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Health disparities and inequalities in prevalence of diabetes in the Kingdom of Saudi Arabia

International Journal for Equity in Health volume 23, Article number: 186 (2024) Cite this article

Abstract

Background

Over time, global health systems have witnessed significant improvements in the delivery and coverage of healthcare services. Nevertheless, the increasing prominence of non-communicable diseases remains a persistent challenge. Diabetes is one such non-communicable chronic disease that poses a threat with respect to both mortality and morbidity. This study investigated the socio-economic determinants and inequalities in the prevalence of diabetes in the Kingdom of Saudi Arabia according to data collected from the 2018 Saudi Family Health Survey conducted by the General Authority for Statistics.

Methods

The analysis was limited to a sample of 11,528 respondents aged ≥ 18 years, selected across all 13 regions of Saudi Arabia, with complete responses for all variables of interest. Socio-economic determinants in diabetes prevalence were explored with univariate, bivariate, and multivariate logistic regression analyses. Furthermore, inequalities were visualised and quantitatively estimated according to construction of a concentration curve and calculation of the concentration index.

Results

The prevalence of diabetes among the 11,528 respondents was 11.20%. Age, education, income, and residence area were significant determinants of diabetes prevalence, with a greater risk of diabetes found in older participants (odds ratio [OR]: 12.262, 95% confidence interval [CI]: 9.820–15.313, p < 0.01) compared to younger participants. Inequality analysis showed a negative education-based concentration index (–0.235, p < 0.01), indicating that diabetes prevalence is concentrated among people with relatively less formal education. For males, the income-based concentration index was significantly positive, whereas the education-based concentration index was significantly negative, indicating a greater concentration of diabetes among Saudi men with higher incomes and less education.

Conclusion

These findings emphasize the need to prioritize policies and strategies for diabetes prevention and control with considerations of the socio-economic inequalities in prevalence. Key areas of focus should include improving education levels across all regions, raising awareness about diabetes and implementing nutritional interventions.

Introduction

Global health systems have witnessed remarkable progress over the last several decades, resulting in significant improvements in the delivery and coverage of healthcare services. Despite these advancements, non-communicable diseases (NCDs) remain a persistent challenge and are posing an increasing threat to public health as they become dominant contributors to global morbidity and mortality [1]. In particular, diabetes has become an increasingly prominent NCD, with an estimated 5 million deaths in the 20–79 years age group worldwide attributed to diabetes in 2015 [2]. The global prevalence of diabetes is estimated to reach 578 million by 2030, and is estimated to surpass 700 million by 2045 [3]. Thus, the prevalence of diabetes, along with diabetes-related deaths and healthcare expenditure, continues to rise worldwide. Given these statistics, it is imperative for governments to prioritize the implementation of strategies aimed at addressing the global impact of diabetes.

Diabetes encompasses a collection of metabolic disorders characterized by high blood glucose levels [4]. Various factors, including physiological, genetic, and socio-economic aspects, influence the risk of developing this chronic condition [5]. Risk factors include the aging population and unhealthy habits, including poor diets [6, 7]. The combination of lifestyle changes and cultural shifts over time has resulted in a rise in physical inactivity and a high prevalence of obesity, further exacerbating the incidence of diabetes [8]. People with diabetes face higher morbidity and mortality risks compared to those of the general population, presenting social and financial challenges to healthcare systems worldwide.

As a chronic disease, individuals living with diabetes require lifelong medical follow-up and face a higher risk of complications and susceptibility to other health issues [9]. This places a significant burden on the healthcare system and leads to increased out-of-pocket healthcare expenses [10]. In 2015, the global health expenditure associated with diabetes reached approximately 673 billion US dollars, with projections estimating a burden of 802 billion US dollars by 2040 [2]. These costs primarily stem from treating diabetes-related complications, creating a burden on healthcare delivery, access, and coverage. This underscores the importance of addressing the far-reaching consequences of diabetes as a global concern.

Research on socio-economic inequalities in diabetes prevalence has yielded diverse findings. Richards et al. [11] identified an inverse correlation of diabetes prevalence with unemployment and alcohol consumption. In Denmark, Tapager et al. [12] revealed a consistent link between municipality socio-economic disadvantage and diabetes prevalence. Similarly, inequality in the prevalence of diabetes was identified in all regions of Bangladesh [13] and Iran [14]. In India, Maiti et al. [15] found a higher diabetes prevalence among individuals with lower education levels, lower socio-economic status, and those residing in rural areas. Other studies identified a family history of diabetes as a significant risk factor, including a study conducted by Moradpour et al. [8] in Iran. In South Africa, Sidahmed et al. [16] reported a higher diabetes prevalence among women compared to men, which was consistent with findings in Argentina reported by Rojas-Roque et al. [17]. However, Wang & Wild [18] reported a lower diabetes prevalence for women than men in Scotland.

As the largest country in the Middle East (covering an area of ~ 2,150,000 km2), the Kingdom of Saudi Arabia (KSA) faces particularly notable social and financial challenges linked to the rising prevalence of diabetes. The country primarily finances its healthcare system through revenue generated from oil production and exportation, with healthcare receiving a significant share of the national budget [19]. Despite the availability and provision of free healthcare services delivered in public facilities, healthcare costs are covered through private insurance and/or out-of-pocket expenditures for a majority of the workforce (~ 56%) [20, 21].

