Malnutrition and associated factors among hospitalized adult patients at a tertiary hospital, Northern Uganda: a cross-sectional study

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However, malnutrition in hospitalized patients is often overlooked, underdiagnosed, and frequently inadequately addressed in clinical practice. We determined the prevalence and associated factors of malnutrition in hospitalized adult patients at Lira Regional Referral Hospital (LRRH), Uganda. Methods: We conducted a cross-sectional study at medical wards of LRRH during November and December 2023. The study included patients aged ≥18years through consecutive sampling method. We excluded those who were too unwell to respond to the research questions. Socio-demographic and clinical characteristics were obtained through interviewer-administered questionnaires. Malnutrition was assessed using the Malnutrition Universal Screening Tool (MUST), which utilizes body mass index (BMI) scores for classification. Individuals with BMI scores <18.5 kg/m² were categorized as undernourished, those with BMI scores <18.5 kg/m² or ≥25 kg/m² were classified as malnourished, and BMI scores of 18.5-24.9 kg/m² were considered normal. Malnutrition was further categorized based on weight loss percentages, using the Subjective Global Assessment (SGA) tool: normal (weight loss 0-10%). Modified Poisson regression was used to evaluate associations between undernutrition and independent variables. Results: In total, 423 patients were recruited with median age of 40 (inter-quartile range [IQR]: 24-63) years; 223 (53%) were female. Overall, 176 (42%, 95% CI: 37-46%) had malnutrition; 116 (27%) were undernourished, 73 (17%) were mild/moderately undernourished, and 43 (10%) severely undernourished. Being aged >64 years (aPR 1.19, 95% CI: 1.01- 1.39), and having adequate dietary intake (aPR 0.91, 95% CI: 0.82-0.99), were independently associated with under-nutrition. Conclusion: Approximately 4 out of every 10 patients screened at LRRH had malnutrition. Patients of advanced age were more likely to be undernourished, while those with adequate dietary intake were less likely to be undernourished. The high prevalence of malnutrition highlights the need for increased attention to nutritional assessment and intervention in clinical practice, particularly for older patients. Adequate dietary intake and post-discharge nutritional interventions could reduce the magnitude of under-nutrition and potentially enhance clinical outcomes in this setting. Malnutrition Prevalence Nutrition status Hospitalized patients Uganda Figures Figure 1 INTRODUCTION Globally, malnutrition in adult hospitalized patients imposes a significant burden on the healthcare system, with an estimated prevalence of 13%-69% [ 1 – 3 ]. Malnutrition among hospitalized patients is an independent risk factor for poor outcomes, including prolonged length of stay, increased mortality, higher morbidity, poor quality of life, and an increased risk of re-admissions [ 4 – 7 ]. Recent findings from selected African hospitals (South Africa, Ghana, and Kenya) revealed that 54% of patients experienced malnutrition before admission, 37% developed complications requiring medical intervention, 1.7% died during hospitalization, and 14% died within 3 months post-discharge [ 8 ]. Furthermore, a study done in Southwestern Uganda reported a malnutrition prevalence of 25–59% and a mortality rate of 18% [ 9 ]. Malnutrition in hospitalized patients is associated with several factors including literacy levels, adequacy of dietary intake, old age, comorbid diseases such as HIV/AIDS, tuberculosis, Cancer, and Liver disease [ 2 , 4 , 6 , 10 ]. Poor appetite, low food intake, and increased metabolic requirements in sick patients, are other factors contributing to malnutrition [ 11 ]. Micronutrient deficiencies are “hidden” and more difficult to assess to diagnose malnutrition; however, body mass Index (BMI) can be efficiently used [ 12 ]. Despite the adverse outcomes associated with malnutrition in hospitalized patients, malnutrition in hospitalized patients is often overlooked, underdiagnosed, and frequently inadequately addressed in clinical practice [ 13 ]. In Uganda, screening for malnutrition in hospitalized adult patients is not routine, resulting in limited data on prevalence and associated factors, particularly at Lira Regional Referral Hospital. Previous studies in this study population in Uganda have been conducted in the southwestern region of the country [ 14 , 15 ], with limited data from the northern region. Assessing the magnitude of malnutrition is crucial for informing policy decisions regarding optimizing nutritional screening for hospitalized adult patients so as to enhance clinical outcomes. This study determined the prevalence of malnutrition and associated factors among hospitalized adult patients at Lira Regional Referral Hospital (LRRH) in Northern Uganda. METHODS Study population, design and setting This was a cross-sectional study conducted among admitted patients at the Medical Ward of LRRH during November and December 2023. This study was conducted at the general (male and female) medicine wards of LRRH, Lira City in Lira district Northern Uganda. LRRH serves a population of about 2.2 million people from nine districts within Lango sub-region (Lira, Apac, Oyam, Kwania, Kole, Amolatar, Alebtong, Dokolo and Otuke districts). The Hospital has a bed capacity of 350. On average, the monthly admission rate for both male and female medical wards totals approximately 400 patients. Study participants were patients aged ≥ 18 years. We excluded those who were too unwell to respond to study questions, and those whose weight and height could not be taken (unable to stand). Sample size determination This study used the Kish Leslie formula (1965), to estimate the sample size of the participants to be enrolled. Where: n = sample size (number of the participants required). z = score at 95% i.e. 1.96 at 0.05 level of confidence. p = known proportion of malnutrition among hospitalized adult patients (0.5). e = acceptable deviation (0.05). n = 1.96 ×1.96 ×0.5(1-0.5) / (0.05×0.05) n = 384 The study assumed 10% of non-response at the desired margin of error of 5% at a 95% confidence interval. In the sample size calculation, a prevalence of 50% was assumed due to the lack of comparable local prevalence data. This yielded a final desired sample size of 423 participants. Study procedure and operational definitions The patients were accessed from the time of admission on the wards, where they were informed about the study. Participants were recruited by trained research assistants who were health workers (two Bachelor midwives and a Nutritionist). Participants who were eligible for the study were given detailed information about the nature and purpose of the study. A trained research assistant issued the consent document to the participants and they were given the opportunity to ask clarifying questions before providing consent and completing a pre-coded structured interview questionnaire. This questionnaire captured data on demographic and socio-economic characteristics. Weight and height, medical history and physical examination of the patient were taken from the ward when bed was screened for privacy and confidentiality. Height Measurement This was taken once using a height meter (stadiometer) that was positioned against the wall. The patients were asked to remove shoes and heavy clothes, stood upright, feet flat, and heels against the height stick and they looked straight ahead. The researcher lowered the head plate until it touched the top of the head gently before reading and documenting the measurement to the nearest 1.0 cm. Weight Measurement A calibrated Secca weighing scale was used to take weight once which was regularly checked for accuracy ensuring that they read zero before participants standing on them. Weighing was done in light clothing and without shoes to the nearest 0.1 kg and documented. Adult hospital malnutrition Adult hospital malnutrition status was defined by MUST scores that uses body mass Index (BMI), mid upper arm circumference (MUAC), unplanned weight loss and disease effect. Participants with BMI of < 18.5kg/m2 or ≥ 25kg/m2 ( ≥ 25-29.9kg/m2 – overweight and ≥ 30.0kg/m2 – obese) were considered to be malnourished, participants with BMI of < 18.5kg/m2 were considered to be undernourished, while those with BMI between 18.5–24.9 kg/m2 were considered to be normal. Participants were categorized based on their unplanned weight loss scores over the last 3–6 months: those with 10% as severely malnourished. Adult malnutrition was then classified by SGA scores that uses 5 components of medical history (weight change, dietary intake, gastrointestinal symptoms, functional capacity, disease and its relation to nutritional requirements) and 3 components of brief physical examination, signs of fat and muscle wasting, and body fluid weight gain. The patient was then classified as; SGA A (Well-nourished), when there was no decrease in food/nutrient intake; <5% weight loss; no/minimal symptoms affecting food intake; no deficit in function; no deficit in fat or muscle mass, SGA B (Mildly/moderately malnourished), when there was a definite decrease in food/nutrient intake; 5%-10% weight loss without stabilization or gain; mild/some symptoms affecting food intake; moderate functional deficit or recent deterioration; mild/moderate loss of fat and/or muscle mass. SGA C (Severely malnourished), when there was a severe deficit in food/nutrient intake; >10% weight loss which is ongoing; significant symptoms affecting food/ nutrient intake; severe functional deficit. Nutritional intake and adequacy The patient nutrient intake was determined using the 24-Hour Dietary Recall focusing on the routine from morning to evening before the onset of the current illness. This enabled assessment of the nutrient intake of the patient i.e. energy intake, carbohydrate, protein and fats. Nutritional adequacy was assessed using the Subjective Global Assessment (SGA) tool, which considers a diet adequate if it includes at least three meals per day containing all three macronutrients: carbohydrates, proteins, and fats. Inadequate nutrition was defined as a reduction in meal frequency, often caused by loss of appetite due to illness or as a result of suboptimal dietary intake, hypocaloric liquids, full fluids, or starvation. The severity of malnutrition was categorized based on the duration of inadequate nutrition, with less than two weeks considered mild, more than two weeks but less than one month considered moderate, and more than one month considered severe [ 16 ]. Data analysis Data were entered into Microsoft Excel and exported to STATA software version 17 (Stata Corp, College Station, Texas, USA), for data analysis. Independent variables were analyzed using descriptive statistics. Continuous variables were as described as median and interquartile range while categorical variables described as percentageseduce the errors during entry process before analysis. Prevalence was calculated as the total number of patients with malnutrition expressed as a percentage of the total participants. This was then reported with its 95% confidence interval. We used modified Poisson regression to evaluate associations between