide, with the fastest growth occurring in developing countries [1]. It has been estimated that 366
million adults (7.0%) had diabetes worldwide in 2011 [2], with a higher proportion in developing countries than in developed nations. By 2030, the number of adults with diabetes is projected to increase by 50.7%; the greatest increase will occur in sub-Saharan Africa (90.5%), where the proportion of undiagnosed diabetes is also the highest ∼80% [2]. However, there
∗ Corresponding author at: South African Medical Research Council and University of Cape Town, P.O. Box 19070, Tygerberg, 7505 Cape Town, South Africa. Tel.: +27 21 938 0529; fax: +27 21 938 0460. E-mail address: [email protected] (A.P. Kengne). 1751-9918/$ – see front matter © 2012 Primary Care Diabetes Europe. Published by Elsevier Ltd. All rights reserved. http://dx.doi.org/10.1016/j.pcd.2012.05.002
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is a scarcity of nationally representative and reliable survey data on undiagnosed diabetes, and for most African countries published estimates were derived from small-scale surveys or based on extrapolations using data from neighboring countries [2]. Consequently, there is a need for more robust data on undiagnosed diabetes. Such figures are key to any prevention strategy, as undiagnosed diabetes mellitus contributes significantly to the overall disease-related morbidity and mortality [3]. Failure to diagnose or delayed diagnosis deprives people with diabetes from the benefits of cost-effective interventions that have shown positive impact of long-term complications of diabetes. Targeted opportunistic screening has been advocated as a mean for improving diabetes detection [4,5]. This typically involves using the opportunity of a contact with the healthcare system of people at high risk of undiagnosed diabetes, to also screen them for diabetes. Such an approach may be ineffective in settings where health service utilization by the population is still low. In such settings, there is a need for approaches to either screen those at high-risk of undiagnosed diabetes in the community, or to connect them to healthcare facilities for screening. Such strategies however, have not been assessed in sub-Saharan Africa. Evaluating community-based screening program is therefore necessary in order to investigate how best to detect undiagnosed diabetes in developing countries. There is very little information on their limitations, which may include the practicality and cost of tracking persons with high screening values to ensure that they receive confirmatory testing as well as proper care once diabetes has been identified. We therefore assessed the prevalence and determinants of undiagnosed diabetes mellitus in a self-selected urban population of adults Cameroonians who reported for a community-based hypertension screening campaign, advertised through mass media.
2.
Materials and methods
2.1.
Design, study population and settings
This was a cross-sectional population-based study conducted in the regional capitals of the Center (Yaoundé), Littoral (Douala), North-West (Bamenda) and West (Bafoussam) administrative regions (out of a total of 10) in Cameroon. According to the Cameroon’s National Institute of Statistics [6], the four participating regions had a total population of 9.98 million individuals in 2010 (51% of the country’s total population: 19.41 millions). In this same year, 57.5% of the national population was aged 15 years and above, and about 52% resided in urban area [6]. The study protocol was approved by the Cameroon National Ethics Committee and signed informed consent obtained from each participant.
2.2.
Invitation to screening
During the three consecutive weeks leading to the survey, daily announcements were made through radio and television, inviting all interested adults (aged 15 years and above) to report to any of the screening sites in participating cities on
the same pre-specified date. Advertisement for screening was also conducted in churches and market places.
2.3.
Study assessments
On May 17th 2011, screening was conducted by trained medical personnel, simultaneously in the four study cities (see investigators’ list in Appendix A). All eligible participants received a face-to-face interview during which data were collected on demographics, smoking habits, alcohol consumption, physical activity, parental history of hypertension, personal medical history including physician diagnosed hypertension and drug treatment, diabetes mellitus, gout and dyslipidemia.
2.3.1.
Anthropometry
Blood pressure (BP) was measured in a seated subject after at least 10 min rest, without alcohol intake or smoking. Measurements were performed on the right arm with the use of automated sphygmomanometers (OMRON M3 HEM-7200-E Omron Matsusaka Co. Ltd., Japan) and appropriate cuff sizes. Weight (in kg) was measured with the use of an automated scale in subjects with light dresses, and height (in m) measured using a wooden platform and a height rule. Body mass index (BMI – in kg/m2 ) was computed as weight (kg)/[height2 (m2 )]. Waist circumference was measured midway between the iliac crest and the lower rib margin and the hip circumference measured at the intertrochanteric level. Waist-to-hip ratio (WHR) was computed as waist (cm)/hip (cm) circumferences.
2.3.2.
Glucose assay
Capillary blood glucose (CBG) was measured after a fasting period of 9–14 h with a glucometer (Accu-chek Aviva® ; Roche Solutions for Diagnosis, F. Hoffmann-La Roche Ltd., Germany). The ACCU-CHEK Aviva test strips are whole-blood referenced and have been calibrated to deliver plasma-like values. A blood sample was obtained from a fingertip for the determination of CBG.
2.4.
Definitions
Participants were considered to have diabetes if they were treated with hypoglycemic medications (insulin and/or oral diabetes medications) or if their fasting blood glucose levels reached 7.0 mmol/l (126 mg/dl) or more, as defined by the World Health Organization (WHO) [7]. Respondents were classified as hypertensive if they had a systolic (and/or diastolic) BP of 140 (90) mmHg or higher, or if they were on blood pressure lowering medications over the last 15 consecutive days. Waist circumference >94 cm in men or 80 cm in women was considered to be high. Excessive alcohol consumption was based on intake of either more than three (two for women) standard glasses of wine per day or more than 10 (five for women) local beers (1 local beer contains 28 g of alcohol) per week. Participants who smoked at least one cigarette per day were classified as current smokers and those who had stopped smoking for more than 3 years were classified as former smokers. Regular non-work related physical activity was considered
p r i m a r y c a r e d i a b e t e s 6 ( 2 0 1 2 ) 229–234
231
in participants reporting at least 25 min of intense physical activity, once a week or more.
