Socioeconomic and Cultural Determinants of Blood Donation Practices Among Underserved Populations

ABSTRACT

Background

Blood donation is a very crucial practice to make a safe and reliable blood supply, however, there are still some populations with lower blood donation rates who have barriers that often limit their participation.

Objective

The aim of this study was to identify the social, economic, and cultural factors that influence blood donation in rural communities.

Methods

A community‐based cross‐sectional survey was carried out in two rural districts. The data collection process involved administering a pretested structured questionnaire to the participants. The data were analyzed using descriptive statistics, χ ² tests, t‐tests, and logistic regression, where the significance level was set at p < 0.05.

Results

Non‐donors showed less awareness of blood donation eligibility (50% vs. 85% of donors; p < 0.001) and perceived safety (60% vs. 92%; p < 0.001). The most common reasons indicated by non‐donors for not participating in blood donations were lack of awareness (30%; 95% CI: 22.5–38.3), fear of pain or discomfort (25%; 95% CI: 18.0–33.4), cultural or religious objections (18%; 95% CI: 12.0–25.9), and financial constraints like transport costs and lost wages (15%; 95% CI: 9.6–22.3). Case of logistic regression revealed that lack of awareness (OR 3.4, 95% CI: 1.9–6.2) and cultural/religious beliefs (OR 2.7, 95% CI: 1.3–5.5) were the significant predictors of non‐donor status.

Conclusion

Targeted interventions consisting of community education, culturally sensitive campaigns, and financial support for transport and time costs are urgently called for to increase donation rates and strengthen blood supply systems in Kenya.

1. Introduction

Blood transfusion turns out to be a primary component of modern medicine and, as such, it supports surgical procedures, trauma care, emergency obstetrics, and also the management of hematological disorders [1, 2]. Thus, a safe and adequate blood supply is a necessity for reducing morbidity and mortality throughout the world [3, 4]. On the contrary, the global evidence illustrates that a vast number of low‐ and middle‐income countries (LMICs) suffer from blood supply shortages, and the demand is often over the available supply [5, 6]. Shortages of this kind are particularly severe in Sub‐Saharan Africa, where they are one of the causes of maternal mortality, childhood anemia, and emergency care deaths that could have been prevented [7, 8]. But still, in many LMICs, the donation of blood voluntarily is not as popular as it is in high‐income countries. In Kenya, approximately 300,000–350,000 units of blood are collected annually, falling significantly short of the national target of 500,000 units [9, 10, 11]. In contrast, many high‐income countries consistently meet or exceed their national blood supply targets through well‐established voluntary donation systems [1, 3]. What is worse is that the rural and underserved populations are the ones contributing the least. Previous studies reveal that the barriers to blood donation consist of many factors, such as lack of information, myths, cultural and religious beliefs, and financial problems [5, 12]. However, most of this evidence comes from urban areas or regions outside East Africa, thus leaving a gap in knowledge concerning the specific difficulties faced by the rural poor.

To encourage such and to make the most of the supply, it would be essential to grasp the barriers and design properly tailored interventions to improve the rates of donations and the overall strength of the blood supply system in the country [13, 14, 15]. In the absence of this type of information, health policies might not correctly identify the locally specific problems that are discouraging people from participating in blood donation drives. The current research thus set out to explore social, cultural, and economic factors influencing blood donation among underserved rural populations in Kenya, with a specific focus on comparing donors and non‐donors.

2. Methods

2.1. Study Design and Setting

This study was classified as a community‐based and cross‐sectional. These districts were chosen intentionally because they have always been characterized by low blood donation rates and a lack of health facilities with transfusion services. This study is reported in accordance with Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) guidelines, which are appropriate for cross‐sectional study designs.

2.2. Participants and Eligibility Criteria

The adults aged 18–60 years living in the districts under study constituted the study population. The two types of participants who qualified for the study were blood donors registered (had donated at least once within the last 2 years) and non‐donors (had never donated blood). A very strict set of exclusion criteria was such that people with serious illnesses at the time of the survey, people unable to give informed consent, and individuals who were unable to communicate effectively in either of the two study languages (English or the predominant local language) were excluded. Despite the availability of translated questionnaires, participants with significant communication difficulties (e.g., dialect variations or comprehension challenges) were not included, which may have introduced selection bias by underrepresenting certain subpopulations.

