This article has Open Peer Review reports available.
Use of medical services and medicines attributable to type 2 diabetes care in Yaoundé, Cameroon: a cross-sectional study
© The Author(s). 2017
Received: 10 November 2016
Accepted: 29 March 2017
Published: 11 April 2017
The increasing numbers of people with type 2 diabetes (T2D) is a global concern and especially in sub-Saharan Africa, where diabetes must compete for resources with communicable diseases. Diabetes intensifies health care utilisation and leads to an increase in medical care costs. However, In Cameroon like in most developing countries, data on the impact of diabetes on the medical health system are scarce. We aimed to analyse the use of medical services and medicines attributable to T2D care in Yaoundé, Cameroon.
We conducted a cross-sectional study comparing the use of medical services and medicines on 500 people with T2D attending the diabetic outpatient units of three hospitals in Yaoundé and 500 people without diabetes matched for age, sex and residence. We performed multivariate logistic and quantile regressions to assess the effect of diabetes on the use of medical services and medicines and the presence of other chronic health problems. Models were adjusted for age, educational level, marital status, occupation and family income.
Overall, the rate of use of health services was found to be greater in people with T2D than those without diabetes. People with T2D had greater odds of having an outpatient visit to any clinician (OR 97.1 [95% CI: 41.6–226.2]), to be hospitalised (OR 11.9 [95% CI: 1.6–87.9]), to take at least one medicine (OR 83.1 [37.1–185.8]) compared with people without diabetes. We also observed an association between diabetes and some chronic diseases/diabetes complications including hypertension (OR 9.2 [95% CI: 5.0–16.9]), cardiovascular diseases (OR 1.9 [95% CI: 0.8–4.9]), peripheral neuropathy (OR 6.2 [95% CI: 3.4–11.2]), and erectile dysfunction (OR 5.8 [95% CI: 2.7–12.1]).
This study showed that the presence of diabetes is associated with an increased use of health care services and medicines as well as with some chronic diseases/diabetes complications.
Diabetes is a challenging problem for public health worldwide with an estimated 415 million people or 8.8% of adults aged 20–79 affected in 2015–75% of whom live in low- and middle-income countries. With current trends, it has been projected that about 642 million people, or one adult in ten, will have diabetes 2040 . The progression of diabetes is part of a broader epidemiological transition from communicable to non-communicable diseases [2–8]. An estimated 14.2 million adults aged 20–79 have diabetes in Africa, representing a regional prevalence of 2.1–6.7%. Africa has the highest proportion of undiagnosed diabetes; over two thirds (66.7%) of people with diabetes are unaware they have the disease . Moreover, Africa is facing a health time bomb with diabetes having an increasing impact on people of working age; more than three quarters of deaths due to diabetes in 2015 occurred in people under the age of 60 . The burden of diabetes in sub-Saharan Africa is already substantial, and patients with this condition make significant demands on health care resources . In Cameroon, diabetes was not considered a public health priority until after the results of a 2003 epidemiological study revealed that diabetes was a growing potential threat to health . In 2015, the prevalence of diabetes was 5.3% in the 20 to 79 years age group with more than 14,998 diabetes-related deaths . This transition has been attributed to the population aging, increasingly sedentary lifestyles and obesity [1, 2]. With the ongoing population aging, it is expected that Africa will continue to experience an increase in the number of patients as well as those with related comorbidities. This is also associated with considerable consequences for health care and related costs [11–18]. The costs associated with diabetes usually include the increased use of health services, loss of productivity and disability. Diabetes therefore imposes a large economic burden on individuals and families, national health systems and nations as a whole, making the disease a significant obstacle to sustainable economic development. Data on the use of resources for diabetes care in sub-Saharan Africa are particularly scarce, difficult to generalize and sometimes outdated, although they all indicate that the diabetes-caused use of medical services and medications is substantial [19–22].