2.5.
Handling of participants with high glucose level
Participants with diabetes (known or screened-detected) received on-site medical counseling and were referred back to their attending physician (known cases), or to specialists within the vicinity of the participant’s residential area for workup and chronic management.
2.6.
Statistical analysis
Data analysis used the Statistical Package for Social Sciences (SSPS Inc., Chicago, IL) version 17.0 software. Results are summarized as count and percentages for qualitative variables and mean and standard deviation (SD) for quantitative variables. Age-adjusted prevalence was calculated using the Cameroon National population’s age structure in 2010 as the standard population [8], and direct standardization methods [9]. Comparison of groups was done using Chi-square tests and equivalents for qualitative variables, and Student’s t-test and analysis of the variance (ANOVA) for quantitative variables. Logistic regressions analyses were used to investigated potential determinants of screened-detected diabetes. A p-value <0.05 indicated statistically significant results.
3.
Results
3.1.
Screening implementation (available data)
Of the 2120 subjects who attended screening, 1591 (75%) received a fasting capillary glucose test and were therefore included in the current analyses. Participants with short fasting duration did not receive the test and were excluded. In Online Appendix Table 1 we present the baseline characteristics of study participants included and excluded from the analytical sample. Differences between those included and the excluded were small in magnitude, clinically trivial and did not attain statistical significance for many characteristics. For instance, mean baseline variables (participants in the primary analysis vs. those excluded) were 43.8 vs. 42.8 years for age (p = 0.69), 27.5 vs. 27.9 kg/m2 for BMI (p = 0.12), 137 vs. 134 mmHg for systolic blood pressure (p = 0.01) and 81 vs. 81 mmHg for diastolic blood pressure (p = 0.19).
3.2.
Characteristics of participants by age groups
The baseline characteristics of the participants to the screening program including the risk factors for diabetes are shown in Table 1. The number of screening attendees varied across screening sites, largely by virtue of differences in the underlying populations of each region of the country. The majority of attendees were men (52%), with median age of 43.7 years. Of those assessed, only 9% (143) reported having previously been diagnosed with diabetes, 27% (431) had a family history of hypertension, 91% had never smoked, 43% (686) were inactive, 16.3% (259) and 64.2% were obese or overweight with a mean BMI of 27.5 kg/m2 (SD = 5.4). The mean values of BMI,
Fig. 1 – Crude prevalence of diabetes by sex and age groups.
systolic and diastolic BP, as well as the proportions of participants with known diabetes, high waist girth, any hypertension or known hypertension were significantly higher with increasing age (Table 1).
3.3.
Prevalence of diabetes and dysglycemia
Of those participants included, 243 (15.3%) had prevalent diabetes including 143 (58.8%) who were already known with the condition. The crude prevalence of diabetes was 13.7% in men and 17.0% in women (p = 0.07) for any diabetes, and 6.0% vs. 6.6% for screened-detected diabetes (p = 0.62). The sex-specific age adjusted prevalence (men vs. women) were 10.1% (95% confidence interval: 8.1–12.1%) and 11.2% (9.1–13.3%) for any diabetes, and 4.6% (2.6–6.6%) and 5.1% (3.0–7.2%) for screened-detected diabetes. The prevalence of diabetes increased with increasing age in men and women (Fig. 1). Trends were mostly linear (all p ≤ 0.001 for linear trend) except for known diabetes in women (p = 0.07). Among those without diabetes, 78 (5.8%) had impaired fasting glycemia defined by a fasting glucose level ≥6.1 mmol/l (110 mg/dl). The equivalents in men and women were 45 (6.2%) and 33 (5.2%), respectively (p = 0.48).
3.4.
Determinants of screened-detected diabetes
Table 2 shows the crude and adjusted odds ratio for variables associated with the prevalence of undiagnosed diabetes. Older
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p r i m a r y c a r e d i a b e t e s 6 ( 2 0 1 2 ) 229–234
Table 1 – Characteristics of the study population. Variable
Overall
<35 years
35–44 years
45–54 years
55+ years
p-Trend
N Men, n (%) Median age, years Region, n (%) Center, n (%) Littoral, n (%) North-West and West, n (%) Parental hypertension, n (%) Dyslipidemia, n (%) Gout, n (%) Known diabetes, n (%) Smoking Never, n (%) Former, n (%) Current, n (%) Physical inactivity, n (%) Excessive alcohol, n (%) Body mass index Normal, n (%) Overweight, n (%) Obese, n (%) Mean, kg/m2 (SD) High waist girth, n (%) Mean systolic blood pressure, mmHg (SD) Mean diastolic blood pressure, mmHg (SD) Any hypertension, n (%) Known hypertension, n (%)
1591 834 (52) 43.7
459 230 (50) 27.5
336 212 (63) 39.2
332 212 (64) 49.6
229 180 (79) 60.7
0.45
630 (40) 893 (56) 68 (4) 431 (27) 12 (1) 18 (1) 143 (9)
223 (49) 234 (51) 2 (0) 113 (25) 2 (0) 2 (0) 11 (2)
152 (45) 198 (59) 11 (3) 105 (31) 3 (1) 4 (1) 18 (5)
145 (44) 241 (73) 12 (4) 123 (37) 5 (2) 2 (1) 32 (10)
110 (48) 220 (96) 43 (19) 90 (39) 2 (1) 10 (4) 82 (36)
1445 (91) 62 (4) 84 (5) 905 (57) 259 (16)
432 (94) 12 (3) 15 (3) 257 (56) 48 (10)
326 (97) 8 (2) 27 (8) 215 (64) 71 (21)
344 (104) 25 (8) 29 (9) 222 (67) 79 (24)
199 (87) 17 (7) 13 (6) 211 (92) 61 (27)
569 (36) 572 (36) 450 (28) 27.5 (5.4) 695 (44) 137 (27) 81 (17) 769 (48) 244 (15)