independent variables and malnutrition as the dependent variable. Variables that had a p-value < 0.2 at bivariate analysis were entered into a multivariable regression model to identify factors independently associated (p < 0.05) with malnutrition. Ethical considerations The ethical clearance was obtained from MUST Research and Ethics committee (MUST-REC 2023 − 937 and IRB 2023-08) before the data was collected. Administrative clearance was sought from the Hospital Director LRRH. Written informed consent was obtained from all the study participants. We abided by the principles of the Helsinki Declaration and CIOMS-2002 guidelines for human research, safeguarding against physical or moral harm. RESULTS Study Flow Chart Out of the 429 screened from November to December 2023, we present results for 423 participants. Six patients were excluded, including 5 who were too unwell to respond (Fig. 1 ). Table 1 Socio-demographic and clinical characteristics of hospitalized adult patients at Lira Regional referral hospital, Northern Uganda. Characteristics Median(IQR) Frequency Percentage Age 40 (24 63) Age 18–40 213 50.35 41–64 115 27.19 > 64 95 22.46 Sex Male 200 47.28 Female 223 52.72 Occupation Student 90 21.28 Peasant 223 52.72 Business 65 15.37 Civil servant 36 8.51 House wife 9 2.13 Residence City 116 27.42 Town council 96 22.70 Rural/village 211 49.88 Education No formal education 93 22.22 Primary 133 31.44 Secondary 140 33.10 Tertiary 56 13.24 Marital status Married 207 48.94 Single living together 144 26.95 Divorced/separated 35 8.27 Widowed 57 15.84 Number of household occupants < 4 241 57.97 ≥ 4 182 43.23 Alcohol abuse No alcohol 234 55.32 Alcohol 189 44.68 Dietary intake Inadequate 285 67.38 Adequate 138 32.62 Diabetes Yes 395 93.38 No 28 6.62 Hypertension Yes 353 83.24 No 70 16.55 Cancer Yes 418 98.82 No 5 1.18 HIV/AIDS Yes 362 85.58 No 61 14.42 TB Yes 394 93.14 No 29 6.86 Heart failure Yes 399 94.33 No 24 5.57 Liver disease Yes 384 90.78 No 39 9.22 Kidney disease Yes 395 93.38 No 28 6.62 Characteristics of study participants Out of 423 participants, 223 (52.72%) were female. The participants had a median age of 40 (24–63 years). The majority (n = 285; 67.37%) had insufficient food consumption. The participants' medical characteristics revealed a high prevalence of chronic illnesses: 70 patients (16.54%) had hypertension, while 61 (14.42%) had HIV/AIDS. Other chronic illnesses included liver disease in 39 (9.21%) participants, tuberculosis in 29 (6.85%), diabetes in 28 (6.61%), kidney disease in 28 (6.61%), heart failure in 24 (5.67%), and cancer in 5 (1.18%) participants (Table 1 ). Prevalence of malnutrition Of the 423 participants, 176 had malnutrition, giving the prevalence of malnutrition among adult hospitalized patients at LRRH of 42% (95%CI: 36.99–46.38); 4% (n = 17) were obese, 10% (n = 43) overweight, and 27% (n = 116) underweight. The prevalence of undernutrition, among the hospitalized adult patients at LRRH was 27% (n = 116; 95%CI: 23.37–31.88). Among the 116 undernourished patients, 73 (63%) had mild/ moderate under-nutrition and 43 (37%) had severe undernutrition. Factors associated with undernutrition At multivariable analysis (Table 2 ), age above 64 years and inadequate dietary intake were independently associated with adult hospitalized under-nutrition. The prevalence of undernutrition in hospitalized patients aged above 64 years was 1.2 times higher (adjusted prevalence ratio [aPR] = 1.20, 95% CI: 1.01–1.39, p = 0.035) than in patients aged below 64 years. Additionally, Patients who had adequate dietary intake were less likely to have undernutrition than those who had inadequate dietary intake (aPR = 0.91, 95% CI: 0.82–0.99, p = 0.041) Table 2 Factors associated with under-nutrition among hospitalized adult patients at Lira Regional Referral Hospital, Northern Uganda. Variable Undernourished n = 116 No Undernourished n = 307 Bivariate analysis p-value Multivariate Analysis aPR (95% Cl) p-value cPR (95% CI) Gender Male 58 (50.00) 142 (46.25) REF Female 58 (50.00) 165 (53.75) 0.97 (0.89–1.06) 0.492 Age 18–40 52 (44.83) 161 (52.44) REF 41–64 25 (21.55) 90 (29.22) 0.97 (0.88–1.08) 0.601 1.00 (0.89–1.13) 0.998 > 64 39 (33.62) 56 (18.24) 1.18 (1.06–1.31) 0.002 1.2 (1.01–1.34) 0.035 Occupation Student 29 (25.00) 61 (19.87) REF Peasant 79 (63.79) 144 (48.53) 1.01 (0.91–1.12) 0.861 0.97 (0.81–1.17) 0.742 Business 7 (6.03) 58 (18.89) 0.81 (0.70–0.93) 0.003 0.87 (0.71–1.06) 0.176 Civil servant 6 (5.17) 30 (9.77) 0.86 (0.72–1.01) 0.072 0.81 (0.64–1.01) 0.064 Housewife 00 (0.00) 9 (2.93) 0.72 (1.26–1.51) 0.036 0.79 (0.56–1.11) 0.172 Residence City 25 (21.55) 91 (29.64) REF Town council 25 (21.55) 71 (23.13) 1.05 (0.93–1.18) 0.465 1.03 (0.91–1.16) 0.683 Rural/Village 66 (26.90) 145 (23.13) 1.10 (0.99–1.22) 0.059 1.00 (0.89–1.12) 0.996 Educational level No Formal education 35 (30.17) 59 (19.22) REF Primary 38 (32.76) 95 (30.95) 0.92 (0.82–1.03) 0.146 0.95 (0.83–1.08) 0.400 Secondary 25 (21.55) 115 (37.46) 0.82 (0.73–0.92) 0.001 0.89 (0.76–1.05) 0.165 Tertiary 18 (15.52) 38 (12.38) 0.95 (1.33–1.59) 0.495 1.11 (0.89–1.38) 0.370 Married/Living with a partner 45 (38.79) 162 (52.77) REF Single 34 (29.31) 80 (26.06) 1.08 (0.98–1.20) 0.119 1.05 (0.89–1.24) 0.549 Divorced 12 (10.34) 23 (7.49) 1.13 (0.97–1.33) 0.122 1.08 (0.92–1.26) 0.373 Widowed 25 (21.55) 42 (13.68) 1.17 (1.03–1.32) 0.013 0.97 (0.82–1.13) 0.679 No. Household occupants < 4 64 (55.17) 177 (57.65) REF ≥ 4 52 (44.83) 130 (42.35) 1.02 (0.94–1.11) 0.646 Substance abuse No alcohol 61 (52.59) 173 (56.35) REF Alcohol 55 (47.42) 134 (43.65) 1.03(0.95–1.12) 0.488 Dietary intake Inadequate 91 (78.45) 139 (63.19) REF Adequate 25 (21.55) 133 (36.81) 0.88 (0.80–0.95) 0.003 0.91 (0.82- 1.00) 0.041 Diabetes No 109 (93.97) 286 (93.16) REF Yes 7 (6.03) 21 (6.84) 0.97 (0.82–1.15) 0.769 Hypertension No 103 (88.79) 250 (81.43) REF Yes 13 (11.21) 57 (18.57) 0.89 (0.80–1.01) 0.069 0.89 (0.79- 1.00) 0.060 Cancer No 113 (97.41) 305 (99.35) REF Yes 3 (2.59) 2 (0.65) 1.39 (0.94–2.06) 0.100 1.26 (0.86–1.84) 0.240 HIV/AIDS No 92 (79.31 270 (87.95) REF Yes 24 (20.69) 37 (12.05) 1.15 (1.019- 1.30 0.024 Tuberculosis No 101 (87.07) 293 (95.44) REF Yes 15 (12.95) 14 (4.56) 1.30 (1.10–1.53) 0.002 1.15 (0.96–1.38) 0.121 Heart Failure No 110 (94.83) 289 (94.14) REF Yes 6 (5.67) 18 (5.86) 0.97 (0.81–1.17) 0.785 Liver disease No 110 (95.69) 274 (92.51) REF Yes 6 (5.17) 33 (10.75) 0.88 (0.76–1.01) 0.077 0.87 (0.75–1.04) 0.077 Kidney disease No 111 (95.69) 284 (92.51) REF Yes 5 (4.31) 23 (7.49) 0.90 (0.76–1.07) 0.241 DISCUSSION In this study, 4 out of every 10 hospitalized adult patients screened at LRRH had malnutrition. Being aged ≥ 64 years and adequate dietary intake were found to be significantly associated with undernutrition. In this study, a considerable proportion of adult hospitalized patients had general malnutrition (42%), which is higher than the 24% prevalence that was reported in Iran [ 17 ]. The disparity could be due differences in age ranges; The Iranian study included patients aged 18–65, potentially excluding malnourished individuals aged 65 and older. Moreover, in our study, malnutrition was more prevalent among participants aged 64 and older compared to those under 64. A systematic review and meta-analysis study among hospitalized patients with COVID-19 [ 18 ] reported a higher prevalence of 49%. The high prevalence may result from COVID-19's impact on the immune system, leading to excessive inflammation and cytokine storms, which can impair nutrient absorption due to gut epithelial cell damage and increased metabolic activity [ 19 ]. A 2014 systematic review in Latin American countries reported a similar 40% prevalence rate to our study, possibly due to similarities such as study population and consideration of comorbid diagnoses like cancer, HIV/AIDS, heart failure, and chronic liver disease at admission [ 2 ]. The high prevalence of malnutrition underscores the importance of enhancing nutritional assessment and intervention in clinical practice. Providing nutrition support during hospitalization and post-discharge should be prioritized among hospitalized patients to improve both short- and long-term clinical outcomes, as demonstrated elsewhere[ 20 ]. This study found out that patients aged ≥ 64 years and above were more likely to have malnutrition compared to younger patients. This aligns with existing literature indicating that aging is associated with physiological changes such as sensory and digestive system changes, affecting digestion, absorption, appetite, and nutritional status [ 21 ]. Other factors contributing to malnutrition in older adults include decreased gastrointestinal hormone secretions (e.g., ghrelin), with parallel changes in anorectic signaling (e.g., neuropeptide Y, peptide YY (PYY), orexin A, leptin, cholecystokinin (CCK) leading to an altered appetite regulation in advanced age [ 22 ]. Furthermore, psychological factors like stress and anxiety, that are more prevalent in old age, may impair gastrointestinal motility and nutrient absorption [ 21 ]. The findings underscore the importance of targeted nutritional strategies, particularly for older patients by promoting and supporting health and nutrition education to increase the level of awareness of good nutrition. In this study, patients who had adequate dietary intake were less likely to have under-nutrition than those who had inadequate. This also aligns with the existing literature indicating that patients who reported reduced dietary intake had the highest percentage (84.6%) of moderate to severe under-nutrition [ 23 ]. Later in a chronic inadequacy dietary intake, where the main source of amino acids for gluconeogenesis is from skeletal muscle proteins. The smooth muscles of the gut breaks down rather more rapidly than the skeletal muscles during the initial stages of starvation [ 24 ]. This results into crypt hypoplasia, reduced secretions with loss of disaccharidases, and atrophy of microvilli with compromised gap junction linkages between epithelial cells that are significant for small intestinal paracellular absorption pathway mechanism and overall dysfunctional consequences of impaired digestion and absorption of nutrients leading into malnutrition. Thus, adequate dietary intake could reduce the magnitude of under-nutrition and potentially enhance clinical outcomes among hospitalized adult patients. Limitations This study has some limitations that should be considered when interpreting the findings. Firstly, this study based on subjective global assessment tool to classify hospitalized adult malnutrition as; normal, mild/moderate or severe malnutrition. Laboratory markers (serum albumin, transferrin, C- reactive protein, and total lymphocyte count) malnutrition assessments could have been used; this may have introduced social desirability bias and potentially led to an underestimation of prevalence of malnutrition. Secondly, this study used 24-hour recall to estimate dietary adequacy, which may not reflect long-term dietary habits. This may have led to underestimating the prevalence of this exposure, potentially biasing the observed effect size towards the null. Finally, there was no follow-up to assess changes in nutritional status at discharge, potentially underestimating malnutrition prevalence. Conclusions In this study, malnutrition was identified in 4 