2.3. Sample Size and Sampling

The sample size of 238 was calculated using Cochran's formula for cross‐sectional surveys, considering a prevalence of barriers of 50%, a confidence level of 95%, and a 6.5% margin of error. The sample for the study was then increased to 250 participants. A stratified random sampling method was employed in which 125 blood donors were selected at random from the registries of the blood donation centers, and 125 non‐donors were recruited from community health programs by using household sampling lists.

2.4. Variables and Measures

The major dependent variable was that of non‐donor status, which was equated with never donating blood. The following were the independent variables: sociodemographic factors such as age, sex, education level, occupation; knowledge and awareness variables included knowledge of eligibility criteria, national blood need awareness, and perceived donation safety; barriers were lack of awareness, pain fear, cultural or religious objections, financial constraints (transportation costs, lost wages), and health concerns (fear of infection, etc.). All variables were categorized as binary or categorical for analysis. The “underserved population” was defined through geographic classification and through specific measurable indicators, which were used to measure the concept. The research used measurable indicators which included (i) distance to the nearest health facility (which measured distances greater than 5 km and distances shorter than 5 km) and (ii) education level (which defined primary or lower education as one category and secondary or higher education as another category), (iii) employment status (which used informal or unemployment status to define one group and formal employment status to define the other group), and (iv) self‐reported difficulty in accessing healthcare services (yes/no). At least two criteria were used to identify participants as members of an underserved population. The selected indicators provided relevant information about healthcare access disparities which exist in Kenyan healthcare systems while also enabling researchers to compare their results with similar studies.

2.5. Data Collection

The study used a structured questionnaire​ administered through direct interactions with study participants. The questionnaire primarily consisted of closed‐ended questions, which included multiple‐choice and categorical response options and contained a limited number of Likert‐scale items for measuring attitudes and perceptions. No open‐ended qualitative responses were collected. The questionnaire included approximately 35 items, which assessed sociodemographic information, blood donation knowledge, perceived obstacles, and donation attitude. It was developed based on previously published studies [8, 15] and World Health Organization guidelines on blood donation practices. Pretest of the instrument was conducted with 20 participants. While making small changes to improve understanding and adapt to local cultural norms. Participant recruitment was conducted by the use of blood donation registries to contact donors and community health volunteers for household listings to reach out to non‐donors. The full questionnaire is available as Supporting Information S1. The full questionnaire, including all items and response options, is provided in the Supporting Information S1 to enhance reproducibility. Representative items included: “Are you aware of the eligibility criteria for blood donation?” (Yes/No), “Do you believe blood donation is safe?” (Yes/No), and “What are your main reasons for not donating blood?” (multiple‐choice options).

2.6. Bias Control

A variety of strategies were put in place to reduce bias: Selection bias: stratified random sampling guaranteed equal representation of donors and non‐donors; Information bias: standardized questionnaires and trained enumerators minimized variability due to the interviewers; Recall bias: participants were asked about their experiences very recently (within the last 2 years) to help with the accuracy; Language bias: preferred language of participants was used for administering surveys. The study results face potential selection bias because the research involved two separate groups of participants who were either donors or non‐donors. The blood donation system tests showed equal results through stratified sampling, but the participants' prior experience with blood donation systems created different response patterns. Two languages were used for the survey, and people who could not speak these languages were excluded without determining how many people spoke those languages within the study population. This may limit generalizability.

2.7. Data Analysis

Data were analyzed using the R Statistical software version 4.3.2. To summarize demographic characteristics, knowledge, and reported barriers, descriptive statistics were used. Categorical variables were shown as frequencies and percentages with 95% confidence intervals (CI), and continuous variables as means with standard deviations (SD). Comparisons between donors and non‐donors were done by means of the χ ² test for categorical variables and independent t‐test for continuous variables. Logistic regression models were used to examine the links between donor status and possible barriers after controlling for age, gender, and education. A p value of less than 0.05 was considered statistically significant.

2.8. Ethical Considerations

The study observed ethics according to the Helsinki declaration, and all participants signed a voluntary written informed consent form before enrollment. Confidentiality regarding participants was maintained throughout the study; all the data were anonymized and stored securely. Additionally, the participants were informed that they would withdraw from the study without penalty at any time. All methods were performed in accordance with the relevant guidelines and regulations.