242 (53) 143 (31) 74 (16) 25.7 (4.5) 130 (28) 125 (19) 74 (14) 115 (25) 9 (2)
108 (32) 139 (41) 114 (34) 28.2 (5.7) 165 (49) 133 (23) 81 (17) 138 (41) 25 (7)
107 (32) 146 (44) 145 (44) 28.5 (5.4) 208 (63) 142 (28) 85 (18) 226 (68) 66 (20)
112 (49) 144 (63) 117 (51) 27.9 (5.2) 192 (84) 151 (29) 87 (17) 190 (83) 144 (63)
<0.001
age (p < 0.001) for trends across age categories, region other than the central of the country (p < 0.001), excessive alcohol intake (p = 0.02) were associated with a higher prevalence of undiagnosed diabetes. Parental hypertension, dyslipidemia,
0.83 0.64 0.01 <0.001 0.20
0.91 0.01 <0.001
<0.001 <0.001 <0.001 <0.001 <0.001 <0.001
presence of gout, smoking, physical inactivity, BMI, high waist girth, increasing SBP or DBP, as well as known hypertension were not associated with the prevalence of undiagnosed diabetes. However, the association of any hypertension (newly
Table 2 – Crude and adjusted odds ratios for predictors of undiagnosed diabetes among self-selected urban dwellers in Cameroon. Variable Sex Age
Region
Parental hypertension Dyslipidemia Gout Smoking
Physical activity Excessive alcohol Body mass index High waist girth Systolic BP Diastolic BP Any hypertension Known hypertension a
Category (n) Men (770) <35 years (448) 35–44 years (343) 45–54 years (366) ≥55 years (291) Center (577) Littoral (840) Others (31) Yes (382) Yes (178) Yes (10) Never (1317) Former (51) Current (80) No (853) Yes (235) Normal (517) Overweight (519) Obese (412) Yes (615) Per 10 mmHg Per 5 mmHg Yes (662) Yes (167)
Unadjusted OR (95%CI) 0.87 (0.58–1.31) 1 (reference) 1.41 (0.70–2.83) 2.86 (1.55–5.26) 3.34 (1.79–6.20) 1 (reference) 0.46 (0.30–0.69) 0.98 (0.29–3.32) 0.98 (0.62–1.56) 4.56 (0.91–22.91) 3.42 (0.72–16.32) 1 (reference) 2.28 (1.00–5.21) 1.37 (0.61–3.07) 1.15 (0.76–1.74) 1.81 (1.12–2.91) 1 (reference) 1.56 (0.92–2.62) 1.94 (1.15–3.28) 1.80 (1.19–2.70) 1.11 (1.04–1.19) 1.05 (1.02–1.09) 1.94 (1.28–2.95) 1.39 (0.78–2.47)
Age, sex, body mass index and region adjusted; CI, confidence interval; OR, odd ratio.
p 0.51 <0.001
0.001
0.93 0.06 0.12 0.12
0.51 0.01 0.04
0.005 0.003 0.003 0.002 0.26
Adjusted OR (95%CI)a 0.87 (0.57–1.33) 1 (reference) 1.37 (0.67–2.79) 2.92 (1.55–5.50) 3.72 (1.94–7.13) 1 (reference) 0.36 (0.23–0.55) 0.59 (0.17–2.05) 1.00 (0.62–1.61) 4.33 (0.83–22.42) 4.06 (0.81–20.36) 1 (reference) 2.05 (0.86–4.89) 1.63 (0.70–3.81) 0.99 (0.63–1.50) 1.84 (1.11–3.06) 1 (reference) 1.27 (0.74–2.18) 1.66 (0.94–2.90) 1.25 (0.77–2.05) 1.07 (0.99–1.16) 1.03 (0.99–1.08) 1.61 (1.00–2.58) 0.87 (0.46–1.64)
p 0.51 <0.001
<0.001
>0.99 0.08 0.09 0.17
0.89 0.02 0.21
0.37 0.10 0.10 0.05 0.67
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diagnosed or already know high SBP and/or DBP) with undiagnosed diabetes was borderline significant.
4.
Discussion
A community-based screening intervention among urban Cameroonians was successful in attracting persons with a high prevalence of risk factors for diabetes. The use of capillary blood glucose to detect diabetes appears to be feasible and acceptable. The yield of new cases was high, with a prevalence of undiagnosed diabetes of 4.5 in women and 5.1% in men, which represented 50% of overall number of people with diabetes. Comparison of these results with previous surveys is somehow difficult, as the latter used different methods (e.g. measurement of venous blood glucose). The results of this report show that the prevalence of undiagnosed DM is lower than that reported earlier with more objective methodology in the same setting. In 1994, Mbanya et al. [10] reported an age-standardized urban prevalence of diabetes of 0.8% and 1.6% in males and females urban dwellers, respectively, with a 57% proportion of undiagnosed diabetes. These results were based on 75-g oral glucose tolerance test and WHO criteria. Ten years later in 2003, the prevalence of diabetes in Cameroon had increased by almost 10-fold, with no improvement in the proportion of undiagnosed diabetes [11]. The slightly lower proportion of people with previously undiagnosed disease observed in our study than in previous surveys may be related to either a relatively low accuracy of our testing method, a rising awareness of diabetes in Cameroon, or a selection bias in our study. The use of the more accurate 2-h post load glucose testing may have yielded a higher prevalence of undiagnosed diabetes. However, such a test is logistically challenging. The prevalence of undiagnosed diabetes in this study is higher than that found in urban areas other African countries like Ghana [12] or Kenya [13]. It is also higher than that observed in the US [14], but comparable to that of China [15]. The observed high prevalence of undiagnosed disease in urban populations of Cameroon may be explained by the increase in risk factors resulting from changes in lifestyle, diet, economic development and increase in life expectancy [16]. Although some of the risk factors for diabetes were not significant associated with prevalent undiagnosed diabetes, this may be due to the lack of precision in the measurement of some of these risk such as lifestyle factors (use of questionnaire). It was not surprising to find that undiagnosed diabetes was associated with older age and higher prevalence of hypertension. The observed association of undiagnosed diabetes with the area of residence may be due to the differential screening rates across participating cities, or differences in the background profile of the general population across cities, not directly captured in the current study.