Given the high demand for the treatment and care of diabetes as a chronic disease, its prevalence further strains the already overburdened healthcare services in the KSA [22]. The rapid economic development occurring in the KSA in recent years has led to cultural shifts and lifestyle changes, including physical inactivity and the adoption of unhealthy habits [23]. Coupled with the rising trend in population aging resulting from improved health standards, the country has experienced a growing disease burden, especially NCDs such as diabetes. Therefore, research on diabetes holds significant implications for the welfare of individuals and the healthcare system in the KSA.

Despite the significant concern surrounding diabetes in the KSA, research tackling this topic remains scarce. Most studies in this field have primarily focused on the general prevalence of NCDs, with limited attention given to socio-economic inequalities specifically related to diabetes [24,25,26]. Al-Hanawi et al. [27] highlighted challenges in attempting to investigate socio-economic inequalities in diabetes prevalence in the KSA due to the lack of up-to-date data. They were compelled to utilize a dataset from 2013, which is significantly outdated considering the changes that have occurred over time.

The Saudi Family Health Survey (FHS) of 2018, conducted under the authority of the General Authority for Statistics (GaStat) [28], can help to fill this gap. Therefore, in this study, the FHS 2018 data were analysed to identify the socio-economic determinants and inequalities in the prevalence of diabetes in the KSA using univariate, bivariate, multivariate logistic regression, concentration curve, and concentration index techniques. Gaining a current and improved understanding of these socio-economic disparities can provide a vital resource for policymakers to guide the development of targeted diabetes education, prevention, and intervention strategies.

Materials and methods

Data

Data from the FHS were used in this study [28]. The FHS field survey is conducted on a three-year basis to collect population-level statistics in the KSA with respect to education and health, including geographical details, living situation, marriage and children, births and deaths, household income and expenditure, and health status (including the presence of chronic diseases and conditions such as diabetes, high blood pressure, and asthma, among others) [28]. The survey represents a collaboration between GaStat and the health sector (including the Ministry of Health and the Saudi Health Council) with additional participation from private and academic institutions. The sample was randomly selected among a representative population covering all administrative regions in the KSA with a total of 15,265 responses.

The questionnaire was designed by experts in the field of health statistics from GaStat with consideration of World Health Organization recommendations, standards, and definitions. The present analysis was based only on complete responses for the variables of concern, resulting in a total sample of 11,528 respondents.

Measurements

The FHS included a question about whether the respondent has received a diagnosis or/and informed with of various NCDs, including diabetes, high blood pressure, and asthma. The outcome variable (i.e., dependent variable) for this study was self-reported diabetes prevalence scored as a binary variable with a value of 1 if the respondent reported having diabetes and 0 otherwise.

The independent variables to assess the socio-economic determinants and inequalities in the prevalence of diabetes (dependent variable) included income and education level as socio-economic status indicators. Other demographic independent variables considered were age (< 40 years = 0 and ≥ 40 years = 1), sex (1 = male, 0 = female), marital status (1 = married, 0 = unmarried, including never married, divorced, and widowed), education level (below primary school = reference, primary school, intermediate school, high school, and higher education), monthly income (in Saudi Riyal [SR]; 1 SR = USD 0.27: <3000 = reference category, 3000 to < 5000, 5000 to < 7000, 7000 to < 10,000, 10,000 to < 15,000, 15,000 to < 20,000, 20,000 to < 30,000, and 30,000 or more), and region of residence among the thirteen administrative regions (Riyadh = reference, Mekkah, Madenah, Albaha, Aseer, Jazan, Najran, Aljouf, Tabouk, Haiel, Qaseem, Eastern Region, and Northern Borders). The decision to separate age groups according to a threshold of 40 years was based on the typical age of ≥ 40 as the onset of type 2 diabetes [29, 30].

Statistical analysis

The variations in socio-economic and demographic factors among respondents were evaluated with univariate analyses. Bivariate analysis was then used to compare the associated frequencies in different independent variables according to the dependent variable (diabetes presence/absence) using cross-tabulation with the Chi-square test. The independent associations of each socio-economic factor with the prevalence of diabetes were assessed by multivariate logistic regression models controlling for age, sex, marital status, and region of residence as covariates. Inequalities in the prevalence of diabetes according to socio-demographic factors were visualized using a concentration curve and quantified by calculation of the concentration index [31]. The influence of sex and regional inequalities on the prevalence of diabetes was further assessed, as these factors have previously been associated with health disparities [32,33,34].

A concentration curve represents the relationship between the cumulative percentage of a health variable (y-axis) and the cumulative share of the population in a socio-economic status indicator (x-axis; ranked from the lowest to the highest) [35]. This enables a visual assessment of the degree of inequality in diabetes prevalence; for example, with respect to income and education level, a curve above (below) the line of equality (i.e. the 45-degree line) indicates that diabetes prevalence is concentrated among those with lower income/less education (higher income/higher education). Inequality is considered to be greater when the concentration curve lies further from the line of equality.