3. Results

3.1. Participant Characteristics

A total of 263 individuals were approached for participation, of whom 250 consented and completed the survey, yielding a response rate of 95%. Thirteen individuals declined participation primarily due to a lack of time or disinterest. The research results show high response rates, which lower the chances of nonresponse bias, but this bias still exists as a possibility. A total of 250 participants were surveyed, including 125 blood donors and 125 non‐donors (response rate: 95%). The mean age of participants was 33.8 years (SD ± 7.0), with no significant age difference between donors (33.5 ± 7.2 years) and non‐donors (34.1 ± 6.8 years). Males comprised 57.5% of the total sample. Most participants had attained secondary (27.5%) or tertiary education (55.5%), while 17% had completed only primary school (Table 1). Based on the predefined criteria, 62.4% of participants resided more than 5 km from the nearest health facility, 41.2% had primary‐level education or below, 48.8% were unemployed or engaged in informal employment, and 55.6% reported difficulty accessing healthcare services. Overall, 68.0% of participants met at least two criteria and were classified as belonging to underserved populations.

Table 1.

Demographic characteristics of study participants (n = 250).

Characteristic	Donors (n = 125)	Non‐donors (n = 125)	Total (n = 250)	p value
Age (years, mean ± SD)	33.5 ± 7.2	34.1 ± 6.8	33.8 ± 7.0	0.42
Gender (%)				0.37
Male	55.0	60.0	57.5
Female	45.0	40.0	42.5
Education level (%)				0.08
Primary school	12.0	22.0	17.0
Secondary school	30.0	25.0	27.5
Tertiary education	58.0	53.0	55.5

3.2. Reported Barriers to Blood Donation

Among non‐donors, the most common barrier was lack of awareness of blood donation benefits or eligibility (30.0%; 95% CI: 22.5–38.3), followed by fear of pain or discomfort (25.0%; 95% CI: 18.0–33.4) and cultural or religious objections (18.0%; 95% CI: 12.0–25.9). Financial constraints such as transport costs and lost wages were reported by 15.0% (95% CI: 9.6–22.3). A smaller proportion (12.0%; 95% CI: 7.3–18.8) cited health concerns, mainly fear of contracting infections (Table 2 and Figure 1).

Table 2.

Reported barriers to blood donation among non‐donors (n = 125).

Barrier	n (%)	95% CI
Lack of awareness	38 (30.0)	22.5–38.3
Fear of pain/discomfort	31 (25.0)	18.0–33.4
Cultural/religious beliefs	23 (18.0)	12.0–25.9
Financial constraints	19 (15.0)	9.6–22.3
Health concerns (e.g., fear of HIV)	15 (12.0)	7.3–18.8

Figure 1.

Barriers to blood donation among non‐donors (n = 125). Lack of awareness (30%) and fear of pain or discomfort (25%) were the most common barriers, followed by cultural or religious beliefs (18%), financial constraints (15%), and health concerns such as fear of infection (12%).

Whereas non‐donors more frequently cite barriers to donating blood, donors have cited them as well, but at a relatively lower frequency. For example, fear of pain was reported by 12% of donors compared to 25% of non‐donors, and financial constraints by 8% of donors versus 15% of non‐donors. This suggests that although these barriers do not completely prevent donation among donors, they may still influence donation frequency and consistency.

3.3. Knowledge and Attitudes Toward Blood Donation

Donors consistently demonstrated higher knowledge and more positive attitudes compared to non‐donors (Table 3). Awareness of the national need for blood was reported by 90.0% of donors versus 55.0% of non‐donors (χ² = 28.6, p < 0.001). Similarly, knowledge of eligibility criteria was higher among donors (85.0% vs. 50.0%, χ² = 30.4, p < 0.001). Belief in the safety of donation was reported by 92.0% of donors compared to 60.0% of non‐donors (χ² = 33.1, p < 0.001) (Table 3 and Figure 2).

Table 3.

Knowledge and attitudes toward blood donation among donors and non‐donors (n = 250).

Knowledge/attitude	Donors (%)	Non‐donors (%)	χ² (df = 1)	p value
Awareness of blood donation need	90.0	55.0	28.6	< 0.001
Knowledge of eligibility criteria	85.0	50.0	30.4	< 0.001
Belief in safety of donation	92.0	60.0	33.1	< 0.001

Figure 2.