4.1.
Strengths and limitations
This study was the first large-scale implementation of community-based screening for diabetes in the four main cities of Cameroon. Despite the somewhat self-selected nature of the sample due to the invitation process, the study
233
was population-based and covered the four main cities of Cameroon, and thus largely reflected the urban population of the country. Previous community-based studies on diabetes in Cameroon have been limited to one or two cities, and consequently covered less people [10]. The representativeness of our sample size may have been limited by the lack of information on some study participants on their diabetes status as well as on individual characteristics, but comparisons showed that there were no differences between those with and without complete data. We used capillary glucose measurements for diabetes screening and diagnosis, which is approved by the WHO [7]. Although relatively less accurate than venous blood measurements; it represents a cheap and reliable option for large-scale screening in developing countries settings. Some of have suggested the use of risk tools to select those at risk diabetes who would then undergo biochemical testing in a screening program, as a viable option. However, such an approach would be limited in Cameroon and sub-Saharan Africa at large by a lack of population-specific or validated screening tools for use in these settings [17]. Furthermore, studies in other developing countries have shown that capillary glucose measurement may be superior to risk scores to stratify the population [18]. Given the lack of evidence on the effectiveness of diabetes screening, many have advocated opportunistic screening and advised against community-based screening, due to potential difficulties in following-up detected people [19]. However, in a context with a nascent or non-existent primary care system and where health service utilization by the population is still low, opportunistic screening may be ineffective. Community-based screening therefore appears as a solution to connect people with undiagnosed disease to healthcare facilities. Furthermore, such an approach, using mass media as an invitation tool may maximize uptake in all subgroups of the population. A one and off community-based screening in developing countries along the model described in this study is feasible, but repeated rounds of screening may be needed to capture the at risk individuals who participated to a previous round and may have developed the disease in the interval time.
5.
Conclusions
Despite the limitations, these results strongly suggest that community-based screening for diabetes in developing countries is feasible and acceptable, and will yield a high proportion of undiagnosed diabetes as half of people with diabetes in Cameroon and by extension in most African countries remain undiagnosed. This indicates the potentially huge impact of a policy for screening. However, health systems may need additional resources to manage the extra number of newly detected cases [20,21]. The results from this study may help inform the design of screening policies in Cameroon and other developing countries, especially in a context where very few, if not none of sub-Saharan African countries have issued recommendations on detection of undiagnosed diabetes. However, more research is needed to determine the benefit of such an undertaking in terms of outcomes, in developing countries.
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Conflict of interest statement [4]
The authors state that they have no conflict of interest.
Acknowledgments The authors would like to express their gratitude to the following bodies and individuals for their contribution to the success of this study: (1) The Cameroon Ministry of public health for providing the blood pressure devices and health staff; (2) The executive committee and members of the Cameroon Cardiac Society for their support for data collection; (3) Servier Pharma Cameroon for a financial contribution to support data processing and drafting of the manuscript; (4) All specialist physicians, enthusiastic general practitioners, medical students and nurses involved in data collection; and (5) All those who helped at the different stages of the project and the fellow Cameroonians who volunteered across the four participating cities to take part in the study.
Appendix A. Cameroon Cardiac Society (CCS) investigators group: Douala: Anastase Dzudie, Hamadou Ba, Henri Roger Ngote, Christian Biholong, Félicité Kamdem, Armel Djomou, Jules Njebet, Cyrille Ouambo, Roger Mboulley Kotto, Barbara Bouellet Abeng, Marielle Epacka Ewane Lobe and Yves Monkam. Yaounde: Alain Menanga, Pierre Mintom, Euloge Yiagnigni, Mérimée Ouankou, Christophe Nouedoui, Pierre Ndobo, Marie Ntep, Monique Kenfack, Sylvie Ndongo, Martine Tchuem, Edvine Wawo, Walinjom F.T. Muna, Katleen Ngu Blackett and Samuel Kingue. Bafoussam: Charles Kouam Kouam. Bamenda: Joseph Abah.
Appendix B. Supplementary data Supplementary data associated with this article can be found, in the online version, at http://dx.doi.org/10.1016/j. pcd.2012.05.002.
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Contents lists available at SciVerse ScienceDirect
Primary Care Diabetes journal homepage: http://www.elsevier.com/locate/pcd
Original research
Prevalence and determinants of undiagnosed diabetes in an urban sub-Saharan African population Justin B. Echouffo-Tcheugui a , Anastase Dzudie b , Marielle E. Epacka b , Simeon P. Choukem b , Marie S. Doualla b , Henry Luma b , Andre P. Kengne c,d,e,∗ a
Hubert Department of Global Health, Rollins School of Public Health, Emory University, Atlanta, GA, USA Department of Internal Medicine, General Hospital of Douala, Douala, Cameroon c The Georges Institute for Global Health, Sydney, Australia d Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht, The Netherlands e South African Medical Research Council & University of Cape Town, Cape Town, South Africa b
a r t i c l e
i n f o
a b s t r a c t
Article history:
Aims: To report the prevalence of undiagnosed diabetes and its determinants among adults
Received 28 February 2012
Cameroonian urban dwellers.
Received in revised form
Methods: On May 17th 2011, a community-based combined screening for diabetes and hyper-
26 April 2012
tension was conducted simultaneously in four major Cameroonian cities. Adult participants
Accepted 14 May 2012
were invited through mass media. Fasting blood glucose was measured in capillary blood.
