Socioeconomic inequality in the prevalence of anemia: decomposing analysis among Kurdish people

Abstract

Introduction

Anemia is a prevalent public health issue affecting millions worldwide, particularly among vulnerable populations. This study examines anemia prevalence among Iran's Kurdish population, revealing socioeconomic inequality and emphasizing the need for targeted interventions.

Materials & methods

A cross-sectional analysis was conducted in 2019 using baseline data from the Dehgolan Prospective Cohort Study, involving 3,869 adults aged 35–70. Anemia was defined according to WHO guidelines, and socioeconomic status was assessed through household asset indices and educational attainment. Logistic regression and concentration index methods were employed to analyze the data.

Results

The prevalence of anemia was found to be 4.4%, with a higher rate in females (6.1%) compared to males (2.2%). Significant disparities were observed based on education and wealth, with illiterate individuals showing a prevalence of 5.5%. The analysis revealed that education and gender were the most influential factors contributing to socioeconomic inequality in anemia prevalence.

Conclusion

The study highlights the critical role of socioeconomic factors in the prevalence of anemia among the Kurdish population. Addressing these inequalities is essential for improving health outcomes and developing effective public health strategies.

Introduction

Anemia is a significant public health concern due to its high prevalence and impact on adults'health and well-being. It may result in symptoms such as fatigue, weakness, shortness of breath, palpitations, tachycardia and impaired cognitive function [1, 2]. In adults, can lead to decreased work productivity, and increased healthcare costs [3]. By disrupting oxygenation, and reducing exercise tolerance and quality of life, it's one of the main determinants of the complications of various diseases worldwide. [4, 5] Hemoglobin levels were assessed through complete blood count (CBC) using the Sysmex XP- 300 analyzer to determine anemia status based on World Health Organization (WHO) guidelines [6]. Iron deficiency anemia was classified as having a hemoglobin concentration of less than 13 g/dL in males and less than 12 g/dL in females. These cutoff values are widely accepted as the thresholds for diagnosing anemia, as hemoglobin levels below these concentrations are associated with impaired oxygen-carrying capacity and increased risk of adverse health outcomes.

The global prevalence of anemia across all age was estimated of 22.8% corresponding to 1.2 prevalent cases. A significant disparity in the prevalence of anemia has been noted across different demographic groups and geographic regions. Children under the age of 5, women, and countries within sub-Saharan Africa and south Asia have been identified as particularly vulnerable to the burden of anemia [7].The burden of anemia has affected 27% of the world's population so developing countries alone account for more than 89%. [8] recently a large study reported the prevalence of anemia among Iranian adults aged 35 and above at 8.83% [9].

Anemia arises from a complex interplay of factors, with iron deficiency being the predominant cause. Various correlates based on its underlying pathophysiology, such as infectious diseases, nutritional deficiencies and chronic conditions are generally the most common pathogenesis of anemia [10]. Predisposing socioeconomic factors have a significant influence on human health. In developing areas, lower socioeconomic households often have higher rates of anemia [11]. Addressing these disparities is crucial for improving health and reducing inequalities. Understanding the impact of socioeconomic factors allows for targeted interventions and policies to improve the well-being of vulnerable populations. Although there have been numerous reports of anemia among children, woman [12] and older adults [13], few studies attempt to investigate anemia in younger adults of general population and its underlying socioeconomic disparity in minority population. Despite the growing body of literature on anemia, there remains a significant gap in understanding its prevalence and the socioeconomic factors influencing it specifically among minority populations in Iran, particularly the Kurdish community. Previous studies, such as those by Akbarpour [14], Zamani [15] and Mashreghi [16], have primarily focused on broader demographics without delving into specific ethnic or socioeconomic disparities. In contrast, numerous studies have been conducted in other countries and regions examining the socio-economic status and its relationship with anemia across various populations [17–19]. This highlights the necessity of our study, as addressing these gaps is essential for developing targeted health interventions that can effectively reduce anemia prevalence in this vulnerable population. Therefor we utilized data from Dehgolan prospective cohort study (DehPCS) to estimate prevalence and underling factors of anemia and its socioeconomic inequality in Kurd population in west of Iran.