The African Diabetes Impact Study was initiated in sub-Saharan Africa because of the paucity of data on the socio-economic cost of diabetes and the inadequacy of national policies for the prevention and control of diabetes. A Pan African multicentre study was initiated with the purpose of increasing knowledge on type 2 diabetes, with focus on socio-economic aspects. In this study, we aimed to analyse the use of medical services and medicines attributable to T2D care in Yaoundé, Cameroon.
We conducted a cross-sectional study, comparing the use of medical services and medicines in people with T2D and people without diabetes.
Sample size and statistical power
Data to calculate statistical power for an economic study in sub-Saharan Africa are not available. However, applied to the formula for matched samples , results from a similar recently completed study in Shanghai  suggest that 500 cases and 500 controls will yield statistically significant results. This sample size also gives > 90% power to detect a difference in proportions of 10 percentage points or larger at an alpha of 0.05.
Recruitment of people with type 2 diabetes
People with T2D were recruited consecutively from outpatient diabetes clinics at three different hospitals in Yaoundé: the Yaoundé Central Hospital, where 80% of cases were obtained, the Biyem-Assi District Hospital, and the Cité Verte District Hospital. We consecutively interviewed 500 people with T2D attending the outpatient diabetes units of these three hospitals.
Recruitment of people without diabetes
In order to obtain participants without diabetes comparable to the participants with diabetes, we asked the latter to identify five people living in their neighbourhood who were comparable to them by age (+/− 5 years) and sex. We therefore contacted and recruited one control subject for each case using this information. Potential controls who said they had been diagnosed with diabetes were excluded. One completed control interview was obtained for each selected person with T2D. All participants were aged between 20 and 80 years.
Trained field workers conducted identical structured face-to-face interviews with all participants in their homes to ascertain recent use of medical care services, medicines and the presence of other chronic diseases using a questionnaire (Additional file 1).
Estimation of the use of medical services
The study team relied on participants recall to ascertain the use of medical services. To increase accuracy, we limited recall to a 90-day window and, to calibrate the window, we asked respondents to name and associate a well-remembered event that occurred approximately 90 days ago. In order to obtain information on the use of medicines, interviewers asked participants to show to them the medicines they were using. To further increase accuracy, we asked for details only about the most recent hospital outpatient visit, overnight hospitalization, and purchase of each medicine. The study team then used the descriptions of this subset of the most recent events to estimate the characteristics of all events of the same kind, such as reasons for admissions and visits and mean length of hospital stay. Although participants were asked about visits to hospital emergency wards, very few of such visits were reported to support analysis. We therefore excluded data on emergency-ward visits from this report. The recall window for hospitalizations was 365 days. We also enquired about other chronic health conditions such as known complications of diabetes (kidney diseases; erectile dysfunction or loss of libido; amputation of toes, foot, or leg; peripheral neuropathy; depression, eye diseases) and others (cardiovascular diseases, cancer, lung diseases, HIV/AIDS).
Interview responses were recorded on the paper interview form and subsequently entered into an electronic data base created using the EPI Data version 3.1 software. Prior to analysis, we cleaned variables in the database and discrepancies were then re-checked against the hard-copy records.
Results are expressed as means ± SD or median (25th – 75th percentiles) and frequencies (percentages) for quantitative and qualitative variables respectively. For univariate analyses, comparisons were made between people with diabetes and people without diabetes using paired Student t tests for continuous variables; McNemar test was used for qualitative variables with fewer than three modalities and a Stuart-Maxwell test for qualitative variables with at least three modalities.
Multivariate logistic and quantile regression models were used to assess the effect of diabetes on the use of medical services, medicines and the presence of other chronic health problems, from which odds ratios (OR) or median regression coefficient were calculated as well as their corresponding 95% confidence intervals (CI) or their standard errors where appropriate. Models were adjusted for age, educational level, marital status, occupation and family income.
Statistical tests were two-sided. Results were interpreted as significant only when p-value were <0.05. Statistical analyses were performed using Stata software (version 13.1®, Stata Institute).
Ethical clearance was obtained from the National Ethics Committee for Health Research on Humans of the Ministry of Public Health of Cameroon. Written informed consent was obtained for all participants enrolled.
