Prevalence and Determinants of Good Glycemic Control among Patients with Type 2 Diabetes Mellitus in Southeast Nigeria

2.1. Study design

This hospital-based cross-sectional study was conducted at the Diabetes Clinic of the University of Nigeria Teaching Hospital (UNTH), Ituku-Ozalla, Enugu State, southeastern Nigeria. UNTH is a major tertiary referral center providing specialized diabetes care to patients from Enugu State and neighboring regions. The study population comprised adult patients aged 18 years and above with a confirmed diagnosis of type 2 diabetes mellitus attending follow-up visits at the diabetes clinic during the study period.

2.2. Eligibility criteria

The inclusion criteria were adult patients with a confirmed diagnosis of type 2 diabetes mellitus who had been attending the clinic for at least six months and who provided written informed consent. Patients who were pregnant or those who were critically ill and unable to respond to questions were excluded from the study.

2.3. Sample size determination

The minimum sample size for the study was determined using Cochran’s formula for cross-sectional studies:

To account for an anticipated 10% non-response rate, the sample size was adjusted to 196. Therefore, a total of 196 participants were included in the study.

2.4. Sampling technique

A systematic random sampling technique was used to recruit participants. The total number of eligible diabetic patients attending the clinic on each clinic day was first estimated. This number was divided by the number of participants expected to be recruited per day to obtain the sampling interval (K-value). Every Kth eligible patient was then selected from the clinic register on each day of data collection. When a selected participant declined to participate or did not meet the inclusion criteria, the next eligible patient was chosen. This process continued daily until the required sample size was achieved.

2.5. Data collection instrument

Data were collected using a semi-structured, interviewer-administered questionnaire designed to capture information on socio-demographic characteristics, clinical history, knowledge of diabetes, self-care practices, and medication adherence. The questionnaire also included sections for laboratory parameters, including fasting blood glucose (FBG) levels and glycated hemoglobin (HbA1c) values where available. Due to limited availability of HbA1c testing, fasting blood glucose (FBG) was used as the primary measure of glycemic control. Good glycemic control was defined as FBG ≤125 mg/dL, while poor glycemic control was defined as FBG >125 mg/dL.

2.6. Pretesting of the instrument

The questionnaire was pretested among 15 diabetic patients attending a different outpatient clinic within the same hospital to ensure clarity, relevance, and cultural appropriateness of the items. Feedback from the respondents was used to identify ambiguous or confusing questions, which were revised accordingly. The pretest also provided an estimate of the average time required to complete the questionnaire and helped ensure logical flow and consistency of responses.

2.7. Reliability of the questionnaire

Reliability testing was carried out using Cronbach’s alpha to assess the internal consistency of the instrument. The pretested questionnaires were coded such that higher scores indicated greater knowledge or adherence to treatment. Data from the pretest were analyzed using SPSS version 27, and the Cronbach’s alpha value obtained for the knowledge and practice items was 0.79, indicating good internal reliability.

2.8. Study duration

The study was conducted over a six-month period, from May 2025 to October 2025. This duration allowed sufficient time for recruitment of participants, data collection, and quality assurance processes.

2.9. Data management and analysis

All completed questionnaires were checked for completeness and consistency before data entry. Data were coded and entered into the Statistical Package for the Social Sciences (SPSS) version 27 for analysis. Descriptive statistics, including means, standard deviations, frequencies, and percentages, were used to summarize socio-demographic and clinical variables. Bivariate analysis using the Chi-square (χ²) test was performed to identify factors associated with glycemic control. Variables that were statistically significant at the bivariate level (p < 0.05) were subsequently entered into a multivariate logistic regression model to determine independent predictors of good glycemic control. Missing data were handled using complete-case analysis, whereby observations with missing values for specific variables were excluded from the respective analyses but retained in other analyses where data were available. The number of valid responses (n) used in each analysis was reported where applicable. Statistical significance was set at a p-value of less than 0.05.