Materials and methods

Participants

This cross-sectional study was conducted in 2019, involving individuals participated in the enrollment phase of the DehPCS, which is a part of the larger PERSIAN cohort study [20]. Dehgolan is located in the southern part of Kurdistan Province in Iran. The region has a population of approximately 68,000, predominantly consisting of Kurds ethnic. Its economy is largely centered around agriculture. The detailed information on the DehPCS has been explained elsewhere [21]. Briefly the purpose of this cohort study was to identify the risk factors of the most common non-communicable diseases in the Kurd population. Participants were recruited in this study with a systematic random 1-stage cluster sampling method. We carried out a comprehensive census and subsequently selected 50% of the 721 blocks using systematic random sampling. The initial block for sampling was determined randomly. The process began with the first building located on the right-hand side of the nearest street to the cohort study center. All households within the selected blocks were thoroughly assessed, and participants were chosen based on predefined eligibility criteria. The inclusion criteria consisted of individuals aged 35–70, holding Iranian citizenship, and being permanent residents of Dehgolan with at least one year of residency and no intention to migrate. In this study, individuals with incomplete data on the required hematologic laboratory parameters were excluded from the analysis. Additionally, pregnant women, individuals with an untreated mental disorder such as psychosis and those who were unable to effectively answer questions were also excluded from the study sample. Finaly, out of 4400 individuals invited to participate, 404 declined, and 127 lacked laboratory data, resulting in a final sample size of 3869 participants. The study protocol was approved by the ethics committee of the Kurdistan University of Medical Sciences (IR.MUK.REC.1402.082). Written informed consent to participate in the survey was obtained from all participants.

Data collection and measurements

The data was collected using a structured checklist administered through in-person interviews conducted by a proficient interviewer fluent in Kurdish. The study included the collection of key demographic variables such as gender, age, and marital status. Participants were classified into three distinct smoking status categories: current smokers with a history of smoking at least 100 cigarettes in their lifetime, individuals who had quit smoking, and those who had never smoked [22]. The criteria for alcohol consumption are met when an individual drinks at least one standard alcoholic drink per week for a minimum duration of 6 months. This standard drink is defined as 200 mL of beer or 45 mL of other alcoholic beverage, consumed once a week for at least 6 months [23]. Similarly, the use of drugs is classified as Illicit/illegal drug use when it occurs at least once a week for a minimum duration of 6 months.

The Body Mass Index (BMI) was determined by dividing the participant's weight in kilograms by the square of their height in meters (kg/m²). According to the guidelines set by the World Health Organization (WHO) [24], participants were categorized into three BMI groups. Those with a BMI ≤ 24.9 kg/m² were classified as having a normal weight. Individuals with a BMI ranging from 25.0 to 29.9 kg/m2 were categorized as overweight, while those with a BMI ≥ 30.0 kg/m² were classified as obese. Physical activity levels were evaluated by utilizing the metabolic equivalent of task (MET) as a standardized measure of energy expenditure across different activities. This allowed for the calculation of MET values for each participant based on their self-reported physical activity over a 24-h period [25].

Socioeconomic status

Socioeconomic status (SES) was evaluated by considering both household asset index and educational attainment. The asset index was developed using the principal component analysis (PCA) method, and the data were segmented into five quartiles based on the index scores [26]. This household asset index served as a composite measure of wealth, taking into account the ownership of assets like a house and the area of living space (measured in square meters), as well as possession of items such as a mobile phone, laptop or PC, are considered. Additional assets include appliances like a freezer, washing machine, dishwasher, vacuum cleaner, and types of televisions (regular color TV or Plasma TV). Other factors include access to the internet, owning a motorcycle, and having a car. It functioned as a proxy for long-term economic status, allowing for the ranking of households based on their asset index score and categorization into wealth quintiles. The participants'educational attainment was categorized based on the highest level of education completed and the number of years spent on education. The categories for education were defined as follows: illiterate, indicating zero years of education; 1–6 years of education; 7–12 years of education; and university, indicating completion of more than 12 years of education.

