Demographic and Socio-economic Correlates of Nutrition Status of Adult Cancer Patients: A Case of Texas Cancer Center, Kenya

Demographic and Socio-economic Correlates of Nutrition Status of Adult Cancer Patients: A Case of Texas Cancer Center, Kenya | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Demographic and Socio-economic Correlates of Nutrition Status of Adult Cancer Patients: A Case of Texas Cancer Center, Kenya ELIZABETH ACHIENG ODUOR, ALFRED OWINO ODONGO, WILLY KIBOI This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4748045/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background Cancer has become a serious global health threat following its increasing incidence and prevalence rates. The study's objective was to establish any existing relationships between demographic and socio-economic characteristics and the nutrition status of adult cancer patients. This was aimed at gathering more data for evidence-based interventions to establish factors that influence nutrition status among cancer patients, given only the existence of scanty information on the same. Methods In this cross-sectional study, 384 patients were randomly selected. Data on demographic and socio-economic characteristics was collected through interviewer-administered questionnaires. Nutrition status data was determined through body mass index which was computed from the weight and height measurements. Chi-square and logistic regression were used to assess the correlates of nutrition status. Results The study revealed that 41% (n = 157) of participants had optimal nutrition. The mean BMI was 25.0kg/m 2 ± 4.25SD, with 17% being underweight, 28% overweight, and 14% obese. The majority of the participants, 89% (n = 343), were above 36 years and 66% (n = 254) were female. Those with secondary education had the highest frequency 41% (n = 158). The monthly household income category with the highest number of respondents was less than 15,000 Kenya shillings (n = 196; 51%), while most of the study participants, 54% (n = 209), had household sizes of 4–6. Factors found to be associated with the participants' nutrition status included age (AOR = 6.73; 95% CI = 1.88–24.11; p-value = 0.003), occupation status (AOR = 2.57; 95% CI = 1.42–4.68; p-value = 0.002), sex (AOR = 2.64; 95% CI = 1.33–5.26; p-value = 0.006), household size (AOR = 1.79; 95% CI = 1.03–3.12; p-value = 0.039), and level of education (AOR = 10.81; 95% CI = 1.29–90.66; p-value = 0.028). Conclusion This study found that malnutrition was common among the study participants, and this could be a result of low income among the families and intake of low nutrients. Therefore, approaches such as increasing the availability, affordability, and accessibility of nutrient-dense foods are key when designing and prioritizing interventions. Figures Figure 1 Figure 2 BACKGROUND OF THE STUDY A lower socioeconomic level (SES) is linked to a higher cancer incidence and worse survival rates. This is due to the discrepancies in survival rates between social groups, including variations in tumor biology, patient comorbidity, disease stage at diagnosis, accessibility to medication, and treatment methods (Arends et al., 2017). In 2020 alone, there were 18.1 million new cancer cases worldwide, with 9.3 million occurring in men and 8.8 million in women. Africa reported 1.1 million new cancer cases and 711,429 cancer-related deaths, with a prevalence of 2.2 million cases (WHO, 2020). In Kenya, the cancer incidence rate stands at 47,887 with 32,987 cancer-related deaths (WHO, 2020). Malnutrition risk may not even be properly handled even when it is acknowledged. Only a fraction of the cancer patients who are at risk of malnutrition got nutritional intervention, according to hospital studies in Europe (Muscaritoli et al., 2017). One frequently mentioned potential explanation for the connection between SES and cancer outcomes is a difference in disease stage. While numerous studies from high-income countries (HICs) have explored the relationship between SES, cancer stage at diagnosis, and survival, there is little research on these topics in low- and middle-income nations (LMICs). This study fills a major gap in knowledge due to the LMICs' exploding cancer burden, and the socioeconomic, and demographic contrasts between their people and those in HICs. The overall aim of this study was to assess the demographic and socio-economic factors of adult cancer patients at the Texas Cancer Center, focusing on the level of education, gender, household size, monthly household income, age, and employment status, aiming to fill existing gaps in cancer-related information and establish relationships between these variables and nutrition status. This paper presents a description of the demographic and socioeconomic status of the populations attending the Texas Cancer Center and their associations with nutritional outcomes in adult cancer patients. This study also provides updated baseline data to inform the design for future research, and to develop strategies that aim to address malnutrition among cancer patients. METHODS Study Site This investigation was conducted at the Texas Cancer Center, as it offers comprehensive services, including laboratory procedures, cancer screening, treatment, and palliative care, delivered by a multidisciplinary team. Research Design An analytical cross-sectional study design was employed in this study. Target Population The study targeted cancer patients, with the accessible population comprising adults (aged 18 and above) with stage I, II, III, and IV cancer undergoing treatment at the Texas Cancer Center. The sample aimed to evaluate the nutrition status of patients undergoing cancer treatment alongside other relevant variables. Inclusion Criteria: The study included all adult outpatients and inpatients diagnosed with cancer at the Texas Cancer Center who provided consent for participation. Exclusion Criteria: Excluded from the study were critically ill patients and individuals meeting the inclusion criteria but unable to participate due to other personal commitments. Sample Size The study sample size was determined using Cochran's formula for an infinite population. The sample size was set at 384 participants. Participants were selected from a sampling frame using a systematic random sampling method based on a predetermined interval of 2, whereby the first participant was randomly selected. Data Collection Instruments Data collection involved the use of semi-structured questionnaires to collect data on age, household size, monthly household income, sex, education status, and occupation status. Weight and height measurements were obtained using a height board and weighing scale. Data Collection Procedures Before administering the questionnaire, participants provided informed written consent. The demographic and socio-economic characteristics (gender, age, household size, monthly household income, highest level of education, and employment status) data were collected through the use of a structured questionnaire. Nutrition status was determined using Body Mass Index (BMI), calculated from weight and height measurements. Nutrition status categories were normal nutrition (BMI of 18.5-24.9kg/m 2 ), Underweight (BMI of less than 18.5kg/m 2 ), Over-weight (BMI of 25-30kg/m), and obese (BMI of over 35kg/m2). Validity and Reliability of Data Collection Tools Data collection tools underwent pre-testing and validation by a panel of experts, while reliability was assessed through the test-retest method. Data Analysis and Presentation Collected data was reviewed to assess if the questionnaires were well filled. The assessed socio-economic and demographic factors (gender, age, household size, monthly household income, level of education, and occupation status) were then grouped, as the nutrition status was categorized as normal nutrition (BMI of 18.5-24.9kg/m2), underweight (BMI of less than 18.5kg/m2), over-weights (BMI of 25-30kg/m2) or obese (BMI of over 35kg/m2). Data analysis was done using STATA version 17. Inferential statistics, such as Pearson's chi-square and logistic regression were utilized to explore associations between nutrition status and respondents' demographic and socioeconomic characteristics. A p-value of < 0.05 indicated existing statistical significance, within a confidence interval of 95%. When bivariate logistics regression was done, any values with a p-value of < 0.05 (crude odds ratio- COR) were fitted in a multivariate regression analysis to establish the predictors of nutrition status and socio-economic and demographic factors of the cancer patients (Adjusted odds ratio- AOR). Findings were presented through tables and graphs. RESULTS Demographic and socio-economic characteristics of the study population The majority 89% (n = 343) of the participants were aged 36 years with the mean age being 44 years ± 2.5SD. More than half 66% of the respondents were female (n = 254). Those with secondary education had the highest frequency 41% (n = 158). The monthly household income category with the highest number of respondents was less than 15000 Kenya shillings (n = 196; 51%), while > 45000 shillings was the least household income category received by the study participants as represented by 9% (n = 33). The mean household income was 17000 ± 3000 shillings. Most of the study participants had household sizes of 4–6 as represented by 54% (n = 209) of the respondents, while the least represented household size was > 9 (n = 2; 1%) persons. The majority 55% (n = 212) of the study respondents were self-employed (Table 1 ). Table 1 Socioeconomic and demographic characteristics of the respondents Characteristic Frequency (N = 384) Percentage (%) Sex Female 254 66 a Male 130 34 Age category (years) 18–35 41 11 ≥36 343 89 a Mean age ± SD (years) 44 ± 2.5 Education status No formal education 33 9 Primary 131 34 a Secondary 158 41 Tertiary 62 16 Household monthly income (KSh) ≤15000 196 51 a 15001–30000 127 33 30001–45000 28 7 >45000 33 9 Mean ± SD (Kshs) 17000 ± 3000 Household size 1–3 128 33 4–6 209 54 a 7–9 45 12 >9 2 1 Occupation status Employed 103 27 Self-employed 212 55 a Unemployed 69 18 a Majority of the respondents The nutrition status of the respondents Respondents with normal nutrition (BMI of 18.5-24.9kg/m2) were 41% (157) while those with the least representation 14% (n = 54) obese (Fig. 1 ). The nutritional status of the respondents was further classified as being normal or malnourished as shown below (Fig. 2 ). The study observed that a significant portion, comprising 59% (n = 227), of its participants suffered from malnutrition. Those who were malnourished had a BMI of either 24.9kg/m2. The average BMI was 25.0kg/m2 ± 4.25SD (Fig. 2 ). Relationship between nutrition status and socioeconomic and demographic characteristics of the respondents On multivariate analyses, age (p = 0.003), occupation status (p = 0.002), sex (p = 0.006), and household size (p = 0.039) had a significant relationship with nutrition status. In terms of education, primary (p = 0.028), secondary (p = 0.010) and tertiary (p = 0.006) levels of education had an association with nutrition status. The study observed that those aged 36–41 years were 6.73 more likely to be malnourished compared to those younger than them. Self-employed respondents (AOR = 2.57; 95% CI = 1.42,4.68) had higher odds of being malnourished compared to their employed and unemployed counterparts. Regarding sex, females had a 2.64 greater likelihood of being malnourished compared to males. Respondents who had a household size of 4–6 were 1.79 more likely to be malnourished than those who had household sizes of less than 4 (Table 2 ). Table 2 Relationship between nutrition status and socioeconomic and demographic characteristics of the respondents