Therapeutic Adherence and Glycemic Control in the Population with Diabetes in Ceuta (Spain), a Multicultural City: A Cross-Sectional Study

Abstract

Diabetes mellitus constitutes a significant global public health problem. It is a chronic disease characterized by persistent hyperglycemia, which is a consequence of inadequate insulin secretion, deficient insulin action, or a combination of both factors. A crucial component in the effective management of this pathology is therapeutic adherence, as it helps prevent complications, improve patient quality of life, reduce associated mortality, and decrease the need for hospitalization. In this context, it is crucial to implement a comprehensive care model that offers continuous support and a multidisciplinary approach. Primary care should be central, coordinating the entire care process. Understanding the clinical and social characteristics of people with diabetes is key to guiding more effective interventions. Objective: The objective of this study was to describe the sociodemographic and anthropometric characteristics, degree of metabolic control, and treatment adherence in patients with diabetes mellitus enrolled in primary care programs in Ceuta. Materials and Methods: This was a descriptive, observational, and cross-sectional study conducted during the second half of 2024. The study population included individuals enrolled in the primary care diabetes program in Ceuta. We analyzed sociodemographic variables with a self-administered questionnaire, the level of therapeutic adherence using the MMAS-8 scale, and glycemic control through glycosylated hemoglobin (HbA1c) values. Results: The sample consisted of 370 individuals, with 50.3% being men. The average age was 62.82 years (SD = 13.46). A significant portion of participants, 61.07%, had no formal education or had only received primary education. Additionally, 84.9% of the participants had at least one other associated chronic pathology. Regarding adherence, 36.8% of the patients showed a high level, and for all patients, the mean HbA1c value was 7.5% (SD = 1.55). Furthermore, our analysis revealed statistically significant associations between cultural background and both therapeutic adherence (weak positive correlation: r = 0.213, p ≤ 0.001; multivariate significance: sig: <0.001; Exp(B) = 2.448) and glycemic control (multivariate significance: sig: <0.001; Exp(B) = 2.686). Conclusions: We observed high treatment adherence in the study population, with HbA1c values within the limits recommended by the World Health Organization for older adults. Furthermore, a relationship between cultural background and both treatment adherence and glycemic control was identified. This suggests a need for further research into these and other social determinants, like study level or monthly income, in future studies.

1. Introduction

Diabetes mellitus is one of the leading causes of morbidity and mortality worldwide. Recent estimates from 2021 indicate that approximately 529 million people globally were living with this disease, with an age-standardized global prevalence of 6.1%. The regions experiencing the highest prevalence rates were North Africa and the Middle East (9.3%) and Oceania (12.3%) [1].

In Spain, data obtained in 2020 showed that 8.2% of men and 6.9% of women were living with some form of diabetes [2]. Diabetes mellitus is a chronic endocrine disease characterized by persistent hyperglycemia, which stems from either a deficit in insulin production, a resistance to its action, or a combination of both [3,4]. This metabolic alteration does not just have direct clinical consequences, but it also significantly impacts the quality of life of those affected, especially when the disease is not adequately controlled. A critical factor in effectively managing diabetes is therapeutic adherence. The World Health Organization (WHO) defines this as “the extent to which a person’s behavior—taking medication, following a diet, and/or executing lifestyle changes—corresponds with agreed recommendations from a health care provider” [5]. A lack of adherence significantly hinders metabolic control and is directly linked to the onset of complications like diabetic retinopathy, along with an increase in mortality and hospital admissions for cardiovascular diseases. Treatment adherence is fundamental, as it contributes to reducing mortality and hospital admissions due to cardiovascular diseases [6].

Various studies have shown that the presence of comorbidities in people with diabetes is associated with a lower treatment adherence [7,8,9]. The coexistence of multiple chronic diseases represents an additional challenge for therapeutic management, due to the complexity of treatment and the burden it places both on the patient and the healthcare system [10,11]. These conditions lead to greater utilization of healthcare services, a decrease in quality of life, and an increased mortality risk [12,13].

Given this reality, it is essential to adopt care models that integrate a multidisciplinary approach and ensure the continuity of care. Primary care, due to its proximity to the patient and holistic perspective, should play a central role in coordinating these complex processes [14].