out of every 10 patients admitted at LRRH, Northern Uganda. Factors significantly associated with malnutrition included participants aged ≥ 64 years, adequate dietary intake. Increased attention to nutritional assessment and intervention in clinical practice in this region particularly for older patients could potentially enhance overall patient care and good outcomes in hospital setting. Furthermore, given the high prevalence of malnutrition, nutrition support during hospitalization and post-discharge should be considered among hospitalized patients to improve both short- and long-term clinical outcomes in this study population. Abbreviations BMI Body Mass Index CCK Cholecystokinin COVID-19 Corona Virus Disease-19 HAM Hospitalized Adult Malnutrition HIV/AIDS Human Immunodeficiency Virus/ Acquired Immune-Deficiency Syndromes LRRH Lira Regional Referral Hospital MUAC Mid-Upper Arm Circumference MUST Malnutrition Universal Screening Tool PYY Peptide YY SGA Subjective Global Assessment TB Tuberculosis Declarations Consent for publication Not applicable Competing Interests: The authors have no conflict of interests to declare. Funding This work did not receive any funding from external source. Author Contribution S.O., R.M, conceived, designed the study. S.O. drafted the initial manuscript; R.M, and D.C.A reviewed and edited the manuscript. S.O, J.S, V.M and R.O.O collected the data, S.O, R.A E.E and M.M designed data collection questionnaires. S.O and R.A analyzed the data. All authors read and approved the final manuscript. Acknowledgement We thank the patients who participated in the study, the research assistants who helped in data collection and the staff and management of Lira Regional Referral Hospital who contributed in various ways towards the success of the study. We acknowledge Mbarara University of Science and Technology and Lira University for granting opportunity to study Data availability The datasets generated and analyzed during the study are available from the corresponding author on request. References Ligthart-Melis GC, et al. Frailty, sarcopenia, and malnutrition frequently (co-) occur in hospitalized older adults: a systematic review and meta-analysis. J Am Med Dir Assoc. 2020;21(9):1216–28. Correia MIT, Perman MI, Waitzberg DL. Hospital malnutrition in Latin America: A systematic review. Clin Nutr. 2017;36(4):958–67. Inciong JFB, et al. Hospital malnutrition in northeast and southeast Asia: A systematic literature review. Clin Nutr ESPEN. 2020;39:30–45. Bellanti F, et al. 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BMC Geriatr. 2017;17(1):136. Norman K, Haß U, Pirlich M. Malnutrition in older adults—recent advances and remaining challenges. Nutrients. 2021;13(8):2764. Barcus GC, et al. Nutrition Screening, Reported Dietary Intake, Hospital Foods, and Malnutrition in Critical Care Patients in Malawi. Nutrients. 2021;13(4):1170. Emery P. Metabolic changes in malnutrition. Eye. 2005;19(10):1029–34. 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. 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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-4330592","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":298763644,"identity":"e67e0aec-24b9-438e-b615-c72f926b425b","order_by":0,"name":"Samuel Okello","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABBUlEQVRIiWNgGAWjYNCCAgYDBnbmBoYPPDZAHmPjAcJaDICQmbGBcYZMGkhLA/FamDlsDoP5eLXIt59Ok/hgYGPMz8zY+Jgh57zd2vbDQFtqbKJxmn8md5vkDIM0M8lmxmbjgjO3k7edSQRqOZaW24DTSbnbpHkMDtsYHGZsk57ZczvZ7ABQC2PDYZxa5PvfbpP+A9RiD9LC++9cstn5h/i1MNwA2sJgcNjMgBmohYfngJ3ZDQK2GNx4u9myxyDNWOIwY7PhDJ7kBLMbQFsS8PhFvj93440fFTaG/e3NBx984LGzNzuf/vDBhxob3A5DB4lglQnEKgcBe1IUj4JRMApGwcgAABomYXyf+5xgAAAAAElFTkSuQmCC","orcid":"","institution":"Mbarara University of Science and Technology","correspondingAuthor":true,"prefix":"","firstName":"Samuel","middleName":"","lastName":"Okello","suffix":""},{"id":298763645,"identity":"89fa7432-bbaf-46f5-9348-237e285944c1","order_by":1,"name":"Victor Muyambi","email":"","orcid":"","institution":"Mbarara University of Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"Victor","middleName":"","lastName":"Muyambi","suffix":""},{"id":298763646,"identity":"6aee316c-ff14-4a55-8c35-9d88c57f8256","order_by":2,"name":"David Collins Agaba","email":"","orcid":"","institution":"Mbarara University of Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"David","middleName":"Collins","lastName":"Agaba","suffix":""},{"id":298763647,"identity":"9c5f8059-6e01-43df-b69e-cfc325ca50ba","order_by":3,"name":"Jimmy Odongo Ogwal","email":"","orcid":"","institution":"Mbarara University of Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"Jimmy","middleName":"Odongo","lastName":"Ogwal","suffix":""},{"id":298763648,"identity":"0d447d69-82d3-4caa-ab51-9b2bccb0413b","order_by":4,"name":"John Semuwemba","email":"","orcid":"","institution":"Mbarara University of Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"John","middleName":"","lastName":"Semuwemba","suffix":""},{"id":298763650,"identity":"3924dfe6-104b-4d98-a259-4f316afd1503","order_by":5,"name":"Ronald Omolo Ouma","email":"","orcid":"","institution":"Mbarara University of Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"Ronald","middleName":"Omolo","lastName":"Ouma","suffix":""},{"id":298763651,"identity":"ad8d2e3a-61be-4679-b131-a832fbcc57c7","order_by":6,"name":"Marvin Musinguzi","email":"","orcid":"","institution":"Lira University","correspondingAuthor":false,"prefix":"","firstName":"Marvin","middleName":"","lastName":"Musinguzi","suffix":""},{"id":298763652,"identity":"0ae5b200-89f3-495f-a736-1169f3b68cf7","order_by":7,"name":"Rebecca Awilli","email":"","orcid":"","institution":"Lira University","correspondingAuthor":false,"prefix":"","firstName":"Rebecca","middleName":"","lastName":"Awilli","suffix":""},{"id":298763653,"identity":"7c1712dc-4f20-4d48-bfb1-10a65898de4e","order_by":8,"name":"Ekung Emmanuel","email":"","orcid":"","institution":"Lira University","correspondingAuthor":false,"prefix":"","firstName":"Ekung","middleName":"","lastName":"Emmanuel","suffix":""},{"id":298763654,"identity":"b4f47a1d-68d1-46de-a56b-3156bd143817","order_by":9,"name":"Richard Migisha","email":"","orcid":"","institution":"Mbarara University of Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"Richard","middleName":"","lastName":"Migisha","suffix":""}],"badges":[],"createdAt":"2024-04-26 15:45:47","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4330592/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4330592/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":56040078,"identity":"8b154641-1c13-4e9c-916a-ba1df36b80f6","added_by":"auto","created_at":"2024-05-07 19:13:59","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":51817,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eFlow chart showing the recruitment of the study participants and the reasons for exclusion\u003c/em\u003e\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-4330592/v1/abee0011252fc4040f939f21.png"},{"id":56077270,"identity":"e79dbab8-f144-44bf-9ea7-6f5ad54ac462","added_by":"auto","created_at":"2024-05-08 08:54:03","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1185625,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4330592/v1/1a6e280a-2222-4956-ba3d-6d9f25e51f34.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Malnutrition and associated factors among hospitalized adult patients at a tertiary hospital, Northern Uganda: a cross-sectional study","fulltext":[{"header":"INTRODUCTION","content":"\u003cp\u003eGlobally, malnutrition in adult hospitalized patients imposes a significant burden on the healthcare system, with an estimated prevalence of 13%-69% [\u003cspan additionalcitationids=\"CR2\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Malnutrition among hospitalized patients is an independent risk factor for poor outcomes, including prolonged length of stay, increased mortality, higher morbidity, poor quality of life, and an increased risk of re-admissions [\u003cspan additionalcitationids=\"CR5 CR6\" citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Recent findings from selected African hospitals (South Africa, Ghana, and Kenya) revealed that 54% of patients experienced malnutrition before admission, 37% developed complications requiring medical intervention, 1.7% died during hospitalization, and 14% died within 3 months post-discharge [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Furthermore, a study done in Southwestern Uganda reported a malnutrition prevalence of 25\u0026ndash;59% and a mortality rate of 18% [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eMalnutrition in hospitalized patients is associated with several factors including literacy levels, adequacy of dietary intake, old age, comorbid diseases such as HIV/AIDS, tuberculosis, Cancer, and Liver disease [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Poor appetite, low food intake, and increased metabolic requirements in sick patients, are other factors contributing to malnutrition [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Micronutrient deficiencies are \u0026ldquo;hidden\u0026rdquo; and more difficult to assess to diagnose malnutrition; however, body mass Index (BMI) can be efficiently used [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eDespite the adverse outcomes associated with malnutrition in hospitalized patients, malnutrition in hospitalized patients is often overlooked, underdiagnosed, and frequently inadequately addressed in clinical practice [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. In Uganda, screening for malnutrition in hospitalized adult patients is not routine, resulting in limited data on prevalence and associated factors, particularly at Lira Regional Referral Hospital. Previous studies in this study population in Uganda have been conducted in the southwestern region of the country [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e], with limited data from the northern region. Assessing the magnitude of malnutrition is crucial for informing policy decisions regarding optimizing nutritional screening for hospitalized adult patients so as to enhance clinical outcomes. This study determined the prevalence of malnutrition and associated factors among hospitalized adult patients at Lira Regional Referral Hospital (LRRH) in Northern Uganda.\u003c/p\u003e"},{"header":"METHODS","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\n\u003ch2\u003eStudy population, design and setting\u003c/h2\u003e\n\u003cp\u003eThis was a cross-sectional study conducted among admitted patients at the Medical Ward of LRRH during November and December 2023. This study was conducted at the general (male and female) medicine wards of LRRH, Lira City in Lira district Northern Uganda. LRRH serves a population of about 2.2\u0026nbsp;million people from nine districts within Lango sub-region (Lira, Apac, Oyam, Kwania, Kole, Amolatar, Alebtong, Dokolo and Otuke districts). The Hospital has a bed capacity of 350. On average, the monthly admission rate for both male and female medical wards totals approximately 400 patients. Study participants were patients aged\u0026thinsp;\u0026ge;\u0026thinsp;18 years. We excluded those who were too unwell to respond to study questions, and those whose weight and height could not be taken (unable to stand).