The Open Public Health Journal




ISSN: 1874-9445 ― Volume 13, 2020
RESEARCH ARTICLE

The Prevalence and Correlates of Pre-Diabetes and Diabetes Mellitus Among Public Category Workers in Akure, Nigeria



Isaac Aladeniyi1, Oladele Vincent Adeniyi2, *, Olufunmilayo Fawole3, Mary Adeolu4, Daniel Ter Goon5, Anthony Idowu Ajayi6, Joshua Iruedo7
1 Department of Epidemiology and Medical Statistics, Faculty of Public Health, University of Ibadan, Ibadan, Nigeria.
2 Department of Family Medicine, Cecilia Makiwane Hospital, East London Hospital Complex, Walter Sisulu University, East London, South Africa.
3 Department of Epidemiology and Medical Statistics, Faculty of Public Health, University of Ibadan, Ibadan, Nigeria.
4 Nigeria State Health Investment Project, Oke eda, Akure, Nigeria.
5 School of Nursing Sciences, Faculty of Science and Agriculture, University of Fort Hare, East London, South Africa.
6 Department of Sociology, Faculty of Social Sciences and Humanities, University of Fort Hare, East London, South Africa.
7 Department of Family Medicine, Walter Sisulu University, Mthatha, South Africa.

Abstract

Background:

Limited epidemiological data on pre-diabetes and diabetes mellitus among public service workers, considered an at-risk population, may undermine the government’s efforts toward addressing the scourge of non-communicable diseases in Nigeria. This study aimed to address this gap by determining the prevalence of pre-diabetes and diabetes mellitus (DM), and to examine their correlates.

Methods:

We conducted a workplace cross-sectional survey of 4828 public service workers across 47 ministries, departments and agencies in Ondo State, Nigeria. An adapted World Health Organisation (WHO) STEPwise surveillance questionnaire was utilised to obtain relevant items of demographic factors, medical history and lifestyle behaviour. Height, weight, blood pressure and fasting blood sugar were measured according to standard protocols. Pre-diabetes and DM were defined as fasting blood glucose 5.6-6.9mmol/L and greater than or equal to 7.0mmol/L, respectively. We performed univariate and multivariate model analyses to determine the associated factors of pre-diabetes and DM.

Results:

Overall, 2299 men and 2529 women participated in the study. The mean age of the participants was 40.4 years (SD±9.7) and the age range was 19 to 76 years. The prevalence of pre-diabetes and DM was 11.7% (n=563) and 5.3% (n=254), respectively. Women had a higher prevalence of pre-diabetes than men did (12.5% versus 10.8%). In univariate analysis, the following factors were associated with pre-diabetes and DM; aging (p<0.0001), marital status (p<0.0001), lower level of education (p=0.008), body mass index (BMI) (p<0.0001) and hypertension (p<0.0001). In multivariate model analysis, after adjusting for confounding factors, age ≥45 years (OR=1.8, 95%CI 1.3-2.4), lower level of education (OR=1.7, 95%CI 1.2-2.4), hypertension (OR=2.0, 95%CI 1.5-2.6) and overweight/obesity (OR=2.2, 95%CI 1.6-3.0) were the independent and significant determinants of DM.

Conclusion:

We found a high prevalence of pre-diabetes and DM in the study population. Cardio-metabolic screening of public category workers might contribute significantly towards bridging the gap of undiagnosed DM in the study setting.

Keywords: Diabetes mellitus type 2, Dysglycaemia, Hypertension, Nigeria, Obesity, Akure, Pre-diabetes, Public category workers.


Article Information


Identifiers and Pagination:

Year: 2017
Volume: 10
First Page: 167
Last Page: 176
Publisher Id: TOPHJ-10-167
DOI: 10.2174/1874944501710010167

Article History:

Received Date: 27/04/2017
Revision Received Date: 04/06/2017
Acceptance Date: 13/08/2017
Electronic publication date: 26/09/2017
Collection year: 2017

© 2017 Aladeniyi et al..

open-access license: This is an open access article distributed under the terms of the Creative Commons Attribution 4.0 International Public License (CC-BY 4.0), a copy of which is available at: https://creativecommons.org/licenses/by/4.0/legalcode. This license permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.