Available online 7 June 2012
Results: Of the 2120 respondents, 1591 (52% being men) received a fasting glucose test. The median age was 43.7 years, and 64.2% were overweight or obese. The sex-specific
Keywords:
age adjusted prevalence (for men and women) were 10.1% (95% confidence interval [CI]:
Diabetes mellitus
8.1–12.1%) and 11.2% (95%CI: 9.1–13.3%) for any diabetes, and 4.6% (95%CI: 2.6–6.6%) and
Screening
5.1% (95%CI: 3.0–7.2%) for screened-detected diabetes, respectively. The prevalence of dia-
Prevalence
betes increased with increasing age in men and women (all p ≤ 0.001 for linear trend). Older
Cameroon
age (p < 0.001), region of residence (p < 0.001), excessive alcohol intake (p = 0.02) were signif-
Sub-Saharan Africa
icantly associated with screened-detected diabetes, while physical inactivity, body mass index, and high waist girth were not significantly associated with the same outcome. Conclusions: Prevalence of undiagnosed diabetes is very high among Cameroonian urban dwellers, indicating a potentially huge impact of screening for diabetes, thus the need for more proactive policies of early detection of the disease. © 2012 Primary Care Diabetes Europe. Published by Elsevier Ltd. All rights reserved.
1.
Introduction
Since the 1980s data have indicated an escalating pattern of diabetes worldwide, with the fastest growth occurring in developing countries [1]. It has been estimated that 366
million adults (7.0%) had diabetes worldwide in 2011 [2], with a higher proportion in developing countries than in developed nations. By 2030, the number of adults with diabetes is projected to increase by 50.7%; the greatest increase will occur in sub-Saharan Africa (90.5%), where the proportion of undiagnosed diabetes is also the highest ∼80% [2]. However, there
∗ Corresponding author at: South African Medical Research Council and University of Cape Town, P.O. Box 19070, Tygerberg, 7505 Cape Town, South Africa. Tel.: +27 21 938 0529; fax: +27 21 938 0460. E-mail address: [email protected] (A.P. Kengne). 1751-9918/$ – see front matter © 2012 Primary Care Diabetes Europe. Published by Elsevier Ltd. All rights reserved. http://dx.doi.org/10.1016/j.pcd.2012.05.002
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is a scarcity of nationally representative and reliable survey data on undiagnosed diabetes, and for most African countries published estimates were derived from small-scale surveys or based on extrapolations using data from neighboring countries [2]. Consequently, there is a need for more robust data on undiagnosed diabetes. Such figures are key to any prevention strategy, as undiagnosed diabetes mellitus contributes significantly to the overall disease-related morbidity and mortality [3]. Failure to diagnose or delayed diagnosis deprives people with diabetes from the benefits of cost-effective interventions that have shown positive impact of long-term complications of diabetes. Targeted opportunistic screening has been advocated as a mean for improving diabetes detection [4,5]. This typically involves using the opportunity of a contact with the healthcare system of people at high risk of undiagnosed diabetes, to also screen them for diabetes. Such an approach may be ineffective in settings where health service utilization by the population is still low. In such settings, there is a need for approaches to either screen those at high-risk of undiagnosed diabetes in the community, or to connect them to healthcare facilities for screening. Such strategies however, have not been assessed in sub-Saharan Africa. Evaluating community-based screening program is therefore necessary in order to investigate how best to detect undiagnosed diabetes in developing countries. There is very little information on their limitations, which may include the practicality and cost of tracking persons with high screening values to ensure that they receive confirmatory testing as well as proper care once diabetes has been identified. We therefore assessed the prevalence and determinants of undiagnosed diabetes mellitus in a self-selected urban population of adults Cameroonians who reported for a community-based hypertension screening campaign, advertised through mass media.
2.
Materials and methods
2.1.
Design, study population and settings
This was a cross-sectional population-based study conducted in the regional capitals of the Center (Yaoundé), Littoral (Douala), North-West (Bamenda) and West (Bafoussam) administrative regions (out of a total of 10) in Cameroon. According to the Cameroon’s National Institute of Statistics [6], the four participating regions had a total population of 9.98 million individuals in 2010 (51% of the country’s total population: 19.41 millions). In this same year, 57.5% of the national population was aged 15 years and above, and about 52% resided in urban area [6]. The study protocol was approved by the Cameroon National Ethics Committee and signed informed consent obtained from each participant.
2.2.
Invitation to screening
During the three consecutive weeks leading to the survey, daily announcements were made through radio and television, inviting all interested adults (aged 15 years and above) to report to any of the screening sites in participating cities on
the same pre-specified date. Advertisement for screening was also conducted in churches and market places.
2.3.
Study assessments
On May 17th 2011, screening was conducted by trained medical personnel, simultaneously in the four study cities (see investigators’ list in Appendix A). All eligible participants received a face-to-face interview during which data were collected on demographics, smoking habits, alcohol consumption, physical activity, parental history of hypertension, personal medical history including physician diagnosed hypertension and drug treatment, diabetes mellitus, gout and dyslipidemia.
2.3.1.
Anthropometry
Blood pressure (BP) was measured in a seated subject after at least 10 min rest, without alcohol intake or smoking. Measurements were performed on the right arm with the use of automated sphygmomanometers (OMRON M3 HEM-7200-E Omron Matsusaka Co. Ltd., Japan) and appropriate cuff sizes. Weight (in kg) was measured with the use of an automated scale in subjects with light dresses, and height (in m) measured using a wooden platform and a height rule. Body mass index (BMI – in kg/m2 ) was computed as weight (kg)/[height2 (m2 )]. Waist circumference was measured midway between the iliac crest and the lower rib margin and the hip circumference measured at the intertrochanteric level. Waist-to-hip ratio (WHR) was computed as waist (cm)/hip (cm) circumferences.
2.3.2.