Ultimately, diabetes mellitus constitutes a priority public health issue, the complexity of which requires a comprehensive understanding of the factors that influence its progression. Identifying these factors is essential to design effective strategies that prevent complications, optimize the use of healthcare resources, and reduce the burden on healthcare services. While numerous studies have explored social determinants and cultural differences in diabetes management, the specific dynamics of acculturation and their impact on health outcomes can vary significantly when addressing therapeutic adherence and glycemic control in the diabetic population of Ceuta, a multicultural city with unique demographic characteristics. Given the distinct processes of acculturation and diverse cultural background present in this region, a dedicated investigation into this particular population is crucial to understand these influencing factors and guide more effective local interventions.

The aim of this study was to describe the sociodemographic and anthropometric characteristics, degree of metabolic control, and treatment adherence in patients diagnosed with diabetes mellitus who are enrolled in the primary care diabetes program in the autonomous city of Ceuta.

2. Materials and Methods

2.1. Study Design and Population

A descriptive, observational, and cross-sectional study was conducted during the second half of 2024. The study population consisted of patients diagnosed with diabetes mellitus that were enrolled in a primary care diabetes program in the autonomous city of Ceuta.

The inclusion criteria were as follows: being over 18 years old (with no upper age limit), having a confirmed diagnosis of diabetes mellitus, having a recent determination of glycated hemoglobin (HbA1c) performed within the three months prior to the end of the study, being enrolled in the primary care diabetes program in Ceuta, having proficiency in Spanish, and having provided written informed consent to participate in the study.

Those under 18 years of age, those without an HbA1c determination within the reference period (the last three months), those not enrolled in the primary care diabetes program in Ceuta, those who did not speak Spanish, or those who did not sign the informed consent were excluded from the study.

The sample size was calculated considering that the total number of people with diabetes in the primary care program in Ceuta was 6222. The ideal sample size for a 95% confidence level (α = 0.05) and a precision of ±5 was 362 people.

The individuals invited to participate in the study were those attending consultations at their health center within the framework of the diabetes care program. Each potential participant was provided with a detailed explanation of the study’s objectives and characteristics, including a review of certain relevant clinical data contained in their medical history. Subsequently, signing of the informed consent was requested as an essential requirement for inclusion in the study. This constituted a convenience sampling method.

2.2. Variables and Data Collection

The sociodemographic variables analyzed in this study included age, sex, marital status, number of children, educational level, monthly income, family background, and tobacco consumption (expressed in the number of cigarettes smoked per day), as well as the presence of family support in the management of diabetes and the identification of the person fulfilling that role. The classification of participants into the binary ‘European’ and ‘Maghrebian’ groups was a decision of this study, as these represent the two predominant communities in Ceuta. This approach facilitates a direct comparison of differences in diabetes management between them. The choice of cultural category was made by each patient through the self-administered questionnaire, along with the other sociodemographic variables. For the purposes of this cross-sectional study, no additional subdivisions were considered within these groups. Data collection was carried out using a structured questionnaire, which was completed by the participants after they were provided with an explanation and had signed the informed consent for participation in the study.

For the variable ‘treatment adherence,’ the MMSA-8 questionnaire was used, which was adapted for diabetes patients and validated in Spanish by Martínez Pérez [15]. This is a self-administered survey with eight questions, seven of which are answered with ‘yes’ or ‘no.’ However, question 5 is worded inversely to prevent the respondent from making the mistake of answering ‘no’ to all questions. For question 8, responses are given on a Likert-type scale with five options. The score assigned to all ‘no’ responses is 1, except for question 5, which is scored 0. ‘Yes’ responses are scored 0, except for the response to question 5, which is scored 1. For question 8, since it five 5 responses, the first, second, third, fourth, and fifth options are scored 1, 0.75, 0.5, 0.25, and 0, respectively. The score range is from 0 to 8, with a score of 8 indicating high treatment adherence, scores of 7 and 6 indicating medium adherence, and a score of 5 or lower indicating low adherence. This scale has been used in interventions and programs designed to improve treatment adherence in patients with diabetes. It is used as an initial assessment tool to plan specific interventions [16]. MMSA-8 es practical, reliable and validated, and is used in various clinical and research settings for different diseases and in several countries. In addition, it is mentioned that its results coincide with those of other measurement methods, which confirms its reliability [17].