Variables Malnutrition COR (95% CI) P value AOR (95% CI) b P value Yes No Age category 18–23 8 2 1.00 1.00 24–29 24 6 1.38(0.31,6.09) 0.659 1.08(0.19,6.23) 0.932 30–35 11 9 0.84(0.39,1.81) 0.670 0.38(0.03,3.99) 0.417 36–41 36 3 0.22(0.07,0.68) 0.008 6.73(1.88,24.11) 0.003 42–47 17 17 1.55(0.49,4.82) 0.448 0.58(0.20,1.71) 0.329 > 47 131 120 0.09(0.08,0.98) 0.049 0.89(0.17,4.69) 0.897 Occupational status Unemployed 37 32 1.00 1.00 Self -Employed 149 43 2.14(1.36,3.36) 0.001 2.57(1.42,4.68) 0.002 Employed 41 82 0.00(0.00,0.00) 0.000 1.88(0.80,4.44) 0.149 Level of education No education 16 10 1.00 1.00 Primary 105 34 1.76(0.51,6.10) 0.373 10.81(1.29,90.66) 0.028 Secondary 73 85 0.49(0.19,1.29) 0.151 11.12(1.78,69.68) 0.010 Tertiary 33 28 0.77(0.31,1.96) 0.596 12.29(2.07,72.92) 0.006 Sex Male 106 93 1.00 1.00 Female 121 64 0.47(0.24,0.91) 0.025 2.64(1.33,5.26) 0.006 Monthly income 1000–15000 54 108 1.00 1.00 15001–30000 50 28 0.42(0.15,1.21) 0.010 1.43(0.27,7.49) 0.672 30001–45000 15 13 1.19(0.20,7.08) 0.849 3.07(0.58,16.28) 0.189 > 45000 108 8 0.29(0.11,0.81) 0.018 5.85(0.65,52.62) 0.115 Household size 1–3 36 94 1.00 1.00 4–6 112 47 1.71(1.11,2.63) 0.014 1.79(1.03,3.12) 0.039 7–9 77 16 5.06(4.82,5.81) 0.631 4.31(0.35,4.33) 0.693 > 9 2 0 5.80(3.75,7.76) 0.675 0.78(0.22,2.34) 0.738 AOR Adjusted Odds Ratio, COR Crude Odds Ratio, CI Confidence Interval, b Adjusted for demographic and socio-economic characteristics (highest level of education, sex, age, occupation status, household size, and monthly household income). DISCUSSION Most of the study participants 59% (n = 227) were malnourished. The mean BMI was 25.0kg/m2 ± 4.25SD. The malnutrition rate in this study was close (58.4%) to those obtained by Siegel et al., 2017 in a cross-sectional study carried out in Ethiopia to establish the prevalence and risk factors of malnutrition among adult cancer patients receiving chemotherapy treatment in a cancer center. However, these results were higher when compared to a study done by Maciel et al., 2012 where he found malnutrition rates among cancer patients to be 29.4%. These rates were also higher (34%) when compared to results obtained from a study carried out in Ghana, by Apprey et al., 2014, to assess the rate of malnutrition in a study population suffering from cancer. These high rates of malnutrition among the study population could be because of cancer itself and the aggressive treatments employed against it, such as chemotherapy, radiation therapy, and surgical interventions, which often unleash multiple side effects that directly impact nutritional intake (Patel, et al., 2018). Moreover, the metabolic demands of cancer, coupled with potential malabsorption issues arising from the disease process or treatment-related gastrointestinal disturbances, can further compromise nutritional status (Rock et al., 2020). Beyond the physiological challenges, cancer patients frequently experience unintended weight loss, partly attributable to increased energy expenditure and partly to the body's response to the tumor burden. Psychological factors, including anxiety, depression, and the emotional toll of confronting a life-threatening illness, can also influence dietary behaviours, potentially leading to decreased food intake and poor nutritional choices (Muscaritoli,2017). Furthermore, financial constraints may limit access to nutritious foods and specialized dietary support, increasing the risk of malnutrition. Additionally, the presence of tumors can directly interfere with nutrient utilization and metabolism, exacerbating the challenge of maintaining adequate nutrition (Bozzetti et al., 2017). Addressing malnutrition in the context of cancer care necessitates a broad approach. This approach should encompass nutritional counselling tailored to individual patient needs, dietary modifications to accommodate taste changes and gastrointestinal issues, and the provision of appetite stimulants or antiemetics to alleviate treatment-related side effects. Furthermore, supportive care interventions, including psychosocial support and financial assistance programs, play a crucial role in addressing the complex interplay of factors contributing to malnutrition in cancer patients. By prioritizing comprehensive nutritional support as an integral component of cancer treatment, healthcare providers can help optimize patient outcomes, and enhance quality of life. The majority of the participants in this study were aged 36 years and above and they represented 89% (n = 343) of the study population. The mean age was 44 years ± 2.5SD. These results were similar to those obtained in a cancer study by Fitzmaurice et al.,2017, where they also found that, with age, one's probability of being diagnosed with cancer increased. However, they also found that this was the reverse with oesophageal cancer, whereby more younger people were diagnosed with cancer as opposed to the elderly ones. Bray et al., 2018 also found that cancer was common among those aged 55 years and above and the incidence rates increased with increasing age. Longer exposure to environmental factors and weaker immune systems could have been the contributing factor to having more participants aged 36 years and above having cancer compared to their younger counterparts (Limin et al., 2018). This could be a result of older individuals having had more time exposed to environmental factors such as carcinogens, toxins, and infectious agents that can contribute to cancer development. Prolonged exposure to these factors over many years may increase the likelihood of cancer initiation. Besides, the immune system plays a crucial role in detecting and eliminating abnormal cells, including those that could become cancerous. As people age, their immune system may weaken, making it less effective at recognizing and destroying cancer cells. More than half the respondents were female (n = 254) and represented 66% of the study population. Similarly, Arends et al., 2017 found that men generally have lower prevalence and incidence rates of cancer in comparison to their female counterparts, due to the sex differences that eventually affect cancers of all types. However, contrary to the findings in this study, when Bray et al did a study in 2018, they found that the rate of men who had bladder cancer was up to four times more than that of the number of women with the same, whereas Siegel et al., 2017 found that gender differences do not have an impact on cancer prevalence as this is highly dependent on the type of cancer. The high cancer rates among women compared to men could be attributed to the health-seeking behaviours of women compared to their male counterparts. Women tend to seek healthcare more proactively than men (Calder et al., 2018). They are often more likely to schedule regular check-ups and screenings, which can lead to earlier detection of health issues. Other than that, women may tend to be more open about their health concerns and more likely to discuss symptoms with friends, family, or healthcare providers. This openness can lead to earlier recognition and diagnosis of health issues. Those with lower levels of education- primary education and no formal education- were found to be more (n = 164; 43%), compared to those with higher levels of education- secondary and tertiary education- among the study respondents (n = 220; 57%). This could be a result of lower levels of education often being associated with lower health literacy, which may result in a lack of awareness about cancer risk factors, symptoms, and preventive measures. Limited health literacy can affect individuals' ability to understand and follow medical advice. Given that educational attainment is associated with higher rates of certain behavioral risk factors for cancer, such as smoking, poor diet, and lack of physical activity, individuals with higher education levels may be more informed about healthy lifestyle choices and have better access to resources that support those choices (Arends et al., 2017). The monthly household income category with the highest number of respondents was 1000–15000 shillings (n = 196; 51%), while > 45000 shillings was the least household income category received by the study participants as represented by 9% (n = 33). The mean household income was 17000 ± 3000 shillings. Contrary to the findings in this study, Fitzmaurice et al.,2017 found that those with higher income rates had a higher prevalence and incidence rate of cancer compared to those who had lower income in Japan. This could be because economic instability among those with lower income is often associated with a lack of health insurance or limited access to healthcare services which can result in delayed diagnosis, limited preventive care, and reduced access to cancer screenings. On the other hand, those with higher income tend to adopt healthier lifestyles, with access to nutritious food, fitness resources, and education about the risks of unhealthy behaviors, in addition to living in cleaner environments with reduced exposure to carcinogens (Siegel et al., 2017). Most of the study participants had household sizes of 4–6 as represented by 54% (n = 209) of the respondents, while the least represented household size was > 9 (n = 2; 1%). Household members provide crucial social support to patients. Family members or housemates may offer emotional support, assistance with daily tasks, and companionship during treatment. It provides an opportunity for more individuals to be available to take on caregiving responsibilities (Fuchs et al., 2022). This can be especially beneficial for cancer patients who may require assistance with activities of daily living, transportation to medical appointments, and emotional support. The lower representation of family sizes more than nine could be attributed to the fact that the financial impact of cancer treatment can be significant. The majority of the study respondents 55% (n = 212) were self-employed, with the least, 18% (n = 69) being unemployed. Most of the respondents could have been self-employed as this often provides greater flexibility in work hours and schedules. This flexibility can be crucial for cancer patients who may need to accommodate medical appointments, treatment sessions, and periods of rest or recovery. Being self-employed allows individuals to have more control over their work environment. This can be particularly important for cancer patients who may have specific needs related to their health, such as the ability to work from home or create a workspace that suits their comfort and well-being. Additionally, self-employment offers the opportunity to tailor work arrangements to fit individual needs. Cancer patients may find it easier to adapt their workload, take breaks as needed, and manage their workload in a way that supports their health. The fraction of respondents who are not working (18%) could be because cancer and its treatments can result in physical health challenges such as fatigue, pain, and side effects that make it difficult for individuals to maintain regular work schedules and perform job duties. However, due to their unemployment status, they are likely to be faced with challenges of cancer management such as lack of access to treatment and drugs due to limited finances. When logistics regression analysis was done, those aged 36–41 years were 6.73 more likely to be malnourished than those aged less than 36 years. These findings were similar to those of a cross-sectional study carried out by MacIntosh et al.,2019, which depicted an increased likelihood of cancer patients being malnourished as they aged. Older adults might be at higher risk of malnutrition due to age-related changes in metabolism and muscle mass (Caillet et al., 2017). Different age groups may experience these side