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\n\u003ch2\u003eSample size determination\u003c/h2\u003e\n\u003cp\u003eThis study used the Kish Leslie formula (1965), to estimate the sample size of the participants to be enrolled.\u003c/p\u003e\n\u003cp\u003e\u003cimg style=\"width: 162px;\" src=\"data:image/png;base64,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\" alt=\"\" /\u003e\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eWhere:\u003c/p\u003e\n\u003cp\u003en\u0026thinsp;=\u0026thinsp;sample size (number of the participants required).\u003c/p\u003e\n\u003cp\u003ez\u0026thinsp;=\u0026thinsp;score at 95% i.e. 1.96 at 0.05 level of confidence.\u003c/p\u003e\n\u003cp\u003ep\u0026thinsp;=\u0026thinsp;known proportion of malnutrition among hospitalized adult patients (0.5).\u003c/p\u003e\n\u003cp\u003ee\u0026thinsp;=\u0026thinsp;acceptable deviation (0.05).\u003c/p\u003e\n\u003cp\u003en\u0026thinsp;=\u0026thinsp;1.96 \u0026times;1.96 \u0026times;0.5(1-0.5) / (0.05\u0026times;0.05)\u003c/p\u003e\n\u003cp\u003en\u0026thinsp;=\u0026thinsp;384\u003c/p\u003e\n\u003cp\u003eThe study assumed 10% of non-response at the desired margin of error of 5% at a 95% confidence interval. In the sample size calculation, a prevalence of 50% was assumed due to the lack of comparable local prevalence data. This yielded a final desired sample size of 423 participants.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\n\u003ch2\u003eStudy procedure and operational definitions\u003c/h2\u003e\n\u003cp\u003eThe patients were accessed from the time of admission on the wards, where they were informed about the study. Participants were recruited by trained research assistants who were health workers (two Bachelor midwives and a Nutritionist). Participants who were eligible for the study were given detailed information about the nature and purpose of the study. A trained research assistant issued the consent document to the participants and they were given the opportunity to ask clarifying questions before providing consent and completing a pre-coded structured interview questionnaire. This questionnaire captured data on demographic and socio-economic characteristics. Weight and height, medical history and physical examination of the patient were taken from the ward when bed was screened for privacy and confidentiality.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eHeight Measurement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis was taken once using a height meter (stadiometer) that was positioned against the wall. The patients were asked to remove shoes and heavy clothes, stood upright, feet flat, and heels against the height stick and they looked straight ahead. The researcher lowered the head plate until it touched the top of the head gently before reading and documenting the measurement to the nearest 1.0 cm.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eWeight Measurement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA calibrated Secca weighing scale was used to take weight once which was regularly checked for accuracy ensuring that they read zero before participants standing on them. Weighing was done in light clothing and without shoes to the nearest 0.1 kg and documented.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\n\u003ch2\u003eAdult hospital malnutrition\u003c/h2\u003e\n\u003cp\u003eAdult hospital malnutrition status was defined by MUST scores that uses body mass Index (BMI), mid upper arm circumference (MUAC), unplanned weight loss and disease effect. Participants with BMI of \u0026lt;\u0026thinsp;18.5kg/m2 or \u003cstrong\u003e\u0026ge;\u003c/strong\u003e\u0026thinsp;25kg/m2 (\u003cstrong\u003e\u0026ge;\u003c/strong\u003e\u0026thinsp;25-29.9kg/m2 \u0026ndash; overweight and \u0026ge;\u0026thinsp;30.0kg/m2 \u0026ndash; obese) were considered to be malnourished, participants with BMI of \u0026lt;\u0026thinsp;18.5kg/m2 were considered to be undernourished, while those with BMI between 18.5\u0026ndash;24.9 kg/m2 were considered to be normal. Participants were categorized based on their unplanned weight loss scores over the last 3\u0026ndash;6 months: those with \u0026lt;\u0026thinsp;5% were categorized as normal, 5\u0026ndash;10% as mild/moderate malnutrition, and \u0026gt;\u0026thinsp;10% as severely malnourished.\u003c/p\u003e\n\u003cp\u003eAdult malnutrition was then classified by SGA scores that uses 5 components of medical history (weight change, dietary intake, gastrointestinal symptoms, functional capacity, disease and its relation to nutritional requirements) and 3 components of brief physical examination, signs of fat and muscle wasting, and body fluid weight gain. The patient was then classified as;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSGA A\u003c/strong\u003e (Well-nourished), when there was no decrease in food/nutrient intake; \u0026lt;5% weight loss; no/minimal symptoms affecting food intake; no deficit in function; no deficit in fat or muscle mass,\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSGA B\u003c/strong\u003e (Mildly/moderately malnourished), when there was a definite decrease in food/nutrient intake; 5%-10% weight loss without stabilization or gain; mild/some symptoms affecting food intake; moderate functional deficit or recent deterioration; mild/moderate loss of fat and/or muscle mass.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSGA C\u003c/strong\u003e (Severely malnourished), when there was a severe deficit in food/nutrient intake; \u0026gt;10% weight loss which is ongoing; significant symptoms affecting food/ nutrient intake; severe functional deficit.\u003c/p\u003e\n\u003cdiv id=\"Sec7\" class=\"Section3\"\u003e\n\u003ch2\u003eNutritional intake and adequacy\u003c/h2\u003e\n\u003cp\u003eThe patient nutrient intake was determined using the 24-Hour Dietary Recall focusing on the routine from morning to evening before the onset of the current illness. This enabled assessment of the nutrient intake of the patient i.e. energy intake, carbohydrate, protein and fats.\u003c/p\u003e\n\u003cp\u003eNutritional adequacy was assessed using the Subjective Global Assessment (SGA) tool, which considers a diet adequate if it includes at least three meals per day containing all three macronutrients: carbohydrates, proteins, and fats.\u003c/p\u003e\n\u003cp\u003eInadequate nutrition was defined as a reduction in meal frequency, often caused by loss of appetite due to illness or as a result of suboptimal dietary intake, hypocaloric liquids, full fluids, or starvation. The severity of malnutrition was categorized based on the duration of inadequate nutrition, with less than two weeks considered mild, more than two weeks but less than one month considered moderate, and more than one month considered severe [\u003cspan class=\"CitationRef\"\u003e16\u003c/span\u003e].\u003c/p\u003e\n\u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\n\u003ch2\u003eData analysis\u003c/h2\u003e\n\u003cp\u003eData were entered into Microsoft Excel and exported to STATA software version 17 (Stata Corp, College Station, Texas, USA), for data analysis.\u003c/p\u003e\n\u003cp\u003eIndependent variables were analyzed using descriptive statistics. Continuous variables were as described as median and interquartile range while categorical variables described as percentageseduce the errors during entry process before analysis.\u003c/p\u003e\n\u003cp\u003ePrevalence was calculated as the total number of patients with malnutrition expressed as a percentage of the total participants. This was then reported with its 95% confidence interval. We used modified Poisson regression to evaluate associations between independent variables and malnutrition as the dependent variable. Variables that had a p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.2 at bivariate analysis were entered into a multivariable regression model to identify factors independently associated (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) with malnutrition.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\n\u003ch2\u003eEthical considerations\u003c/h2\u003e\n\u003cp\u003eThe ethical clearance was obtained from MUST Research and Ethics committee (MUST-REC 2023\u0026thinsp;\u0026minus;\u0026thinsp;937 and IRB 2023-08) before the data was collected. Administrative clearance was sought from the Hospital Director LRRH. Written informed consent was obtained from all the study participants. We abided by the principles of the Helsinki Declaration and CIOMS-2002 guidelines for human research, safeguarding against physical or moral harm.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"RESULTS","content":"\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eStudy Flow Chart\u003c/h2\u003e \u003cp\u003eOut of the 429 screened from November to December 2023, we present results for 423 participants. Six patients were excluded, including 5 who were too unwell to respond (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" 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\u003eSocio-demographic and clinical characteristics of hospitalized adult patients at Lira Regional referral hospital, Northern Uganda.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\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=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eCharacteristics\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMedian(IQR)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eFrequency\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ePercentage\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003eAge 40 (24 63)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e18\u0026ndash;40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e213\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e50.35\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e41\u0026ndash;64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e115\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e27.19\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e22.46\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eSex\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e200\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e47.28\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e223\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e52.72\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003eOccupation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eStudent\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e21.28\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePeasant\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e223\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e52.72\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBusiness\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e15.37\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCivil servant\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e8.51\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHouse wife\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.13\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eResidence\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e116\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e27.42\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTown council\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e22.70\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRural/village\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e211\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e49.88\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eEducation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo formal education\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e22.22\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePrimary\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e133\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e31.44\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSecondary\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e140\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e33.10\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTertiary\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e13.24\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eMarital status\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMarried\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e207\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e48.94\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSingle living together\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e144\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e26.95\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDivorced/separated\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e8.27\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWidowed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e15.84\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eNumber of household occupants\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e241\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e57.97\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e182\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e43.23\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eAlcohol abuse\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo alcohol\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e234\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e55.32\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAlcohol\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e189\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e44.68\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eDietary intake\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eInadequate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e285\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e67.38\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAdequate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e138\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e32.62\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eDiabetes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e395\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e93.38\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e6.62\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eHypertension\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e353\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e83.24\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e16.55\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eCancer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e418\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e98.82\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.18\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eHIV/AIDS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e362\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e85.58\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e14.42\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eTB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e394\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e93.14\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e6.86\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eHeart failure\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e399\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e94.33\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e5.57\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eLiver disease\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e384\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e90.78\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e9.22\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eKidney disease\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e395\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e93.38\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e6.62\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eCharacteristics of study participants\u003c/h2\u003e \u003cp\u003eOut of 423 participants, 223 (52.72%) were female. The participants had a median age of 40 (24\u0026ndash;63 years). The majority (n\u0026thinsp;=\u0026thinsp;285; 67.37%) had insufficient food consumption.\u003c/p\u003e \u003cp\u003eThe participants' medical characteristics revealed a high prevalence of chronic illnesses: 70 patients (16.54%) had hypertension, while 61 (14.42%) had HIV/AIDS. Other chronic illnesses included liver disease in 39 (9.21%) participants, tuberculosis in 29 (6.85%), diabetes in 28 (6.61%), kidney disease in 28 (6.61%), heart failure in 24 (5.67%), and cancer in 5 (1.18%) participants (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003ePrevalence of malnutrition\u003c/h2\u003e \u003cp\u003eOf the 423 participants, 176 had malnutrition, giving the prevalence of malnutrition among adult hospitalized patients at LRRH of 42% (95%CI: 36.99\u0026ndash;46.38); 4% (n\u0026thinsp;=\u0026thinsp;17) were obese, 10% (n\u0026thinsp;=\u0026thinsp;43) overweight, and 27% (n\u0026thinsp;=\u0026thinsp;116) underweight.\u003c/p\u003e \u003cp\u003eThe prevalence of undernutrition, among the hospitalized adult patients at LRRH was 27% (n\u0026thinsp;=\u0026thinsp;116; 95%CI: 23.37\u0026ndash;31.88). Among the 116 undernourished patients, 73 (63%) had mild/ moderate under-nutrition and 43 (37%) had severe undernutrition.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eFactors associated with undernutrition\u003c/h2\u003e \u003cp\u003eAt multivariable analysis (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e), age above 64 years and inadequate dietary intake were independently associated with adult hospitalized under-nutrition. The prevalence of undernutrition in hospitalized patients aged above 64 years was 1.2 times higher (adjusted prevalence ratio [aPR]\u0026thinsp;=\u0026thinsp;1.20, 95% CI: 1.01\u0026ndash;1.39, p\u0026thinsp;=\u0026thinsp;0.035) than in patients aged below 64 years. Additionally, Patients who had adequate dietary intake were less likely to have undernutrition than those who had inadequate dietary intake (aPR\u0026thinsp;=\u0026thinsp;0.91, 95% CI: 0.82\u0026ndash;0.99, p\u0026thinsp;=\u0026thinsp;0.041)\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\u003eFactors associated with under-nutrition among hospitalized adult patients at Lira Regional Referral Hospital, Northern Uganda.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"17\"\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 \u003cdiv align=\"left\" class=\"colspec\" colname=\"c14\" colnum=\"14\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c15\" colnum=\"15\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c16\" colnum=\"16\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c17\" colnum=\"17\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eUndernourished n\u0026thinsp;=\u0026thinsp;116\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eNo Undernourished n\u0026thinsp;=\u0026thinsp;307\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eBivariate analysis\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003eMultivariate\u003c/p\u003e \u003cp\u003eAnalysis\u003c/p\u003e \u003cp\u003eaPR (95% Cl)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c14\" namest=\"c12\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c16\" namest=\"c15\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"1\" nameend=\"c17\" namest=\"c17\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003ecPR (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c12\" namest=\"c9\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c15\" namest=\"c13\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c17\" namest=\"c16\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eGender\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c12\" namest=\"c9\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c15\" namest=\"c13\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c17\" namest=\"c16\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e58 (50.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e142 (46.25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eREF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c12\" namest=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c15\" namest=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c17\" namest=\"c16\"\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\" colname=\"c3\"\u003e \u003cp\u003e58 (50.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e165 (53.75)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.97 (0.89\u0026ndash;1.06)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.492\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c12\" namest=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c15\" namest=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c17\" namest=\"c16\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAge\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c12\" namest=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c15\" namest=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c17\" namest=\"c16\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e18\u0026ndash;40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e52 (44.83)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e161 (52.44)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eREF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c12\" namest=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c15\" namest=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c17\" namest=\"c16\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e41\u0026ndash;64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e25 (21.55)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e90 (29.22)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.97 (0.88\u0026ndash;1.08)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.601\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c12\" namest=\"c9\"\u003e \u003cp\u003e1.00 (0.89\u0026ndash;1.13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c15\" namest=\"c13\"\u003e \u003cp\u003e0.998\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c17\" namest=\"c16\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e39 (33.62)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e56 (18.24)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.18 (1.06\u0026ndash;1.31)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c12\" namest=\"c9\"\u003e \u003cp\u003e1.2 (1.01\u0026ndash;1.34)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c15\" namest=\"c13\"\u003e \u003cp\u003e0.035\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c17\" namest=\"c16\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eOccupation\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c12\" namest=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c15\" namest=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c17\" namest=\"c16\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eStudent\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e29 (25.