For the variables weight, height, diagnosis of hypertension, heart disease, hypercholesterolemia, hypertriglyceridemia, duration of diabetes onset, type of pharmacological treatment received, and the last HbA1c value recorded, the patient’s Electronic Health Record (EHR) was consulted. The EHR is a set of documents, assessments of different health episodes, a sheet of analytical values, and imaging tests of the patient. For the clinical variables corresponding to hypertension, heart disease, hypercholesterolemia, and hypertriglyceridemia, a review of each participant’s EHR was carried out. The presence of these conditions was considered when at least one episode was recorded by the healthcare professional in which the corresponding diagnosis was explicitly stated.

In the first phase, a descriptive study of the variables included in the study was conducted. Quantitative variables were expressed as means and standard deviations, while qualitative variables were expressed as absolute numbers and percentages.

In the second phase, a bivariate study was performed. After checking for normality, non-parametric tests were used. For correlation analysis, Spearman’s Rho was applied; this was used to correlate the variables after checking for normality. Since the variables did not meet the normality criteria, a non-parametric test was used for correlation. Multivariate analysis was performed with the variables that showed correlation in order to study the complex interactions between variables, control for confounding variables, and identify independent predictors. The dependent variables were dichotomized following the recommendations found in the literature. For HbA1c, a value of 7% was established, as recommended by the Spanish Society of Endocrinology and Nutrition [18]. For the adherence to treatment variable, the threshold for good adherence was set at a score of 8, as established by the author of the MMSA-8 scale [15]. Data analysis was conducted using the SPSS statistical package, version 29, for MAC.

2.3. Ethical Considerations

The project was approved by the Primary Care Directorate of Ceuta with the code REGAGE24s00044127488. The informed consent of all study participants was requested and obtained. The confidentiality of the information used was ensured in accordance with the current Spanish legislation (Organic Law 15/1999, of 13 December, on the Protection of Personal Data) and respecting Law 14/2007, of 3 July, on Biomedical Research. Access to the medical records was carried out using a computer that was specifically designated for this purpose, in compliance with the applicable Spanish legislation (Law 41/2002, of 14 November, which regulates the autonomy of patients and their rights and obligations regarding information and clinical documentation, as well as Law 3/2005, of 7 March, which modifies Law 3/2001, of 28 May, regulating informed consent and the patient’s medical history).

3. Results

The study sample consisted of a total of 370 individuals who completed the sociodemographic questionnaire and signed the informed consent form to participate in the research. Of the 370 individuals studied, 50.3% identified as male, and 49.7% identified as female. The age range of the participants was 24 to 94 years, with a mean age of 62.82 (SD: 13.46). A significant portion of participants, 61.07%, had no formal education or had only received primary education; specifically, 10% of the males and 20.81% of the females had no education, while 16.75% of the males and 13.51% of the females had primary education. Regarding body mass index (BMI), a large proportion of the sample was categorized as overweight (24.05% male, 21.89% female) or obese (13.78% male, 21.89% female), notably with a higher percentage of females presenting obesity. In terms of monthly income, a considerable proportion of the participants reported low earnings (EUR 0–500), particularly among females (16.75% compared to 7.02% in males). The sample was almost equally distributed between European (25.4% male, 23.78% female) and Maghrebian (24.86% male, 25.94% female) cultural background. Regarding family support, 30.5% of the patients reported having no one to provide support, while 48.1% received support from their partners, 15.7% from their children, 3.0% from their parents, and 2.7% from close relatives. The remaining sociodemographic and anthropometric characteristics are summarized in

Table 1. Summary of sociodemographic and anthropometric data by gender.

