Burden of hypertension and type 2 diabetes in Tamale Metropolis: a case study of Tamale teaching hospital

Abstract

Hypertension and type 2 diabetes mellitus are a pair of prevalent chronic non-communicable ailments that present significant obstacles to the global well-being of the public. The study investigates the burden of Hypertension and Type 2 Diabetes in Tamale metropolis. The target population used for the study was all outpatients from the Tamale Teaching Hospital. Secondary source of data was employed for the study. The findings reveal significant variations in hypertension prevalence across demographic categories, with the highest rates found in the 55–64 age group at 31.4% (95% CI: 26.3%—36.5%) and all age groups demonstrating statistical significance (p < 0.0001). Findings further show the prevalence of Type 2 diabetes peaks at 28.8% in the 65–74 age group, while the 25–34 age range has the lowest rate at 1.5%. Findings from the regression analysis reveal that gender and patients’ place of residence significantly influence type 2 diabetes, while age is the only variable that shows a statistically significant association with hypertension. The study concluded that Type 2 diabetes and hypertension is prevalent among older age, higher weight, male gender, self-employed, and lower education. The study recommends for implementation of targeted screening programs focusing on older adults, especially females, and those in self-employed, for Type 2 diabetes and Hypertension.

Introduction

Hypertension and type 2 diabetes mellitus are a pair of prevalent chronic non-communicable ailments that present significant obstacles to the global well-being of the public. Hypertension, which is defined by high blood pressure, and type 2 diabetes, a metabolic disease caused by lack of insulin and hyperglycemia, frequently coexist and work together to increase the risk of cardiovascular events, kidney damage, and death [8, 71]. According to Chobanian et al. [21] and Kasper (2015), hypertension is projected to be the cause of 7.5 million deaths annually worldwide, accounting for a significant portion of the world's sickness burden. The Global Health Observatory Report estimates that in 2008, around 40% of persons 25 years of age and older had hypertension overall (Institute of Medicine Committee, 2010). 972 million persons worldwide were predicted to have hypertension in 2000; 333 million lived in economically developed nations and 639 million in economically developing nations [51]. It is anticipated that 1.56 billion persons worldwide would have hypertension by 2025 [51]

Approximately 1.13 billion people worldwide suffer with hypertension, and the incidence of this condition is on the rise, especially in low- and middle-income countries, according to the World Health Organization (WHO, 2023). Similarly, type 2 diabetes is predicted to affect 463 million people worldwide by 2030, contributing to an epidemic-like situation (Magliano et al., 2021). In earlier times, the area was closely tied to infectious ailments; nonetheless, transitions in disease patterns, urbanization, lifestyle shifts, and an older demographic have led to a surge in non-communicable disease (NCD) occurrences [60].

With prevalence rates showing significant variation among countries, hypertension in particular is a growing concern throughout the African continent. According to a thorough analysis, the prevalence of hypertension in Africa is roughly 30%, which is much higher than the global average [64]. According to Zeru et al. [90], non-infectious diseases, particularly diabetes mellitus (DM) and hypertension, have alarmingly increased in Ethiopia. According to a meta-analysis carried out in Ethiopia, the prevalence of hypertension was 20.63% and the prevalence of type 2 diabetes was 6.5% [80]. In Ethiopia, the challenges associated with diabetes and the associated heart-related issues, such as hypertension, nerve problems, kidney problems, coronary artery disease, and strokes, have become significantly more urgent [33].

Due in large part to a lack of adequate medical facilities, limited access to essential medications, and challenges with disease monitoring and management, high blood pressure and type 2 diabetes are prevalent on the African continent. Since the healthcare systems in many African nations are not prepared to handle the rising demand for NCD care, there are significant gaps in prevention, diagnosis, and treatment services [63]. Moreover, variations in the prevalence of type 2 diabetes and hypertension among different African populations are caused by differences in economic standing and the availability of medical resources. According to [60], vulnerable groups—such as those who are poor, live in rural areas, or reside in informal settlements—often encounter obstacles when trying to access healthcare facilities. They also run a higher risk of having their NCDs misdiagnosed or inadequately addressed.