Statistical analysis

In this research, participant data was analyzed with STATA version 17 software. The study calculated the prevalence of anemia and used summary statistics (mean and standard deviation for continuous variables; frequency and percentage for categorical variables) to describe sample characteristics. Additionally, logistic regression was employed to explore the relationship between the outcome variable and predictor variables, considering potential confounding factors. The study assessed socioeconomic inequality using the concentration curve and concentration index. Concentration curve depict the cumulative percentage of participants ranked by SES (y-axis) relative to the cumulative percentage of the anemia outcome variable (x-axis). To quantify inequality, the concentration index was calculated using the standardized Wagstaff formula (Eq. 1), which $\mu$ denote mean value of outcome y, n is the sample size and R denotes the individual’s fractional rank in the asset distribution. which yields a value between − 1 and 1, where zero represents perfect equality [27]. Additionally, the concentration index decomposition method was applied to quantify the contribution of various determinants to anemia inequality. Statistical significance for all analyses was defined as a p-value of less than 0.05.

$$C=\frac{2}{n\mu }\sum _{i=1}^n{y}_i{R}_i-1$$ 1

Results

The study involved 3,869 participants, of which 2,176 (56.2%) were women. The mean age of participants was 48.4 ± 8.9 with the majority (89.4%) belonging to the age group of 35–60 years. 31.3% of participants was illiterate and 12.9% had more than 12 years of education experience (Table 1).

Table 1.

Prevalence of anemia and their association with socioeconomic variables in multivariable logistic regression among Dehgolan Prospective Cohort Study participants

Variables	N (%)a	Anemia status		Adjusted odds ratio (95%CI)b	P-value
		n	(%)
Sex
Female	2176 (56.24%)	133	6.11%	Ref
Male	1693 (43.75%)	38	2.24%	0.43 (0.26–0.70)	0.001
Age categories
35–45	1894 (48.95%)	90	4.75%	Ref
46–60	1565 (40.44%)	64	4.09%	0.77 (0.52–1.13)	0.187
≥ 61	410 (10.61%)	17	4.15%	0.69(0.37–1.28)	0.235
Education
Illiterate	1201 (31.34%)	66	5.50%	Ref
1–5	1093 (28.52%)	56	5.12%	1.03 (0.67–1.59)	0.878
6–12	1042 (27.19%)	38	3.65%	0.80 (0.46–1.40)	0.444
University	496 (12.94%)	8	1.61%	0.37 (0.14–0.94)	0.038
Marital status
Married	3517 (91.77%)	149	4.24%	-
Single	315 (8.22%)	19	6.03%	-
Job
No	2009 (52.64%)	118	5.87%	-
Yes	1807 (47.35%)	50	2.77%	-
Chronic disease
No	2136 (55.43%)	85	3.98%	-
Yes	1717 (44.56%)	86	5.01%	-
Smoking
No smoker	2929 (76.53%)	142	4.85%	Ref
Ex-smoker	317 (8.28%)	14	4.42%	1.56 (0.84–2.89)	0.151
Smoker	581 (15.18%)	15	2.58%	1.06 (0.57–1.99)	0.831
Use alcohol
No	3327 (87.76%)	161	4.84%	Ref
Yes	464 (12.23%)	7	1.51%	0.46 (0.20–1.04)	0.062
Use drugs
No	3352 (88.44%)	157	4.68%	-
Yes	438 (11.55%)	11	2.51%	-
BMI d
< 25	953 (24.69%)	43	4.51%	Ref
25–29.9	1652 (42.80%)	69	4.18%	0.72 (0.48–1.08)	0.117
≥ 30	1254 (32.49%)	59	4.70%	0.69 (0.45–1.07)	0.099
Met category
Low	1566 (40.81%)	66	4.21%	-
Moderate	1736 (45.24%)	88	5.07%	-
Vigorous	535 (13.94%)	17	3.18%	-	3790
Socio-economic status
Q1 (poorest)	750 (19.78%)	47	6.27%	Ref
Q2	757 (19.97%)	31	4.10%	0.69 (0.43–1.11)	0.125
Q3	757 (19.97%)	33	4.36%	0.82 (0.51 − 1.32)	0.412
Q4	767 (20.23%)	32	4.17%	0.89 (0.54–1.46)	0.0.640
Q5 (richest)	759 (20.02%)	24	3.16%	0.96 (0.53–1.74)	0.906
Total

^an (%) = number (percent)

^bCI = Confidence interval

^dBMI: Body Massive Index

The findings indicate a total prevalence of anemia was 4.4% (95% CI, 3.5–5.1), with 2.72 percentage points higher in females (6.11%) compared to males (2.24%). Anemia prevalence was 3.89 percentage points higher in the illiterate group (5.50%) compared to those who had more than 12 years of education experience (1.61%). Furthermore, anemia increased across wealth index quantiles, with the prevalence in the poorest group being about 2 times higher than in the richest group. Additionally, individuals with no history of alcohol and drug use had approximately 3 times and 1.9 times higher prevalence of anemia compared to users, respectively (Table 1).