The concentration index was used to quantify the degree of inequality in the prevalence of diabetes according to a socio-economic characteristic, which is calculated as twice the area between the concentration curve and the line of equality [35]. The concentration index ranges from − 1, indicating that the prevalence of diabetes is disproportionately concentrated among individuals with relatively low education or income, to + 1, indicating that the prevalence of diabetes is disproportionately concentrated among individuals with relatively high education or income. This study used income and education as the measures of socio-economic status, which enabled ranking individuals from the poorest to the richest or the lowest to the highest education level, to estimate the concentration index.

Results

Univariate analysis

Table 1 summarizes the descriptive statistics of the dependent and independent variables. At the time of the survey, the prevalence of diabetes was approximately 11.20% (n = 1291) for the total sample of 11,528 respondents with complete responses for the variables of interest. Less than half the sample was aged 40 years and above, 45% were female, and one-third were unmarried. Slightly less than 20% of the respondents had a below primary school education level and had completed higher education, respectively. The monthly income of approximately one-quarter of the respondents was less than 5000 SR, while only approximately 5% of the respondents indicating earning ≥ 30,000 SR monthly. Most of the survey respondents were from Mekkah and Riyadh regions.

Table 1 Summary statistics of the study population (n = 11,528)
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Bivariate analysis

The associations of diabetes prevalence with the socio-economic characteristics based on the bivariate analysis are presented in Table 2. The prevalence of diabetes was significantly associated with age (χ2 = 120.030, p < 0.01), in which diabetes was more frequently reported among those aged ≥ 40 years (21.88%) than those aged < 40 years (1.79%). There were significant associations of diabetes prevalence with marital status (χ2 = 198.633, p < 0.01) and education level (χ2 = 385.650, p < 0.01). Compared with that of respondents with higher education (7.64%) and secondary school education (6.25%), the diabetes prevalence was significantly higher (21.46%) among those with below primary school education. Moreover, diabetes was highly associated with income, with a greater prevalence among people reporting a monthly income of ≥ 30,000 SR (14.88%) than for those reporting a monthly income below 3000 SR (8.95%). Finally, there was a significant association between diabetes prevalence and region of residence (χ2 = 75.260, p < 0.01), with a higher prevalence in Qassim (14.27%) and a lower prevalence in Northern Border (4.62%).

Table 2 Bivariate analysis of the association of diabetes prevalence with socio-economic characteristics
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Income-related and education-related inequalities in diabetes prevalence

The income-based concentration curve among those reporting having diabetes almost completely overlapped with the line of equality; however, this curve was slightly below the equality line, suggesting that diabetes is somewhat concentrated among the rich (Fig. 1). By contrast, the education-based concentration curve was clearly above the line of equality, indicating a greater prevalence of diabetes among the less well-educated people in the KSA (Fig. 2). As the concentration curve for education was farther away from the line of equality, this analysis suggests that education level has a stronger impact on the inequal distribution of diabetes prevalence for the population of the KSA.

Fig. 1
figure 1

Income-based concentration curve for diabetes prevalence

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Fig. 2
figure 2

Education-based concentration curve for diabetes prevalence

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Since the concentration curves in Figs. 1 and 2 cannot provide the magnitude of the inequality, we further used the Wagstaff concentration index to quantify and compare the degree of inequalities in diabetes prevalence according to income and education as the key socio-economic factors of interest (Table 3).

Table 3 Inequality indices for diabetes prevalence according to variations in income and education in the Kingdom of Saudi Arabia
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At the national level, the education-based concentration index was significantly (P < 0.01) negative, confirming a concentration of diabetes among those with lower education levels, whereas the concentration of the income-based index was not significant at the national level. Both indices were statistically significant (P < 0.01) in females and males; however, the direction of the effect differed according to sex. Both indices were significantly negative among females, indicating that diabetes prevalence is concentrated among women with a lower income level and lower education. By contrast, the male-specific income-based concentration index was significantly positive, whereas the male-specific education-based concentration index was significantly negative, indicating that the diabetes prevalence is concentrated among men in Saudi Arabia with less education but with higher income level.

All regions demonstrated significantly negative education-based concentration indices, indicating that the concentration of diabetes among less educated people is a consistent trend throughout the country. However, the income-based concentration indices varied according to region, ranging from insignificantly negative (i.e. in Albaha, Aljouf, Eastern Region, Haiel, Jazan, and Najran) to significantly positive (i.e. in Aseer and Northern Border). Although the income-based indices for Mekkah and Qassim were also positive, they were not statistically significant.

Regression analysis

Multivariate logistic regression analysis was further performed to assess the potential impacts of other variables on the observed associations between diabetes prevalence and socio-economic factors in the KSA (Table 4). Model 1 showed a higher likelihood of reporting diabetes among all higher-income categories compared with the lower-income category (below 3000 SR), with an odds ratio (OR) of 1.421 (95% confidence interval [CI]: 1.010–1.997, p < 0.05) for those reporting a monthly income of ≥ 30,000 SR. Model 2 showed that compared with people educated below the primary school level, those with higher education had a lower likelihood of having diabetes (OR: 0.568, 95% CI: 0.462–0.698, p < 0.01). The significance of the ORs for income and education categories was retained in Model 3. Therefore, the likelihood of diabetes increased with increasing income level and decreased with increasing education level.