Knowledge and attitudes toward blood donation among donors and non‐donors (n = 125) and non‐donors (n = 125). Donors reported higher awareness of the need for blood, better knowledge of eligibility criteria, and greater belief in donation safety compared to non‐donors.

3.4. Multivariable Logistic Regression Analysis

The study used multivariable logistic regression analysis to determine which factors predict non‐donor status after researchers controlled for age, gender, and education level. Participants who lacked awareness about donation had three times higher odds of being non‐donors (OR 3.4, 95% CI: 1.9–6.2, p < 0.001). Cultural and religious beliefs had an independent relationship with non‐donor status (OR 2.7, 95% CI: 1.3–5.5, p = 0.006). The adjusted model showed that fear of pain, financial constraints, and other factors did not reach statistical significance.

3.5. Economic and Cultural Factors

Economic barriers (transportation cost, time away from work) were reported by 18.0% of non‐donors compared with 8.0% of donors (χ² = 6.9, p = 0.009). Cultural or religious objections were significantly more common among non‐donors (15.0%) compared with donors (5.0%) (χ² = 7.9, p = 0.005).

4. Discussion

This cross‐sectional study was conducted to investigate the social, economic, and cultural factors which hinder the blood donation process for the underserved rural population of Kenya. The study revealed that lack of awareness was the top‐most barrier, almost one‐third of the non‐donors mentioned it. The fear of pain and cultural or religious beliefs formed the other significant deterrents. The third barrier was financial constraints, which included transportation costs and lost wages. However, logistic regression analysis indicated that lack of awareness and cultural objections continued to be strong independent determinants of non‐donor status even after controlling for demographic characteristics. Although donors reported similar types of barriers, their lower frequency suggests that these individuals may possess stronger motivating factors that outweigh perceived obstacles. The current survey did not assess motivations for donation, but this finding shows that barriers alone do not account for donation behavior and demonstrates the need to study both internal drivers and social incentives to understand why donors continue their support.

Another finding of the study was the connection between education, knowledge, and donation behavior, which was very significant. Non‐donors were the ones with much lower levels of formal education, and this may partially explain the observed differences in knowledge levels and perceptions of donation safety. The shown patterns imply that educational attainment may, in fact, indirectly influence the donation decisions based on knowledge and misconceptions. While less frequently mentioned, non‐donors' fears about health risks, such as acquiring infections through blood donation, were also present and should not be ignored. Awareness of eligibility criteria and trust in donation safety were higher among donors (Table 3 and Figure 2), highlighting the role of knowledge and perceived safety in influencing donation behavior.

There was a consistency in these findings with the findings from low and middle‐income country studies conducted in other settings [16, 17]. For example, in Nigeria, a study identified that lack of awareness and misconceptions were the main barriers to voluntary blood donation [18, 19]. These findings are consistent with studies conducted in other low‐ and middle‐income countries, where lack of awareness, fear, and misconceptions have been identified as major barriers to blood donation. However, direct comparisons with other regions within Kenya remain limited due to a lack of comparable rural‐focused studies [20, 21, 22]. The research shows that rural areas face more severe transportation costs, and their blood donation centers are more difficult to access than other areas because these two factors create obstacles that prevent people from donating blood. The study identified barriers that affect multiple groups, but their effects become more intense in underserved communities because multiple socioeconomic and access‐related disadvantages overlap. The research findings confirm existing barriers while showing how these barriers operate in rural areas with limited structural options. Cultural and religious objections are also mentioned as a barrier in other similar studies. In the same way, cultural and religious objections have been pointed out in South Asia and Sub‐Saharan Africa, where people's reluctance to give is affected by traditional beliefs [23, 24]. The economic difficulties seen in our research, especially with transport costs and loss of wages, are similar to those found in India and Tanzania, where the poor were not able to donate blood because of inequalities in the system [13]. The obstacles which this rural underserved group faces show high similarity to the obstacles which urban populations and general populations in Kenya and other low‐ and middle‐income countries face because these groups share three main barriers which people do not know about, and they operate from their dominant fears and their incorrect beliefs. In this study, financial constraints were reported by 15% of non‐donors and were significantly more common among non‐donors than donors (15% vs. 8%, p = 0.009), supporting evidence from similar LMIC settings where indirect costs limit participation in blood donation [25, 26].