* Address correspondence to this author at the Department of Family Medicine and Rural Health, Walter Sisulu University, Cecilia Makiwane Hospital/East London Hospital Complex, East London, South Africa; Tel: +27437082351; +27793110232; E-mails: vincoladele@gmail.com; oadeniyi@wsu.ac.za





1. BACKGROUND

Diabetes mellitus (DM) is a chronic debilitating non-communicable disease (NCD) and a major cardiovascular disease (CVD) risk factor [1Mbanya JC MA, Sobngwi E, Assah FK, Enoru ST. Diabetes in sub-Saharan Africa. Lancet 2010; 375(9733): 2254-66.
[http://dx.doi.org/10.1016/S0140-6736(10)60550-8]
, 2Maher DSJ. Research on health transition in Africa: time for action. Lancet 2011; 28(9): 5.
[http://dx.doi.org/10.1186/1478-4505-9-5]
]. An estimated 415 million adults were living with DM in 2015; 215.2 million men and 199.5 million women [3International Diabetes Federation IDF Diabetes Atlas. In: 7th edn edn Brussels, Belgium. 2015; pp. 1-144.]. Shaw et al. [4Shaw JE, Sicree RA, Zimmet PZ. Global estimates of the prevalence of diabetes for 2010 and 2030. Diabetes Res Clin Pract 2010; 87(1): 4-14.
[http://dx.doi.org/10.1016/j.diabres.2009.10.007] [PMID: 19896746]
] projected that 439 million adults would have DM by the year 2030, with a significant increase (69%) in its prevalence in developing countries in comparison to a 20% increase in developed countries. These projections may fall short of current estimates due to an accelerated increase in the incidence of DM worldwide.

In sub-Saharan Africa, the burden of DM among adults aged 20 to 79 years is estimated at 14.2 million, with four countries: South Africa, DR Congo, Nigeria and Ethiopia; accounting for nearly half of all adults living with DM in the region [3International Diabetes Federation IDF Diabetes Atlas. In: 7th edn edn Brussels, Belgium. 2015; pp. 1-144.]. Nigeria is estimated to have 1.6 million individuals with DM [3International Diabetes Federation IDF Diabetes Atlas. In: 7th edn edn Brussels, Belgium. 2015; pp. 1-144.]. Underpinning the pandemic of DM is rapid urbanisation, dietary changes, decreased physical activity, alcohol use disorders, smoking and other unhealthy lifestyles [2Maher DSJ. Research on health transition in Africa: time for action. Lancet 2011; 28(9): 5.
[http://dx.doi.org/10.1186/1478-4505-9-5]
, 5Ahasan HN, Islam MZ, Alam MB, et al. Prevalence and risk factors of type-2 diabetes mellitus among Secretariat employees of Bangladesh. J Med 2011; 12(2): 125-30.
[http://dx.doi.org/10.3329/jom.v12i2.8419]
].

About 193 million people living with DM are undiagnosed worldwide; half of all individuals with DM in the developed world are undiagnosed in comparison with two-thirds (67%) in sub-Saharan Africa [3International Diabetes Federation IDF Diabetes Atlas. In: 7th edn edn Brussels, Belgium. 2015; pp. 1-144.]. The rates of undiagnosed DM vary by region and screening programmes [6Hall V, Thomsen RW, Henriksen O, Lohse N. Diabetes in Sub Saharan Africa 1999-2011: Epidemiology and public health implications. A systematic review. BMC Public Health 2011; 11(1): 564.
[http://dx.doi.org/10.1186/1471-2458-11-564] [PMID: 21756350]
]. Some of the reasons for variations are a lack of diagnostic tools and glucose monitoring equipment and the high cost of treatment. Individuals with undiagnosed DM are unlikely to seek health care or to adopt lifestyle changes. DM complications such as chronic kidney disease, heart failure, retinopathy and neuropathy are diagnosed in the majority of patients [7Harris MI, Klein R, Welborn TA, Knuiman MW. Onset of NIDDM occurs at least 4-7 yr before clinical diagnosis. Diabetes Care 1992; 15(7): 815-9.
[http://dx.doi.org/10.2337/diacare.15.7.815] [PMID: 1516497]
-10Flores-Le Roux JA, Comin J, Pedro-Botet J, et al. Seven-year mortality in heart failure patients with undiagnosed diabetes: An observational study. Cardiovasc Diabetol 2011; 10(1): 39.
[http://dx.doi.org/10.1186/1475-2840-10-39] [PMID: 21569580]
].