However, model 3 further showed a nearly 12-fold increased likelihood of diabetes among people aged ≥ 40 years (OR: 12.262, 95% CI: 9.820–15.313, p < 0.01). Moreover, regional differences in the likelihood of reporting diabetes were evident, in which residents of Jazan, Najran, and Northern Border were significantly less likely to report diabetes than residents of Riyadh.

Table 4 Association between diabetes prevalence and socio-economic factors
Full size table

Discussion

By employing a range of statistical techniques, including univariate, bivariate, multivariate logistic regression, and concentration curves and indices, based on self-weighted data from the 2018 FHS, this study highlights the socio-economic disparities in the prevalence of diabetes in the KSA. These findings thus offer crucial targets for the development of effective diabetes education, prevention, and intervention programs. Given that disparities in the prevalence of diabetes were linked to various factors, including sex, education, income, and region of residence, the government must prioritize strategies that address these associated socio-economic factors to reduce the prevalence of diabetes and improve its treatment and management.

The bivariate analysis revealed a significant association between the prevalence of diabetes and age (χ2 = 120.030, p < 0.01), in which diabetes was more concentrated among individuals aged 40 years and above (21.88%) compared to those aged below 40 years (1.79%). Similarly, there was an approximately 12-times higher likelihood of reporting diabetes among individuals aged 40 years and above (OR: 12.262, 95% CI: 9.820–15.313, p < 0.01) compared to younger individuals below 40 years. Numerous studies support this finding, demonstrating that the prevalence of diabetes varies across different age groups, with the older population facing a higher risk compared to younger individuals [36, 37]. This is not surprising considering that the natural aging process leads to reduced physical activity, a weakened immune system, and various healthcare access challenges, all of which contribute to an increased disease burden among the older population [38, 39].

The results also indicate a greater prevalence of diabetes among individuals with lower levels of education. In particular, an education level below primary school was significantly associated with a higher prevalence of diabetes (21.46%) compared to that of higher education levels, including a university degree or above (7.64%). The multivariate analysis confirmed that a lower education level was an independent predictor of reporting having diabetes. This result is in line with findings in several other countries and regions, including Ghana [37], Europe [40], France [41], and others [8, 42]. People with lower education levels may face greater exposure to risk factors and thus have higher susceptibility to developing diabetes compared to those with more favourable living conditions [43].

Education plays a crucial role in creating awareness and enabling individuals to translate information into practical behaviours that help mitigate the risks of chronic diseases [44]. Conversely, individuals with lower levels of education may be more prone to engaging in risky behaviours due to a lack of awareness [45, 46]. Indeed, there is evidence that people with lower education levels are less likely to participate in diabetes training programs [47, 48]. These educational disparities ultimately contribute to differential risks of diabetes prevalence and treatment. Moreover, individuals with lower educational attainment often face limited access to healthcare services and lack health insurance coverage [49, 50]. These results highlight that government-led diabetes intervention policies should explicitly target and address these disparities related to education.

This study demonstrates a concentration of diabetes prevalence among individuals reporting a monthly income of ≥ 30,000 SR (14.88%) compared to those with a monthly income below 3000 SR (8.95%). The likelihood of reporting diabetes was higher among all higher-income categories compared to the lower-income category (< 3000 SR monthly). These findings suggest a concentration of diabetes prevalence among the rich, as evidenced by the concentration curve falling below the line of equality. Some studies have presented a contrasting relationship between income and diabetes prevalence, whereas others align with the present findings [5, 12]. For instance, Richards et al. [11] discovered that a 1% increase in per-capita income corresponds to a 0.92% increase in diabetes prevalence. Mutyambizi et al. [51] revealed a significant concentration of self-reported diabetes and total diabetes (including both diagnosed and undiagnosed cases) among individuals with higher wealth in South Africa. Su et al. [45] identified a significant correlation between income and diabetes prevalence in China. These positive associations between income and diabetes prevalence could be explained by the escalation of adverse health behaviours such as physical inactivity, smoking, and alcohol consumption with rising income levels [52]. Moreover, considering the International Diabetes Federation’s estimate of approximately 40% undiagnosed diabetes cases in Saudi Arabia, the elevated prevalence among affluent individuals identified in this study may stem from the higher diagnosis and detection rates for diabetes among the wealthy due to their superior access to healthcare [3]. Conducting additional research specifically targeting undiagnosed diabetes could provide a clearer understanding of the actual prevalence of the disease in the KSA.

The income-based and education-based concentration indices were statistically significant at the 1% level for both sexes, although with different patterns and directions. Both indices were significantly negative for females, suggesting that the prevalence of diabetes is concentrated among women with lower income levels and less levels of education. Studies have consistently shown that women tend to have a higher risk of diabetes compared to men in similar settings [53, 54]. This can largely be attributed to biological factors such as insulin resistance and abdominal adiposity, which are more prevalent among females and increase their susceptibility to diabetes [37]. Additionally, women exhibit higher rates of physical inactivity and are more prone to obesity, further exacerbating their susceptibility to diabetes [54]. These factors are particularly amplified among women with lower levels of education and income, as they may face even more unfavourable conditions that put them at a greater risk of developing diabetes.