Even though the health risks associated with hypertension and type 2 diabetes are becoming more widely recognized, less is known about the epidemiological patterns, risk factors, and management issues associated with these conditions in sub-Saharan African cities like Tamale Metropolis in Ghana. Existing literature primarily focuses on high-income countries and broader regional analyses, overlooking the unique contextual factors influencing the burden of these diseases in low-resource settings. Furthermore, little thorough study has been done on the intersections of type 2 diabetes and hypertension, especially when it comes to co-morbidity and related consequences. Understanding the interplay between these conditions is crucial for developing holistic intervention strategies tailored to the complex healthcare needs of affected populations. Thus, in an effort to close the research gap and provide targeted public health interventions and locally-specific healthcare delivery techniques, this study examines the prevalence of type 2 diabetes and hypertension in the Tamale Metropolis.

Materials and methods

Study area

The Tamale Metropolis was the study area of the research. The Northern Region is home to 26 districts, one of which being Tamale Metropolis. The Sagnarigu District and Savelugu Municipality form its northern and western borders, while the Mion District and the East Gonja District form its eastern and southern, and the Central Gonja District forms its southwestern, neighboring ones. Its location is in the very heart of the area. The land area of the Metropolis is around 646,90,180 square kilometers (GSS, 2021). In terms of latitude and longitude, the Metropolis is situated between 9º16 and 9º 34 north and 0º 36 and 0º 57 west.

A total of 374,744 people live in the Metropolis, including 189,693 women and 185,051 men (50.6% and 49.4%, respectively). The populations of the region and the country, respectively, are roughly 16.2% and 1.2% (GSS, 2021). With 89,011 households and an overall household size of 4.1 people, the density of population is 825 per sq km, which is lower than the area average of 5.2. Around 27.2% of people can read and write exclusively in English, 9.4% can read and write in Ghanaian, and 61.8% can read and write in both English and Ghanaian. The population has 0.4% literate in English and 0.4% in French, for a total literacy rate of 1.1% for the three languages (English, French, and a Ghanaian language). There are more female illiterates than male illiterates. The map of Tamale Metropolitan Assembly is shown in Fig. 1 below.

Fig. 1.

The map of Tamale metropolitan assembly

Target population

The target population for the study comprised all outpatients at Tamale Teaching Hospital.

Study population

The study population consisted of outpatients diagnosed with type 2 diabetes and hypertension, as documented in the outpatient morbidity register of the Tamale Teaching Hospital. Cases were chosen via a past review of secondary data from the hospital's Health Information System (HIS), encompassing records from January 2023 to December 2023. Only patients with a verified diagnosis of one or both diseases, as recorded by a healthcare professional at an outpatient consultation, were incorporated into the analysis. A total of 205 qualified cases that satisfied these inclusion criteria were extracted for the study. Patients with incomplete records or ambiguous diagnoses were eliminated to guarantee data precision and uniformity.

Source of data

The information was obtained from a secondary source. Data regarding type 2 diabetes and hypertension cases were obtained from the Health Information System (HIS) of the Tamale Teaching Hospital since it serves as the reference center within the northern sector of Ghana.

Study variables

For the study, two sets of variables were chosen. These are both independent and dependent variables. The glucose level of people with type 2 diabetes and their blood pressure served as the dependent variables. The socio-economic characteristics (level of education, age, occupation, weight, place of residence, and gender) of patients were the independent variables for this study.

Data analysis

The data was analyzed using both descriptive and inferential statistical techniques. Descriptive statistics include the cross-tabulation and the percentages. An inferential technique called multivariate multiple regression was used to model the socio-economic traits linked with type 2 diabetes and hypertension.

Model specification

The Multivariate Multiple Regression model is a suitable analytical tool in cases where one finds two dependent variables together with several independent variables. This particular regression method clarifies the variability of several dependent variables by means of a set of independent variables and allows their simultaneous modeling, therefore recognizing their interrelationships.

Model Structure

The multivariate multiple regression model can be written as:

where:

Y is an n × m matrix of dependent variables, with n observations and mm dependent variables.

X is an n × p matrix of independent variables, with p predictors.

is a p × m matrix of coefficients.

is an n × m matrix of errors or residuals.

Estimation of coefficient

Coefficient B can be estimated using multivariate ordinary least squares (OLS), which minimizes the sum of squared residuals. This is often done using matrix algebra:

Assumptions of multivariate multiple regression

The Multivariate Multiple Regression model bases its principal assumptions on the following premises regarding the underlying data:

• i.The dependent variables is continuous.
• ii.Linearity
• iii.Independence: The observations should be independent of each other, implying that the residuals (errors) for one observation are not correlated with the residuals of another.
• iv.Homoscedasticity: The residuals (errors) should have constant variance across all levels of the independent variables, meaning that the spread of the residuals should be roughly the same at all points along the range of the predictors.
• v.Normality: The residuals (errors) should be approximately normally distributed, particularly important for hypothesis testing and constructing confidence intervals.