Variable				n	%
Sex	Male			186	50.3
	Female			184	49.7
Age	18–29	Male		5	1.35
		Female		3	0.85
	30–39	Male		6	1.62
		Female		8	2.16
	40–49	Male		21	5.67
		Female		14	3.78
	50–59	Male		34	9.18
		Female		39	10.54
	60–69	Male		67	18.1
		Female		58	15.67
	70–79	Male		42	11.35
		Female		33	8.91
	Over 80	Male		11	2.97
		Female		29	7.83
Marital status	Single	Male		25	6.75
		Female		11	2.97
	Married	Male		145	39.18
		Female		108	29.18
	Widowed	Male		10	2.7
		Female		53	14.32
	Divorced	Male		6	1.62
		Female		12	3.24
BMI	Normal weight	Male		46	12.43
		Female		22	5.94
	Overweight	Male		89	24.05
		Female		81	21.89
	Obesity	Male		51	13.78
		Female		81	21.89
Monthly income	0–500	Male		26	7.02
		Female		62	16.75
	501–1000	Male		42	11.35
		Female		49	13.24
	1001–1500	Male		43	11.62
		Female		39	10.54
	1501–2000	Male		33	8.91
		Female		18	4.86
	Over 2000	Male		42	11.35
		Female		16	4.32
Cultural background	European	Male		94	25.4
		Female		88	23.78
	Maghrebian	Male		92	24.86
		Female		96	25.94
Family support	No	Male		55	14.86
		Female		58	15.86
	Yes	Partner	Male	105	28.37
			Female	73	19.73
		Children	Male	12	3.24
			Female	46	12.43
		Parents	Male	8	2.16
			Female	3	0.81
		Close relatives	Male	6	1.62
			Female	4	1.08
Education level	No education	Male		37	10
		Female		77	20.81
	Primary education	Male		62	16.75
		Female		50	13.51
	Secondary education	Male		23	6.21
		Female		26	7.02
	High School	Male		39	10.54
		Female		18	4.86
	University	Male		25	6.75
		Female		13	3.51

.

To assess the level of treatment adherence and glycemic control, we analyzed the MMSA-8 scale scores and HbA1c values, respectively. Our findings indicate that a total of 36.8% of the diabetes patients achieved a high treatment adherence (a score of 8 on the MMAS-8 scale for diabetes patients). The mean HbA1c for all patients was found to be 7.5% (SD: 1.55). The comprehensive clinical data and comorbidities of the diabetes diagnosed population in Ceuta are summarized in

Table 2. Clinical data and comorbidities of the diabetic population in Ceuta by gender.

Variable			n	%
MMSA-8 Score	Low adherence	Male	38	10.27
		Female	55	14.86
	Medium adherence	Male	73	19.73
		Female	68	18.38
	High adherence	Male	75	20.27
		Female	61	16.48
HbA1c (mean)	7.5 (SD 1.55)	Male	7.46 (D.E. 1.57)
		Female	7.54 (D.E. 1.53)
Years of evolution (median)	12.02 (SD 6.94)	Male	11.47 (D.E. 6.94)
		Female	12.59 (D.E. 6.91)
Treatment	No pharmacological treatment	Male	2	0.54
		Female	11	2.97
	Oral anti-diabetic (OAD)	Male	105	28.38
		Female	92	24.86
	Insulin	Male	26	7.02
		Female	17	4.59
	OAD + insulin	Male	53	14.32
		Female	64	17.3

.

Figure 1 summarizes the findings of our study, clearly illustrating the differences in HbA1c levels between the group of European and Maghrebian origin.

An analysis of comorbidities revealed that 84.9% of the patients diagnosed with diabetes suffered from at least one other associated chronic condition such as hypertension, heart disease or various dyslipidemias. Specifically, 17% of the total sample were diagnosed with hypertension, 0.3% suffered from some type of heart disease and 7.3% had some form of dyslipidemia. Further detailing patient characteristics,

Table 3. Smoking habits and comorbidities by gender.

				N	%
Smoking	No		Male	147	39.73
			Female	172	46.49
	Yes	Less than 5	Male	7	1.98
			Female	5	1.35
		Between 6 and 15	Male	19	5.13
			Female	3	0.81
		More than 16	Male	13	3.51
			Female	4	1.08
Hypertension	Yes		Male	122	32.97
			Female	134	36.22
Heart disease	Yes		Male	32	8.65
			Female	15	4.05
Hypercholesterolemia	Yes		Male	122	32.98
			Female	113	30.54
Hypertriglyceridemia	Yes		Male	62	16.76
			Female	54	14.59

provides a comprehensive overview of smoking habits and the prevalence of key comorbidities, disaggregated by gender. Figure 2 reflects the comorbidities of the conditions in the sample of people with diabetes.