effects differently due to their unique physiological and lifestyle factors. Regarding sex, females had a 2.64 greater likelihood of being malnourished in comparison to their male counterparts. This was in line with a study carried out in Taiwan by Mathew et al. 2016 which found a positive correlation between gender and nutrition status. Given that the majority of my study population was female (66%), this could have translated to the aspect that more females had early interventions made about any nutrition-related side effects that they were facing, hence nutrient optimization. Level of education was associated with nutrition status. These results were similar to those of a study in India which found the prevalence of underweight, stunting, and wasting to be 38%, 41%, and 22%, respectively, with the rate of malnutrition being significantly higher among those with lower levels of education (Pokhrel et al.,2016 ). These two studies show that a lack of education could lead to incorrect dietary choices hence increasing the likelihood of one being malnourished. However, in a study by Hermans et al., 2023, no association was found between the level of education and the nutrition status of cancer patients Education plays a role in health literacy and understanding the importance of proper nutrition. Respondents with higher education levels may be more proactive in seeking and following nutritional advice, potentially leading to better nutrition status (Wardle et al., 2013). Individuals with a secondary education level, as noted by Wardle et al.,2013, tend to possess a moderate grasp of health and nutrition, making them more likely to access and comprehend information regarding the management of nutrition-related side effects in the context of cancer treatment. Nevertheless, this group can still benefit from targeted education and support to optimize their dietary choices during this challenging period. On the other hand, individuals with tertiary education may have greater access to resources and a more comprehensive understanding of nutrition, leading to proactive information-seeking and informed dietary decisions. However, this group may also face higher expectations regarding their ability to self-manage nutrition during cancer treatment. In contrast, respondents with only primary education, as per NHS (2018), may encounter limited access to health information and resources, emphasizing the need for additional support and education to navigate the challenges of nutrition-related side effects during cancer treatment. Finally, those without any formal education face significant barriers to understanding and managing such side effects, necessitating special attention to ensure they receive basic nutritional guidance and support throughout their cancer treatment journey. Household size had a significant relationship with the nutrition status of the study participants. This could be an indication of the potential role of family support. A different study in North-eastern Peninsular Malaysia found that household size (P = 0.024) was a significant risk factor for household food security and eventually nutrition status for cancer members of the household (Mohamadpour et al., 2018). This depicts the great impact that household size has on nutrition status, given that the two studies were carried out in a third-world country and a developed country respectively. Household size can have notable implications for the prevalence of nutrition-related side effects. Larger households may face resource constraints and require careful planning to meet the nutritional needs of both the cancer patient and the entire family. In contrast, smaller households may have more flexibility in accommodating the dietary needs of the cancer patient. Understanding the household size of respondents is crucial for tailoring interventions and support programs for cancer patients. Those who were self-employed were twice as likely to be malnourished than those who were either unemployed or employed by institutions. These results differed from those obtained by Hweidi et al., 2021 who found that unemployed respondents had the highest odds of being malnourished compared to the self-employed and employed participants. This suggests that the occupation of the respondents has a substantial impact on their nutrition status. Self-employed individuals may not have flexibility in managing their work schedules and taking time off for medical appointments or managing side effects due to the need to make higher profits for their businesses. They might face financial uncertainties, especially if their businesses are impacted by their cancer treatment or if they lack health insurance and paid sick leave (Kawakita et al., 2016). Self-employed cancer patients may also face a heightened risk of malnutrition compared to both employed and unemployed individuals due to a lack of access to employee benefits such as paid sick leave, health insurance, and employer-sponsored wellness programs, which can provide financial and practical support during cancer treatment. This can result in increased financial strain and limited resources to afford nutritious foods or access supportive services like nutritional counselling. Additionally, the flexible nature of self-employment may lead individuals to prioritize work commitments over personal health, potentially neglecting dietary needs or delaying seeking medical attention for cancer-related symptoms. Moreover, self-employed individuals may experience greater stress and anxiety about maintaining their livelihoods and business responsibilities while undergoing cancer treatment, which can further impact appetite and dietary habits. Overall, the combination of financial challenges, lack of benefits, and increased work-related stressors makes self-employed cancer patients particularly vulnerable to malnutrition compared to their employed and unemployed counterparts. Understanding the occupation of respondents is essential for tailoring interventions and support programs for cancer patients. For self-employed individuals, interventions could focus on financial support, such as access to health insurance or guidance on managing business responsibilities during treatment. For those who are employed, interventions may include educating employers about the specific needs of employees with cancer and providing resources for employees to balance work and treatment. In addition, supportive workplace policies, such as flexible hours or remote work options, can benefit employed individuals managing cancer and its side effects (Sharp et al., 2019). For respondents who are not employed, interventions might focus on providing access to financial assistance programs and support systems to ensure they can manage their nutrition and treatment effectively. CONCLUSION The study's findings indicated that the majority of respondents were female, with primary education and falling within low-income brackets. Occupation, education, age, gender, and household size were identified as the factors influencing nutritional status. Abbreviations AOR Adjusted Odds Ratio COR Crude Odds Ratio SD Standard Deviation HICs High-income countries (HICs) SES Socio-economic Status LMICs low- and middle-income nations BMI Body Mass Index. Declarations Acknowledgment The authors would wish to thank the staff at Texas Cancer Center for their support, and t o the study participants for their input in making this study a success. Funding None. Availability of data and materials Data supporting the findings of this study are found within the document. Author's contribution EO conceived, designed, collected data, analyzed data, and wrote the manuscript. AO and WK helped with designing the study, as well as reading and approving the final manuscript. Competing interest The authors declare that they have no competing interests. Consent for publication Not applicable. Ethics approval and consent to participate Ethical approval was obtained from the Mount Kenya Institutional, Scientific, and Ethical Review Committee (MKU/ISERC/2685), with permissions secured from the Texas Cancer Center and NACOSTI (Reference number: 247263). Informed consent was obtained from participants after explaining the objectives of the study, and confidentiality was strictly maintained by the use of codes to de-classify personal identification information. Oral consent was used due to the low literacy rate in the population used in this study. 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Smokeless tobacco use and the risk of head and neck cancer: pooled analysis of US studies in the INHANCE consortium. American Journal of Epidemiology, 184(10), 703–716 Limin D, Demark-Wahnefried, W.; Peterson, B.L.; Winer, E.P.; Marks, L.; Aziz, N.; Marcom, P.K.; Blackwell, K.; Rimer, B.K. Changes in Weight, Body Composition, and Factors Influencing Energy Balance Among Premenopausal Breast Cancer Patients Receiving Adjuvant Chemotherapy. J. Clin. Oncol. 2018, 19 , 2381–2389 Maciel, B.J., Pedrosa, F. &Coelho, C.P. (2012). Nutritional status and adequacy of enteral nutrition in pediatric cancer patients at a reference center in northeastern Brazil Nutricionhospitalaria,27 (4), 1099 − 105. doi:10.3305/nh.2012.27.4.5869 MacIntosh CG, Morley JE, Horowitz M, Chapman IM: Anorexia of Ageing. Nutrition. 2019, 16: 983–995. 10.1016/S0899-9007(00)00405-6 Mathew AC, Das D, Sampath S, Vijayakumar M, Ramakrishnan N, Ravishankar SL. Prevalence and correlates of malnutrition among elderly in an urban area in Coimbatore. Indian J Public Health. 2016; 60:112–117 Mohamadpour M, Sharif ZM, Keysami MA. Food insecurity, health and nutritional status among sample of palm-plantation households in Malaysia. J Health Popul Nutr. 2018; 30:291–302 Muscaritoli M, Lucia S, Farcomeni A, Lorusso V, Saracino V, Barone C, Plastino F, Gori S, Magarotto R, Carteni G, Chiurazzi B, Pavese I, Marchetti L, Zagonel V, Bergo E, Tonini G, Imperatori M, Iacono C, Maiorana L, Pinto C, Rubino D, Cavanna L, Di Cicilia R, Gamucci T, Quadrini S, Palazzo S, Minardi S, Merlano M, Colucci G, Marchetti P; PreMiO Study Group.Oncotarget. 