Pre-diabetes is defined as impaired fasting glucose (IFG) of 5.6 - 6.9mmol/L and/or when the 2-hour postprandial blood glucose is between 7.8 and 11.1mmol/L (impaired glucose tolerance) or both (impaired homeostasis) [11Aroda VR, Ratner R. Approach to the patient with prediabetes. J Clin Endocrinol Metab 2008; 93(9): 3259-65.
[http://dx.doi.org/10.1210/jc.2008-1091] [PMID: 18772457]
, 12Association AD. Standards of medical care in diabetes--2009. Diabetes Care 2009; 32(Suppl. 1): S13-61.
[http://dx.doi.org/10.2337/dc09-S013] [PMID: 19118286]
]. A pre-diabetic condition results in a two to ten fold increase in the absolute risk for developing DM and a comparable risk for cardiovascular disease [13Pranoto AP. The Indonesian Diabetes Association. Guidelines on the management and prevention of prediabetes. Indonesian. J Intern Med 2014; 46(4)]. There is compelling evidence to support early prevention measures and management of patients with pre-diabetes in order to reduce the incidence and complications of DM [14Alberti KG, Zimmet P, Shaw J. International Diabetes Federation: A consensus on Type 2 diabetes prevention. Diabet Med 2007; 24(5): 451-63.
[http://dx.doi.org/10.1111/j.1464-5491.2007.02157.x] [PMID: 17470191]
].

Despite evidence of the growing influence of pre-diabetes and DM on cardiovascular health, there is scanty information on the prevalence and associated factors of pre-diabetes and DM among public service workers in Ondo State, Nigeria. Such data is crucial for crafting workplace intervention strategies to address non-communicable diseases in this population. The aim of this study was to estimate the prevalence of pre-diabetes and DM among public service workers in Akure, Ondo State, Nigeria; and also to examine their associated factors.

2. METHODS

2.1. Study Area and Design

We undertook a workplace population study (cross-sectional study) of public service workers drawn from the 47 ministries, departments and agencies (MDAs) in Akure, the state capital of Ondo State, Nigeria. The majority of the MDAs are located within the state secretariat at Alagbaka, Akure.

2.2. Participants and Sample Size

All public category workers (about 50 000) in Akure, Ondo State were eligible to participate in the study. The majority of the workers (about 30 000) in Ondo State work across various ministries, departments and agencies in Akure, while the rest work outside the state capital. A convenient sample of workers (N=5000), corresponding to one-sixth of the workers in Akure across the various MDAs, was considered adequate for sample representativeness. Participants were included if they were available and had observed the minimum eight-hour fasting protocol for the study. However, we excluded workers who were younger than 18 years, pregnant or lactating women from the study. A communique detailing the purpose, process and specified dates for each ministry was sent to the relevant authorities and all workers. Each MDA was allocated three days for testing to ensure the active participation of all workers.

2.3. Data Collection

We employed and trained 12 professional nurses as research assistants who took measurements and conducted the interviews. In total, 4828 workers participated in the study. A number of eligible workers were excluded after confirming that they had not observed the mandatory eight-hour fasting protocol for the study. All participants were selected serially across the various MDAs over a period of three months (June – August, 2015).

The consent forms and questionnaires were written in both English and Yoruba (the local language). Participants were interviewed using an adapted version of the World Health Organisation (WHO) STEP wise questionnaire for the surveillance of non-communicable disease (NCD) risk factors at the country level [15World Health Organization: STEP wise approach to chronic disease risk factor surveillance (STEPS) In: 2010; 7 Available at: http://www.whoint/chp/steps/riskfactor/en/indexhtml]. The questionnaire was pre-tested in a pilot study that included 25 workers in the Ministry of Health and finalised after necessary amendments.

The pilot study was utilised to validate the study instrument and pilot data were not included in the main study. The questionnaire included items on sex, age, grade level of employment, marital status, smoking status, alcohol intake, diet, hours of sleep and physical activity. Level of education was defined according to the grade level attained in school and participants were categorised as having no formal education, primary (grade 1–6), secondary (7–12), tertiary (first degree in university or colleges of higher learning) or post-graduate (minimum of second degree). Public service workers were categorised based on their grade level into: senior management staff (13–17), middle level staff (8–12) and junior management staff (less than 8). Participants were questioned on daily consumption of red meat (Western-type diet), cigarette smoking status (considered smokers if they had ever smoked cigarettes, not only if they currently smoked), excessive consumption of alcohol (if they had ever consumed three or more units of alcohol per day for men and two for women) [16Definition, diagnosis and classification of diabetes mellitus and its complications: Report of a WHO Consultation Part 1: Diagnosis and classification of diabetes mellitus. Geneva, Switzerland: World Health Organization 1999.]. Physical activity was based on self-reporting and participants were categorised as inactive (sedentary lifestyle) if they spent eight or more hours in a sitting position per day. Additional information on prior access to NCD screening was obtained by self-reporting. Participants were asked if whether or not they ever had their blood pressure and blood sugar measured by health workers.