2.10. Ethical considerations

Ethical approval for this study was obtained from the Health Research Ethics Committee (HREC) of the University of Nigeria Teaching Hospital (UNTH), Ituku-Ozalla, Enugu, with approval number NHREC/05/01/2008B-FWA00002458-1RB00002323, as indicated on the ethical clearance certificate. The study was conducted in accordance with internationally accepted ethical principles, including the Declaration of Helsinki (2013 revision) and the Council for International Organizations of Medical Sciences (CIOMS) International Ethical Guidelines for Health-Related Research Involving Humans (2016). Prior to data collection, the purpose of the study, procedures involved, potential benefits, and any possible risks were clearly explained to all participants. Written informed consent was obtained from each participant before enrollment into the study. Participation was voluntary, and respondents were informed of their right to decline participation or withdraw from the study at any point without any consequences. Confidentiality and anonymity were strictly maintained throughout the study. Personal identifiers such as names were not recorded; instead, unique codes were assigned to each participant. All collected data were securely stored in password-protected electronic devices and handled only by the research team.

3.1. Sociodemographic and clinical information

The study population consisted of elderly participants (57.1%) with a mean age of 62 ± 11 years, while the mean age at diabetes diagnosis was 48 ± 12 years ( Table 1). Females accounted for the majority (60.2%), and most participants were of Igbo ethnicity (89.3%) and married (78.6%). Nearly half resided in urban areas (49.5%), and majority had attained tertiary education (34.2%). Clinically, most participants had been living with diabetes for more than 10 years (44.4%) and were mainly managed with oral tablets (38.3%). Over half of the respondents (52.0%) had fasting blood glucose levels in the diabetic range.

3.2. History of diabetes-related complications

Retinopathy was the most frequently reported complication, affecting 11.7% of respondents, followed by diabetic foot ulcer and neuropathy or numbness, each reported by 8.2% of participants ( Figure 1). Hypertension (2.6%) and recurrent infections (1.0%) were less commonly reported.

Figure 1. History of health complications from diabetes (N = 196).

The result show that retinopathy 11.7%, diabetic foot ulcers 8.2%, neuropathy or numbness 8.2%, hypertension 2.6%, and recurrent infections 1.0%.

3.3. Knowledge of diabetes

According to the findings, 45.4% correctly recognized that diabetes results from insulin deficiency, and 36.7% knew that a healthy target fasting blood glucose level is below 125 mg/dL ( Table 2). Knowledge regarding beneficial lifestyle practices was moderate, with 48.5% acknowledging the role of regular physical activity and 44.9% recognizing the importance of high-fiber foods in glycemic control. Awareness of more specific indicators, such as HbA1c reflecting average blood glucose over 2–3 months, was very low (13.3%), and only 20.4% understood that stress can influence blood glucose levels. Misconceptions were also evident, with many respondents incorrectly believing that missing medication doses or neglecting foot care in the absence of neuropathy would not affect diabetes outcomes. Additionally, the overall knowledge of diabetes with majority of participants reported to have poor knowledge accounting for 77% ( Figure 2). 14.8% had moderate knowledge, and only 5.6% demonstrated good knowledge. 2.6% (not shown) had no computable knowledge score (no response for any knowledge question). Knowledge levels were classified using the cut-offs: poor (<60%), moderate (60–79%), and good (≥80%).

Figure 2. Overall knowledge of diabetes (N = 196).

Most participants had poor knowledge (77%), 14.8% had moderate knowledge, and only 5.6% demonstrated good knowledge. 2.6% (not shown) had no computable knowledge score (no response for any knowledge question).

3.4. Medication adherence assessment using the 8-item morisky medication adherence scale (MMAS-8)

Approximately one-third of participants reported sometimes forgetting to take their medication (33.3%) or stopping their medication when they perceived their diabetes to be under control (32.8%). Around a quarter reported missing doses in the previous two weeks (28.0%) or stopping medication without consulting their doctor (27.0%). Most participants (82.0%) reported taking their medication on the day prior to the survey, although 39.7% indicated that taking medication every day felt inconvenient. Difficulty remembering to take medications was also reported by many participants, with 27.5% experiencing occasional forgetfulness and 16.9% reporting this sometimes ( Table 3). Overall medication adherence based on MMAS-8 scores show that low adherence (57.7%), 25.9% had medium adherence, and 15.9% demonstrated high adherence, with 1 (0.5%) missing response ( Figure 3). Adherence levels were classified as high (score = 8), medium (6 to <8), and low (<6).

Figure 3. MMAS-8 overall score.

The figure shows the overall medication adherence based on MMAS-8 scores with majority reported low adherence (57.7%), 25.9% had medium adherence, and 15.9% demonstrated high adherence, with 1 (0.5%) missing response (not shown). Adherence levels were classified as high (score = 8), medium (6 to <8), and low (<6).