2017 Aug 10;8(45):79884–79896. doi: 10.18632/oncotarget.20168. eCollection 2017 Oct 3.PMID: 29108370 NHS. (2018). Eating well with a learning disability: Easy Read. Retrieved from https://www.england.nhs.uk/wp-content/uploads/2018/07/eating-well-with-a-learning-disability Patel Ariza. Role of nutritional factors in pathogenesis of cancer. Food Quality Safe. 2018;2(1):27–36 Pokhrel, K, Nanishi, K, Poudel (2016) Undernutrition among infants and children in Nepal: maternal health services and their roles to prevent it. Matern Child Health J 20, 2037–2049 Rock CL et al. American cancer society guideline for diet and physical activity for cancer prevention. CA: Cancer J Clin. 2020;70(4):245–271 Sharp, L., Johansson, H., Leinonen, M. K., Rönmark, E., Yli-Tuomi, T., Boman, C., ... & Heinrich, J. (2019). Health-related quality of life in young adults with asthma, allergic rhinitis and comorbid asthma and allergic rhinitis. Clinical and Experimental Allergy, 49(3), 355–363 Siegel RL, Miller KD, Jemal A. Cancer Statistics, 2017. CA Cancer J Clin. 2017;67:7–30. https://doi.org/10.3322/caac.21387 Wardle, J., Haase, A. M., Steptoe, A., Nillapun, M., Jonwutiwes, K., Bellisie, F., ... & Wagner, C. (2013). Gender differences in food choice: the contribution of health beliefs and dieting. Annals of Behavioral Medicine, 26(1), 3–15 WHO International Agency for Research on Cancer 2020 Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4748045","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":330347780,"identity":"2cbeded1-103a-4351-a4a3-da27bba2bfc3","order_by":0,"name":"ELIZABETH ACHIENG ODUOR","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABBklEQVRIie3RMWvCQBTA8ZODm67NGhDiV3jl4MSlfpUcgbgkdOiSrYOQSXCN3+K6dG3kIFPANdKCg6CrJYuFop5xcji1m8P95/vxePcQstnuMHBmK7wFJeSi5K6fEM/JxLBG6NmD3EAwCjFK1KusIg6b8pG538tcIRSw3kVSqrcjEZPUE7Kz8jWZik8zKXArVfGRKEr4QLYj0CQXH0bSSvFPuo/1Lt0hpWF8A8HkNGVBNXGL+P3rKiGa7PSPzUdc/cF+ANXLpiHSSGgzRciq5AH1iQ9VM2V6nUyyMGQ0J09ZQyBgprv0x7N1/avJ2A2K9sOOdJwsYvU2MZ/SFPzvuc1ms9nOOwDsaXaOOumGugAAAABJRU5ErkJggg==","orcid":"","institution":"Mount Kenya University","correspondingAuthor":true,"prefix":"","firstName":"ELIZABETH","middleName":"ACHIENG","lastName":"ODUOR","suffix":""},{"id":330347781,"identity":"8e28f88a-6580-4005-989e-73eb12916fa5","order_by":1,"name":"ALFRED OWINO ODONGO","email":"","orcid":"","institution":"Mount Kenya University","correspondingAuthor":false,"prefix":"","firstName":"ALFRED","middleName":"OWINO","lastName":"ODONGO","suffix":""},{"id":330347782,"identity":"8b9689e5-5adf-4bae-9bf5-d7147e539ab7","order_by":2,"name":"WILLY KIBOI","email":"","orcid":"","institution":"Mount Kenya University","correspondingAuthor":false,"prefix":"","firstName":"WILLY","middleName":"","lastName":"KIBOI","suffix":""}],"badges":[],"createdAt":"2024-07-16 08:00:00","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4748045/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4748045/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":62730657,"identity":"53be4f89-620b-49fe-8419-312b8decd514","added_by":"auto","created_at":"2024-08-18 23:18:49","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":178703,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003e\u003cstrong\u003eNutrition status categories of the respondents\u003c/strong\u003e\u003c/em\u003e\u003c/p\u003e","description":"","filename":"Picture1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4748045/v1/e8af17c263a02ee3053d6827.jpg"},{"id":62730013,"identity":"290e2be4-3861-46a2-9470-523a5ca631df","added_by":"auto","created_at":"2024-08-18 23:10:49","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":105813,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003e\u003cstrong\u003eThe nutrition status of the respondents\u003c/strong\u003e\u003c/em\u003e\u003c/p\u003e","description":"","filename":"Picture2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4748045/v1/df1b15a550bd282458173f31.jpg"},{"id":79815883,"identity":"4b88bf09-1aea-4c6d-bc36-bbcc179b1a5c","added_by":"auto","created_at":"2025-04-03 07:46:50","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1297575,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4748045/v1/e876a398-22fc-4a6d-8519-294df1e8149b.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"\u003cp\u003eDemographic and Socio-economic Correlates of Nutrition Status of Adult Cancer Patients: A Case of Texas Cancer Center, Kenya\u003c/p\u003e","fulltext":[{"header":"BACKGROUND OF THE STUDY","content":"\u003cp\u003eA lower socioeconomic level (SES) is linked to a higher cancer incidence and worse survival rates. This is due to the discrepancies in survival rates between social groups, including variations in tumor biology, patient comorbidity, disease stage at diagnosis, accessibility to medication, and treatment methods (Arends et al., 2017). In 2020 alone, there were 18.1\u0026nbsp;million new cancer cases worldwide, with 9.3\u0026nbsp;million occurring in men and 8.8\u0026nbsp;million in women. Africa reported 1.1\u0026nbsp;million new cancer cases and 711,429 cancer-related deaths, with a prevalence of 2.2\u0026nbsp;million cases (WHO, 2020). In Kenya, the cancer incidence rate stands at 47,887 with 32,987 cancer-related deaths (WHO, 2020). Malnutrition risk may not even be properly handled even when it is acknowledged. Only a fraction of the cancer patients who are at risk of malnutrition got nutritional intervention, according to hospital studies in Europe (Muscaritoli et al., 2017). One frequently mentioned potential explanation for the connection between SES and cancer outcomes is a difference in disease stage. While numerous studies from high-income countries (HICs) have explored the relationship between SES, cancer stage at diagnosis, and survival, there is little research on these topics in low- and middle-income nations (LMICs). This study fills a major gap in knowledge due to the LMICs' exploding cancer burden, and the socioeconomic, and demographic contrasts between their people and those in HICs. The overall aim of this study was to assess the demographic and socio-economic factors of adult cancer patients at the Texas Cancer Center, focusing on the level of education, gender, household size, monthly household income, age, and employment status, aiming to fill existing gaps in cancer-related information and establish relationships between these variables and nutrition status. This paper presents a description of the demographic and socioeconomic status of the populations attending the Texas Cancer Center and their associations with nutritional outcomes in adult cancer patients. This study also provides updated baseline data to inform the design for future research, and to develop strategies that aim to address malnutrition among cancer patients.\u003c/p\u003e"},{"header":"METHODS","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy Site\u003c/h2\u003e \u003cp\u003eThis investigation was conducted at the Texas Cancer Center, as it offers comprehensive services, including laboratory procedures, cancer screening, treatment, and palliative care, delivered by a multidisciplinary team.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eResearch Design\u003c/h2\u003e \u003cp\u003eAn analytical cross-sectional study design was employed in this study.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eTarget Population\u003c/h2\u003e \u003cp\u003eThe study targeted cancer patients, with the accessible population comprising adults (aged 18 and above) with stage I, II, III, and IV cancer undergoing treatment at the Texas Cancer Center. The sample aimed to evaluate the nutrition status of patients undergoing cancer treatment alongside other relevant variables.\u003c/p\u003e \u003cp\u003eInclusion Criteria: The study included all adult outpatients and inpatients diagnosed with cancer at the Texas Cancer Center who provided consent for participation.\u003c/p\u003e \u003cp\u003eExclusion Criteria: Excluded from the study were critically ill patients and individuals meeting the inclusion criteria but unable to participate due to other personal commitments.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eSample Size\u003c/h2\u003e \u003cp\u003eThe study sample size was determined using Cochran's formula for an infinite population. The sample size was set at 384 participants. Participants were selected from a sampling frame using a systematic random sampling method based on a predetermined interval of 2, whereby the first participant was randomly selected.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eData Collection Instruments\u003c/h2\u003e \u003cp\u003eData collection involved the use of semi-structured questionnaires to collect data on age, household size, monthly household income, sex, education status, and occupation status. Weight and height measurements were obtained using a height board and weighing scale.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eData Collection Procedures\u003c/h2\u003e \u003cp\u003e Before administering the questionnaire, participants provided informed written consent. The demographic and socio-economic characteristics (gender, age, household size, monthly household income, highest level of education, and employment status) data were collected through the use of a structured questionnaire.\u003c/p\u003e \u003cp\u003eNutrition status was determined using Body Mass Index (BMI), calculated from weight and height measurements. Nutrition status categories were normal nutrition (BMI of 18.5-24.9kg/m\u003csup\u003e2\u003c/sup\u003e), Underweight (BMI of less than 18.5kg/m\u003csup\u003e2\u003c/sup\u003e), Over-weight (BMI of 25-30kg/m), and obese (BMI of over 35kg/m2).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eValidity and Reliability of Data Collection Tools\u003c/h2\u003e \u003cp\u003eData collection tools underwent pre-testing and validation by a panel of experts, while reliability was assessed through the test-retest method.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eData Analysis and Presentation\u003c/h2\u003e \u003cp\u003eCollected data was reviewed to assess if the questionnaires were well filled. The assessed socio-economic and demographic factors (gender, age, household size, monthly household income, level of education, and occupation status) were then grouped, as the nutrition status was categorized as normal nutrition (BMI of 18.5-24.9kg/m2), underweight (BMI of less than 18.5kg/m2), over-weights (BMI of 25-30kg/m2) or obese (BMI of over 35kg/m2). Data analysis was done using STATA version 17. Inferential statistics, such as Pearson's chi-square and logistic regression were utilized to explore associations between nutrition status and respondents' demographic and socioeconomic characteristics. A p-value of \u0026lt;\u0026thinsp;0.05 indicated existing statistical significance, within a confidence interval of 95%. When bivariate logistics regression was done, any values with a p-value of \u0026lt;\u0026thinsp;0.05 (crude odds ratio- COR) were fitted in a multivariate regression analysis to establish the predictors of nutrition status and socio-economic and demographic factors of the cancer patients (Adjusted odds ratio- AOR). Findings were presented through tables and graphs.\u003c/p\u003e \u003c/div\u003e"},{"header":"RESULTS","content":"\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eDemographic and socio-economic characteristics of the study population\u003c/h2\u003e \u003cp\u003eThe majority 89% (n\u0026thinsp;=\u0026thinsp;343) of the participants were aged 36 years with the mean age being 44 years \u003csup\u003e\u0026plusmn;\u003c/sup\u003e 2.5SD. More than half 66% of the respondents were female (n\u0026thinsp;=\u0026thinsp;254). Those with secondary education had the highest frequency 41% (n\u0026thinsp;=\u0026thinsp;158). The monthly household income category with the highest number of respondents was less than 15000 Kenya shillings (n\u0026thinsp;=\u0026thinsp;196; 51%), while\u0026thinsp;\u0026gt;\u0026thinsp;45000 shillings was the least household income category received by the study participants as represented by 9% (n\u0026thinsp;=\u0026thinsp;33). The mean household income was 17000\u0026thinsp;\u0026plusmn;\u0026thinsp;3000 shillings. Most of the study participants had household sizes of 4\u0026ndash;6 as represented by 54% (n\u0026thinsp;=\u0026thinsp;209) of the respondents, while the least represented household size was \u0026gt;\u0026thinsp;9 (n\u0026thinsp;=\u0026thinsp;2; 1%) persons. The majority 55% (n\u0026thinsp;=\u0026thinsp;212) of the study respondents were self-employed (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSocioeconomic and demographic characteristics of the respondents\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCharacteristic\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFrequency (N\u0026thinsp;=\u0026thinsp;384)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePercentage (%)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSex\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e254\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e66\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e130\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e34\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAge category (years)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e18\u0026ndash;35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e343\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e89 \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMean age\u0026thinsp;\u0026plusmn;\u0026thinsp;SD (years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e44\u0026thinsp;\u0026plusmn;\u0026thinsp;2.