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e61 (19.87)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eREF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c12\" namest=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c15\" namest=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c17\" namest=\"c16\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003ePeasant\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e79 (63.79)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e144 (48.53)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.01 (0.91\u0026ndash;1.12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.861\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c12\" namest=\"c9\"\u003e \u003cp\u003e0.97 (0.81\u0026ndash;1.17)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c15\" namest=\"c13\"\u003e \u003cp\u003e0.742\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c17\" namest=\"c16\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eBusiness\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7 (6.03)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e58 (18.89)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.81 (0.70\u0026ndash;0.93)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c12\" namest=\"c9\"\u003e \u003cp\u003e0.87 (0.71\u0026ndash;1.06)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c15\" namest=\"c13\"\u003e \u003cp\u003e0.176\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c17\" namest=\"c16\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eCivil servant\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6 (5.17)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e30 (9.77)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.86 (0.72\u0026ndash;1.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.072\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c12\" namest=\"c9\"\u003e \u003cp\u003e0.81 (0.64\u0026ndash;1.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c15\" namest=\"c13\"\u003e \u003cp\u003e0.064\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c17\" namest=\"c16\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eHousewife\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e00 (0.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e9 (2.93)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.72 (1.26\u0026ndash;1.51)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.036\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c12\" namest=\"c9\"\u003e \u003cp\u003e0.79 (0.56\u0026ndash;1.11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c15\" namest=\"c13\"\u003e \u003cp\u003e0.172\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c17\" namest=\"c16\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eResidence\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c12\" namest=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c15\" namest=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c17\" namest=\"c16\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eCity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e25 (21.55)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e91 (29.64)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eREF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c12\" namest=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c15\" namest=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c17\" namest=\"c16\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eTown council\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e25 (21.55)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e71 (23.13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.05 (0.93\u0026ndash;1.18)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.465\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c12\" namest=\"c9\"\u003e \u003cp\u003e1.03 (0.91\u0026ndash;1.16)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c15\" namest=\"c13\"\u003e \u003cp\u003e0.683\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c17\" namest=\"c16\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eRural/Village\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e66 (26.90)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e145 (23.13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.10 (0.99\u0026ndash;1.22)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.059\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c12\" namest=\"c9\"\u003e \u003cp\u003e1.00 (0.89\u0026ndash;1.12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c15\" namest=\"c13\"\u003e \u003cp\u003e0.996\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c17\" namest=\"c16\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eEducational level\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c12\" namest=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c15\" namest=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c17\" namest=\"c16\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eNo Formal education\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e35 (30.17)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e59 (19.22)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eREF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c12\" namest=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c15\" namest=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c17\" namest=\"c16\"\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\" colname=\"c3\"\u003e \u003cp\u003e38 (32.76)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e95 (30.95)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.92 (0.82\u0026ndash;1.03)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.146\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c12\" namest=\"c9\"\u003e \u003cp\u003e0.95 (0.83\u0026ndash;1.08)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c15\" namest=\"c13\"\u003e \u003cp\u003e0.400\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c17\" namest=\"c16\"\u003e\u0026nbsp;\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\" colname=\"c3\"\u003e \u003cp\u003e25 (21.55)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e115 (37.46)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.82 (0.73\u0026ndash;0.92)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c12\" namest=\"c9\"\u003e \u003cp\u003e0.89 (0.76\u0026ndash;1.05)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c15\" namest=\"c13\"\u003e \u003cp\u003e0.165\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c17\" namest=\"c16\"\u003e\u0026nbsp;\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\" colname=\"c3\"\u003e \u003cp\u003e18 (15.52)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e38 (12.38)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.95 (1.33\u0026ndash;1.59)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.495\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c12\" namest=\"c9\"\u003e \u003cp\u003e1.11 (0.89\u0026ndash;1.38)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c15\" namest=\"c13\"\u003e \u003cp\u003e0.370\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c17\" namest=\"c16\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eMarried/Living with a partner\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e45 (38.79)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e162 (52.77)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eREF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c12\" namest=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c15\" namest=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c17\" namest=\"c16\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eSingle\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e34 (29.31)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e80 (26.06)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.08 (0.98\u0026ndash;1.20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.119\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c12\" namest=\"c9\"\u003e \u003cp\u003e1.05 (0.89\u0026ndash;1.24)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c15\" namest=\"c13\"\u003e \u003cp\u003e0.549\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c17\" namest=\"c16\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eDivorced\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12 (10.34)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e23 (7.49)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.13 (0.97\u0026ndash;1.33)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.122\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c12\" namest=\"c9\"\u003e \u003cp\u003e1.08 (0.92\u0026ndash;1.26)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c15\" namest=\"c13\"\u003e \u003cp\u003e0.373\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c17\" namest=\"c16\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eWidowed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e25 (21.55)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e42 (13.68)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.17 (1.03\u0026ndash;1.32)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.013\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c12\" namest=\"c9\"\u003e \u003cp\u003e0.97 (0.82\u0026ndash;1.13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c15\" namest=\"c13\"\u003e \u003cp\u003e0.679\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c17\" namest=\"c16\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eNo. Household occupants\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c12\" namest=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c15\" namest=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c17\" namest=\"c16\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e64 (55.17)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e177 (57.65)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eREF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c12\" namest=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c15\" namest=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c17\" namest=\"c16\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e52 (44.83)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e130 (42.35)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.02 (0.94\u0026ndash;1.11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.646\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c12\" namest=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c15\" namest=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c17\" namest=\"c16\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSubstance abuse\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c12\" namest=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c15\" namest=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c17\" namest=\"c16\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eNo