3.5. Determinants of glycemic control

3.5.1. Access to healthcare factor

According to the results, 19.9% reported always being able to afford diabetes medications without financial strain, and 16.8% often managed to do so, while 27.0% could sometimes afford them, and 27.8% rarely or never could ( Table 4). Despite financial challenges, 63.3% were able to monitor their blood glucose as recommended over the past six months. Attendance at follow-up appointments was moderate, with 46.4% attending always and 26.5% often. Furthermore, the reason for not checking blood glucose in the last 6 months is presented in Figure 4 with the major reason reported to be unavailability of instruments or test strips (14.8%), while financial strain (6.1%), fear of needles (2.0%), and being newly diagnosed (2.0%) were less frequent.

Figure 4. Reason for not checking blood glucose in the last 6 months.

The most common reason was unavailability of instruments or test strips (14.8%), while financial strain (6.1%), fear of needles (2.0%), and being newly diagnosed (2.0%) were less frequent.

3.5.2. Psychological and emotional factors

Almost half (46.4%) experienced stress or anxiety related to diabetes at least sometimes, whereas 42.1% reported rarely or never feeling stressed. Feelings of depression or hopelessness were less common, with 23.9% reporting these symptoms often or sometimes, compared with 65.8% who rarely or never experienced them. Family and social support were high: 55.1% rated support as very high (5), 23.5% as high (4), and 7.7% as moderate (3). Only 1.5% each reported very low or low support (ratings 1 and 2), while 10.7% provided no response ( Table 5).

3.5.3. Economic and social determinants

The majority reported modest earnings, with 33.7% earning between 50,000–100,000 NGN monthly and 28.1% earning less than 50,000 NGN ( Table 6). Only 10.7% reported an income exceeding 200,000 NGN, suggesting that most participants belonged to low- to middle-income groups. Despite the relatively low income levels, financial hardship appeared to have limited impact on meal consistency. Three-quarters of respondents (75.0%) indicated that they never skipped meals due to financial difficulties, while 12.8% did so occasionally and only 1.5% reported daily meal skipping.

3.5.4. Health beliefs and motivation

Most participants believed that diabetes can be effectively controlled through medication and lifestyle modification, with 73.5% strongly agreeing and 19.9% agreeing ( Table 7). Maintaining optimal blood glucose levels was considered very important by 76.5% of respondents. More than half (55.1%) reported having full control over their blood glucose levels, while 35.2% indicated having partial control. Additionally, 57.7% reported receiving structured diabetes education within the past year.

3.6. Association between demographic factors and overall diabetes knowledge

Age, gender, residence, marital status, employment, and duration of diabetes did not show statistically significant associations with knowledge levels (all p > 0.05) ( Table 8). However, educational attainment demonstrated a significant association with overall diabetes knowledge (χ² = 24.194, p = 0.000). Participants with tertiary education were more likely to have good knowledge (90.9%) compared to those with no formal education or only primary schooling, who predominantly exhibited poor knowledge levels. Although elderly participants (58.9%) accounted for a higher proportion of those with poor knowledge, middle-aged adults showed relatively better understanding.

3.7. Association between demographics and medication adherence

Younger adults demonstrated relatively higher proportions of medium and high adherence (54.5% and 27.3%, respectively), whereas older adults constituted the largest proportion of participants with low adherence (53.6%). The association between age group and medication adherence was statistically significant (χ² = 14.673, p = 0.005). Employment status also showed a significant association with adherence (p = 0.010). Participants who were employed or self-employed demonstrated better adherence patterns compared with unemployed participants, who were more frequently classified as having low adherence ( Table 9). Type of diabetes treatment was significantly associated with medication adherence (p = 0.004). Participants receiving oral hypoglycemic agents showed the highest proportion of high adherence (28.6%), while those on insulin therapy or lifestyle-only interventions were more frequently classified as low adherers. Similarly, fasting blood glucose (FBG) levels were significantly associated with adherence (p = 0.040). Participants with FBG levels in the diabetic range (≥125 mg/dL) had the highest proportion of low adherence compared with those with normal or impaired glucose levels. Other factors, including gender, tribe, marital status, residence, education level, and age at diabetes diagnosis, were not significantly associated with medication adherence (all p > 0.05).