Socio-demographic characteristics of study participants
People with T2D
People without diabetes
Mean age, mean ± SD
55.11 ± 10.6
Age classes, n (%)
Gender, n (%)
Diabetes duration, years median (1st–3rd)
Diabetes duration, years
Educational level, n (%)
Marital status, n (%)
Not living in couple
Living in couple
Occupation, n (%)
Not working because of ill, n (%)
Family size, mean (SD)
Family income, USD/person/year median (1st-3rd) a
Tobacco smoking, n (%)
Use of outpatient services
Use of medical services and medicines by the study population
Use of services
People with T2D
People without diabetes
OR (95% CI)a
median (95% CI)
Use of outpatient services
Estimated percentage of people with at least 1 visit to hospital, last 90 days, n (%)
Estimated percentage of people with at least 1 outpatient visit to hospital, last 90 days, n (%)
Median outpatient visits at the hospital per person, among those with at least 1 outpatient visit during the last 90 days, median (1st-3rd)
Estimated percentage of people with at least 1 outpatient visit to any clinician, last 90 days, n (%)
Median total outpatient visits to any clinician, last 90 days, median (1st-3rd)
Use of inpatient services
Estimated percentage of people with at least 1 inpatient admission, last 90 days, n (%)
Median length of stay, most recent hospital admission, if admitted last 90 days, median (1st-3rd)
Estimated annual percentage of people with at least 1 inpatient admission, n (%)
Use of medicines
Estimated percentage of people with at least one medicine, n (%)
Considering visits to any clinician, people with T2D had 97 times more odds to have a visit than those without diabetes (OR 97.1 [95% CI: 41.6–226.2], p < 0.001). Among people with T2D, outpatient visits did not vary significantly by age or by duration of diagnosed diabetes (all p > 0.05).
Use of inpatient services
Table 2 shows the use of inpatient services by participants in the two groups. Nine percent of people with T2D reported at least one admission during the preceding 90 days, compared to 0.4% in the non-diabetic group (OR 10.9 [95% CI: 1.5–81.6], p < 0.05).
The median hospital length of stay, among those admitted during the preceding 90 days, was 8 (25th–75th percentiles: 5–15) for people with T2D and 2 (25th–75th percentiles: 1–3) for those without diabetes. Moreover 47 people with T2D spent at least one night at the hospital during the past year and therefore had 11 times more odds of being hospitalised compared to people without diabetes (OR 11.9 [95% CI: 1.6–87.9], p < 0.05).
Reasons for inpatient admissions and outpatient visits
Self-reported causes of inpatient admissions and outpatient visits
Hospital outpatient visits
People with T2D
People with T2D
People without diabetes
Use of medicines
Nearly all people with T2D (93.6%) possessed and reported that they were on at least one medication at the time they were interviewed, compared to one-fifth (22.8%) of people without diabetes, (OR 83.1 [95% CI: 37.1–185.8]), p < 0.001. Moreover the use of medicines increased with age and with the duration of diabetes (all p < 0.05).
Among people with T2D, 89.6% were on a glucose lowering agent, 41.6% on an antihypertensive treatment, 3.2% on a lipid lowering agent, and 5.6% on daily aspirin.
Chronic health problems associated with diabetes
Health chronic problems declared by the study population
People with T2D
People without diabetes
OR (95% CI)**
Erectile dysfunction or loss of libido
Foot or leg ulcer/Amputation
Diabetes imposes a large economic burden on the national healthcare system. The estimation of the impact of T2D on the use of health services and medicines by people with T2D is an indicator of this burden. In Cameroon, there is a limited data on the impact of T2D on the healthcare system. In this study, in order to assess the use of medical services, medicines and the presence of chronic health conditions, we conducted a cross-sectional study comparing the outcomes in a group of 500 people with T2D and 500 people without diabetes in Yaoundé, matched for age (+/− 5 years), sex and residence.