Although the education-based concentration index was also significantly negative for males, indicating that the prevalence of diabetes is concentrated among men with lower levels of education in the KSA, they exhibited a significantly positive income-based concentration index, indicating that the prevalence of diabetes is concentrated among men with higher income levels. Men generally exhibit a lower prevalence of diabetes compared to women [55]. However, among men with low levels of education, the prevalence of diabetes tends to be high due to limited awareness of the disease, resulting in increased exposure to its risks [42]. Moreover, as men are often the primary source of the family income and maintain control over wealth in the KSA, they may be more likely to adopt unhealthy habits such as smoking and unhealthy diets, further increasing their risk of diabetes [52]. Given these gender inequalities in diabetes prevalence and risk factors, it is essential to implement targeted and gender-sensitive measures for prevention and education. Gender-specific interventions are necessary as policies addressing diabetes in women may not be applicable to men, given their distinct needs.

The education-based concentration indices were significantly negative across all regions in the KSA, indicating that the prevalence of diabetes is concentrated among individuals with lower levels of education throughout the country. However, there were also regional variations in the likelihood of reporting diabetes, with higher prevalence in Qassim, Tabuk, Mekkah, and Riyadh compared to the Northern Border, Jazan, and Najran. This regional disparity is further supported by the income-based concentration indices, which were positive for certain regions (Aseer and Northern Border) but negative for others (Riyadh and Madenah). Therefore, the observed regional differences in diabetes prevalence are likely influenced by associated disparities in income levels, leading to the adoption of different lifestyles and varying degrees of diabetes risk exposure. Furthermore, variations in healthcare access across regions, with some regions experiencing regional deprivation while others having better access, likely contribute to these disparities [56]. Similar regional variations in diabetes prevalence have been reported in the related literature for various countries, including Pakistan [53], Bangladesh [13], Iran [14] and China [5]. Therefore, diabetes prevention and intervention programs should consider location-specific effects when designing policies to address the prevalence of diabetes.

This study is subject to certain limitations that should be considered when interpreting the results. The conclusions and discussions should be contextualized, considering that health inequalities have various structural determinants, including commercial and environmental factors, that can impact prevalence but were not implicitly examined in this analysis. Furthermore, the use of self-reported data is subject to biases, including differences in access to healthcare and diabetes awareness, which can influence the accuracy of reported diabetes cases. It would be beneficial to include a more objective and standardized measure of diabetes that considers both diagnosed and undiagnosed cases. Additionally, the focus of the study was limited to socio-economic determinants and inequalities in the prevalence of diabetes; thus, further research could explore socio-economic inequalities in other dimensions such as race or ethnicity and citizenship status. Another limitation is that the study primarily focused on diabetes as a whole without distinguishing between different types, such as type 2 diabetes and other variations. Future research could delve into inequality determinants when differentiating between various types of diabetes. Nonetheless, the study offers valuable insights that can inform the implementation of strategies to address inequalities in diabetes prevalence.

Conclusions

This study provides new insight into the socio-economic determinants and inequalities related to diabetes prevalence in Saudi Arabia. Data from the 2018 FHS were analysed using various techniques such as univariate, bivariate, multivariate logistic regression, concentration curves, and concentration indices. The results align closely with existing literature on the topic, reinforcing current understanding in the field. The prevalence of diabetes demonstrates variations according to gender and region of residence. The older generation tends to experience a higher prevalence of diabetes compared to the younger generation. The concentration indices based on income and education reveal that high diabetes prevalence is associated with lower levels of education but higher levels of income. These findings emphasize the need to prioritize policies and strategies for diabetes prevention and control that address socio-economic inequalities in diabetes prevalence. Key areas of focus should include improving education levels across all regions, raising awareness about diabetes including through the use of social media, implementing nutritional interventions, and reducing income disparities. Moreover, deliberate efforts should be made to modify risk factors such as obesity and physical inactivity. Public policies need to incorporate strategies that promote healthy habits and lifestyles, ultimately leading to a reduction in diabetes prevalence, complications, and the burden on the healthcare system.

Data availability

The datasets generated and/or analysed during the current study are not publicly available due to privacy, confidentiality, and other restrictions. Access to data can be gained through the General Authority for Statistics in Saudi Arabia via https://www.stats.gov.sa/en.

References

  1. Heller O, Somerville C, Suggs LS, Lachat S, Piper J, Aya Pastrana N, et al. The process of prioritization of non-communicable diseases in the global health policy arena. Health Policy Plann. 2019;34(5):370–83.

    Article  Google Scholar 

  2. Ogurtsova K, da Rocha Fernandes J, Huang Y, Linnenkamp U, Guariguata L, Cho NH, et al. IDF Diabetes Atlas: global estimates for the prevalence of diabetes for 2015 and 2040. Diabetes Res Clin Pract. 2017;128:40–50.

    Article  PubMed  CAS  Google Scholar 

  3. Saeedi P, Petersohn I, Salpea P, Malanda B, Karuranga S, Unwin N, et al. Global and regional diabetes prevalence estimates for 2019 and projections for 2030 and 2045: results from the International Diabetes Federation Diabetes Atlas. Diabetes Res Clin Pract. 2019;157:107843.