To identify potential associations between various factors, a correlation analysis was performed. In this analysis, a moderate positive relationship between participants’ cultural background and their monthly income (r = 0.507; p < 0.001) was identified, suggesting that certain cultural backgrounds may be associated with a higher socioeconomic status. Regarding treatment adherence, statistically significant correlations were observed, although they were of a weak magnitude. Specifically, a weak positive correlation was found between therapeutic adherence and cultural background, with some influence on treatment follow-up. Furthermore, a very weak negative correlation between educational level and treatment adherence was identified (r = −0.198; p < 0.001), suggesting that a higher educational level and treatment may not necessarily be associated with better adherence in this particular sample. No correlation was found between BMI and treatment adherence or HbA1c control. The key correlations are detailed in

Table 4. Key correlations.

	Age	Education Level	Smoking	Monthly Income	Family Support	Cultural Background	HbA1c	Adherence to Treatment	BMI
Age
Education level	−0.261 (<0.001)
Smoking	−0.163 (0.002)	0.232 (<0.001)
Monthly income	0.033 (0.525)	0.506 (<0.001)	0.189 (<0.001)
Family support	0.023 (0.663)	0.020 (0.696)	0.10 (0.851)	−0.004 (0.931)
Cultural background	−0.259 (<0.001)	0.366 (<0.001)	0.124 (0.017)	0.507 (<0.001)	0.077 (0.138)
HbA1c	−0.110 (0.035)	0.177 (<0.001)	0.049 (0.351)	−0.218 (<0.001)	0.034 (0.516)	0.288 (<0.001)
Adherence to treatment	−0.076 (0.147)	0.198 (<0.001)	0.319 (0.370)	0.148 (0.004)	0.121 (0.020)	0.213 (<0.001)	−0.112 (0.031)
BMI	−0.087 (0.097)	0.125 (0.16)	−0.138 (0.008)	−0.168 (0.001)	0.002 (0.968)	0.013 (0.796)	−0.054 (0.298)	0.082 (0.116)

.

Table 5. Multivariate analysis.

	B	Standard Error	Wald	df	Sig	Exp(B)
HbA1c–Education level	0.005	0.098	0.003	1	0.959	1.005
HbA1c–monthly income	−0.110	0.103	1.147	1	0.284	0.896
HbA1c–cultural background	0.988	0.221	19.974	1	<0.001	2.686
Adherence to treatment–education level	−0.181	0.097	3.473	1	0.062	0.834
Adherence to treatment–monthly income	0.089	0.103	0.736	1	0.391	1.093
Adherence to treatment–cultural background	0.895	0.222	16.504	1	<0.001	2.448

Dependent variables: HbA1c (0 = good control; 1 = poor control); adherence to treatment (0 = good adherence; 1 = poor adherence). Independent variables: cultural background (0 = European; 1 = Maghrebian); education level (0 = no education; 1 = primary education; 2 = secondary education; 3 = high school; 4 = university); monthly income (0 = 0–500; 1 = 501–1000; 2 = 1001–1500; 3 = 1501–2000; 4 = over 2000).

summarizes the main results of the multivariate analysis conducted of HbA1c and treatment adherence variables, along with the variables that correlated with both. Regarding the HbA1c variable, the variable that showed statistical significance was cultural background (p < 0.001), with an exposure coefficient of 2.321. Similarly, the treatment adherence variable showed statistical significance (p < 0.001), as well as an odds ratio of 2.448. The other variables included in the multivariate analysis did not show statistical significance.

4. Discussion

The findings of this study in Ceuta, a multicultural city, highlight that social and cultural determinants, particularly cultural background, show a stronger correlation with diabetes management than other socioeconomic variables such as educational level and income, which underscores the complexity in managing this disease. This study aimed to describe the sociodemographic and anthropometric characteristics, the degree of metabolic control, and treatment adherence in individuals diagnosed with diabetes mellitus who are enrolled in the primary care diabetes management program in the autonomous city of Ceuta.

Despite 36.75% of the patients demonstrating a high level of treatment adherence, the mean HbA1c value for all patients was 7.5% (SD = 1.55), which is above the values recommended by the Spanish Society of Endocrinology and Nutrition (SEEN) as a general target (below 7%) [18]. The SEEN sets a target of 8–8.5% for older individuals, frail individuals, and/or those with a limited life expectancy, at high risk of hypoglycemia, with low self-care capacity and limited family support. While the sample is within that target range for HbA1c control, not all participants are elderly, are frail, or have a limited life expectancy, and the family support in the sample is relatively high.