The 95% confidence limits for prevalence of Type 2 diabetes and Hypertension disease

The 95% confidence limits for the prevalence of disease are a statistical tool used to determine the range of values within which the true population prevalence is expected to fall with 95% probability based on a particular sample[16].

In order to determine the 95% confidence limits for illness prevalence, the formula below is used:

where:

The value of the z-score for a 95% confidence interval = 1.96.

The total number of people in the population (N).

The number of patients in the population (n).

Ethical considerations

This study utilized secondary data obtained from Tamale Teaching Hospital and did not involve the prospective recruitment of human participants by the researchers. Data were accessed for the research from 10th June, 2024 to 10th November, 2024. The data were not publicly available but were accessed with the appropriate institutional permissions. All data collected during the study were kept confidential and only accessible to the research team. Participants'names and other identifying information were not included in any reports or publications resulting from the study. Ethical approval was obtained from the University for Development Studies Institutional Review Board (UDSIRB) before the study was conducted.

Results

The demographic features of patients with type 2 diabetes are presented in Table 1. The age distribution reveals a substantial concentration in older age groups, with the most of the patients (28.8%) being within the age range of 65–74 years old. There is a larger prevalence of type 2 diabetes among females, with 76.1% of patients being female as opposed to 23.9% of patients being male. 45.4% of patients weighed between 66 and 86 kg, and 11.2% of patients weighed between 87 and 107 kg. A good number of the patients (73%) are self-employed, as shown by their occupational status.

Table 1.

Demographic characteristics of type 2 diabetes patients

Category	Frequency	Percent
Age
25–34	3	1.5
35–44	25	12.2
45–54	53	25.9
55–64	39	19.0
65–74	59	28.8
75–84	22	10.7
85–94	4	2.0
Total	205	100.0
Weight
45–65	84	41.0
66–86	93	45.4
87–107	23	11.2
108–128	5	2.4
Total	205	100.0
Gender
Male	49	23.9
Female	156	76.1
Total	205	100.0
Occupation
Unemployed	10	4.9
Self-employed	149	73.0
Public or private sector employee	23	11.3
Pensioner	22	10.8
Total	204	100.0
Education
None	115	56.1
Primary	23	11.2
JHS	5	2.4
SHS	4	2.0
Tertiary	58	28.3
Total	205	100.0

Table 2 shows that the 55–64 age group accounts for the largest proportion of hypertension patients, accounting for 31.4% of the total population of Patients with Hypertension. Few (15.7%) of the patients have age between 45 and 54. Among the patients who have hypertension, the weight distribution shows that the biggest proportion, which accounts for 50.2% of the total, falls within the range of 70–90 kg, followed by the range of 46–69 kg, which accounts for 41.3% of the total. This demonstrates that individuals with a normal weight can also be impacted by hypertension, despite the fact that being overweight or obese is associated with an increased risk of having the condition at some point in their lives. Based on the gender distribution, males have a higher (67.7%) hypertension compared to females (32.3%). According to the occupational status of patients, the highest proportion of them are self-employed, which accounts for 67.7% of the total. Public or private sector employees make up 19.3% of the total, and retirees account for 9.4% of the total. Patients’ educational backgrounds reveal that 49.8%of them have not gotten any kind of formal education, while 32.7% have completed their studies at a university level.

Table 2.

Demographic characteristics of Hypertension patients

Category	Frequency	Percentage
Age
25–34	29	13.0
35–44	22	9.9
45–54	35	15.7
55–64	70	31.4
65–74	42	18.8
75–84	25	11.2
Total	223	100.0
Weight
46–69	92	41.3
70–90	112	50.2
91–111	14	6.3
112–132	5	2.2
Total	223	100.0
Gender
Male	151	67.7
Female	72	32.3
Total	223	100.0
Occupation
Unemployed	8	3.6
Self-employed	151	67.7
Public or private sector employee	43	19.3
Pensioner	21	9.4
Total	223	100.0
Education
None	111	49.8
Primary	21	9.4
JHS	8	3.6
SHS	10	4.5
Tertiary	73	32.7
Total	223	100.0