alcohol\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e61 (52.59)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e173 (56.35)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eREF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c15\" namest=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c17\" namest=\"c16\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eAlcohol\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e55 (47.42)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e134 (43.65)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.03(0.95\u0026ndash;1.12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.488\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c12\" namest=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c15\" namest=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c17\" namest=\"c16\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDietary intake\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c12\" namest=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c15\" namest=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c17\" namest=\"c16\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eInadequate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e91 (78.45)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e139 (63.19)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eREF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c12\" namest=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c15\" namest=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c17\" namest=\"c16\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAdequate\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e25 (21.55)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e\u003cb\u003e133 (36.81)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.88 (0.80\u0026ndash;0.95)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e0.003\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c12\" namest=\"c9\"\u003e \u003cp\u003e0.91 (0.82- 1.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c15\" namest=\"c13\"\u003e \u003cp\u003e\u003cb\u003e0.041\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c17\" namest=\"c16\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDiabetes\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c12\" namest=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c17\" namest=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003e109 (93.97)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e286 (93.16)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eREF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c12\" namest=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c17\" namest=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003e7 (6.03)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e21 (6.84)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.97 (0.82\u0026ndash;1.15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.769\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c12\" namest=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c17\" namest=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHypertension\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c12\" namest=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c17\" namest=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003e103 (88.79)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e250 (81.43)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eREF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c12\" namest=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c17\" namest=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003e13 (11.21)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e57 (18.57)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.89 (0.80\u0026ndash;1.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.069\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c12\" namest=\"c9\"\u003e \u003cp\u003e0.89 (0.79- 1.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e0.060\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c17\" namest=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCancer\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c12\" namest=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c17\" namest=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003e113 (97.41)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e305 (99.35)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eREF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c12\" namest=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c17\" namest=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003e3 (2.59)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2 (0.65)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.39 (0.94\u0026ndash;2.06)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c12\" namest=\"c9\"\u003e \u003cp\u003e1.26 (0.86\u0026ndash;1.84)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e0.240\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c17\" namest=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHIV/AIDS\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c12\" namest=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c17\" namest=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003e92 (79.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e270 (87.95)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eREF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c12\" namest=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c17\" namest=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003e24 (20.69)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e37 (12.05)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.15 (1.019- 1.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.024\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c12\" namest=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c17\" namest=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTuberculosis\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c12\" namest=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c17\" namest=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003e101 (87.07)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e293 (95.44)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eREF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c12\" namest=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c17\" namest=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003e15 (12.95)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e14 (4.56)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.30 (1.10\u0026ndash;1.53)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c12\" namest=\"c9\"\u003e \u003cp\u003e1.15 (0.96\u0026ndash;1.38)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e0.121\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c17\" namest=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHeart Failure\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c12\" namest=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c17\" namest=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003e110 (94.83)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e289 (94.14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eREF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c12\" namest=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c17\" namest=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003e6 (5.67)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e18 (5.86)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.97 (0.81\u0026ndash;1.17)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.785\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c12\" namest=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c17\" namest=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eLiver disease\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c12\" namest=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c17\" namest=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003e110 (95.69)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e274 (92.51)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eREF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c12\" namest=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c17\" namest=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003e6 (5.17)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e33 (10.75)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.88 (0.76\u0026ndash;1.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.077\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c12\" namest=\"c9\"\u003e \u003cp\u003e0.87 (0.75\u0026ndash;1.04)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e0.077\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c17\" namest=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eKidney disease\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c12\" namest=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c17\" namest=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003e111 (95.69)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e284 (92.51)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eREF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c12\" namest=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c17\" namest=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003e5 (4.31)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e23 (7.49)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.90 (0.76\u0026ndash;1.07)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.241\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c12\" namest=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c17\" namest=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"DISCUSSION","content":"\u003cp\u003eIn this study, 4 out of every 10 hospitalized adult patients screened at LRRH had malnutrition. Being aged\u0026thinsp;\u0026ge;\u0026thinsp;64 years and adequate dietary intake were found to be significantly associated with undernutrition.