The normalized concentration index values for wealth index and education level indicate a significant concentration of anemia among the socioeconomically disadvantaged population. Table 2 and Fig. 1, indicating negative CI values of − 0.135 and − 0.155 for wealth index and education level, respectively, highlight the extent of this concentration.

Table 2.

Socioeconomic-related inequality for prevalence of Iron deficiency anemia

	Normalized concentration index	Confidence interval 95%	p-value
Wealth index	− 0.135	− 0.046, − 0.225	0.003
Education level	− 0.155	− 0.068, − 0.243	0.0005

Fig. 1.

Concentration Index (CI) Values for Education and Asset Index. A illustrates the negative concentration index value of − 0.155 for education level, B shows a CI of − 0.135 for the wealth index. Concentration curve 1. The cumulative outcome proportion of anemia by education years

To identify the various contributors to socioeconomic inequality in anemia, we conducted a multiple logistic regression analysis. The final model, as shown in Tables 3 and 4, includes gender, age, education level, smoking, alcohol use, BMI categories, and wealth index quintile as potential contributors to inequality.

Table 3.

The decomposition analysis results for anemia based on the asset index

		Coefficient	Mean	Elasticity	Concentration index	Contribution	Contribution Sum	Percentage contribution
Gender	Reference: female	− 0.035	0.44	− 0.35	0.19	− 0.066	− 0.07	51.04
Age groups	Reference: 35–45						0.02	− 13.45
	46–60	− 0.011	0.40	− 0.10	− 0.04	0.004
	> 60	− 0.015	0.10	− 0.04	− 0.36	0.013
Education	Reference: Illiterate						− 0.07	55.21
	1–5	0.002	0.29	0.01	− 0.08	− 0.001
	6–12	− 0.009	0.27	− 0.06	0.20	− 0.011
	> 12	− 0.030	0.13	− 0.09	0.67	− 0.059
SES	Reference: poorest						0.01	− 9.10
	Poor	− 0.015	0.20	− 0.07	− 0.40	0.027
	middle	− 0.009	0.20	− 0.04	− 0.01	0.0002
	rich	− 0.005	0.20	− 0.02	0.40	− 0.010
	very rich	− 0.002	0.20	− 0.01	0.80	− 0.006
BMI	Reference: < 24.9						− 0.01	3.97
	25–29.9	− 0.015	0.43	− 0.15	0.06	− 0.008
	> = 30	− 0.016	0.32	− 0.12	− 0.02	0.003
Smoking status	Smoking status						0.00	1.22
	ex	0.022	0.08	0.04	− 0.04	− 0.002
	smoker	0.003	0.15	0.01	0.02	0.000
Alcohol use	Reference: No	− 0.033	0.12	− 0.09	0.15	− 0.013	− 0.01	10.44
	sum						− 0.128	99.335
	Resi						− 0.001	0.665
	Anemia P	0.044
	CI						− 0.129

Table 4.

The decomposition analysis results for anemia based on the education status

		Coefficient	Mean	Elasticity	Concentration index	Contribution	Contribution Sum	Percentage contribution
Gender	Reference: female	− 0.035	0.44	− 0.35	0.20	− 0.070	− 0.070	47.00
Age groups	Reference: 35–45						0.038	− 25.67
	46–60	− 0.011	0.40	− 0.10	− 0.20	0.021
	> 60	− 0.015	0.10	− 0.04	− 0.48	0.017
Education	Reference: Illiterate						− 0.104	70.31
	1–5	0.002	0.29	0.01	− 0.09	− 0.001
	6–12	− 0.009	0.27	− 0.06	0.46	− 0.026
	> 12	− 0.030	0.13	− 0.09	0.87	− 0.077
SES	Reference: poorest						0.012	− 8.13
	Poor	− 0.015	0.20	− 0.07	− 0.25	0.017
	middle	− 0.009	0.20	− 0.04	− 0.06	0.002
	rich	− 0.005	0.20	− 0.02	0.14	− 0.003
	very rich	− 0.002	0.20	− 0.01	0.56	− 0.004
BMI	Reference: < 24.9						0.008	− 5.68
	25–29.9	− 0.015	0.43	− 0.15	0.06	− 0.009
	> = 30	− 0.016	0.32	− 0.12	− 0.14	0.017
Smoking status	Smoking status						− 0.001	0.63
	ex	0.022	0.08	0.04	− 0.04	− 0.002
	smoker	0.003	0.15	0.01	0.08	0.001
Alcohol use	Reference: No	− 0.033	0.12	− 0.09	0.19	− 0.018	− 0.018	11.87
	Sum						− 0.134	90.343
	Resi						− 0.014	9.657
	Anemia P	0.044
	CI						− 0.148