2.4. Measurements

Participants with abnormal measurements were provided with referral forms to the staff clinics or state specialist hospital in Akure. Glycaemia was measured using ACCUTRENDR test strips for capillary blood glucose (fasting state). Pre-diabetes was defined as a fasting blood glucose of between 5.6 and 6.9 mmol/l. DM was defined as being pre-diagnosed with such by a clinician and/or receiving anti-diabetic medications and/or a fasting glucose level greater than or equal to 7.0mmol/l. Blood pressure (systolic and diastolic) was measured in accordance with the standard protocol [17Seedat Y, Rayner B, Veriava Y. South African hypertension practice guideline 2014. Cardiovasc J Afr 2015; 26(2): 90-0. [erratum].
[PMID: 25629715]
] with a validated Microlife BP A100 Plus model which provided an average of two readings for each participant. Hypertension was defined as an average of two systolic blood pressure readings of > 140mmHg and/or diastolic of > 90mmHg and/or if the individual was on current treatment for hypertension [18James PA, Oparil S, Carter BL, et al. 2014 evidence-based guideline for the management of high blood pressure in adults: report from the panel members appointed to the Eighth Joint National Committee (JNC 8). JAMA 2014; 311(5): 507-20.
[http://dx.doi.org/10.1001/jama.2013.284427] [PMID: 24352797]
].

Body weight was measured in light clothing to the nearest 0.5 kg in the standing position using a Soehnle Scale (Soenle-Waagen Gmbh Co., Muurhardt, Germany). Height was measured by stadiometer in a standing position with closed feet (without shoes to the nearest 0.5cm), holding the breath in full inspiration and with a Frankfurt line of vision [19Committee WE. 1995.]. Body mass index (BMI) was calculated as weight divided by height in square metres. BMI was categorised in accordance with WHO criteria [16Definition, diagnosis and classification of diabetes mellitus and its complications: Report of a WHO Consultation Part 1: Diagnosis and classification of diabetes mellitus. Geneva, Switzerland: World Health Organization 1999.] as <18.5kg/m2, 18.5–24.9kg/m2, 25.5–29.9kg/m2 and >30.0kg/m2 as underweight, normal, overweight and obese respectively.

2.5. Data Analysis

Data were analysed using the Statistical Package for Social Science (SPSS) version 21 for windows (SPSS Inc., Chicago, IL, USA). Data were expressed as mean value ± standard deviations (SD) for continuous variables. Frequencies (n) and proportions (%) were reported for categorical variables. Counts (frequency = n) and proportions (%) were reported for categorical variables. Percentages were compared using the chi-square test. Student’s t-test was used to compare means between groups. We calculated the univariate odds ratios (ORs) using the Maentel-Haenszel test, and multivariate ORs and their 95% confidence intervals (95%CIs) using logistic regression to identify the predictors of DM in our sample. A logistic regression model analysis was performed, adjusted for sex, age, level of education, hours of sleep, formal exercise programme, excessive alcohol intake, cigarette smoking and red meat consumption. A p-value of < 0.05 was considered statistically significant.

3. RESULTS

A total of 4828 participants, 2299 (48%) were men and 2529 (52%) women with a male: female ratio of 1:1. The mean age of the participants was 40.4 years (SD±9.7) and the age range was 19 – 76 years. The majority of the participants had at least a secondary education (86.5%), were married (76.6%), and of middle-level category (53.2%). Men compared to women were more likely to smoke cigarettes, consume red meats daily and consume alcohol excessively. A sedentary lifestyle (spending up to 8 hours daily in a sitting position) was reported by 24.5% of study participants with no significant difference between the sexes (Table 1).

Table 1
Baseline characteristics.


Prior screening for DM occurred in 67.5% of the participants, with women (69.5%) more likely to access blood glucose screening than men (65.4%) (p=0.007). Prior diagnosis of DM occurred in 198 participants (4.1%) with no sex differences between the two groups (p=0.063). The prevalence of pre-diabetes and DM was 11.7% (n=563) and 5.3% (n=254) respectively. Women had a higher prevalence of pre-diabetes than men (12.5% versus 10.8%). There was a positive linear association between age of participants and DM (Table 2). DM was strongly associated with marital relationship, level of education, increasing body mass index and hypertension.

Table 2
Prevalence of diabetes by background characteristics.


After adjusting for a confounding factor (marital status) in the multivariate logistic regression model analysis, aging, lower level of education, overweight/obesity and hypertension remained significant and independent predictors for DM (Table 3). A two-fold increased risk of developing DM was found among workers with ages greater than or equal to 45 years, primary or no education, those with concomitant hypertension and overweight/obesity.

Table 3
Multivariate predictors of diabetes.