A key strength of our study lies in the sample size. Our cohort of 370 participants exceeded the calculated sample size of 362, which was required for a 95% confidence level and a precision of ±5. This sample is also larger than those in previous studies, such as Represas Carrera’s [19] (200 individuals) and Fortin’s [20] (104 individuals). This robust representativeness of the diabetic population within Ceuta’s primary care programs validates the reliability of our finding and reinforces the applicability of our conclusions within this specific context.

Regarding sex, the prevalence of diabetes mellitus is consistently higher in men than in women according to Elissen [21] and Nair [22], a pattern also observed in studies by Torres [23] and Represas Carrera [19]. The data obtained from the sample in this study, where 50.3% of the participants were men and 49.7% women, show similar trends, reinforcing the representativeness of our cohort in terms of gender distribution compared to other studied populations.

The average age of participants in this study is lower than that of participants in Fortín’s study [20] (66 years) and Represas Carrera’s study [19] (69.77 years), but higher than that of Torres’ study [23] (57.7 years). These difference in the mean age of the various groups studied across the articles can be attributed to several factors. One such factor es the inclusion criteria. In this regard, both the studies by Represas Carrera and Torres share the criterion of participants being over 18 years old, similar to our study. Fortin’s study did not specify a particular age but rather focused on a primary criterion of having an HbA1c greater than 10%. Another potential cause for this difference in mean age could stem from the patient recruitment methods. In the case of Fortin’s study, patients attended a consultation and if their HbA1c analysis showed a determination greater than 10%, they were invited to participate. For both Represas Carrera and Torres’ studies, patients were contacted via telephone and given an appointment at the diabetes clinic. Fortin’s study utilized a convenience sampling approach by including patients with a specific HbAc1 threshold. In our study, patients were recruited during their visits to the consultation for any interaction with their diabetes nurse (e.g., collecting results, picking up glucose meters or information-seeking consultations). This disparity might also be due to patient recruitment occurring in different years, implying that the prevalence of diabetes could have shifted over these time differences. The sampling method might also have influenced this disparity.

The mean HbA1c in Torres’ study [23] was 6.1%, and in Represas Carrera’s [19], it was 6.96%, both lower than the mean of 7.5% observed in our sample. On the other hand, Albuja Chaves’ study [24] reported an HbA1c of 7.6%, slightly higher than ours, while Fortin [20] presented a mean of 11. The observed variations in mean HbA1c across studies warrant a deeper analysis beyond mere numerical comparison. Our study’s mean age of 62.82 years and the significant proportion of participants with no formal or only primary education (61.07%) likely contribute to the higher HbA1c compared to studies with younger or more educated cohorts. For instance, Torres’ sample had a mean age of 57.7 years, suggesting a younger population potentially more amenable to stricter glycemic targets. Conversely, Fortin’s study specifically targeted patients with HbA1c higher than 10%, naturally yielding a much higher mean, highlighting a focus on individuals with pronounced poor control rather than a general diabetic population. Clinically, our mean HbA1c of 7.5%, while above the SEEN’s general target of <7%, aligns with recommendations for older adults, frail individuals or those whit limited life expectancy (8–8.5%). This suggests thar the primary care diabetes program in Ceuta may be achieving acceptable control for this higher-risk segment of its population, which is crucial given the high prevalence of associated chronic condition (84.9%). However, for younger patients or those with fewer comorbidities, an HbA1c of 7.5% still poses an elevated risk for long-term micro- and macrovascular complications. Therefore, while the program demonstrates effectiveness for certain vulnerable groups, it necessitates tailored, more intensives strategies to achieve stricter glycemic targets in the broader, younger population. These adaptive intervention are particularly pertinent given the unique multicultural context of Ceuta and the observed association between cultural background and glycemic control.

The civil status is not recorded in all studies, but in Fortín’s study [20], 61% of participants lived with a partner, compared to 68.4% of the sample in this study. The civil status of patients can significantly influence diabetes management and associated health outcomes. According to the ADA, social context and health determinants play a crucial role in diabetes management. This includes aspects related to civil status, whereby single, married, divorced, or widowed individuals may have different needs and behaviors in relation to self-care and treatment adherence [25].