    Article  PubMed  Google Scholar 

  4. Shaw JE, Sicree RA, Zimmet PZ. Global estimates of the prevalence of diabetes for 2010 and 2030. Diabetes Res Clin Pract. 2010;87(1):4–14.

    Article  PubMed  CAS  Google Scholar 

  5. Wang Z, Li X, Chen M. Socioeconomic factors and inequality in the prevalence and treatment of diabetes among middle-aged and elderly adults in China. Journal of diabetes research. 2018;2018.

  6. Hunt KJ, Schuller KL. The increasing prevalence of diabetes in pregnancy. Obstet Gynecol Clin N Am. 2007;34(2):173–99.

    Article  Google Scholar 

  7. Flor LS, Campos MR. The prevalence of diabetes mellitus and its associated factors in the Brazilian adult population: evidence from a population-based survey. Revista Brasileira De Epidemiologia. 2017;20:16–29.

    Article  PubMed  Google Scholar 

  8. Moradpour F, Rezaei S, Piroozi B, Moradi G, Moradi Y, Piri N, et al. Prevalence of prediabetes, diabetes, diabetes awareness, treatment, and its socioeconomic inequality in west of Iran. Sci Rep. 2022;12(1):17892.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  9. De Silva AP, De Silva SHP, Haniffa R, Liyanage IK, Jayasinghe KSA, Katulanda P, et al. A survey on socioeconomic determinants of diabetes mellitus management in a lower middle income setting. Int J Equity Health. 2016;15:1–11.

    Article  Google Scholar 

  10. Biswas T, Islam MS, Linton N, Rawal LB. Socio-economic inequality of chronic non-communicable diseases in Bangladesh. PLoS ONE. 2016;11(11):e0167140.

    Article  PubMed  PubMed Central  Google Scholar 

  11. Richards SE, Wijeweera C, Wijeweera A. Lifestyle and socioeconomic determinants of diabetes: evidence from country-level data. PLoS ONE. 2022;17(7):e0270476.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  12. Tapager I, Bender AM, Andersen I. A decade of socioeconomic inequality in type 2 diabetes area-level prevalence: an unshakeable status quo? Scand J Public Health. 2023;51(2):268–74.

    Article  PubMed  Google Scholar 

  13. Sarker AR, Khanam M. Socio-economic inequalities in diabetes and prediabetes among Bangladeshi adults. Diabetol Int. 2022;13(2):421–35.

    Article  PubMed  Google Scholar 

  14. Perseh L, Peimani M, Ghasemi E, Nasli-Esfahani E, Rezaei N, Farzadfar F, et al. Inequalities in the prevalence, diagnosis awareness, treatment coverage and effective control of diabetes: a small area estimation analysis in Iran. BMC Endocr Disorders. 2023;23(1):17.

    Article  Google Scholar 

  15. Maiti S, Akhtar S, Upadhyay AK, Mohanty SK. Socioeconomic inequality in awareness, treatment and control of diabetes among adults in India: evidence from National Family Health Survey of India (NFHS), 2019–2021. Sci Rep. 2023;13(1):2971.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  16. Sidahmed S, Geyer S, Beller J. Socioeconomic inequalities in diabetes prevalence: the case of South Africa between 2003 and 2016. BMC Public Health. 2023;23(1):324.

    Article  PubMed  PubMed Central  Google Scholar 

  17. Rojas-Roque C, Hernández-Vásquez A, Azañedo D, Bendezu-Quispe G. Socioeconomic inequalities in the prevalence of diabetes in Argentina: a repeated cross-sectional study in Urban women and men. Int J Environ Res Public Health. 2022;19(15):8888.

    Article  PubMed  PubMed Central  Google Scholar 

  18. Wang J, Wild SH. Marked and widening socioeconomic inequalities in type 2 diabetes prevalence in Scotland. J Epidemiol Community Health. 2022;76(5):482–4.

    Article  Google Scholar 

  19. Al-Hanawi MK, Alsharqi O, Almazrou S, Vaidya K. Healthcare finance in the Kingdom of Saudi Arabia: a qualitative study of householders’ attitudes. Appl Health Econ Health Policy. 2018;16:55–64.

    Article  PubMed  Google Scholar 

  20. Al-Hanawi MK, Mwale ML, Qattan AM. Health insurance and out-of-pocket expenditure on health and medicine: heterogeneities along income. Front Pharmacol. 2021;12:638035.

    Article  PubMed  PubMed Central  Google Scholar 

  21. Alkhamis AA. The association between insured male expatriates’ knowledge of health insurance benefits and lack of access to health care in Saudi Arabia. BMC Public Health. 2018;18:1–9.

    Article  Google Scholar 

  22. Al-Hanawi MK, Alsharqi O, Vaidya K. Willingness to pay for improved public health care services in Saudi Arabia: a contingent valuation study among heads of Saudi households. Health Econ Policy Law. 2020;15(1):72–93.

    Article  PubMed  Google Scholar 

  23. Alzeidan R, Rabiee F, Mandil A, Hersi A, Fayed A. Non-communicable disease risk factors among employees and their families of a Saudi university: an epidemiological study. PLoS ONE. 2016;11(11):e0165036.