The results in Table 3 above indicate significant variations in the prevalence of hypertension among patients across different demographic categories, such as age, weight, gender, occupation, and education level. The age group with the highest prevalence is 55–64 years, accounting for 31.4%. Significantly, all age groups exhibit p-values that are statistically significant (0.0001), providing substantial evidence against the null hypothesis that there is no association between age and the prevalence of hypertension. The weight categories also demonstrate a clear association, with the highest prevalence in the 70–90 kg range at 50.2% (though not statistically significant since p = 0.050) and a notable 41.3% in the 46–69 kg range. In terms of gender, the prevalence is markedly higher among males (67.7%) compared to females (32.3%), with both proportions showing strong statistical significance. The occupational status further highlights disparities, with those in the self-employed category exhibiting a prevalence of 67.7%. The prevalence among unemployed individuals is significantly lower, at 3.6%. Educational attainment also impacts hypertension prevalence, with those having no formal education showing a prevalence of 49.8%

Table 3.

Prevalence of Hypertension among patients

Category	Proportion	Confidence Interval (95%)	P-value
Age 25–34	0.130	(0.086, 0.174)	0.0001
Age 35–44	0.099	(0.060, 0.138)	0.0001
Age 45–54	0.157	(0.112, 0.202)	0.0001
Age 55–64	0.314	(0.263, 0.365)	0.0001
Age 65–74	0.188	(0.141, 0.235)	0.0001
Age 75–84	0.112	(0.073, 0.151)	0.0001
Weight 46–69	0.413	(0.363, 0.463)	0.0001
Weight 70–90	0.502	(0.455, 0.549)	0.50
Weight 91–111	0.063	(0.034, 0.092)	0.0001
Weight 112–132	0.022	(0.004, 0.040)	0.0001
Male	0.678	(0.634, 0.722)	0.0001
Female	0.323	(0.278, 0.368)	0.0001
Unemployed	0.036	(0.016, 0.056)	0.0001
Self-employed	0.678	(0.634, 0.722)	0.0001
Public or private sector employee	0.193	(0.143, 0.243)	0.0001
Pensioner	0.094	(0.057, 0.131)	0.0001
None	0.498	(0.448, 0.548)	0.50
Primary	0.094	(0.057, 0.131)	0.0001
JHS	0.036	(0.016, 0.056)	0.0001
SHS	0.045	(0.020, 0.070)	0.0001
Tertiary	0.327	(0.277, 0.377)	0.0001

Results from Table 4 indicate that, the prevalence of Type 2 diabetes noticeably rises with age, peaking at 28.8% in the 65–74 age range. On the other hand, the age group of 25–34 has the lowest prevalence at 1.5%. The weight category follows a similar pattern, with the 45–65 age group showing the highest prevalence (41.0%). Nevertheless, a p-value of 0.500 suggests that the 66–86 category does not produce statistically significant results. There are clear disparities in prevalence between the sexes, with females showing a substantially higher prevalence (76.1%) than males (23.9%). All age, gender, and occupation groups show statistically significant p-values (all less than 0.0001), according to the results in Table 4. When considering occupation, it is observed that those who are self-employed have the highest prevalence at 72.7%, whereas the unemployed exhibit the lowest prevalence at 4.9%. The role of education level is also critical, as individuals with no formal education exhibit a prevalence rate of 56.1% Table 5.

Number of obs = 205
F(6, 116) = 2.23
Prob > F = 0.0452
R-squared = 0.600
Adj R-squared = 0.579

Table 4.

Prevalence of Type 2 diabetes among patients

Category	Subcategory	Frequency	Confidence Interval (95%)	P-value
Age	25–34	3	(0.000, 0.031)	0.0001
	35–44	25	(0.078, 0.166)	0.0001
	45–54	53	(0.203, 0.315)	0.0001
	55–64	39	(0.139, 0.241)	0.0001
	65–74	59	(0.230, 0.346)	0.0001
	75–84	22	(0.067, 0.147)	0.0001
	85–94	4	(0.001, 0.039)	0.0001
Weight	45–65	84	(0.344, 0.476)	0.0001
	66–86	93	(0.387, 0.521)	0.500
	87–107	23	(0.073, 0.151)	0.0001
	108–128	5	(0.004, 0.044)	0.0001
Gender	Male	49	(0.184, 0.294)	0.0001
	Female	156	(0.706, 0.816)	0.0001
Occupation	Unemployed	10	(0.021, 0.077)	0.0001
	Self-employed	149	(0.671, 0.789)	0.0001
	Public or private sector employee	23	(0.073, 0.153)	0.0001
	Pensioner	22	(0.068, 0.148)	0.0001
Education	None	115	(0.494, 0.628)	0.500
	Primary	23	(0.073, 0.151)	0.0001
	JHS	5	(0.004, 0.044)	0.0001
	SHS	4	(0.001, 0.039)	0.0001
	Tertiary	58	(0.225, 0.341)	0.0001

Table 5.