4.1. Sociodemographic and clinical information

The study population consisted of elderly participants (57.1%) with a mean age of 62 ± 11 years, while the mean age at diabetes diagnosis was 48 ± 12 years. Females represented the majority (60.2%), and most participants were of Igbo ethnicity (89.3%) and married (78.6%). Nearly half resided in urban areas (49.5%), and a significant proportion had tertiary education (34.2%). Clinically, 44.4% had been living with diabetes for more than ten years, and oral hypoglycemic agents were the most common treatment modality (38.3%). More than half (52.0%) had fasting blood glucose levels within the diabetic range, indicating suboptimal glycemic control. The reliance on oral medications may influence adherence patterns and glycemic outcomes, as oral tablets are generally easier to administer compared to insulin therapy. These findings align with global epidemiological trends showing that the burden of type 2 diabetes mellitus (T2DM) is higher among older adults and females. A systematic review and meta-analysis reported that T2DM prevalence increases exponentially after age 50, with women more likely to live longer with the condition due to higher survival rates (Chivese et al., 2019). The prevalence of elderly participants in this cohort imply that diabetes continues to affect older adults, particularly women, which is consistent with global studies showing higher prevalence of type 2 diabetes in aging populations. This emphasizes the need for age-sensitive and gender diabetes interventions and monitoring strategies. Half of respondents had poor glycemic control (FBG ≥125 mg/dL) which is consistent with a longitudinal study conducted in Iran, where glycemic control in men was 59.77% and 57.98% in women in 2016 and 55.96% in men and 53.66% in women in 2021(Nikkhah et al., 2025).

4.2. History of diabetes-related complications

Moreover, retinopathy (11.7%) was the most common complication, followed by diabetic foot ulcers and neuropathy or numbness (8.2% each), while hypertension (2.6%) and recurrent infections (1.0%) were less frequent. The prevalence of microvascular complications (retinopathy, neuropathy) over macrovascular events aligns with another systematic review and meta-analysis by (Ireri et al., 2024) and another cross-sectional study conducted in Kenya (Satapathy et al., 2025) which reported similar complications in diabetic patients. The lower prevalence of macrovascular complications in this cohort might be due to underreporting or early disease detection, but it also reflects improved hypertension management. This finding implies that preventive screening for ocular and neuropathic complications should be prioritized in long-term diabetes care programs in Nigeria.

4.3. Knowledge of diabetes

Overall, diabetes-related knowledge was generally poor among participants, with 77% scoring below 60%. Only 45.4% knew diabetes results from insulin deficiency, and 36.7% correctly identified normal fasting glucose (<125 mg/dL). Awareness of physical activity and dietary fiber was moderate, while knowledge of HbA1c and stress effects was very low (13.3% and 20.4%, respectively). These findings are consistent with previous studies across Africa and Asia, which revealed similar knowledge deficits (Asefa et al., 2024; Sari et al., 2025; Rekha et al., 2023). Similar trend has been consistently reported among diabetic patients in low- and middle-income countries to exhibit inadequate knowledge about diabetes self-care, largely due to fragmented patient education and lack of structured counseling (Woodward et al., 2024). Likewise, a Nigerian study by (Emos E et al., 2021) showed that fewer than half of patients could correctly identify diabetes causes or target glucose levels. This finding implies that diabetes education programs in Nigeria remain insufficient, particularly in explaining long-term indicators such as HbA1c, which are critical for disease monitoring. The low level of awareness emphasizes the need for routine health education integrated into clinical consultations and community-based interventions to improve self-management.

4.4. Medication adherence

Furthermore, medication adherence was moderate, with 57.7% showing low adherence with forgetfulness (33.3%) and stopping medication when feeling well (32.8%) were common barriers. These findings imply that behavioral and perceptual factors, such as treatment fatigue and misperception of wellness, affect adherence. The result of this finding emphasizes that interventions targeting reminder systems, patient counseling, and adherence support are important to improving diabetes outcomes (Khan et al., 2020). The study also revealed that participants on oral tablets had higher adherence (66.7%), highlighting that treatment modality influences medication-taking behavior. A systematic review and meta-analysis of low- and middle-income countries showed similar trends, where forgetfulness and perceived wellness were major contributors to non-adherence (Teo et al., 2024). This supports the implication that behavioral interventions should be context-specific and culturally tailored.