Glucose assay
Capillary blood glucose (CBG) was measured after a fasting period of 9–14 h with a glucometer (Accu-chek Aviva® ; Roche Solutions for Diagnosis, F. Hoffmann-La Roche Ltd., Germany). The ACCU-CHEK Aviva test strips are whole-blood referenced and have been calibrated to deliver plasma-like values. A blood sample was obtained from a fingertip for the determination of CBG.
2.4.
Definitions
Participants were considered to have diabetes if they were treated with hypoglycemic medications (insulin and/or oral diabetes medications) or if their fasting blood glucose levels reached 7.0 mmol/l (126 mg/dl) or more, as defined by the World Health Organization (WHO) [7]. Respondents were classified as hypertensive if they had a systolic (and/or diastolic) BP of 140 (90) mmHg or higher, or if they were on blood pressure lowering medications over the last 15 consecutive days. Waist circumference >94 cm in men or 80 cm in women was considered to be high. Excessive alcohol consumption was based on intake of either more than three (two for women) standard glasses of wine per day or more than 10 (five for women) local beers (1 local beer contains 28 g of alcohol) per week. Participants who smoked at least one cigarette per day were classified as current smokers and those who had stopped smoking for more than 3 years were classified as former smokers. Regular non-work related physical activity was considered
p r i m a r y c a r e d i a b e t e s 6 ( 2 0 1 2 ) 229–234
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in participants reporting at least 25 min of intense physical activity, once a week or more.
2.5.
Handling of participants with high glucose level
Participants with diabetes (known or screened-detected) received on-site medical counseling and were referred back to their attending physician (known cases), or to specialists within the vicinity of the participant’s residential area for workup and chronic management.
2.6.
Statistical analysis
Data analysis used the Statistical Package for Social Sciences (SSPS Inc., Chicago, IL) version 17.0 software. Results are summarized as count and percentages for qualitative variables and mean and standard deviation (SD) for quantitative variables. Age-adjusted prevalence was calculated using the Cameroon National population’s age structure in 2010 as the standard population [8], and direct standardization methods [9]. Comparison of groups was done using Chi-square tests and equivalents for qualitative variables, and Student’s t-test and analysis of the variance (ANOVA) for quantitative variables. Logistic regressions analyses were used to investigated potential determinants of screened-detected diabetes. A p-value <0.05 indicated statistically significant results.
3.
Results
3.1.
Screening implementation (available data)
Of the 2120 subjects who attended screening, 1591 (75%) received a fasting capillary glucose test and were therefore included in the current analyses. Participants with short fasting duration did not receive the test and were excluded. In Online Appendix Table 1 we present the baseline characteristics of study participants included and excluded from the analytical sample. Differences between those included and the excluded were small in magnitude, clinically trivial and did not attain statistical significance for many characteristics. For instance, mean baseline variables (participants in the primary analysis vs. those excluded) were 43.8 vs. 42.8 years for age (p = 0.69), 27.5 vs. 27.9 kg/m2 for BMI (p = 0.12), 137 vs. 134 mmHg for systolic blood pressure (p = 0.01) and 81 vs. 81 mmHg for diastolic blood pressure (p = 0.19).
3.2.
Characteristics of participants by age groups
The baseline characteristics of the participants to the screening program including the risk factors for diabetes are shown in Table 1. The number of screening attendees varied across screening sites, largely by virtue of differences in the underlying populations of each region of the country. The majority of attendees were men (52%), with median age of 43.7 years. Of those assessed, only 9% (143) reported having previously been diagnosed with diabetes, 27% (431) had a family history of hypertension, 91% had never smoked, 43% (686) were inactive, 16.3% (259) and 64.2% were obese or overweight with a mean BMI of 27.5 kg/m2 (SD = 5.4). The mean values of BMI,
Fig. 1 – Crude prevalence of diabetes by sex and age groups.
systolic and diastolic BP, as well as the proportions of participants with known diabetes, high waist girth, any hypertension or known hypertension were significantly higher with increasing age (Table 1).
3.3.
Prevalence of diabetes and dysglycemia
Of those participants included, 243 (15.3%) had prevalent diabetes including 143 (58.8%) who were already known with the condition. The crude prevalence of diabetes was 13.7% in men and 17.0% in women (p = 0.07) for any diabetes, and 6.0% vs. 6.6% for screened-detected diabetes (p = 0.62). The sex-specific age adjusted prevalence (men vs. women) were 10.1% (95% confidence interval: 8.1–12.1%) and 11.2% (9.1–13.3%) for any diabetes, and 4.6% (2.6–6.6%) and 5.1% (3.0–7.2%) for screened-detected diabetes. The prevalence of diabetes increased with increasing age in men and women (Fig. 1). Trends were mostly linear (all p ≤ 0.001 for linear trend) except for known diabetes in women (p = 0.07). Among those without diabetes, 78 (5.8%) had impaired fasting glycemia defined by a fasting glucose level ≥6.1 mmol/l (110 mg/dl). The equivalents in men and women were 45 (6.2%) and 33 (5.2%), respectively (p = 0.48).
3.4.