People with T2D and people without diabetes did not differ on gender and residence. People with T2D were 1.1 years older than people without diabetes. We observed a statistically significant difference in the matching criterion age which probably results from the frequency matching using 5-year age bands. In order to adjust for socio-demographic characteristics, analyses were adjusted for age, educational level, marital status, occupation and family income.
People with T2D had 97 times more odds of having an outpatient visit to any clinician, 11 times more odds of being hospitalised and 83 times more odds of being on at least one medicine, compared with people without diabetes. Considering the proportions, 92% of people with T2D reported at least one outpatient visit during the preceding 90 days, compared to 19.6% of people without diabetes. Also, 9.4% of people with T2D reported at least one inpatient admission compared to 0.4% for people without diabetes. Finally, with regards to the use of medicines, 93.6% of people with T2D were on at least one medication compared to 22.8% for people without diabetes.
These proportions are much higher than those observed in a study using a similar interview schedule design in China, where the proportions from people with T2D were 46.1% for outpatient visits, 3.6% for inpatient days and 66.2% for medicines; compared to 23.3%, 1.6% and 18.3% respectively for people without diabetes . Also, in a multi-centric study in Africa including Cameroon and 3 other countries (Mali, Tanzania and South Africa), the overall proportions were also as high as in Cameroon; the proportions from people with T2D were 90.5% for outpatient visits, 4.9% for inpatient visits, 94.8% for ongoing medication; compared to 15.4, 1.1 and 26.6% respectively for people without diabetes .
Several factors could explain these differences. One important factor is that unlike in a comparable study in China where diabetes was ascertained by a population-based household screening, a majority of diabetes cases remain undiagnosed in Africa . In Africa, testing for diabetes usually occurs after patients present with complications known to arise from diabetes . This means that, as a group, people recognized as having diabetes in Africa are much sicker than their counterparts in many other places, and need more services and medication.
The under-utilization of medical services by the general population is another cause of the big differences we report. Medical care in Africa is often very expensive relative to income . People with diabetes may receive more drugs because their condition brings them into frequent contact with the health care system, increasing both the likelihood of detecting and treating previously undiagnosed diseases and the continuation of medication for known chronic disorders.
A third cause is the fact that people with T2D were recruited from well-organized diabetes clinics located in the capital, with a high concentration of general practitioners, internists and diabetologists. Findings would likely to be different if people with diabetes from other parts of the country were included in the study.
Furthermore, diabetes was associated with several comorbidities. We observed associations between diabetes and other chronic conditions such as hypertension, cardiovascular diseases, peripheral neuropathy, and erectile dysfunction. This probably increases the burden on people with diabetes. Indeed, it is known that the number of comorbidities is a strong predictor for the volume of medical health care utilisation [20, 24–27].
This study has some limitations. Firstly, as it is neither possible nor desirable to affect diabetes experimentally, our research design was observational. However, our cross-sectional study with a comparison group is the most widely used approach and accepted measure of the economic and social impacts of diabetes [11, 14, 15, 28–30]. Secondly, the sample consisted of consecutive users of the outpatient diabetic clinics of hospitals in the capital city, a procedure that probably over-selected people with diabetes who were more ill and more likely to use and more likely to be able to afford the services. Also, the self-reported information raises the likelihood of recall bias but we tried to limit this by considering 3 months for outpatient visits and 1 year for hospitalization. Literature in industrialized countries suggests that patients can remember occurrences and many details of meetings of medical care for those no older than 3 months, and remember the occurrence and even hospitalization details for up to 1 year [25–27, 31, 32]. To the extent that the patient cannot recall, it does usually develop in the direction of under-reporting, particularly during outpatient visits [31, 32]. Any underreporting in our data should have the effect of skewing downwards the estimates of the effects of diabetes on the use of medical care, because the people with T2D had significantly more encounters than people without diabetes.