    Article  PubMed  PubMed Central  Google Scholar 

  24. Al-Hanawi MK, Keetile M. Socio-economic and demographic correlates of non-communicable disease risk factors among adults in Saudi Arabia. Front Med. 2021;8:605912.

    Article  Google Scholar 

  25. Algabbani A, Alqahtani A, BinDhim N. Prevalence and determinants of non-communicable diseases in Saudi Arabia. Food Drug Regul Sci J. 2019;2(2):1.

    Google Scholar 

  26. Mandil AM, Alfurayh NA, Aljebreen MA, Aldukhi SA. Physical activity and major non-communicable diseases among physicians in Central Saudi Arabia. Saudi Med J. 2016;37(11):1243.

    Article  PubMed  PubMed Central  Google Scholar 

  27. Al-Hanawi MK, Chirwa GC, Pulok MH. Socio-economic inequalities in diabetes prevalence in the Kingdom of Saudi Arabia. Int J Health Plann Manag. 2020;35(1):233–46.

    Article  Google Scholar 

  28. GASTAT. The General Authority for Statistics: Family Health Survey. 2018 [cited 2021 21 Jan]; https://www.stats.gov.sa/en/965

  29. Al-Saeed AH, Constantino MI, Molyneaux L, D’Souza M, Limacher-Gisler F, Luo C, et al. An inverse relationship between age of type 2 diabetes onset and complication risk and mortality: the impact of youth-onset type 2 diabetes. Diabetes Care. 2016;39(5):823–9.

    Article  PubMed  CAS  Google Scholar 

  30. Zou W, Ni L, Lu Q, Zou C, Zhao M, Xu X, et al. Diabetes onset at 31–45 years of age is associated with an increased risk of diabetic retinopathy in type 2 diabetes. Sci Rep. 2016;6(1):38113.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  31. Wagstaff A, Paci P, Van Doorslaer E. On the measurement of inequalities in health. Soc Sci Med. 1991;33(5):545–57.

    Article  PubMed  CAS  Google Scholar 

  32. Qattan AM, Boachie MK, Immurana M, Al-Hanawi MK. Socioeconomic determinants of smoking in the Kingdom of Saudi Arabia. Int J Environ Res Public Health. 2021;18(11):5665.

    Article  PubMed  PubMed Central  Google Scholar 

  33. Pande RP, Yazbeck AS. What’s in a country average? Wealth, gender, and regional inequalities in immunization in India. Soc Sci Med. 2003;57(11):2075–88.

    Article  PubMed  Google Scholar 

  34. Thomson KH, Renneberg A-C, McNamara CL, Akhter N, Reibling N, Bambra C. Regional inequalities in self-reported conditions and non-communicable diseases in European countries: findings from the European Social Survey (2014) special module on the social determinants of health. Eur J Public Health. 2017;27(suppl1):14–21.

    Article  PubMed  Google Scholar 

  35. O’Donnell O, Van Doorslaer E, Wagstaff A, Lindelow M. Analyzing health equity using household survey data: a guide to techniques and their implementation. World Bank; 2007.

  36. Ayah R, Joshi MD, Wanjiru R, Njau EK, Otieno CF, Njeru EK, et al. A population-based survey of prevalence of diabetes and correlates in an urban slum community in Nairobi, Kenya. BMC Public Health. 2013;13(1):1–11.

    Article  Google Scholar 

  37. Gatimu SM, Milimo BW, Sebastian MS. Prevalence and determinants of diabetes among older adults in Ghana. BMC Public Health. 2016;16(1):1–12.

    Article  Google Scholar 

  38. Balanda KP, Buckley CM, Barron SJ, Fahy LE, Madden JM, Harrington JM, et al. Prevalence of diabetes in the Republic of Ireland: results from the National Health Survey (SLAN) 2007. PLoS ONE. 2013;8(10):e78406.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  39. Haseen F, Adhikari R, Soonthorndhada K. Self-assessed health among Thai elderly. BMC Geriatr. 2010;10(1):1–9.

    Article  Google Scholar 

  40. Vandenheede H, Deboosere P, Espelt A, Bopp M, Borrell C, Costa G, et al. Educational inequalities in diabetes mortality across Europe in the 2000s: the interaction with gender. Int J Public Health. 2015;60(4):401–10.

    Article  PubMed  Google Scholar 

  41. Bihan H, Laurent S, Sass C, Nguyen G, Huot C, Moulin JJ, et al. Association among individual deprivation, glycemic control, and diabetes complications: the EPICES score. Diabetes Care. 2005;28(11):2680–5.

    Article  PubMed  Google Scholar 

  42. Asadi-Lari M, Khosravi A, Nedjat S, Mansournia M, Majdzadeh R, Mohammad K, et al. Socioeconomic status and prevalence of self-reported diabetes among adults in Tehran: results from a large population-based cross-sectional study (Urban HEART-2). J Endocrinol Investig. 2016;39:515–22.

    Article  CAS  Google Scholar 

  43. Addo J, Agyemang C, Aikins Ad G, Beune E, Schulze MB, Danquah I, et al. Association between socioeconomic position and the prevalence of type 2 diabetes in ghanaians in different geographic locations: the RODAM study. J Epidemiol Community Health. 2017;71(7):633–9.