The ADA’s recommendations emphasize the importance of assessing tobacco use and referring to cessation programs as part of routine care for individuals at risk of diabetes [26]. In our sample, 13.7% of participants were active smokers, a figure similar to the 13% in Torres’ study [21] and slightly higher than the 11% in Fortin’s study [20]. These small variations in smoking rates could reflect differences in public health campaigns, access to cessation programs, or cultural norms in each population. For the patients in our study, this prevalence of smoking, while not the highest, represents a crucial modifiable risk factor. The prevalence of smoking observed in our sample is a contributing factor to the challenges of glycemic control and an increased risk of cardiovascular complications in this population, highlighting the need for specific and focused interventions for smoking cessation within the primary care program in Ceuta.

Previous studies in Canada and the Netherlands have demonstrated an inverse correlation between income level and diabetes prevalence, indicating than lower incomes are associated with a higher risk of developing the disease [27,28]. For example, Dinca-Panaitescu [27] found that earning less than CAD 15,000 per year multiplied the risk of diabetes seven-fold in women and three-fold in men, while Duan [28] found that incomes below EUR 1000 per month increased the risk for type 2 diabetes by 71%. In our sample, 48.4% of participants reported incomes below EUR 1000 per month, with women being the most affected (n = 111). The high proportion of low-income patients in our sample aligns with existing research suggesting a link between socioeconomic disadvantage and increased vulnerability to diabetes. This association may be due to factors such as limited access to healthy foods, inadequate educational resources and barriers to consistent medical care, underscoring the necessity of public health strategies to address socioeconomic inequities in Ceuta.

The influence of diabetes according to different cultural backgrounds is a complex issue with variability among different groups. Buichia’s study addresses how diabetes disproportionately affects the indigenous people of Mexico [29]. In turn, Hanson et al. found that Native Americans had a higher prevalence (34%) compared to African Americans (12.4%) and European Americans (10.4%) [30]. According to the American Academy of Ophthalmology, data on diabetes prevalence show a 15.1% prevalence for Native Americans and Alaska Natives, 12.7% for African Americans, 12.1% for Hispanics, 7.4% for non-Hispanic whites, and 8% for Asians [31,32]. Our study presents a unique distribution, in which 49.2% of people with diabetes were of European descent, and 50.8% were of Maghrebian descent. This nearly equitable distribution between two predominant cultural groups in a multicultural city like Ceuta is particularly novel. Unlike broader racial comparisons in the literature, our study allows for a more direct exploration of how specific cultural dynamics of acculturation in a delimited geographical context can influence therapeutic adherence and glycemic control, as evidenced by the weak positive correlation between cultural background and treatment adherence (r = 0.213, p ≤ 0.001), and multivariate significance for glycemic control (sig: <0.001; Exp(B) = 2.868). This adds a specific layer of knowledge about the impact of local cultural diversity on diabetes management.

Comorbidity in people with diabetes is common, as highlighted in multiple studies [9,19,20,21,22,33]. In the sample studied, individuals with another pathology associated with diabetes represented 84.9%; the presence of diabetes, HTA, and high cholesterol was the highest, at 21.62%. It is worth noting that Sosa’s study [9] investigated a larger number of comorbidities, highlighting HTA and dyslipidemias as the main comorbidities, with these data being similar to those obtained from the sample of this study. They also reported others with lower incidence such as diabetic nephropathy and depression. Represas Carrera’s study [19] indicated that the most common pathologies are HTA and dyslipidemia, which also occurred in the population of this study. Torres [23] specifically highlights the high risk of chronic kidney disease, as also reported by Sosa’s study.

In our sample, a very weak negative correlation between educational level and treatment adherence was observed (r = −0.198; p ≤ 0.001). This finding differs from that of Afaya’s study [34], where secondary education was associated with 3.7-times higher odds of non-adherence to medication compared to tertiary education. Similarly, Strandberg’s study [35] conducted in Norway showed that individuals with primary and secondary education had lower rates of participation in diabetes education programs (Diabetes Start Course) compared to those with a university education. The apparent ‘contradiction’ in our sample, where a higher educational level was not necessarily associated with better adherence, suggests that, in the multicultural context of Ceuta, other factors such as cultural background or family support (which did show a correlation) could mitigate the expected impact of educational level. Our study advances knowledge by challenging a common assumption that this relationship is universally linear and suggesting it can be modulated by the sociocultural context, which implies that educational interventions must be adapted and not assume a direct transfer of knowledge to practice in diverse populations.