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eEducation status\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo formal education\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrimary\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e131\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e34 \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSecondary\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e158\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e41\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTertiary\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHousehold monthly income (KSh)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026le;15000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e196\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e51 \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e15001\u0026ndash;30000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e127\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e33\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e30001\u0026ndash;45000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt;45000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD (Kshs)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e17000\u0026thinsp;\u0026plusmn;\u0026thinsp;3000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHousehold size\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u0026ndash;3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e128\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e33\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4\u0026ndash;6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e209\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e54 \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e7\u0026ndash;9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt;9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eOccupation status\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEmployed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e103\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e27\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSelf-employed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e212\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e55 \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnemployed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"3\"\u003e\u003csup\u003ea\u003c/sup\u003e Majority of the respondents\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eThe nutrition status of the respondents\u003c/h2\u003e \u003cp\u003eRespondents with normal nutrition (BMI of 18.5-24.9kg/m2) were 41% (157) while those with the least representation 14% (n\u0026thinsp;=\u0026thinsp;54) obese (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe nutritional status of the respondents was further classified as being normal or malnourished as shown below (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The study observed that a significant portion, comprising 59% (n\u0026thinsp;=\u0026thinsp;227), of its participants suffered from malnutrition. Those who were malnourished had a BMI of either \u0026lt;\u0026thinsp;18.5kg/m2 or \u0026gt;\u0026thinsp;24.9kg/m2.\u003c/p\u003e \u003cp\u003eThe average BMI was 25.0kg/m2\u0026thinsp;\u0026plusmn;\u0026thinsp;4.25SD (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eRelationship between nutrition status and socioeconomic and demographic characteristics of the respondents\u003c/h2\u003e \u003cp\u003eOn multivariate analyses, age (p\u0026thinsp;=\u0026thinsp;0.003), occupation status (p\u0026thinsp;=\u0026thinsp;0.002), sex (p\u0026thinsp;=\u0026thinsp;0.006), and household size (p\u0026thinsp;=\u0026thinsp;0.039) had a significant relationship with nutrition status. In terms of education, primary (p\u0026thinsp;=\u0026thinsp;0.028), secondary (p\u0026thinsp;=\u0026thinsp;0.010) and tertiary (p\u0026thinsp;=\u0026thinsp;0.006) levels of education had an association with nutrition status. The study observed that those aged 36\u0026ndash;41 years were 6.73 more likely to be malnourished compared to those younger than them. Self-employed respondents (AOR\u0026thinsp;=\u0026thinsp;2.57; 95% CI\u0026thinsp;=\u0026thinsp;1.42,4.68) had higher odds of being malnourished compared to their employed and unemployed counterparts. Regarding sex, females had a 2.64 greater likelihood of being malnourished compared to males. Respondents who had a household size of 4\u0026ndash;6 were 1.79 more likely to be malnourished than those who had household sizes of less than 4 (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eRelationship between nutrition status and socioeconomic and demographic characteristics of the respondents\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"13\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c13\" colnum=\"13\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c5\" namest=\"c3\"\u003e \u003cp\u003eMalnutrition\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c10\" namest=\"c8\"\u003e \u003cp\u003eCOR (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c12\"\u003e \u003cp\u003eAOR (95% CI) \u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c13\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c5\" namest=\"c3\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c10\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAge category\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c6\" namest=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c9\" namest=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c13\" namest=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e18\u0026ndash;23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c5\" namest=\"c3\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c10\" namest=\"c8\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e24\u0026ndash;29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c5\" namest=\"c3\"\u003e \u003cp\u003e24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c10\" namest=\"c8\"\u003e \u003cp\u003e1.38(0.31,6.09)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.659\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e1.08(0.19,6.23)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e0.932\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e30\u0026ndash;35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c5\" namest=\"c3\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c10\" namest=\"c8\"\u003e \u003cp\u003e0.84(0.39,1.81)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.670\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.38(0.03,3.99)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e0.417\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e36\u0026ndash;41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c5\" namest=\"c3\"\u003e \u003cp\u003e36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c10\" namest=\"c8\"\u003e \u003cp\u003e0.22(0.07,0.68)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.008\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e6.73(1.88,24.11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e\u003cb\u003e0.003\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e42\u0026ndash;47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c5\" namest=\"c3\"\u003e \u003cp\u003e17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c10\" namest=\"c8\"\u003e \u003cp\u003e1.55(0.49,4.82)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.448\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.58(0.20,1.71)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e0.329\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c5\" namest=\"c3\"\u003e \u003cp\u003e131\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e120\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c10\" namest=\"c8\"\u003e \u003cp\u003e0.09(0.08,0.98)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.049\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.89(0.17,4.69)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e0.897\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eOccupational status\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c5\" namest=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c10\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eUnemployed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c5\" namest=\"c3\"\u003e \u003cp\u003e37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c10\" namest=\"c8\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eSelf -Employed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c5\" namest=\"c3\"\u003e \u003cp\u003e149\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c10\" namest=\"c8\"\u003e \u003cp\u003e2.14(1.36,3.36)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e2.57(1.42,4.68)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e\u003cb\u003e0.002\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eEmployed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c5\" namest=\"c3\"\u003e \u003cp\u003e41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c10\" namest=\"c8\"\u003e \u003cp\u003e0.00(0.00,0.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e1.88(0.80,4.44)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e0.149\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eLevel of education\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c5\" namest=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c10\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eNo education\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c5\" namest=\"c3\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c10\" namest=\"c8\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003ePrimary\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c5\" namest=\"c3\"\u003e \u003cp\u003e105\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c10\" namest=\"c8\"\u003e \u003cp\u003e1.76(0.51,6.10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.373\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e10.81(1.29,90.66)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e\u003cb\u003e0.028\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eSecondary\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c5\" namest=\"c3\"\u003e \u003cp\u003e73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c10\" namest=\"c8\"\u003e \u003cp\u003e0.49(0.19,1.29)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.151\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e11.12(1.78,69.68)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e\u003cb\u003e0.010\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eTertiary\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c5\" namest=\"c3\"\u003e \u003cp\u003e33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c10\" namest=\"c8\"\u003e \u003cp\u003e0.77(0.31,1.96)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.596\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e12.29(2.07,72.92)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e\u003cb\u003e0.006\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSex\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c8\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c13\" namest=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c5\" namest=\"c3\"\u003e \u003cp\u003e106\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c10\" namest=\"c8\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c5\" namest=\"c3\"\u003e \u003cp\u003e121\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c10\" namest=\"c8\"\u003e \u003cp\u003e0.47(0.24,0.91)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.025\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e2.64(1.33,5.26)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e\u003cb\u003e0.006\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMonthly income\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c5\" namest=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c10\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e1000\u0026ndash;15000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c5\" namest=\"c3\"\u003e \u003cp\u003e54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e108\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c10\" namest=\"c8\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e15001\u0026ndash;30000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c5\" namest=\"c3\"\u003e \u003cp\u003e50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c10\" namest=\"c8\"\u003e \u003cp\u003e0.42(0.15,1.21)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.010\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e1.43(0.27,7.49)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e0.672\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e30001\u0026ndash;45000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c5\" namest=\"c3\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c10\" namest=\"c8\"\u003e \u003cp\u003e1.19(0.20,7.08)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.849\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e3.07(0.58,16.28)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e0.189\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;45000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c5\" namest=\"c3\"\u003e \u003cp\u003e108\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c10\" namest=\"c8\"\u003e \u003cp\u003e0.29(0.11,0.81)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.018\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e5.85(0.65,52.62)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e0.115\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHousehold size\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c5\" namest=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c10\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e1\u0026ndash;3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c5\" namest=\"c3\"\u003e \u003cp\u003e36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c10\" namest=\"c8\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e4\u0026ndash;6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c5\" namest=\"c3\"\u003e \u003cp\u003e112\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c10\" namest=\"c8\"\u003e \u003cp\u003e1.71(1.11,2.63)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.014\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e1.79(1.03,3.12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e\u003cb\u003e0.039\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e7\u0026ndash;9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c5\" namest=\"c3\"\u003e \u003cp\u003e77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c10\" namest=\"c8\"\u003e \u003cp\u003e5.06(4.82,5.81)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.631\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e4.31(0.35,4.33)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e0.693\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c5\" namest=\"c3\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c10\" namest=\"c8\"\u003e \u003cp\u003e5.80(3.75,7.76)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.675\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.78(0.22,2.34)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e0.738\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eAOR Adjusted Odds Ratio, COR Crude Odds Ratio, CI Confidence Interval, \u003csup\u003eb\u003c/sup\u003e Adjusted for demographic and socio-economic characteristics (highest level of education, sex, age, occupation status, household size, and monthly household income).\u003c/p\u003e \u003c/div\u003e"},{"header":"DISCUSSION","content":"\u003cp\u003eMost of the study participants 59% (n\u0026thinsp;=\u0026thinsp;227) were malnourished. The mean BMI was 25.0kg/m2\u0026thinsp;\u0026plusmn;\u0026thinsp;4.25SD. The malnutrition rate in this study was close (58.4%) to those obtained by Siegel et al., 2017 in a cross-sectional study carried out in Ethiopia to establish the prevalence and risk factors of malnutrition among adult cancer patients receiving chemotherapy treatment in a cancer center. However, these results were higher when compared to a study done by Maciel et al., 2012 where he found malnutrition rates among cancer patients to be 29.4%. These rates were also higher (34%) when compared to results obtained from a study carried out in Ghana, by Apprey et al., 2014, to assess the rate of malnutrition in a study population suffering from cancer.\u003c/p\u003e \u003cp\u003eThese high rates of malnutrition among the study population could be because of cancer itself and the aggressive treatments employed against it, such as chemotherapy, radiation therapy, and surgical interventions, which often unleash multiple side effects that directly impact nutritional intake (Patel, et al., 2018). Moreover, the metabolic demands of cancer, coupled with potential malabsorption issues arising from the disease process or treatment-related gastrointestinal disturbances, can further compromise nutritional status (Rock et al., 2020).\u003c/p\u003e \u003cp\u003eBeyond the physiological challenges, cancer patients frequently experience unintended weight loss, partly attributable to increased energy expenditure and partly to the body's response to the tumor burden. Psychological factors, including anxiety, depression, and the emotional toll of confronting a life-threatening illness, can also influence dietary behaviours, potentially leading to decreased food intake and poor nutritional choices (Muscaritoli,2017). Furthermore, financial constraints may limit access to nutritious foods and specialized dietary support, increasing the risk of malnutrition. Additionally, the presence of tumors can directly interfere with nutrient utilization and metabolism, exacerbating the challenge of maintaining adequate nutrition (Bozzetti et al., 2017).\u003c/p\u003e \u003cp\u003eAddressing malnutrition in the context of cancer care necessitates a broad approach. This approach should encompass nutritional counselling tailored to individual patient needs, dietary modifications to accommodate taste changes and gastrointestinal issues, and the provision of appetite stimulants or antiemetics to alleviate treatment-related side effects. Furthermore, supportive care interventions, including psychosocial support and financial assistance programs, play a crucial role in addressing the complex interplay of factors contributing to malnutrition in cancer patients. By prioritizing comprehensive nutritional support as an integral component of cancer treatment, healthcare providers can help optimize patient outcomes, and enhance quality of life.\u003c/p\u003e \u003cp\u003eThe majority of the participants in this study were aged 36 years and above and they represented 89% (n\u0026thinsp;=\u0026thinsp;343) of the study population. The mean age was 44 years\u0026thinsp;\u0026plusmn;\u0026thinsp;2.5SD. These results were similar to those obtained in a cancer study by Fitzmaurice et al.,2017, where they also found that, with age, one's probability of being diagnosed with cancer increased. However, they also found that this was the reverse with oesophageal cancer, whereby more younger people were diagnosed with cancer as opposed to the elderly ones. Bray et al., 2018 also found that cancer was common among those aged 55 years and above and the incidence rates increased with increasing age. Longer exposure to environmental factors and weaker immune systems could have been the contributing factor to having more participants aged 36 years and above having cancer compared to their younger counterparts (Limin et al., 2018). This could be a result of older individuals having had more time exposed to environmental factors such as carcinogens, toxins, and infectious agents that can contribute to cancer development. Prolonged exposure to these factors over many years may increase the likelihood of cancer initiation. Besides, the immune system plays a crucial role in detecting and eliminating abnormal cells, including those that could become cancerous. As people age, their immune system may weaken, making it less effective at recognizing and destroying cancer cells.\u003c/p\u003e \u003cp\u003eMore than half the respondents were female (n\u0026thinsp;=\u0026thinsp;254) and represented 66% of the study population. Similarly, Arends et al., 2017 found that men generally have lower prevalence and incidence rates of cancer in comparison to their female counterparts, due to the sex differences that eventually affect cancers of all types. However, contrary to the findings in this study, when Bray et al did a study in 2018, they found that the rate of men who had bladder cancer was up to four times more than that of the number of women with the same, whereas Siegel et al., 2017 found that gender differences do not have an impact on cancer prevalence as this is highly dependent on the type of cancer. The high cancer rates among women compared to men could be attributed to the health-seeking behaviours of women compared to their male counterparts. Women tend to seek healthcare more proactively than men (Calder et al., 2018). They are often more likely to schedule regular check-ups and screenings, which can lead to earlier detection of health issues. Other than that, women may tend to be more open about their health concerns and more likely to discuss symptoms with friends, family, or healthcare providers. This openness can lead to earlier recognition and diagnosis of health issues.\u003c/p\u003e \u003cp\u003eThose with lower levels of education- primary education and no formal education- were found to be more (n\u0026thinsp;=\u0026thinsp;164; 43%), compared to those with higher levels of education- secondary and tertiary education- among the study respondents (n\u0026thinsp;=\u0026thinsp;220; 57%). This could be a result of lower levels of education often being associated with lower health literacy, which may result in a lack of awareness about cancer risk factors, symptoms, and preventive measures. Limited health literacy can affect individuals' ability to understand and follow medical advice. Given that educational attainment is associated with higher rates of certain behavioral risk factors for cancer, such as smoking, poor diet, and lack of physical activity, individuals with higher education levels may be more informed about healthy lifestyle choices and have better access to resources that support those choices (Arends et al., 2017).\u003c/p\u003e \u003cp\u003eThe monthly household income category with the highest number of respondents was 1000\u0026ndash;15000 shillings (n\u0026thinsp;=\u0026thinsp;196; 51%), while\u0026thinsp;\u0026gt;\u0026thinsp;45000 shillings was the least household income category received by the study participants as represented by 9% (n\u0026thinsp;=\u0026thinsp;33). The mean household income was 17000\u0026thinsp;\u0026plusmn;\u0026thinsp;3000 shillings. Contrary to the findings in this study, Fitzmaurice et al.,2017 found that those with higher income rates had a higher prevalence and incidence rate of cancer compared to those who had lower income in Japan. This could be because economic instability among those with lower income is often associated with a lack of health insurance or limited access to healthcare services which can result in delayed diagnosis, limited preventive care, and reduced access to cancer screenings. On the other hand, those with higher income tend to adopt healthier lifestyles, with access to nutritious food, fitness resources, and education about the risks of unhealthy behaviors, in addition to living in cleaner environments with reduced exposure to carcinogens (Siegel et al., 2017).