Determinants of screened-detected diabetes
Table 2 shows the crude and adjusted odds ratio for variables associated with the prevalence of undiagnosed diabetes. Older
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Table 1 – Characteristics of the study population. Variable
Overall
<35 years
35–44 years
45–54 years
55+ years
p-Trend
N Men, n (%) Median age, years Region, n (%) Center, n (%) Littoral, n (%) North-West and West, n (%) Parental hypertension, n (%) Dyslipidemia, n (%) Gout, n (%) Known diabetes, n (%) Smoking Never, n (%) Former, n (%) Current, n (%) Physical inactivity, n (%) Excessive alcohol, n (%) Body mass index Normal, n (%) Overweight, n (%) Obese, n (%) Mean, kg/m2 (SD) High waist girth, n (%) Mean systolic blood pressure, mmHg (SD) Mean diastolic blood pressure, mmHg (SD) Any hypertension, n (%) Known hypertension, n (%)
1591 834 (52) 43.7
459 230 (50) 27.5
336 212 (63) 39.2
332 212 (64) 49.6
229 180 (79) 60.7
0.45
630 (40) 893 (56) 68 (4) 431 (27) 12 (1) 18 (1) 143 (9)
223 (49) 234 (51) 2 (0) 113 (25) 2 (0) 2 (0) 11 (2)
152 (45) 198 (59) 11 (3) 105 (31) 3 (1) 4 (1) 18 (5)
145 (44) 241 (73) 12 (4) 123 (37) 5 (2) 2 (1) 32 (10)
110 (48) 220 (96) 43 (19) 90 (39) 2 (1) 10 (4) 82 (36)
1445 (91) 62 (4) 84 (5) 905 (57) 259 (16)
432 (94) 12 (3) 15 (3) 257 (56) 48 (10)
326 (97) 8 (2) 27 (8) 215 (64) 71 (21)
344 (104) 25 (8) 29 (9) 222 (67) 79 (24)
199 (87) 17 (7) 13 (6) 211 (92) 61 (27)
569 (36) 572 (36) 450 (28) 27.5 (5.4) 695 (44) 137 (27) 81 (17) 769 (48) 244 (15)
242 (53) 143 (31) 74 (16) 25.7 (4.5) 130 (28) 125 (19) 74 (14) 115 (25) 9 (2)
108 (32) 139 (41) 114 (34) 28.2 (5.7) 165 (49) 133 (23) 81 (17) 138 (41) 25 (7)
107 (32) 146 (44) 145 (44) 28.5 (5.4) 208 (63) 142 (28) 85 (18) 226 (68) 66 (20)
112 (49) 144 (63) 117 (51) 27.9 (5.2) 192 (84) 151 (29) 87 (17) 190 (83) 144 (63)
<0.001
age (p < 0.001) for trends across age categories, region other than the central of the country (p < 0.001), excessive alcohol intake (p = 0.02) were associated with a higher prevalence of undiagnosed diabetes. Parental hypertension, dyslipidemia,
0.83 0.64 0.01 <0.001 0.20
0.91 0.01 <0.001
<0.001 <0.001 <0.001 <0.001 <0.001 <0.001
presence of gout, smoking, physical inactivity, BMI, high waist girth, increasing SBP or DBP, as well as known hypertension were not associated with the prevalence of undiagnosed diabetes. However, the association of any hypertension (newly
Table 2 – Crude and adjusted odds ratios for predictors of undiagnosed diabetes among self-selected urban dwellers in Cameroon. Variable Sex Age
Region
Parental hypertension Dyslipidemia Gout Smoking
Physical activity Excessive alcohol Body mass index High waist girth Systolic BP Diastolic BP Any hypertension Known hypertension a
Category (n) Men (770) <35 years (448) 35–44 years (343) 45–54 years (366) ≥55 years (291) Center (577) Littoral (840) Others (31) Yes (382) Yes (178) Yes (10) Never (1317) Former (51) Current (80) No (853) Yes (235) Normal (517) Overweight (519) Obese (412) Yes (615) Per 10 mmHg Per 5 mmHg Yes (662) Yes (167)
Unadjusted OR (95%CI) 0.87 (0.58–1.31) 1 (reference) 1.41 (0.70–2.83) 2.86 (1.55–5.26) 3.34 (1.79–6.20) 1 (reference) 0.46 (0.30–0.69) 0.98 (0.29–3.32) 0.98 (0.62–1.56) 4.56 (0.91–22.91) 3.42 (0.72–16.32) 1 (reference) 2.28 (1.00–5.21) 1.37 (0.61–3.07) 1.15 (0.76–1.74) 1.81 (1.12–2.91) 1 (reference) 1.56 (0.92–2.62) 1.94 (1.15–3.28) 1.80 (1.19–2.70) 1.11 (1.04–1.19) 1.05 (1.02–1.09) 1.94 (1.28–2.95) 1.39 (0.78–2.47)
Age, sex, body mass index and region adjusted; CI, confidence interval; OR, odd ratio.
p 0.51 <0.001
0.001
0.93 0.06 0.12 0.12
0.51 0.01 0.04
0.005 0.003 0.003 0.002 0.26
Adjusted OR (95%CI)a 0.87 (0.57–1.33) 1 (reference) 1.37 (0.67–2.79) 2.92 (1.55–5.50) 3.72 (1.94–7.13) 1 (reference) 0.36 (0.23–0.55) 0.59 (0.17–2.05) 1.00 (0.62–1.61) 4.33 (0.83–22.42) 4.06 (0.81–20.36) 1 (reference) 2.05 (0.86–4.89) 1.63 (0.70–3.81) 0.99 (0.63–1.50) 1.84 (1.11–3.06) 1 (reference) 1.27 (0.74–2.18) 1.66 (0.94–2.90) 1.25 (0.77–2.05) 1.07 (0.99–1.16) 1.03 (0.99–1.08) 1.61 (1.00–2.58) 0.87 (0.46–1.64)
p 0.51 <0.001
<0.001
>0.99 0.08 0.09 0.17
0.89 0.02 0.21
0.37 0.10 0.10 0.05 0.67
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diagnosed or already know high SBP and/or DBP) with undiagnosed diabetes was borderline significant.
4.