Cultural background is another factor that, in our sample, was weakly positively related to treatment adherence. This finding, which suggests that cultural background has some influence on treatment follow-up, contrasts with the findings of Strandberg et al.’s study, where no such relationship was found [35]. The ‘cultural background’ in our study, classifying participants as of European or Maghrebi descent, reflects the predominant cultural dynamics in Ceuta’s multicultural population and is comparable to ‘geographic ancestries’ or ‘racial/ethnic groups’ discussed in global studies. Immigrants, especially those undocumented, face multiple barriers that hinder diabetes treatment adherence. These barriers include limited access to healthcare services, lack of health insurance, language and cultural barriers, and economic difficulties [36]. Nevertheless, studies consistently show significant disparities in glycemic control and treatment adherence between different racial and ethnic groups. Hunt, in a national diabetes study, found that non-Hispanic Black and Hispanic war veterans had suboptimal glycemic control compared to non-Hispanic white veterans [37].

A study by Nagresbetsky et al. found that BMI is significantly associated with higher HbA1c levels, suggesting that glycemic control is less optimal in individuals with a higher BMI [38]. On the other hand, a study by Cai et al. indicated that baseline BMI is not significantly associated with treatment efficacy [39]. In our sample, no correlation was found between BMI and treatment adherence or HbA1c control (r = 0.082, p = 0.116, for treatment adherence and r = −0.054, p = 0.298, for HbA1c). This finding, rather than resolving the controversy, adds a further perspective. Our study suggests that, at least in the population of Ceuta, BMI might not be the primary predictor of adherence or glycemic control, which could be due to the influence of other sociodemographic and cultural factors acting as stronger determinants. This implies that interventions to improve adherence and control in this population should focus on aspects beyond body weight, such as cultural background or family support, which did show significant associations in our analysis.

Among the main limitations of this study, it is important to highlight its cross-sectional design, which allows for the identification of associations between variables; however, it does not establish causal relationships. Additionally, the measurement of treatment adherence was conducted using a self-report scale (MMAS-8), which may introduce have social desirability biased or recall errors, potentially overestimating the actual adherence. Although the MMSA-8 was administered in Spanish, the language spoken by the study population, it is important to note that the scale was validated in countries with similar cultural background to the Maghreb but not for this specific linguistic and cultural context. Therefore, the results should be interpreted with some caution. The studied population consisted only of individuals enrolled in the primary care diabetes program in Ceuta, which may limit the generalizability of the results to other geographic or population contexts. Furthermore, while basic sociodemographic variables were collected, other factors that could influence adherence, such as health literacy, were not explored. Another relevant limitation is the exclusive use of HbA1c as a measure of glycemic control, without including other metabolic indicators that could provide a more comprehensive view of the patient’s health status. Finally, despite a large portion of participants presenting comorbidities, their specific impact on adherence or metabolic control was not analyzed, limiting the interpretation of these findings and highlighting the need for future studies that explore these factors in more detail.

5. Conclusions

The most important conclusions that can be drawn from this study are that, despite a considerable proportion of the investigated cohort exhibiting high therapeutic adherence, the average metabolic control of diabetes exceeds the reference thresholds established by the Spanish Society of Endocrinology and Nutrition for the general management of the condition. However, this level aligns with predefined glycemic targets for specific subpopulations, such as elderly individuals, frail patients, or those with a limited life expectancy. The vast majority of individuals diagnosed with diabetes in the sample have experienced comorbidities with other chronic conditions, with hypertension and hypercholesterolemia being the most frequent, highlighting the complexity in managing these patients. Several sociodemographic factors show a correlation with therapeutic adherence and glycemic control. A very weak positive correlation between the participants’ educational level and adherence to treatment was identified, aligning with external evidence that suggests an association between lower educational levels and a reduced adherence to medication. Additionally, the results suggest that social and cultural determinants linked to cultural background have a more direct impact on diabetes management than other socioeconomic variables, such as educational level or income, in this specific context. Family support, particularly from the partner, is significant and is an important factor in managing diabetes. In contrast to other studies that associate a higher BMI with suboptimal glycemic control, this study did not find a correlation between BMI and adherence to treatment or HbA1c control in the overall sample.

These findings invite future longitudinal studies to establish causal relationships between sociocultural determinants, therapeutic adherence, and glycemic control and suggest conducting comparative studies in other multicultural cities to determine the generalizability of these results and better understand the global influence of acculturation processes on diabetes management.