Blood donation behavior shows variability because people donate blood according to their individual preferences. The present study used a binary classification of donors and non‐donors which may oversimplify complex behavioral patterns. The study did not differentiate between first‐time donors, repeat donors, or replacement donors. A more nuanced classification will be more helpful for a prospective intervention study in the future. The findings can be further interpreted through the Health Belief Model, which explains that health‐related behaviors depend on three factors, including perceived benefits, perceived barriers, and cues to action [27]. The study reveals that people who lack awareness about blood donation benefits will perceive lower advantages, while people who fear blood donation and hold cultural beliefs about it will face greater obstacles, which will determine their blood donation choices. This paper adds to the existing literature by assigning a numerical value to the relative importance of each obstacle and contrasting donors with non‐donors in the background of underprivileged communities. Among the strengths are stratified sampling, culturally adapted tools, and multivariable analysis employed to find independent predictors. Nevertheless, there are some limitations that should be pointed out. The cross‐sectional design does not allow for causation to be inferred. Self‐ reporting may be affected by recall or social desirability bias. Also, the sample from two rural districts may not represent the entire Kenyan population or its urban areas.

The study results provide direct information that guides policy development and implementation. The strong association between lack of awareness and non‐donor status supports the need for targeted health education campaigns that focus on teaching people about eligibility requirements and safety measures. The study results show that cultural and religious beliefs affect people; therefore, organizations must work together with community leaders and religious leaders to create solutions that respect local customs. Organizations need to establish structural solutions that include mobile donation systems and payment for indirect expenses because financial obstacles, together with transport expenses, create obstacles for their work. The present study findings demonstrate that the recommendations maintain their validity, which exists beyond the existing literature, because they apply to underserved rural areas.

5. Limitations

This study has several limitations that should be acknowledged. Being cross‐sectional in design, it cannot establish causal relationships between the identified factors and blood donation behavior. The use of self‐reported data may have introduced recall or social desirability bias, as participants could overstate or understate their knowledge and attitudes. Additionally, since the sample was limited to two rural districts, the findings may not fully represent other regions or urban populations in Kenya. Some psychological aspects, such as altruism, prior donation experience, and trust in health systems, were not explored in depth, yet they may influence donor motivation. Although stratified random sampling was applied, non‐response and selection bias cannot be completely ruled out. Despite these limitations, the study offers important insights into the social, economic, and cultural barriers to blood donation, providing a valuable foundation for tailored interventions and future longitudinal studies. The study did not differentiate between different types of blood donors, which include first‐time donors, repeat donors, and replacement donors. The questionnaire used in the study relied on previous research and established guidelines, but lacked formal validation through internal consistency testing, which would have proven construct validity. The study findings directly inform each of the recommended interventions. The research shows that people who do not donate blood tend to lack knowledge about blood donation. The study shows that cultural beliefs need to be studied through partnerships with community leaders. The economic barriers that researchers found in their study demonstrate the requirement for mobile donation services and transport assistance as solution methods. The study shows that the recommendations match existing research, but their importance in this study comes from the size of the obstacles found within this specific group of people.

6. Conclusion

This study brings up the issue of targeted interventions to the repeatedly mentioned barriers as a necessary measure. Educational programs that focus on increasing awareness about the need for blood donations and donor eligibility should be prioritized in underserved populations. Furthermore, addressing economic barriers by providing incentives or support for transportation and time off work could help mitigate some of the challenges faced by potential donors. Additionally, culturally sensitive strategies should be developed to address specific cultural and religious beliefs that hinder blood donation. By addressing these barriers, blood donation rates can be increased, ultimately improving the overall blood supply and donor safety in underserved areas.

Author Contributions

Collince Odiwuor Ogolla: conceptualization, investigation, writing – original draft, methodology, formal analysis, resources, writing – review and editing. Benard Guya: writing – review and editing, supervision, software, methodology. Apollo O. Maima: writing – review and editing, supervision, project administration, resources, visualization, funding acquisition.

Funding

The authors have nothing to report.

Ethics Statement

The study observed ethics according to the Helsinki declaration, and all participants signed a voluntary written informed consent form.

Consent

All authors has given their consent for publication of this article.

Conflicts of Interest

The authors declare no conflicts of interest.

Supporting information

Supporting File

Data Availability Statement

The authors confirm that the data supporting the findings of this study are available within the article and the Supporting Information S1. Additional data may be available from the corresponding author upon reasonable request.