4.6. Determinants of glycemic control

Furthermore, determinants of glycemic control indicated that 19.9% of participants could always afford medication, and 27.8% rarely or never could. About 63.3% monitored their blood sugar as recommended, but financial strain and lack of testing materials were major constraints. Similar barriers were reported by another multicentre cross-sectional study conducted in Northwest Ethiopia, where affordability was associated with treatment adherence and glycemic outcomes (Sendekie et al., 2022). This finding implies that economic inequality and limited health insurance coverage can influence glycemic control. Ensuring affordable medication access and subsidized glucose monitoring tools could improve disease management outcomes. Nearly half (46.4%) experienced stress or anxiety about diabetes, and 23.9% reported feelings of depression or hopelessness. High levels of family and social support (78.6%) were observed. These findings align with global evidence that mental health disorders particularly depression and anxiety are common among individuals with diabetes (Abu Bonsra et al., 2025; Wojujutari Ajele & Sunday Idemudia, 2025). Such comorbidities reduced quality of life and impair self-care behaviors, reduce medication adherence, and worsen glycemic outcomes. Consequently, addressing the mental health dimensions of diabetes has become important global health priority. In recognition of this, the World Health Organization’s Global Diabetes Compact (2021) and the WHO Mental Health Action Plan (2013–2030) both emphasize the integration of mental health services into chronic disease management (WHO, 2021). Most participants (61.8%) earned below ₦100,000 monthly, but 75.0% never skipped meals due to financial constraints. Similar socioeconomic profiles have been reported in other Nigerian and Ghanaian studies. Suggesting that even low-income groups prioritize food access despite limited income (Christabel et al., 2024). Stable meal intake implies potential dietary consistency, a protective factor for glucose regulation. Additionally, most participants believed diabetes could be controlled with medication and lifestyle changes (93.4%) and viewed good blood sugar maintenance as very important (76.5%). This high self-efficacy aligns with the Health Belief Model, indicating readiness for behavioral change when supported by health education. Structured diabetes education (57.7%) further suggests a growing awareness base. These findings emphasize the positive role of motivation and health literacy in promoting adherence and improved clinical outcomes. Structured and continuous diabetes education could therefore translate belief into consistent behavior, improving glycemic outcomes over time. The prevalence of strong social support 55.1% observed in this study suggests that social networks and family involvement can might play a protective role in mitigating the psychological distress associated with diabetes. This aligns with the WHO’s people-centered care framework, which encourages community and family engagement in disease management to enhance adherence and treatment continuity (WHO, 2021).

4.7. Association between demographics, knowledge, and adherence

Education was the only sociodemographic factor significantly associated with diabetes knowledge, similarly to another study conducted in North-East Ethiopia (Mulugeta Abate et al., 2025). These findings imply that literacy plays an important role in understanding diabetes management such as medication and dietary adherence, blood glucose monitoring, symptom recognition, and lifestyle modification. This highlights the need to adapt health education strategies tailored to varying literacy levels, using simple language, visual aids, and culturally appropriate communication to enhance diabetes knowledge in Nigeria and beyond (Chen et al., 2024).

Besides, sociodemographic and clinical factors such as Age, employment status, treatment type, and fasting blood glucose were significantly associated with medication adherence. The observed decline in adherence with increasing age is consistent with previous studies indicating that older adults often face challenges such as polypharmacy, cognitive decline, visual impairment, and complex medication regimens that reduce treatment consistency (Stanly et al., 2025). These barriers emphasized the need for age-specific adherence interventions such as simplified dosing schedules, caregiver support, and reminder systems to sustain medication-taking behaviors in elderly populations. The relationship between higher fasting blood glucose levels and lower adherence further reinforces the bidirectional nature of adherence and glycemic control. Similar finding has reported in a systematic review and meta-analysis conducted in low-middle-income countries (Azagew et al., 2025). Poor adherence leads to inadequate glycemic regulation, while persistent hyperglycemia may cause emotional distress or treatment fatigue, which in turn worsen adherence. This cyclical interaction suggests that healthcare providers must monitor blood glucose and assess behavioral adherence as a routine part of diabetes management (Sendekie et al., 2025). Employed participants exhibited better compliance compared to the unemployed. Structured daily routines, financial stability, and greater health literacy among employed individuals likely facilitate consistent medication use and access to healthcare services. Conversely, unemployment may lead to financial constraints, psychological stress, or reduced motivation to adhere to therapy. (Stanly et al., 2025). Differences in treatment type such as monotherapy versus combination therapy also played a role, possibly due to variations in regimen complexity and side-effect profiles. Patients on multiple OADs or insulin often face higher pill burdens and dosing schedules that can discourage consistent adherence. Simplified combination formulations, patient education on medication purpose, and adherence counseling could help mitigate these barriers (Xie et al., 2023).