In conclusion, this study showed that, people with T2D in Cameroon use more resources (outpatient visits, hospitalizations and medicines) than people without diabetes. Although non-communicable diseases like diabetes are commonly thought to be minor problems for health systems and patients in Africa compared to communicable diseases, there can be no doubt that diabetes imposes a significant burden on families and the health care system in Cameroon. This therefore mandates that the attention of policymakers should be drawn and actions informed by evidence should be considered.
We thank all the field workers involved in this study and acknowledge the valuable contribution of the International Diabetes Federation- task force on diabetes health economics.
This study was funded by allocations from the Economic Study Fund of the International Diabetes Federation.
Availability of data and materials
The datasets used and/or analysed during the current study available from the corresponding author on a reasonable request.
With IDF staff, LF led the conception and design of the study with input from CTM and J-CM. CTM, LF, ES and J-CM supervised data collection. CTM performed the statistical analysis with input from LF. CTM took primary responsibility for writing the manuscript. CTM, LF, ZN, ELY, ES and J-CM contributed to editing the manuscript. All the authors approved the final manuscript.
The authors declare that they have no competing interests.
Consent for publication
Ethics approval and consent to participate
Ethical approval was given by the National Ethics Committee for Health Research on Humans of the Ministry of Health of Cameroon. Reference number: N°075/CNE/DNM/08.
Written informed consent was obtained for all participants enrolled.
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
- International Diabetes Federation. IDF Diabetes. 7th ed. Brussels: International Diabetes Federation; 2015.Google Scholar
- Miranda JJ, Kinra S, Casas JP, Smith GD, Ebrahim S. Non-communicable diseases in low- and middle-income countries: context, determinants and health policy. Trop Med Int Health TM IH. 2008;13:1225–34.View ArticlePubMedGoogle Scholar
- McKeown RE. The epidemiologic transition: changing patterns of mortality and population dynamics. Am J Lifestyle Med. 2009;3:19S–26S.View ArticlePubMedPubMed CentralGoogle Scholar
- Gage TB. Are modern environments really bad for us?: revisiting the demographic and epidemiologic transitions. Am. J. Phys. Anthropol. 2005;Suppl 41:96–117Google Scholar
- Kirk D. Demographic transition theory. Popul Stud. 1996;50:361–87.View ArticleGoogle Scholar
- Omran AR. The epidemiologic transition. A theory of the epidemiology of population change. Milbank Mem. Fund Q. 1971;49:509–38.Google Scholar
- Omran AR. The epidemiologic transition theory. A preliminary update. J Trop Pediatr. 1983;29:305–16.View ArticlePubMedGoogle Scholar
- Olshansky SJ, Ault AB. The fourth stage of the epidemiologic transition: the age of delayed degenerative diseases. Milbank Q. 1986;64:355–91.View ArticlePubMedGoogle Scholar
- Unwin N, Setel P, Rashid S, Mugusi F, Mbanya JC, Kitange H, et al. Noncommunicable diseases in sub-Saharan Africa: where do they feature in the health research agenda? Bull World Health Organ. 2001;79:947–53.PubMedPubMed CentralGoogle Scholar
- HoPiT. Cameroon burden of Diabetes (CamBod) Project. Yaoundé,Cameroon; 2004.Google Scholar
- Brown JB, Nichols GA, Glauber HS, Bakst AW. Type 2 diabetes: incremental medical care costs during the first 8 years after diagnosis. Diabetes Care. 1999;22:1116–24.View ArticlePubMedGoogle Scholar