4. DISCUSSION

This study estimated the prevalence and determinants of pre-diabetes and DM among public-category workers in Akure, Nigeria. This study also highlights the growing trend of pre-diabetes and DM among the workforce in this setting. The prevalence of DM was 5.3% in our study sample. We further diagnosed an additional 1.2% of workers living with DM without prior awareness of its existence. This is crucial in view of the high rate of undiagnosed DM in the country and the rest of sub-Saharan Africa.

The prevalence of pre-diabetes was 11.7% in our study sample. This cohort will benefit from lifestyle changes, linkage and retention in care. Therefore, our findings highlight the inherent benefit of conducting workplace screening for cardio-metabolic conditions in the study setting. Prior screening for cardiovascular diseases was reported more frequently by women. This is however, not surprising, given the structural (more women-friendly services) and cultural barriers (perception that men should be strong and healthy) to accessing screening services among men in African settings [20Ogunmola OJ, Olaifa AO, Oladapo OO, Babatunde OA. Prevalence of cardiovascular risk factors among adults without obvious cardiovascular disease in a rural community in Ekiti State, Southwest Nigeria. BMC Cardiovasc Disord 2013; 13(1): 89.
[http://dx.doi.org/10.1186/1471-2261-13-89] [PMID: 24138186]
-24Onwubere B, Ejim E, Okafor C, et al. Pattern of blood pressure indices among the residents of a rural community in South East Nigeria 2011.
[http://dx.doi.org/10.4061/2011/621074]
].

Our finding of 5.3% for DM is slightly higher than the prevalence of DM among ministry workers in Oyo State, Nigeria of 4.7% [25Ojewale LY, Adejumo PO. Type 2 diabetes mellitus and impaired fasting blood glucose in urban south western Nigeria. Int J Diabetes Metab 2012; 21: 1-9.] and 4.8% reported in the predominantly rural and semi-urban communities of Ekiti State, South-west Nigeria [20Ogunmola OJ, Olaifa AO, Oladapo OO, Babatunde OA. Prevalence of cardiovascular risk factors among adults without obvious cardiovascular disease in a rural community in Ekiti State, Southwest Nigeria. BMC Cardiovasc Disord 2013; 13(1): 89.
[http://dx.doi.org/10.1186/1471-2261-13-89] [PMID: 24138186]
]. A similar figure, 5.4% was reported from a population survey consisting of both rural and urban indigenes of Delta State by Oguoma et al. [26Oguoma VM, Nwose EU, Skinner TC, Digban KA, Onyia IC, Richards RS. Prevalence of cardiovascular disease risk factors among a Nigerian adult population: relationship with income level and accessibility to CVD risks screening. BMC Public Health 2015; 15(1): 1.
[PMID: 25563658]
]. However, our study found a higher prevalence (11.7%) of pre-diabetes in comparison to 4.9%, 3.3% and 3.8% reported by Oguoma et al. [26Oguoma VM, Nwose EU, Skinner TC, Digban KA, Onyia IC, Richards RS. Prevalence of cardiovascular disease risk factors among a Nigerian adult population: relationship with income level and accessibility to CVD risks screening. BMC Public Health 2015; 15(1): 1.
[PMID: 25563658]
], Ojewale et al. [25Ojewale LY, Adejumo PO. Type 2 diabetes mellitus and impaired fasting blood glucose in urban south western Nigeria. Int J Diabetes Metab 2012; 21: 1-9.] and Ogunmola et al. [20Ogunmola OJ, Olaifa AO, Oladapo OO, Babatunde OA. Prevalence of cardiovascular risk factors among adults without obvious cardiovascular disease in a rural community in Ekiti State, Southwest Nigeria. BMC Cardiovasc Disord 2013; 13(1): 89.
[http://dx.doi.org/10.1186/1471-2261-13-89] [PMID: 24138186]
], respectively. A similarly high prevalence of pre-diabetes and DM has been reported elsewhere within Nigeria and sub-Saharan Africa [25Ojewale LY, Adejumo PO. Type 2 diabetes mellitus and impaired fasting blood glucose in urban south western Nigeria. Int J Diabetes Metab 2012; 21: 1-9., 27Soewondo P, Pramono LA. Prevalence, characteristics, and predictors of pre-diabetes in Indonesia. Med J Indones 2011; 20(4): 283.
[http://dx.doi.org/10.13181/mji.v20i4.465]
, 28Mayega RW, Guwatudde D, Makumbi F, et al. Diabetes and pre-diabetes among persons aged 35 to 60 years in eastern Uganda: prevalence and associated factors. PLoS One 2013; 8(8): e72554.
[http://dx.doi.org/10.1371/journal.pone.0072554] [PMID: 23967317]
]. However, earlier studies from South-west Nigeria reported a lower pre-diabetes prevalence of 2.2% and 3.3% [25Ojewale LY, Adejumo PO. Type 2 diabetes mellitus and impaired fasting blood glucose in urban south western Nigeria. Int J Diabetes Metab 2012; 21: 1-9., 29Olatunbosun ST, Ojo PO, Fineberg NS, Bella AF. Prevalence of diabetes mellitus and impaired glucose tolerance in a group of urban adults in Nigeria. J Natl Med Assoc 1998; 90(5): 293-301.
[PMID: 9617070]
]. Our results further draw attention to the influence of urbanisation, changing lifestyles and dietary changes in the African population [30Alwan A, Maclean DR, Riley LM, et al. Monitoring and surveillance of chronic non-communicable diseases: progress and capacity in high-burden countries. Lancet 2010; 376(9755): 1861-8.
[http://dx.doi.org/10.1016/S0140-6736(10)61853-3] [PMID: 21074258]
, 31Farag YM, Gaballa MR. Diabesity: An overview of a rising epidemic. Nephrol Dial Transplant 2011; 26(1): 28-35.
[http://dx.doi.org/10.1093/ndt/gfq576] [PMID: 21045078]
].