Discussion
A community-based screening intervention among urban Cameroonians was successful in attracting persons with a high prevalence of risk factors for diabetes. The use of capillary blood glucose to detect diabetes appears to be feasible and acceptable. The yield of new cases was high, with a prevalence of undiagnosed diabetes of 4.5 in women and 5.1% in men, which represented 50% of overall number of people with diabetes. Comparison of these results with previous surveys is somehow difficult, as the latter used different methods (e.g. measurement of venous blood glucose). The results of this report show that the prevalence of undiagnosed DM is lower than that reported earlier with more objective methodology in the same setting. In 1994, Mbanya et al. [10] reported an age-standardized urban prevalence of diabetes of 0.8% and 1.6% in males and females urban dwellers, respectively, with a 57% proportion of undiagnosed diabetes. These results were based on 75-g oral glucose tolerance test and WHO criteria. Ten years later in 2003, the prevalence of diabetes in Cameroon had increased by almost 10-fold, with no improvement in the proportion of undiagnosed diabetes [11]. The slightly lower proportion of people with previously undiagnosed disease observed in our study than in previous surveys may be related to either a relatively low accuracy of our testing method, a rising awareness of diabetes in Cameroon, or a selection bias in our study. The use of the more accurate 2-h post load glucose testing may have yielded a higher prevalence of undiagnosed diabetes. However, such a test is logistically challenging. The prevalence of undiagnosed diabetes in this study is higher than that found in urban areas other African countries like Ghana [12] or Kenya [13]. It is also higher than that observed in the US [14], but comparable to that of China [15]. The observed high prevalence of undiagnosed disease in urban populations of Cameroon may be explained by the increase in risk factors resulting from changes in lifestyle, diet, economic development and increase in life expectancy [16]. Although some of the risk factors for diabetes were not significant associated with prevalent undiagnosed diabetes, this may be due to the lack of precision in the measurement of some of these risk such as lifestyle factors (use of questionnaire). It was not surprising to find that undiagnosed diabetes was associated with older age and higher prevalence of hypertension. The observed association of undiagnosed diabetes with the area of residence may be due to the differential screening rates across participating cities, or differences in the background profile of the general population across cities, not directly captured in the current study.
4.1.
Strengths and limitations
This study was the first large-scale implementation of community-based screening for diabetes in the four main cities of Cameroon. Despite the somewhat self-selected nature of the sample due to the invitation process, the study
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was population-based and covered the four main cities of Cameroon, and thus largely reflected the urban population of the country. Previous community-based studies on diabetes in Cameroon have been limited to one or two cities, and consequently covered less people [10]. The representativeness of our sample size may have been limited by the lack of information on some study participants on their diabetes status as well as on individual characteristics, but comparisons showed that there were no differences between those with and without complete data. We used capillary glucose measurements for diabetes screening and diagnosis, which is approved by the WHO [7]. Although relatively less accurate than venous blood measurements; it represents a cheap and reliable option for large-scale screening in developing countries settings. Some of have suggested the use of risk tools to select those at risk diabetes who would then undergo biochemical testing in a screening program, as a viable option. However, such an approach would be limited in Cameroon and sub-Saharan Africa at large by a lack of population-specific or validated screening tools for use in these settings [17]. Furthermore, studies in other developing countries have shown that capillary glucose measurement may be superior to risk scores to stratify the population [18]. Given the lack of evidence on the effectiveness of diabetes screening, many have advocated opportunistic screening and advised against community-based screening, due to potential difficulties in following-up detected people [19]. However, in a context with a nascent or non-existent primary care system and where health service utilization by the population is still low, opportunistic screening may be ineffective. Community-based screening therefore appears as a solution to connect people with undiagnosed disease to healthcare facilities. Furthermore, such an approach, using mass media as an invitation tool may maximize uptake in all subgroups of the population. A one and off community-based screening in developing countries along the model described in this study is feasible, but repeated rounds of screening may be needed to capture the at risk individuals who participated to a previous round and may have developed the disease in the interval time.
5.
Conclusions
Despite the limitations, these results strongly suggest that community-based screening for diabetes in developing countries is feasible and acceptable, and will yield a high proportion of undiagnosed diabetes as half of people with diabetes in Cameroon and by extension in most African countries remain undiagnosed. This indicates the potentially huge impact of a policy for screening. However, health systems may need additional resources to manage the extra number of newly detected cases [20,21]. The results from this study may help inform the design of screening policies in Cameroon and other developing countries, especially in a context where very few, if not none of sub-Saharan African countries have issued recommendations on detection of undiagnosed diabetes. However, more research is needed to determine the benefit of such an undertaking in terms of outcomes, in developing countries.
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Conflict of interest statement [4]
The authors state that they have no conflict of interest.
Acknowledgments The authors would like to express their gratitude to the following bodies and individuals for their contribution to the success of this study: (1) The Cameroon Ministry of public health for providing the blood pressure devices and health staff; (2) The executive committee and members of the Cameroon Cardiac Society for their support for data collection; (3) Servier Pharma Cameroon for a financial contribution to support data processing and drafting of the manuscript; (4) All specialist physicians, enthusiastic general practitioners, medical students and nurses involved in data collection; and (5) All those who helped at the different stages of the project and the fellow Cameroonians who volunteered across the four participating cities to take part in the study.
Appendix A. Cameroon Cardiac Society (CCS) investigators group: Douala: Anastase Dzudie, Hamadou Ba, Henri Roger Ngote, Christian Biholong, Félicité Kamdem, Armel Djomou, Jules Njebet, Cyrille Ouambo, Roger Mboulley Kotto, Barbara Bouellet Abeng, Marielle Epacka Ewane Lobe and Yves Monkam. Yaounde: Alain Menanga, Pierre Mintom, Euloge Yiagnigni, Mérimée Ouankou, Christophe Nouedoui, Pierre Ndobo, Marie Ntep, Monique Kenfack, Sylvie Ndongo, Martine Tchuem, Edvine Wawo, Walinjom F.T. Muna, Katleen Ngu Blackett and Samuel Kingue. Bafoussam: Charles Kouam Kouam. Bamenda: Joseph Abah.
Appendix B. Supplementary data Supplementary data associated with this article can be found, in the online version, at http://dx.doi.org/10.1016/j. pcd.2012.05.002.
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