- Brown JB, Ramaiya K, Besançon S, Rheeder P, Tassou CM, Mbanya J-C, et al. Use of medical services and medicines attributable to diabetes in Sub-Saharan Africa. PLoS One. 2014;9:e106716.View ArticlePubMedPubMed CentralGoogle Scholar
- Yang W, Zhao W, Xiao J, Li R, Zhang P, Kissimova-Skarbek K, et al. Medical care and payment for diabetes in China: enormous threat and great opportunity. PLoS One. 2012;7:e39513.View ArticlePubMedPubMed CentralGoogle Scholar
- Selby JV, Ray GT, Zhang D, Colby CJ. Excess costs of medical care for patients with diabetes in a managed care population. Diabetes Care. 1997;20:1396–402.View ArticlePubMedGoogle Scholar
- Jönsson B. The economic impact of diabetes. Diabetes Care. 1998;21 Suppl 3:C7–10.View ArticlePubMedGoogle Scholar
- Norlund A, Apelqvist J, Bitzén PO, Nyberg P, Scherstén B. Cost of illness of adult diabetes mellitus underestimated if comorbidity is not considered. J Intern Med. 2001;250:57–65.View ArticlePubMedGoogle Scholar
- Westert GP, Satariano WA, Schellevis FG, van den Bos GA. Patterns of comorbidity and the use of health services in the Dutch population. Eur J Public Health. 2001;11:365–72.View ArticlePubMedGoogle Scholar
- Struijs JN, Baan CA, Schellevis FG, Westert GP, van den Bos GAM. Comorbidity in patients with diabetes mellitus: impact on medical health care utilization. BMC Health Serv Res. 2006;6:84.View ArticlePubMedPubMed CentralGoogle Scholar
- Chale S, Swai A, Mujinja P, McLarty D. Must diabetes be a fatal disease in Africa? Study of costs of treatment. BMJ. 1992;304:1215–8.View ArticlePubMedPubMed CentralGoogle Scholar
- Beran D, Yudkin JS. Diabetes care in sub-Saharan Africa. Lancet Lond Engl. 2006;368:1689–95.View ArticleGoogle Scholar
- Nkegoum A. Coût direct et indirect du diabète en l’absence de complications chroniques a Yaounde’. Cameroun: University of Yaoundé I; 2002.Google Scholar
- World Health Organization. Global report on diabetes. Geneva: World Health Organization; 2016.Google Scholar
- Campbell MJ, Julious SA, Altman DG. Estimating sample sizes for binary, ordered categorical, and continuous outcomes in two group comparisons. BMJ. 1995;311:1145–8.View ArticlePubMedPubMed CentralGoogle Scholar
- Mbanya JCN, Motala AA, Sobngwi E, Assah FK, Enoru ST. Diabetes in sub-Saharan Africa. Lancet. 2010;375:2254–66.View ArticlePubMedGoogle Scholar
- Raina P, Torrance-Rynard V, Wong M, Woodward C. Agreement between self-reported and routinely collected health-care utilization data among seniors. Health Serv Res. 2002;37:751–74.View ArticlePubMedPubMed CentralGoogle Scholar
- Roberts RO, Bergstralh EJ, Schmidt L, Jacobsen SJ. Comparison of self-reported and medical record health care utilization measures. J Clin Epidemiol. 1996;49:989–95.View ArticlePubMedGoogle Scholar
- Brown JB, Adams ME. Patients as reliable reporters of medical care process. Recall of ambulatory encounter events. Med. Care. 1992;30:400–11.View ArticleGoogle Scholar
- American Diabetes Association. Economic costs of diabetes in the U.S. in 2012. 2013. p. 1033–46.Google Scholar
- Köster I, von Ferber L, Ihle P, Schubert I, Hauner H. The cost burden of diabetes mellitus: the evidence from Germany--the CoDiM study. Diabetologia. 2006;49:1498–504.View ArticlePubMedGoogle Scholar
- Jo C. Cost-of-illness studies: concepts, scopes, and methods. Clin Mol Hepatol. 2014;20:327–37.View ArticlePubMedPubMed CentralGoogle Scholar
- Wolinsky FD, Miller TR, An H, Geweke JF, Wallace RB, Wright KB, et al. Hospital episodes and physician visits: the concordance between self-reports and medicare claims. Med Care. 2007;45:300–7.View ArticlePubMedPubMed CentralGoogle Scholar
- Zuvekas SH, Olin GL. Validating household reports of health care use in the medical expenditure panel survey. Health Serv Res. 2009;44:1679–700.View ArticlePubMedPubMed CentralGoogle Scholar