Our findings provide epidemiological data for health managers to institute periodic workplace screenings as a policy specifically to target pre-diabetes and DM among the workforce both in Ondo state and other regions in Nigeria. The broader goal of promoting lifestyles that are associated with low blood pressure and cholesterol, ideal body weight, and non-smoking is supported by our findings. Such a strategy requires tackling the roots of CVD risk factors by health promotion, healthy public policies (workplace NCD screening) and improved physical environments conducive to healthy lifestyles [32Paradis G, Chiolero A. The cardiovascular and chronic diseases epidemic in low- and middle-income countries: A global health challenge. J Am Coll Cardiol 2011; 57(17): 1775-7.
[http://dx.doi.org/10.1016/j.jacc.2010.11.047] [PMID: 21511114]
, 33Labarthe DR. Prevention of cardiovascular risk factors in the first place. Prev Med 1999; 29(6 Pt 2): S72-8.
[http://dx.doi.org/10.1006/pmed.1999.0539] [PMID: 10641821]
].

The high prevalence of pre-diabetes in this study has clinical significance for the workforce of the state and the country, as one-third of this cohort might progress to DM within the next ten years without interventions. If this happens, a further increase in the incidence and prevalence of DM among the workforce is expected. This, by implication, could lead to poor quality of life, disability, premature mortality and lower productivity, as well as strain the already overburdened healthcare system. The strategy of linking the pre-diabetic cohort to care offers the opportunity for initiation of lifestyle interventions. This will potentially prevent or delay the onset of DM and its associated micro- and macro-vascular complications [31Farag YM, Gaballa MR. Diabesity: An overview of a rising epidemic. Nephrol Dial Transplant 2011; 26(1): 28-35.
[http://dx.doi.org/10.1093/ndt/gfq576] [PMID: 21045078]
, 34Rena R, Wing PB, Frederick L. Brancati, George A Bray, Jeanne M Clark: Cardiovascular effects of intensive lifestyle intervention in type 2 diabetes. N Engl J Med 2013; 2013(369): 145-54.].