The distribution of wealth inequality in anemia is largely accounted for by several key factors, as illustrated in Fig. 2a. The model indicates that a substantial 99.3% of wealth inequality can be attributed to the included variables. Notably, education and gender emerged as the most influential factors, contributing approximately 55.2% and 55.0% respectively to the overall wealth disparity. Conversely, age groups and wealth index exhibit a negative contribution of − 13.4% and − 9.1% respectively. These findings underscore the complex interplay of socio-economic factors in shaping wealth distribution within the anemia, highlighting the need for targeted interventions to address underlying disparities.

Fig. 2.

Distribution of Wealth Inequality in Anemia. A presents the asset index, highlighting its role in wealth distribution, while B focuses on education and its impact

The analysis of education inequality in anemia reveals that a significant portion, approximately 90.3%, of the disparity can be explained by key factors as depicted in Fig. 2b. Among these factors, education and gender stand out as the most influential, contributing around 70.3% and 47.0% respectively to the overall wealth gap. In contrast, age groups and wealth index show a negative contribution of − 25.7% and − 8.1% respectively.

Discussion

The findings of the study revealed that the prevalence of anemia in the studied population was 4.4%, with a significantly higher prevalence among women at 6.1%. Comparatively, the prevalence of anemia among American women was similarly reported to be 6.3% [28]. In urban residents of Brazil, the prevalence of anemia was reported to be 1.1%. In contrast, in Oman, the prevalence of anemia was estimated to be 26% in 2018 [29]. Petry et al. [30] underscored the necessity of distinguishing between various types of anemia and advocated for the customization of anemia -reduction strategies and programs due to the considerable heterogeneity in the prevalence of anemia across different countries [30, 31]. Their analysis revealed that in approximately 50% of the assessed countries, the proportion of anemia was lower than previously estimated. They documented that the prevalence of anemia ranged from 9.1% in Vietnam to 76.3% in Sierra Leone, while the prevalence of anemia varied from 0.1% in Georgia to 21.2% in Liberia [30].

The presented results derived from DehPCS showed that the prevalence of anemia was higher in females than in males. This finding is consistent with previous studies that have reported the association of anemia with gender and its biological and social determinants. [32–34] which is justified by the following reasons. Higher physiological requirements in females, food insecurity, poverty, or cultural practices, lower access to health services and preventive interventions, and more exposure to infections which contribute to anemia [35, 36]. Hence, it is critical to address the gender-specific variables of anemia among women and to design effective interventions that can improve their health [36, 37].

The concentration curve of anemia by SES and education was above the line of equality, showing that the low SES and education group had a higher prevalence of anemia which is in line with previous findings [38]. The logistic regression analysis results indicated that none of the variables were significantly associated with anemia. This means that the odds of anemia were not different across the categories of age, marital status, education, SES, and job which was similar to the results of some studies [39]. This finding is contrary to the previous studies, as they detailed that these socio-demographic variables are related to the predominance and chance of anemia in different populations [40–42]. Some possible explanations for this finding are the sample size to detect any significant differences in anemia among the subgroups of the participants; the measurement of anemia was based on a single hemoglobin test, which may not reflect the true anemia status of the participants; A more detailed or relevant classification of the socio-demographic variables would allow for a better comparison of anemia among the participants.