\u003c/p\u003e \u003cp\u003eMost of the study participants had household sizes of 4\u0026ndash;6 as represented by 54% (n\u0026thinsp;=\u0026thinsp;209) of the respondents, while the least represented household size was \u0026gt;\u0026thinsp;9 (n\u0026thinsp;=\u0026thinsp;2; 1%). Household members provide crucial social support to patients. Family members or housemates may offer emotional support, assistance with daily tasks, and companionship during treatment. It provides an opportunity for more individuals to be available to take on caregiving responsibilities (Fuchs et al., 2022). This can be especially beneficial for cancer patients who may require assistance with activities of daily living, transportation to medical appointments, and emotional support. The lower representation of family sizes more than nine could be attributed to the fact that the financial impact of cancer treatment can be significant.\u003c/p\u003e \u003cp\u003eThe majority of the study respondents 55% (n\u0026thinsp;=\u0026thinsp;212) were self-employed, with the least, 18% (n\u0026thinsp;=\u0026thinsp;69) being unemployed. Most of the respondents could have been self-employed as this often provides greater flexibility in work hours and schedules. This flexibility can be crucial for cancer patients who may need to accommodate medical appointments, treatment sessions, and periods of rest or recovery. Being self-employed allows individuals to have more control over their work environment. This can be particularly important for cancer patients who may have specific needs related to their health, such as the ability to work from home or create a workspace that suits their comfort and well-being. Additionally, self-employment offers the opportunity to tailor work arrangements to fit individual needs. Cancer patients may find it easier to adapt their workload, take breaks as needed, and manage their workload in a way that supports their health. The fraction of respondents who are not working (18%) could be because cancer and its treatments can result in physical health challenges such as fatigue, pain, and side effects that make it difficult for individuals to maintain regular work schedules and perform job duties. However, due to their unemployment status, they are likely to be faced with challenges of cancer management such as lack of access to treatment and drugs due to limited finances.\u003c/p\u003e \u003cp\u003eWhen logistics regression analysis was done, those aged 36\u0026ndash;41 years were 6.73 more likely to be malnourished than those aged less than 36 years. These findings were similar to those of a cross-sectional study carried out by MacIntosh et al.,2019, which depicted an increased likelihood of cancer patients being malnourished as they aged. Older adults might be at higher risk of malnutrition due to age-related changes in metabolism and muscle mass (Caillet et al., 2017). Different age groups may experience these side effects differently due to their unique physiological and lifestyle factors.\u003c/p\u003e \u003cp\u003eRegarding sex, females had a 2.64 greater likelihood of being malnourished in comparison to their male counterparts. This was in line with a study carried out in Taiwan by Mathew et al. 2016 which found a positive correlation between gender and nutrition status. Given that the majority of my study population was female (66%), this could have translated to the aspect that more females had early interventions made about any nutrition-related side effects that they were facing, hence nutrient optimization.\u003c/p\u003e \u003cp\u003eLevel of education was associated with nutrition status. These results were similar to those of a study in India which found the prevalence of underweight, stunting, and wasting to be 38%, 41%, and 22%, respectively, with the rate of malnutrition being significantly higher among those with lower levels of education (Pokhrel et al.,2016\u003cb\u003e).\u003c/b\u003e These two studies show that a lack of education could lead to incorrect dietary choices hence increasing the likelihood of one being malnourished. However, in a study by Hermans et al., 2023, no association was found between the level of education and the nutrition status of cancer patients Education plays a role in health literacy and understanding the importance of proper nutrition. Respondents with higher education levels may be more proactive in seeking and following nutritional advice, potentially leading to better nutrition status (Wardle et al., 2013). Individuals with a secondary education level, as noted by Wardle et al.,2013, tend to possess a moderate grasp of health and nutrition, making them more likely to access and comprehend information regarding the management of nutrition-related side effects in the context of cancer treatment. Nevertheless, this group can still benefit from targeted education and support to optimize their dietary choices during this challenging period. On the other hand, individuals with tertiary education may have greater access to resources and a more comprehensive understanding of nutrition, leading to proactive information-seeking and informed dietary decisions. However, this group may also face higher expectations regarding their ability to self-manage nutrition during cancer treatment. In contrast, respondents with only primary education, as per NHS (2018), may encounter limited access to health information and resources, emphasizing the need for additional support and education to navigate the challenges of nutrition-related side effects during cancer treatment. Finally, those without any formal education face significant barriers to understanding and managing such side effects, necessitating special attention to ensure they receive basic nutritional guidance and support throughout their cancer treatment journey.\u003c/p\u003e \u003cp\u003eHousehold size had a significant relationship with the nutrition status of the study participants. This could be an indication of the potential role of family support. A different study in North-eastern Peninsular Malaysia found that household size (P\u0026thinsp;=\u0026thinsp;0.024) was a significant risk factor for household food security and eventually nutrition status for cancer members of the household (Mohamadpour et al., 2018). This depicts the great impact that household size has on nutrition status, given that the two studies were carried out in a third-world country and a developed country respectively. Household size can have notable implications for the prevalence of nutrition-related side effects. Larger households may face resource constraints and require careful planning to meet the nutritional needs of both the cancer patient and the entire family. In contrast, smaller households may have more flexibility in accommodating the dietary needs of the cancer patient. Understanding the household size of respondents is crucial for tailoring interventions and support programs for cancer patients.\u003c/p\u003e \u003cp\u003eThose who were self-employed were twice as likely to be malnourished than those who were either unemployed or employed by institutions. These results differed from those obtained by Hweidi et al., 2021 who found that unemployed respondents had the highest odds of being malnourished compared to the self-employed and employed participants. This suggests that the occupation of the respondents has a substantial impact on their nutrition status. Self-employed individuals may not have flexibility in managing their work schedules and taking time off for medical appointments or managing side effects due to the need to make higher profits for their businesses. They might face financial uncertainties, especially if their businesses are impacted by their cancer treatment or if they lack health insurance and paid sick leave (Kawakita et al., 2016). Self-employed cancer patients may also face a heightened risk of malnutrition compared to both employed and unemployed individuals due to a lack of access to employee benefits such as paid sick leave, health insurance, and employer-sponsored wellness programs, which can provide financial and practical support during cancer treatment. This can result in increased financial strain and limited resources to afford nutritious foods or access supportive services like nutritional counselling. Additionally, the flexible nature of self-employment may lead individuals to prioritize work commitments over personal health, potentially neglecting dietary needs or delaying seeking medical attention for cancer-related symptoms. Moreover, self-employed individuals may experience greater stress and anxiety about maintaining their livelihoods and business responsibilities while undergoing cancer treatment, which can further impact appetite and dietary habits. Overall, the combination of financial challenges, lack of benefits, and increased work-related stressors makes self-employed cancer patients particularly vulnerable to malnutrition compared to their employed and unemployed counterparts. Understanding the occupation of respondents is essential for tailoring interventions and support programs for cancer patients. For self-employed individuals, interventions could focus on financial support, such as access to health insurance or guidance on managing business responsibilities during treatment. For those who are employed, interventions may include educating employers about the specific needs of employees with cancer and providing resources for employees to balance work and treatment. In addition, supportive workplace policies, such as flexible hours or remote work options, can benefit employed individuals managing cancer and its side effects (Sharp et al., 2019). For respondents who are not employed, interventions might focus on providing access to financial assistance programs and support systems to ensure they can manage their nutrition and treatment effectively.\u003c/p\u003e"},{"header":"CONCLUSION","content":"\u003cp\u003eThe study's findings indicated that the majority of respondents were female, with primary education and falling within low-income brackets. Occupation, education, age, gender, and household size were identified as the factors influencing nutritional status.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eAOR\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eAdjusted Odds Ratio\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCOR\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eCrude Odds Ratio\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eSD\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eStandard Deviation\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eHICs\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eHigh-income countries (HICs)\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eSES\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eSocio-economic Status\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eLMICs\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003elow- and middle-income nations\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eBMI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eBody Mass Index.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgment\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors would wish to thank the staff at Texas Cancer Center for their support, and t o the study participants for their input in making this study a success.