Our study found a linear relationship between DM and aging. We found a lower DM prevalence of 1.9% among individuals aged 25–34 years; however, the prevalence reached 21.4% among participants 65 years and above. This further supports the extant literature on the association of aging with an increasing incidence and prevalence of DM at population level [25Ojewale LY, Adejumo PO. Type 2 diabetes mellitus and impaired fasting blood glucose in urban south western Nigeria. Int J Diabetes Metab 2012; 21: 1-9.-28Mayega RW, Guwatudde D, Makumbi F, et al. Diabetes and pre-diabetes among persons aged 35 to 60 years in eastern Uganda: prevalence and associated factors. PLoS One 2013; 8(8): e72554.
[http://dx.doi.org/10.1371/journal.pone.0072554] [PMID: 23967317]
, 35Kirkman MS, Briscoe VJ, Clark N, et al. Diabetes in older adults. Diabetes Care 2012; 35(12): 2650-64.
[http://dx.doi.org/10.2337/dc12-1801] [PMID: 23100048]
]. Older adults (≥65 years) are at higher risk for developing DM due to the combined effects of insulin resistance and impaired islet cell function [35Kirkman MS, Briscoe VJ, Clark N, et al. Diabetes in older adults. Diabetes Care 2012; 35(12): 2650-64.
[http://dx.doi.org/10.2337/dc12-1801] [PMID: 23100048]
]. Insulin resistance is associated with increased body adiposity and physical inactivity in older individuals [36Amati F, Dubé JJ, Coen PM, Stefanovic-Racic M, Toledo FG, Goodpaster BH. Physical inactivity and obesity underlie the insulin resistance of aging. Diabetes Care 2009; 32(8): 1547-9.
[http://dx.doi.org/10.2337/dc09-0267] [PMID: 19401446]
]. With aging, the pancreatic islet cell function and the proliferative capacity declines, leading to new-onset DM [37Maedler K, Schumann DM, Schulthess F, et al. Aging correlates with decreased β-cell proliferative capacity and enhanced sensitivity to apoptosis: a potential role for Fas and pancreatic duodenal homeobox-1. Diabetes 2006; 55(9): 2455-62.
[http://dx.doi.org/10.2337/db05-1586] [PMID: 16936193]
, 38Rankin MM, Kushner JA. Adaptive β-cell proliferation is severely restricted with advanced age. Diabetes 2009; 58(6): 1365-72.
[http://dx.doi.org/10.2337/db08-1198] [PMID: 19265026]
]. It should be noted that the prevalence of DM among the study sample age range 55–64 years was 11.5%; however, the prevalence of pre-diabetes was 17.5% in this age group. Both pre-diabetes and DM should be aggressively managed with lifestyle interventions. Most senior staff were within the age group 55 years and above, the group most affected by pre-diabetes and DM. It is very crucial to preserve the health of this age group, for, among other reasons, institutional memory and mentoring for junior staff in the workplace.

The association of socioeconomic indicators and pre-diabetes/DM is an important finding in our study. The paradox of a high prevalence of DM among individuals with a lower educational attainment can be explained by the aging factor. Most workers with only a primary education or without formal education are older staff within our cohort. Whether the workers with higher educational levels were making lifestyle changes and experiencing a lower prevalence of DM as a result of their educational level is a plausible hypothesis. It is supported by evidence suggesting that increasing knowledge about disease conditions influences dietary decisions and lifestyle adjustments [39Braveman PA, Cubbin C, Egerter S, et al. Socioeconomic status in health research: One size does not fit all. JAMA 2005; 294(22): 2879-88.
[http://dx.doi.org/10.1001/jama.294.22.2879] [PMID: 16352796]
, 40Cai L, He J, Song Y, Zhao K, Cui W. Association of obesity with socio-economic factors and obesity-related chronic diseases in rural southwest China. Public Health 2013; 127(3): 247-51.
[http://dx.doi.org/10.1016/j.puhe.2012.12.027] [PMID: 23433575]
].

We also found that being married or widowed was associated with a high prevalence of pre-diabetes and DM. Pre-diabetes was commoner among the married (12.1%) and widowed (13.8%). People in marital relationships tend to have regular feeding habits for the stable food items (pounded yam and cassava products) which are high-energy diets. It should be noted that there was a gradual increase in the prevalence of DM from 5.3% among junior staff to 7.2% among senior management staff. This supports the finding of Kuntz and Lampert [41Kuntz B, Lampert T. Socioeconomic factors and obesity. Dtsch Arztebl Int 2010; 107(30): 517-22.
[PMID: 20737057]
] and Booth and Hux [42Booth GL, Hux JE. Relationship between avoidable hospitalizations for diabetes mellitus and income level. Arch Intern Med 2003; 163(1): 101-6.
[http://dx.doi.org/10.1001/archinte.163.1.101] [PMID: 12523923]
] which demonstrated a significant association between diabetes prevalence and socioeconomic status.

We found significant associations between pre-diabetes/DM and overweight/obesity, and hypertension in both univariate and multivariate analyses. This is not surprising, given the biological plausibility for clustering of cardio-metabolic conditions in certain individuals. There was a two-fold increased risk of developing DM among individuals who were hypertensive in our study sample. We also found a high DM prevalence of 7.7% and 8.6% among those who were obese and hypertensive respectively. A similar trend of increasing prevalence was observed for pre-diabetes among those who were overweight (12.7%) and obese (15%) respectively. There is a biological explanation for our findings; a strong pathophysiological link between hypertension and DM with obesity, inflammation, oxidative stress, insulin resistance and atherosclerosis being central to the development of these diseases [43Cheung BM, Li C. Diabetes and hypertension: is there a common metabolic pathway? Curr Atheroscler Rep 2012; 14(2): 160-6.
[http://dx.doi.org/10.1007/s11883-012-0227-2] [PMID: 22281657]
-45Ross R. The pathogenesis of atherosclerosis: A perspective for the 1990s. Nature 1993; 362(6423): 801-9.
[http://dx.doi.org/10.1038/362801a0] [PMID: 8479518]
].