Analysis of Variance (ANOVA)

Source	SS	df	MS
Model	964.839024	6	160.806504
Residual	643.226016	116	5.54419066
Total	1608.06504	122	13.180861

At the 5% level of significance, the whole regression model is statistically significant (F-statistic = 2.23, p = 0.0452). The dependent variable is strongly related to one or more of the model's predictors. The model successfully accounts for approximately 60.0% of the variation in the dependent variable, as indicated by the R-squared score of 0.600.

The regression results in Table 6 examine the relationship between socio-economic variables and the burden of Type 2 diabetes. The results indicate that gender and patients’ place of residence have a significant impact on glucose levels, while age, weight, occupation, and education do not have a notable effect. More precisely, the gender coefficient indicates that males have glucose levels that are 1.418 units greater than females, while keeping all other factors constant. The statistical analysis reveals a significant association (p = 0.046) between gender and glucose levels, indicating that gender plays a crucial role in determining glucose levels. Similarly, the coefficient for patients’ place of residence signifies that a one-unit shift in location results in a decrease of 0.028 units in glucose levels, providing all other factors remain constant. The result demonstrates a significant association (p = 0.030) between patients’ place of residence and glucose levels, suggesting that patients’ place of residence has a pivotal influence in determining glucose levels. However, the coefficients for age (−0.030, p = 0.237), weight (−0.018, p = 0.408), occupation (−0.052, p = 0.549), and education (0.064, p = 0.766) are not statistically significant. This indicates that these variables do not have a significant impact on glucose levels in this model. The constant term (11.513) indicates the mean glucose level when all other variables are set to zero.

Number of obs = 223
F(5, 94) = 2.25
Prob > F = 0.0554
R-squared = 0.650
Adj R-squared = 0.631

Table 6.

Regression analysis of the socio-economic factors contributing to the burden of Type 2 diabetes

Glucoselevel	Coef	Std. Err	t	P >|t|
Age	−0.030	0.025	−1.188	0.237
Weight	−0.018	0.022	−0.831	0.408
Gender	1.418	0.702	2.021	0.046
Occupation	−0.052	0.086	−0.602	0.549
Education	0.064	0.213	0.298	0.766
patients’ place	−0.028	0.013	−2.199	0.030
of residence
_cons	11.513	3.003	3.834	0.000

Regression analysis as delineated in Table 7 evaluates the connection between many predictors and the dependent variable using 223 observations. At a 5% significance level, the F-statistic, which has a value of 2.25 and a p-value of 0.0554, indicates that the comprehensive model has a low level of significance. With an R-squared coefficient of 0.650, the independent variables in the model account for 65.0% of the variability in the dependent variable.

Table 7.

Analysis of Variance (ANOVA)

Source	SS	df	MS
Model	2.3133546475	5	0.4626709295
Residual	1.2456525025	94	0.0132462979
Total	3.55900715	99	0.035949567

Regression analysis in Table 8 shows that many socioeconomic variables contribute to hypertension condition. The likelihood of hypertension increases with age, according to the positive and statistically significant age coefficient (Coef. = 0.0033, p = 0.015). Alternatively, this model does not find a statistically significant relationship between hypertension and weight, gender, occupation, or education (p-values > 0.05).

Table 8.