A person’s health and well-being can be influenced by various factors, such as smoking, BMI, drug use, alcohol use, chronic disease, and physical activity measured as metabolic equivalent of task (MET) category. According to a previous study, anemia risk was lower among past or current smokers, and hemoglobin and hematocrit levels were higher among current smokers [43]. However, another study reported that smoking elevated the levels of erythrocytes, hemoglobin, hematocrit, leukocytes, mean corpuscular volume, and mean corpuscular hemoglobin concentration, which could increase the likelihood of developing atherosclerosis, polycythemia vera, chronic obstructive pulmonary disease, and/or cardiovascular diseases [44]. These parameters were not assessed in our study, but they may have implications for our findings. Moreover, alcohol use was positively associated with anemia [43]. However, a study among traders in the Tamale metropolis of Ghana found no significant difference in anemia rates between alcohol consumers and non-consumers [45]. Furthermore, BMI and central obesity were directly related to anemia risk [43]. In conclusion, adopting a healthy lifestyle is an essential strategy to prevent and treat anemia.

This study investigated the socio-economic inequalities in anemia among the residents of Dehgolan city in Iran. The results showed that anemia was more prevalent and concentrated among the low SES group, especially among women. These findings suggest that anemia is a public health problem that is influenced by socio-economic determinants and requires appropriate interventions to reduce the disparities and improve the health outcomes of the population. The results of this study are consistent with previous studies that have reported the association of anemia with socio-economic factors such as education, wealth, occupation, and region [8, 41]. Education is an important factor that affects the nutritional status, health awareness, and health-seeking behavior of individuals [46]. Wealth is another factor that determines the access and affordability of health services and nutritious food. Occupation is related to physical activity, exposure to environmental hazards, and income level of individuals. The region is a factor that reflects the geographic, climatic, and cultural diversity of the population. Gender is a factor that influences the biological, social, and economic aspects of health [46, 47]. Therefore, addressing these factors is essential for reducing the burden of anemia and improving the quality of life of the affected individuals.

This study is strengthened by its large sample size and the consistency of the data, which enhance the reliability of the findings. However, the study has notable limitations. The cross-sectional design restricts causal inferences, and the urban setting may limit the generalizability of the results to rural populations. Additionally, important factors influencing anemia inequality such as dietary intake, genetic disorders, infectious diseases, and cultural practices were not measured or controlled for. Moreover, the potential confounding or interaction effects of unmeasured variables in the logistic regression analysis may have obscured the relationship between anemia and socio-demographic factors. For instance, dietary habits, genetic predispositions, and cultural practices can significantly impact anemia risk and may correlate with socio-demographic variables.

Conclusion

This study underscores the critical issue of inequality in anemia prevalence, revealing that socio-economic factors significantly contribute to this health disparity. With an overall prevalence of 4.4%, the data highlights that women and illiterate individuals are disproportionately affected, with anemia rates markedly higher in these groups. The concentration index values indicate a strong association between socio-economic status and anemia, with education and gender emerging as the most influential contributors to inequality. Notably, the analysis shows that approximately 99.3% of wealth inequality in anemia can be attributed to these socio-economic determinants. Furthermore, the findings reveal that education and gender account for a substantial portion of the disparities, emphasizing the need for targeted interventions. Addressing these inequalities is essential not only for improving anemia outcomes but also for promoting greater health equity among disadvantaged populations.

Authors’ contributions

A.M., A.SH, and M.A. were involved in data collection and drafting the manuscript. Y.M. and F.M. contributed to the study's conception, design, analysis, and interpretation of results. P.M., Gh.M., N.M., A.M.B., and B.P. assisted with result interpretation and provided critical feedback on the manuscript. All authors reviewed and approved the final version of the manuscript.

Funding

The Iranian Ministry of Health and Medical Education has contributed to the funding used in the PERSIAN Cohort through Grant no 700/534.

Data availability

The datasets used and/or analyzed during the current study are available from the corresponding author upon reasonable request.

Declarations

Ethics approval and consent to participate

The study was reviewed and approved by the Research Ethics Committees of Kurdistan University of Medical Sciences. The project was found to be by ethical principles and national norms and standards for conducting Medical Research in Iran. Written informed consent to participate in this study was obtained from all participants'legal guardians. In cases where participants were illiterate or lacked formal education, informed consent was provided by an appropriate representative on their behalf.

Consent for publication

Not applicable.

Competing interests

The authors declare no competing interests.