In designing interventions towards addressing pre-diabetes and DM, an integrated strategy focussing on all the cardiometabolic conditions would yield better results. This is in keeping with the call for action against DM by the International Diabetes Federation [3International Diabetes Federation IDF Diabetes Atlas. In: 7th edn edn Brussels, Belgium. 2015; pp. 1-144.]. While some of the traditional cardiovascular risk factors – cigarette smoking, excessive consumption of alcohol, daily consumption of red meat and sedentary lifestyles – were not statistically significant in our study, the evidence for their control has been documented worldwide [46Inzucchi SE, Bergenstal RM, Buse JB, et al. Management of hyperglycemia in type 2 diabetes, 2015: A patient-centered approach: update to a position statement of the American Diabetes Association and the European Association for the Study of Diabetes. Diabetes Care 2015; 38(1): 140-9.
[http://dx.doi.org/10.2337/dc14-2441] [PMID: 25538310]
, 47Amod A, Ascott-Evans BH, Berg Gl, et al. The 2012 SEMDSA Guideline for the Management of type 2 Diabetes, Journal of Endocrinology, Metabolism and Diabetes of South Africa. In: JEMDSA: Do H, Ed 2012; 17: pp. S1-S94.]

5. STRENGTHS AND LIMITATIONS

Our findings should be interpreted with caution in the light of the convenience sampling and cross-sectional design of the study which does not allow for causal associations to be drawn. Self-reporting of some of the lifestyle measures may have introduced bias. Notwithstanding the limitations of our study, the credibility of our study is supported by the large sample size, involving all MDAs and different grade levels of workers.

In addition, the study offered blood glucose screening for 1571 participants (32.5%) who had never screened for DM. Our study identified a large cohort of pre-diabetic individuals who were linked to a health facility for further evaluation and follow up. Also, this study is a pointer to a need for workplace policy screening for non-communicable diseases as a strategy to further reduce the gap of undiagnosed DM in sub-Saharan Africa. Our study provides useful baseline epidemiological data on pre-diabetes and DM screening among public category workers in Ondo state, as well as workers in the informal sector in the country.

CONCLUSION

There is a high prevalence of pre-diabetes and DM in our study population. An aging population, lower level of education and modifiable conditions such as overweight, obesity and hypertension were the most important associated factors for DM among the public service workers in the study setting. Workplace screenings for pre-diabetes and DM might provide an innovative strategy for reaching this population. Health managers should therefore consider a workplace policy on integrated screening for cardiometabolic diseases as an intervention towards addressing the sustainable development goal agenda on non-communicable diseases in Nigeria and sub-Saharan Africa.

ETHICS APPROVAL AND CONSENT TO PARTICIPATE

Ethical approval was granted by the Ondo State Health Research Ethics Committee (SHREC – AD4693/307). Prior to each day’s interviews, a public lecture was delivered to the participants describing all information regarding the study. Information sheets and consent forms were provided to the participants. All participants provided written informed consent before they were enrolled for the study. Participants were interviewed in a secured room to ensure the privacy and confidentiality of each worker. The project was implemented in accordance with the Helsinki Declaration and confidentiality and privacy of medical information were observed during the course of the study.

HUMAN AND ANIMAL RIGHTS

No animals were used in this research. All research procedures followed were in accordance with the ethical standards of the committee responsible for human experimentation (institutional and national), and with the Helsinki Declaration of 1975, as revised in 2008 (http://www.wma.net/en/20activities/10ethics/10helsinki/).

CONSENT FOR PUBLICATION

All authors approved the submission of this final draft towards publication in a peer reviewed journal.

AVAILABILITY OF DATA AND MATERIALS

Data from this study will be made available on request.

AUTHORS’ CONTRIBUTIONS

IA1, OF3, MA4: conceptualised, designed the protocol and collected data. OVA2, DTG4, JI7: provided intellectual input to the design of the protocol and drafted the manuscript. AIA6: conducted the statistical analysis. All authors read the manuscript and approved the final version.

Funding

This project was partly funded by the Ondo State Ministry of Health which had no influence in the conceptualisation, design or implementation of the study and the outcomes.

CONFLICT OF INTEREST

The authors declare no conflict of interest, financial or otherwise.

ACKNOWLEDGEMENTS

The authors are grateful to the heads of the various ministries, departments and agencies for their unstinting support towards the successful implementation of the project.

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