Regression analysis of the socio-economic factors contributing to the burden of hypertension

BP	Coef	Std. Err	t	P >|t|	[95% Conf. Interval]
Age	0.0032885	0.001322	2.49	0.015	0.0006636	0.0059134
Weight	−0.0011479	0.0013067	−0.88	0.382	−0.0037424	0.0014467
Gender	−0.0321872	0.0408754	−0.79	0.433	−0.1133462	0.0489718
Occupation	−0.0048411	0.0035501	−1.36	0.176	−0.0118898	0.0022077
Education	0.0097275	0.0119821	0.81	0.419	−0.0140632	0.0335182
_cons	1.67451	0.1637545	10.23	0.000	1.349372	1.999649

Socioeconomic status is one of the risk factors for developing hypertension and type 2 diabetes, according to multivariate multiple regression analysis. From Table 9 above, it appears that occupation of patients is linked to lower glucose levels, since the occupation coefficient is statistically significant (Coef. = −0.1478, p = 0.040) in relation to glucose levels. There is no strong influence of age, weight, gender, or education on glucose levels in this model, since the corresponding coefficients are not statistically significant (p-values > 0.05). When all other variables are set to zero, the baseline glucose level is represented by the constant term (_cons = 11.8745, p < 0.0001). The age coefficient in the blood pressure (BP) model is positive and statistically significant (Coef. = 0.0033, p = 0.015), which is in line with the preceding regression study. This finding further supports the idea that getting older is a major contributor to the development of hypertension. This model does not find a statistically significant relationship between blood pressure and gender, weight, employment, or level of education (p-values > 0.05). When all other variables are set to zero, the baseline blood pressure is represented by the constant term (_cons = 1.6745, p < 0.0001).

Table 9.

Multivariate Multiple Regression analysis of the socio-economic factors contributing to the burden of hypertension and type 2 diabetes

	Coef	Std. Err	t	P >|t|	[95% Conf. Interval]
Glucose level
Age	-.0355033	.0263667	−1.35	0.181	-.0878549	.0168484
Weight	.0063572	.0260619	0.24	0.808	-.0453892	.0581036
Gender	.6590786	.8152323	0.81	0.421	-.9595842	2.277741
Occupation	1,478,288	.070804	−2.09	0.040	-.2884117	0072458
Education	.2397521	.2389746	1.00	0.318	-.2347375	. 7,142,417
cons	11.87446	3.265977	3.64	0.000	5.389787	18.35913
BP
Age	0032885	.001322	2.49	0.015	.0006636	.0059134
Weight	0011479	.0013067	−0.88	−0.88	-.0037424	.0014467
Gender -	.0321872	.0408754	−0.79	0.433	-.1133462	.0489718
Occupation -	.0048411	.0035501	1.36	0.176	-.0118898	.0022077
Education	.0097275	.0119821	0.81	0.419	-.0140632	.0335182_
Cons	1.67451	.1637545	10.23	0.000	1.349372	1.999649

Figure 2 reveals a general increase in diabetes mellitus cases from 2015 to 2018, reaching a peak in 2018, followed by fluctuations and a significant drop in 2023. The decomposition of the series indicates that the trend component shows a rising trend until 2018, after which it decreases. The seasonal component is minimal, indicating that the data does not exhibit strong seasonal patterns. The residuals suggest variations that are not explained by the trend or seasonal components. A linear trend graph indicates an average annual increase of approximately 241 cases per year over the given period. This suggests a consistent increase in Type 2 diabetes cases from 2015 to 2018, potentially due to factors that contributed to the rise in cases during these years. The peak in 2018 and subsequent fluctuations suggest potential interventions, changes in reporting practices, or other factors impacting the number of reported cases. The significant drop in 2023 indicates a major shift, possibly due to changes in healthcare policies, better disease management, or other socio-economic factors.

Fig. 2.

Trend analysis of Type 2 diabetes cases

Figure 3 indicate a significant increase in hypertension cases from 2015 to 2018, rising from 41,534 to 52,228, which reflects an approximate increase of 25.8% and highlights a growing concern regarding hypertension in the population. Nonetheless, subsequent to attaining its zenith in 2018, a significant decrease in incidences is observed in the subsequent years. In the year 2019, there was a minor reduction to 48,200 cases, which was followed by a more noticeable decline in the years that followed: 33,572 in 2020, 26,929 in 2021, 22,245 in 2022, and ultimately 15,785 in 2023.

Fig. 3.

Trend analysis of hypertension cases

Discussion

This study highlights the increasing prevalence of hypertension and type 2 diabetes among the elderly population in the Tamale metropolitan area, particularly among those aged 55–74 years. The significant association between aging and these diseases aligns with global evidence, which indicates that age is a major unmodifiable risk factor for chronic noncommunicable diseases [39],Cowie et al., 2018). This study deepens our understanding of diabetes prevalence in urban communities in Ghana and highlights that limitations in preventive treatment may accelerate disease progression among the elderly.