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNone.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eData supporting the findings of this study are found within the document.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor\u0026apos;s contribution\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eEO conceived, designed, collected data, analyzed data, and wrote the manuscript. AO and WK helped with designing the study, as well as reading and approving the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eEthical approval was obtained from the Mount Kenya Institutional, Scientific, and Ethical Review Committee (MKU/ISERC/2685), with permissions secured from the Texas Cancer Center and NACOSTI (Reference number: 247263). Informed consent was obtained from participants after explaining the objectives of the study, and confidentiality was strictly maintained by the use of codes to de-classify personal identification information. Oral consent was used due to the low literacy rate in the population used in this study. Participants retained the right to withdraw from the study, and research assistants were trained to uphold objectivity during the data collection process. Measures were put in place to ensure that collected data was properly and securely stored and only accessible to the investigators.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eApprey, C., Annan, R.A., Arthur, F.K.N., Boateng, S.K. \u0026amp;Animah, K.(2014). The assessment and prediction of malnutrition in children suffering from cancer in Ghana. \u003cem\u003eEuropean Journal of Experimental Biology, 4\u003c/em\u003e(4), 31\u0026ndash;37\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eArends J., Baracos V., Bertz H., Bozzetti F., Calder P., Deutz N., Erickson N., Laviano A., Lisanti M., Lobo D., et al. ESPEN expert group recommendations for action against cancer-related malnutrition. \u003cem\u003eClin. Nutr.\u003c/em\u003e 2017;36:1187\u0026ndash;1196. doi: 10.1016/j.clnu.2017.06.017\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBozzetti F., Calder P., Deutz N., Erickson N., Laviano A., Lisanti M., Lobo D., et al. ESPEN expert group recommendations for action against cancer-related malnutrition. \u003cem\u003eClin. Nutr.\u003c/em\u003e 2017;36:1187\u0026ndash;1196. doi: 10.1016/j.clnu.2017.06.017\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBray, F.; Ferlay, J.; Soerjomataram, I.; Siegel, R.L.; Torre, L.A.; Jemal, A. Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. \u003cem\u003eCA Cancer J. Clin.\u003c/em\u003e 2018, \u003cem\u003e68\u003c/em\u003e, 394\u0026ndash;424\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCaillet, P., Liuu, E., Raynaud, S., \u0026amp; Bonnefoy, M. (2017). Physical and nutritional geriatric evaluation and comprehensive geriatric assessment. In S. G. Extermann (Ed.), Geriatric Oncology (pp. 35\u0026ndash;50). Springer\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCalder M, De Wys, W.D.; Begg, C.; Lavin, P.T.; Band, P.R.; Bennett, J.M.; Bertino, J.R.; Cohen, M.H.; Douglass, H.O., Jr.; Engstrom, P.F.; Ezdinli, E.Z.; et al. Prognostic effect of weight loss prior to chemotherapy in cancer patients. \u003cem\u003eAm. J. Med.\u003c/em\u003e 2018, \u003cem\u003e69\u003c/em\u003e, 491\u0026ndash;497\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFitzmaurice C, Allen C, Barber RM, Barregard L, Bhutta ZA, Brenner H, et al. Global, regional, and national cancer incidence, mortality, years of life lost, years lived with disability, and disability-adjusted life-years for 32 cancer groups, 1990 to 2015: a systematic analysis for the global burden of disease study global burden. JAMA Oncol. 2017;3(4):524\u0026ndash;48\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFuchs HE, Jemal A. Cancer statistics, 2022. \u003cem\u003eCA\u003c/em\u003e: A Cancer Journal for Clinicians 2022; 72(1):7\u0026ndash;33\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHermans, K.E.P.E., Kazemzadeh, F., Loef, C. \u003cem\u003eet al.\u003c/em\u003e Risk factors for cancer of unknown primary: a literature review. \u003cem\u003eBMC Cancer\u003c/em\u003e 23, 314 (2023). https://doi.org/10.1186/s12885-023-10794-6\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHweidi, I. M., Carpenter, C. L., Al-Obeisat, S. M., Alhawatmeh, H. N., Nazzal, M. S., \u0026amp; Jarrah, M. I. (2021, July). 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Oncol.\u003c/em\u003e 2018, \u003cem\u003e19\u003c/em\u003e, 2381\u0026ndash;2389\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMaciel, B.J., Pedrosa, F. \u0026amp;Coelho, C.P. (2012). Nutritional status and adequacy of enteral nutrition in pediatric cancer patients at a reference center in northeastern Brazil \u003cem\u003eNutricionhospitalaria,27\u003c/em\u003e(4), 1099\u0026thinsp;\u0026minus;\u0026thinsp;105. doi:10.3305/nh.2012.27.4.5869\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMacIntosh CG, Morley JE, Horowitz M, Chapman IM: Anorexia of Ageing. Nutrition. 2019, 16: 983\u0026ndash;995. 10.1016/S0899-9007(00)00405-6\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMathew AC, Das D, Sampath S, Vijayakumar M, Ramakrishnan N, Ravishankar SL. Prevalence and correlates of malnutrition among elderly in an urban area in Coimbatore. Indian J Public Health. 2016; 60:112\u0026ndash;117\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMohamadpour M, Sharif ZM, Keysami MA. Food insecurity, health and nutritional status among sample of palm-plantation households in Malaysia. J Health Popul Nutr. 2018; 30:291\u0026ndash;302\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMuscaritoli M, Lucia S, Farcomeni A, Lorusso V, Saracino V, Barone C, Plastino F, Gori S, Magarotto R, Carteni G, Chiurazzi B, Pavese I, Marchetti L, Zagonel V, Bergo E, Tonini G, Imperatori M, Iacono C, Maiorana L, Pinto C, Rubino D, Cavanna L, Di Cicilia R, Gamucci T, Quadrini S, Palazzo S, Minardi S, Merlano M, Colucci G, Marchetti P; PreMiO Study Group.Oncotarget. 2017 Aug 10;8(45):79884\u0026ndash;79896. doi: 10.18632/oncotarget.20168. eCollection 2017 Oct 3.PMID: 29108370\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNHS. (2018). Eating well with a learning disability: Easy Read. Retrieved from https://www.england.nhs.uk/wp-content/uploads/2018/07/eating-well-with-a-learning-disability\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePatel Ariza. Role of nutritional factors in pathogenesis of cancer. Food Quality Safe. 2018;2(1):27\u0026ndash;36\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePokhrel, K, Nanishi, K, Poudel (2016) Undernutrition among infants and children in Nepal: maternal health services and their roles to prevent it. Matern Child Health J 20, 2037\u0026ndash;2049\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRock CL et al. American cancer society guideline for diet and physical activity for cancer prevention. CA: Cancer J Clin. 2020;70(4):245\u0026ndash;271\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSharp, L., Johansson, H., Leinonen, M. K., R\u0026ouml;nmark, E., Yli-Tuomi, T., Boman, C., ... \u0026amp; Heinrich, J. (2019). Health-related quality of life in young adults with asthma, allergic rhinitis and comorbid asthma and allergic rhinitis. Clinical and Experimental Allergy, 49(3), 355\u0026ndash;363\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSiegel RL, Miller KD, Jemal A. Cancer Statistics, 2017. CA Cancer J Clin. 2017;67:7\u0026ndash;30. https://doi.org/10.3322/caac.21387\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWardle, J., Haase, A. M., Steptoe, A., Nillapun, M., Jonwutiwes, K., Bellisie, F., ... \u0026amp; Wagner, C. (2013). Gender differences in food choice: the contribution of health beliefs and dieting. Annals of Behavioral Medicine, 26(1), 3\u0026ndash;15\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWHO International Agency for Research on Cancer 2020\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-4748045/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4748045/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eCancer has become a serious global health threat following its increasing incidence and prevalence rates. The study's objective was to establish any existing relationships between demographic and socio-economic characteristics and the nutrition status of adult cancer patients. This was aimed at gathering more data for evidence-based interventions to establish factors that influence nutrition status among cancer patients, given only the existence of scanty information on the same.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eIn this cross-sectional study, 384 patients were randomly selected. Data on demographic and socio-economic characteristics was collected through interviewer-administered questionnaires. Nutrition status data was determined through body mass index which was computed from the weight and height measurements. Chi-square and logistic regression were used to assess the correlates of nutrition status.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eThe study revealed that 41% (n\u0026thinsp;=\u0026thinsp;157) of participants had optimal nutrition. The mean BMI was 25.0kg/m\u003csup\u003e2 \u0026plusmn;\u003c/sup\u003e 4.25SD, with 17% being underweight, 28% overweight, and 14% obese. The majority of the participants, 89% (n\u0026thinsp;=\u0026thinsp;343), were above 36 years and 66% (n\u0026thinsp;=\u0026thinsp;254) were female. Those with secondary education had the highest frequency 41% (n\u0026thinsp;=\u0026thinsp;158). The monthly household income category with the highest number of respondents was less than 15,000 Kenya shillings (n\u0026thinsp;=\u0026thinsp;196; 51%), while most of the study participants, 54% (n\u0026thinsp;=\u0026thinsp;209), had household sizes of 4\u0026ndash;6. Factors found to be associated with the participants' nutrition status included age (AOR\u0026thinsp;=\u0026thinsp;6.73; 95% CI\u0026thinsp;=\u0026thinsp;1.88\u0026ndash;24.11; p-value\u0026thinsp;=\u0026thinsp;0.003), occupation status (AOR\u0026thinsp;=\u0026thinsp;2.57; 95% CI\u0026thinsp;=\u0026thinsp;1.42\u0026ndash;4.68; p-value\u0026thinsp;=\u0026thinsp;0.002), sex (AOR\u0026thinsp;=\u0026thinsp;2.64; 95% CI\u0026thinsp;=\u0026thinsp;1.33\u0026ndash;5.26; p-value\u0026thinsp;=\u0026thinsp;0.006), household size (AOR\u0026thinsp;=\u0026thinsp;1.79; 95% CI\u0026thinsp;=\u0026thinsp;1.03\u0026ndash;3.12; p-value\u0026thinsp;=\u0026thinsp;0.039), and level of education (AOR\u0026thinsp;=\u0026thinsp;10.81; 95% CI\u0026thinsp;=\u0026thinsp;1.29\u0026ndash;90.66; p-value\u0026thinsp;=\u0026thinsp;0.028).\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eThis study found that malnutrition was common among the study participants, and this could be a result of low income among the families and intake of low nutrients. Therefore, approaches such as increasing the availability, affordability, and accessibility of nutrient-dense foods are key when designing and prioritizing interventions.\u003c/p\u003e","manuscriptTitle":"Demographic and Socio-economic Correlates of Nutrition Status of Adult Cancer Patients: A Case of Texas Cancer Center, Kenya","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-08-18 23:10:45","doi":"10.21203/rs.3.rs-4748045/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"ccb35577-b1c3-40d8-ae61-5084d74e7dd7","owner":[],"postedDate":"August 18th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-04-03T07:38:43+00:00","versionOfRecord":[],"versionCreatedAt":"2024-08-18 23:10:45","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4748045","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4748045","identity":"rs-4748045","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}