    Article  PubMed  Google Scholar 

  44. Shahab JA, Alishan KN. The effect of literacy level on health related-quality of life, self-efficacy and self-management behaviors in diabetic patients. Acta Medica Iranica. 2011;49(3):153–8.

    Google Scholar 

  45. Su R, Cai L, Cui W, He J, You D, Golden A. Multilevel analysis of socioeconomic determinants on diabetes prevalence, awareness, treatment and self-management in ethnic minorities of Yunnan Province, China. Int J Environ Res Public Health. 2016;13(8):751.

    Article  PubMed  PubMed Central  Google Scholar 

  46. Wardle J, Steptoe A. Socioeconomic differences in attitudes and beliefs about healthy lifestyles. J Epidemiol Community Health. 2003;57(6):440–3.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  47. Hale NL, Bennett KJ, Probst JC. Diabetes care and outcomes: disparities across rural America. J Community Health. 2010;35:365–74.

    Article  PubMed  Google Scholar 

  48. Schillinger D, Barton LR, Karter AJ, Wang F, Adler N. Does literacy mediate the relationship between education and health outcomes? A study of a low-income population with diabetes. Public Health Rep. 2006;121(3):245–54.

    Article  PubMed  PubMed Central  Google Scholar 

  49. Madden JM, Graves AJ, Ross-Degnan D, Briesacher BA, Soumerai SB. Cost-related medication nonadherence after implementation of Medicare Part D, 2006–2007. JAMA: J Am Med Association. 2009;302(16):1755.

    Article  CAS  Google Scholar 

  50. McWilliams JM, Meara E, Zaslavsky AM, Ayanian JZ. Health of previously uninsured adults after acquiring Medicare coverage. JAMA. 2007;298(24):2886–94.

    Article  PubMed  CAS  Google Scholar 

  51. Mutyambizi C, Booysen F, Stokes A, Pavlova M, Groot W. Lifestyle and socio-economic inequalities in diabetes prevalence in South Africa: a decomposition analysis. PLoS ONE. 2019;14(1):e0211208.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  52. De Silva AP, De Silva SHP, Haniffa R, Liyanage IK, Jayasinghe S, Katulanda P, et al. Inequalities in the prevalence of diabetes mellitus and its risk factors in Sri Lanka: a lower middle income country. Int J Equity Health. 2018;17:1–10.

    Article  Google Scholar 

  53. Shera A, Jawad F, Maqsood A. Prevalence of diabetes in Pakistan. Diabetes Res Clin Pract. 2007;76(2):219–22.

    Article  PubMed  CAS  Google Scholar 

  54. Xu Y, Wang L, He J, Bi Y, Li M, Wang T, et al. Prevalence and control of diabetes in Chinese adults. JAMA. 2013;310(9):948–59.

    Article  PubMed  CAS  Google Scholar 

  55. Gnavi R, Petrelli A, Demaria M, Spadea T, Carta Q, Costa G. Mortality and educational level among diabetic and non-diabetic population in the Turin Longitudinal Study: a 9-year follow-up. Int J Epidemiol. 2004;33(4):864–71.

    Article  PubMed  Google Scholar 

  56. Jacobs E, Tönnies T, Rathmann W, Brinks R, Hoyer A. Association between regional deprivation and type 2 diabetes incidence in Germany. BMJ Open Diabetes Res Care. 2019;7(1):e000857.

    Article  PubMed  PubMed Central  Google Scholar 

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Funding

This project was funded by the Deanship of Scientific Research (DSR) at King Abdulaziz University, Jeddah, under grant no. GPIP: 129-120-2024. The author, therefore, acknowledges with thanks DSR for technical and financial support.

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Authors and Affiliations

  1. Department of Health Services and Hospitals Administration, Faculty of Economics and Administration, King Abdulaziz University, Jeddah, Saudi Arabia

    Mohammed Khaled Al-Hanawi

  2. Health Economics Research Group, King Abdulaziz University, Jeddah, Saudi Arabia

    Mohammed Khaled Al-Hanawi

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  1. Mohammed Khaled Al-Hanawi
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Contributions

Conceptualization, M.K.A.; data curation, M.K.A.; formal analysis, M.K.A.; investigation, M.K.A.; methodology, M.K.A.; project administration, M.K.A.; software, M.K.A.; writing funding acquisition, M.K.A.;—original draft preparation, M.K.A.; writing—review and editing, M.K.A. The author have read and agreed to the published version of the manuscript.

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Correspondence to Mohammed Khaled Al-Hanawi.

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The secondary data used in this study were based on the survey conducted, commissioned, funded, and managed in 2018 by GaStat that was responsible for all ethical procedures. All participants provided informed consent and all procedures complied with institutional and/or national research committee ethical standards and with the 1964 Helsinki Declaration and subsequent amendments or equivalent ethical standards. The dataset was de-identified prior to analysis by GaStat to allow for secondary data use. GaStat granted permission to use the data and thus no further clearance was necessary as this was performed at the data collection phase.

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Al-Hanawi, M.K. Health disparities and inequalities in prevalence of diabetes in the Kingdom of Saudi Arabia. Int J Equity Health 23, 186 (2024). https://doi.org/10.1186/s12939-024-02265-6

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Keywords

International Journal for Equity in Health

ISSN: 1475-9276

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