An important observation is the significant gender disparity in the incidence of type 2 diabetes, with women accounting for 76.1% of cases. While some global studies indicate higher incidence rates among men [19], regional factors such as social roles, caregiving responsibilities, dietary habits, and access to healthcare may explain why women have a higher prevalence in this context. This highlights the need for gender-responsive intervention measures, including specialized health education for women, community screening measures integrated into maternal and child health services, and strategies to address barriers to lifestyle changes.

Hypertension is most common among those aged 55–64, indicating that middle age is a critical stage for intervention. This aligns with the findings of Fleg [31] and Song et al. [75], who observed that blood pressure typically increases with age, with notable gender differences in older populations. While weight is clearly associated with hypertension and diabetes, not all weight categories yield statistically significant results, highlighting the complexity of using BMI as a predictive variable in resource-limited settings where both undernutrition and overnutrition occur.

The observed co-occurrence of hypertension and type 2 diabetes underscores the necessity of integrated non-communicable disease screening and management regimens at primary healthcare settings. Health services in areas such as Tamale must adjust to the dual burden by enhancing surveillance, facilitating early detection, and integrating culturally pertinent behavioral therapies.

Conclusion

The study concludes that type 2 diabetes is most prevalent among older individuals, particularly those aged 65–74 years (28.8%), with the lowest prevalence observed among those aged 25–34 years (1.5%). The condition is significantly more common in females (76.1%) than in males (23.9%). In terms of weight, the highest proportion of diabetes cases occurred among individuals weighing 66–86 kg (45.4%). Self-employment was strongly associated with the condition, with 73.0% of diabetes patients engaged in self-employment, while only 4.9% were unemployed. Over half of the diabetes patients (56.1%) had no formal education, underscoring the influence of lower educational attainment.

Hypertension was most common among individuals aged 55–64 years (31.4%) and those within the 70–90 kg weight range (50.2%). Unlike diabetes, hypertension was more prevalent in males (67.7%) than females (32.3%). A significant majority of hypertension patients (67.7%) were also in self-employment, while 49.8% had no formal education, highlighting a strong link with socioeconomic disadvantage.

The study also found that gender and residence significantly influence glucose levels related to type 2 diabetes, whereas age, weight, occupation, and education did not show statistically significant effects. In contrast, age was the only socioeconomic factor with a statistically significant positive association with hypertension (p = 0.015), indicating that the risk of hypertension increases with advancing age.

The cross-sectional study design used in this study focused on collecting exposure and outcome data at a single point in time, offering a valuable snapshot of disease prevalence. However, their ability to establish causality or determine temporal relationships is limited, and they may be susceptible to selection bias and confounding. To enhance external validity and generalizability, future research should include multiple study sites, incorporating rural and peri-urban settings across Ghana. This approach would yield a more representative sample and facilitate comparisons across diverse populations.

Authors’ contributions

Author I.M wrote the conceptualization,Methodology and original draft. Author M.A championed the supervision, Validation and editing of the manuscript. E.T and H.M handled the resources,formal analysis and data curation.

Project administration Ishamatu Mohammed Yakubu, Mustapha Alhassan. Resources: Hudu Mohammed, Ebenezer Tawiah Arhin. Supervision: Mustapha Alhassan. Validation: Mustapha Alhassan. Visualization: Ebenezer Tawiah Arhin. Writing – original draft: Ishamatu Mohammed Yakubu. Writing – review & editing: Ishamatu Mohammed Yakubu, Mustapha Alhassan, Ebenezer Tawiah Arhin, Hudu Mohammed.

Funding

This research received no external funding.

Data availability

The data supporting the findings of this study are available from the corresponding author upon reasonable request.

Declarations

Ethics approval and consent to participate

Ethical approval for this study was obtained from the University for Development Studies Institutional Review Board (UDSIRB) (Approval Date: 10th June, 2024) and authorization to conduct the research at the Tamale Teaching Hospital was granted by the Department of Research and Development (Approval Date: 11th June, 2024). The study involved the use of secondary, de-identified data and did not involve direct interaction with patients. All procedures were carried out in accordance with institutional guidelines and adhered to the ethical principles outlined in the Declaration of Helsinki as revised in 2013, ensuring respect for participants’ rights, data confidentiality, and the responsible use of human data.

Consent for publication

Not applicable.

Competing interests

The authors declare no competing interests.