\u003c/p\u003e \u003cp\u003eIn this study, a considerable proportion of adult hospitalized patients had general malnutrition (42%), which is higher than the 24% prevalence that was reported in Iran [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. The disparity could be due differences in age ranges; The Iranian study included patients aged 18\u0026ndash;65, potentially excluding malnourished individuals aged 65 and older. Moreover, in our study, malnutrition was more prevalent among participants aged 64 and older compared to those under 64. A systematic review and meta-analysis study among hospitalized patients with COVID-19 [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e] reported a higher prevalence of 49%. The high prevalence may result from COVID-19's impact on the immune system, leading to excessive inflammation and cytokine storms, which can impair nutrient absorption due to gut epithelial cell damage and increased metabolic activity [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. A 2014 systematic review in Latin American countries reported a similar 40% prevalence rate to our study, possibly due to similarities such as study population and consideration of comorbid diagnoses like cancer, HIV/AIDS, heart failure, and chronic liver disease at admission [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. The high prevalence of malnutrition underscores the importance of enhancing nutritional assessment and intervention in clinical practice. Providing nutrition support during hospitalization and post-discharge should be prioritized among hospitalized patients to improve both short- and long-term clinical outcomes, as demonstrated elsewhere[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThis study found out that patients aged\u0026thinsp;\u0026ge;\u0026thinsp;64 years and above were more likely to have malnutrition compared to younger patients. This aligns with existing literature indicating that aging is associated with physiological changes such as sensory and digestive system changes, affecting digestion, absorption, appetite, and nutritional status [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. Other factors contributing to malnutrition in older adults include decreased gastrointestinal hormone secretions (e.g., ghrelin), with parallel changes in anorectic signaling (e.g., neuropeptide Y, peptide YY (PYY), orexin A, leptin, cholecystokinin (CCK) leading to an altered appetite regulation in advanced age [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. Furthermore, psychological factors like stress and anxiety, that are more prevalent in old age, may impair gastrointestinal motility and nutrient absorption [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. The findings underscore the importance of targeted nutritional strategies, particularly for older patients by promoting and supporting health and nutrition education to increase the level of awareness of good nutrition.\u003c/p\u003e \u003cp\u003eIn this study, patients who had adequate dietary intake were less likely to have under-nutrition than those who had inadequate. This also aligns with the existing literature indicating that patients who reported reduced dietary intake had the highest percentage (84.6%) of moderate to severe under-nutrition [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. Later in a chronic inadequacy dietary intake, where the main source of amino acids for gluconeogenesis is from skeletal muscle proteins. The smooth muscles of the gut breaks down rather more rapidly than the skeletal muscles during the initial stages of starvation [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. This results into crypt hypoplasia, reduced secretions with loss of disaccharidases, and atrophy of microvilli with compromised gap junction linkages between epithelial cells that are significant for small intestinal paracellular absorption pathway mechanism and overall dysfunctional consequences of impaired digestion and absorption of nutrients leading into malnutrition. Thus, adequate dietary intake could reduce the magnitude of under-nutrition and potentially enhance clinical outcomes among hospitalized adult patients.\u003c/p\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eLimitations\u003c/h2\u003e \u003cp\u003eThis study has some limitations that should be considered when interpreting the findings. Firstly, this study based on subjective global assessment tool to classify hospitalized adult malnutrition as; normal, mild/moderate or severe malnutrition. Laboratory markers (serum albumin, transferrin, C- reactive protein, and total lymphocyte count) malnutrition assessments could have been used; this may have introduced social desirability bias and potentially led to an underestimation of prevalence of malnutrition. Secondly, this study used 24-hour recall to estimate dietary adequacy, which may not reflect long-term dietary habits. This may have led to underestimating the prevalence of this exposure, potentially biasing the observed effect size towards the null. Finally, there was no follow-up to assess changes in nutritional status at discharge, potentially underestimating malnutrition prevalence.\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusions","content":"\u003cp\u003eIn this study, malnutrition was identified in 4 out of every 10 patients admitted at LRRH, Northern Uganda. Factors significantly associated with malnutrition included participants aged\u0026thinsp;\u0026ge;\u0026thinsp;64 years, adequate dietary intake. Increased attention to nutritional assessment and intervention in clinical practice in this region particularly for older patients could potentially enhance overall patient care and good outcomes in hospital setting. Furthermore, given the high prevalence of malnutrition, nutrition support during hospitalization and post-discharge should be considered among hospitalized patients to improve both short- and long-term clinical outcomes in this study population.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eBMI\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eBody Mass Index\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eCCK\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eCholecystokinin\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eCOVID-19\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eCorona Virus Disease-19\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eHAM\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eHospitalized Adult Malnutrition\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eHIV/AIDS\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eHuman Immunodeficiency Virus/ Acquired Immune-Deficiency Syndromes\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eLRRH\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eLira Regional Referral Hospital\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eMUAC\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eMid-Upper Arm Circumference\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eMUST\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eMalnutrition Universal Screening Tool\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003ePYY\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ePeptide YY\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eSGA\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eSubjective Global Assessment\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eTB\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eTuberculosis\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003cstrong\u003eConsent for publication\u003c/strong\u003e \u003cp\u003eNot applicable\u003c/p\u003e \u003c/p\u003e\u003cp\u003e \u003ch2\u003eCompeting Interests:\u003c/h2\u003e \u003cp\u003eThe authors have no conflict of interests to declare.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003eThis work did not receive any funding from external source.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eS.O., R.M, conceived, designed the study. S.O. drafted the initial manuscript; R.M, and D.C.A reviewed and edited the manuscript. S.O, J.S, V.M and R.O.O collected the data, S.O, R.A E.E and M.M designed data collection questionnaires. S.O and R.A analyzed the data. All authors read and approved the final manuscript.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003e We thank the patients who participated in the study, the research assistants who helped in data collection and the staff and management of Lira Regional Referral Hospital who contributed in various ways towards the success of the study. We acknowledge Mbarara University of Science and Technology and Lira University for granting opportunity to study\u003c/p\u003e\u003ch2\u003eData availability\u003c/h2\u003e \u003cp\u003eThe datasets generated and analyzed during the study are available from the corresponding author on request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eLigthart-Melis GC, et al. Frailty, sarcopenia, and malnutrition frequently (co-) occur in hospitalized older adults: a systematic review and meta-analysis. J Am Med Dir Assoc. 2020;21(9):1216\u0026ndash;28.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCorreia MIT, Perman MI, Waitzberg DL. Hospital malnutrition in Latin America: A systematic review. Clin Nutr. 2017;36(4):958\u0026ndash;67.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eInciong JFB, et al. Hospital malnutrition in northeast and southeast Asia: A systematic literature review. Clin Nutr ESPEN. 2020;39:30\u0026ndash;45.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBellanti F, et al. Malnutrition in hospitalized old patients: screening and diagnosis, clinical outcomes, and management. Nutrients. 2022;14(4):910.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFelder S, et al. Association of nutritional risk and adverse medical outcomes across different medical inpatient populations. Nutrition. 2015;31(11\u0026ndash;12):1385\u0026ndash;93.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKang MC et al. Prevalence of malnutrition in hospitalized patients: a multicenter cross-sectional study. J Korean Med Sci, 2018. 33(2).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLima J, et al. Decline of nutritional status in the first week of hospitalisation predicts longer length of stay and hospital readmission during 6-month follow-up. Br J Nutr. 2021;125(10):1132\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBlaauw R et al. \u003cem\u003eThe Problem of Hospital Malnutrition in the African Continent.\u003c/em\u003e Nutrients, 2019. 11(9): p. 2028.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOdwee A. \u003cem\u003eMalnutrition and Its Associated Factors among Adults Attending Anti-Retroviral Therapy at Three Selected Hospitals In Bushenyj District, Uganda.\u003c/em\u003e 2018.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAndersen AL, et al. Risk of malnutrition upon admission and after discharge in acutely admitted older medical patients: a prospective observational study. Nutrients. 2021;13(8):2757.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRoberts S, et al. Engaging hospitalised patients in their nutrition care using technology: development of the NUTRI-TEC intervention. BMC Health Serv Res. 2020;20(1):148\u0026ndash;148.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNorman K, Ha\u0026szlig; U, Pirlich M. Malnutrition in Older Adults-Recent Advances and Remaining Challenges. Nutrients. 2021;13(8):2764.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKirkland LL, Shaughnessy E. Recognition and prevention of nosocomial malnutrition: a review and a call to action! Am J Med. 2017;130(12):1345\u0026ndash;50.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAsiimwe SB, et al. Bedside measures of malnutrition and association with mortality in hospitalized adults. Clin Nutr. 2015;34(2):252\u0026ndash;6.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOdwee A, et al. Malnutrition amongst HIV adult patients in selected hospitals of Bushenyi district in southwestern Uganda. Afr Health Sci. 2020;20(1):122\u0026ndash;31.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePlanas M, et al. Nutritional status among adult patients admitted to an university-affiliated hospital in Spain at the time of genoma. Clin Nutr. 2004;23(5):1016\u0026ndash;24.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePoudineh S, et al. A multi-centre survey on hospital malnutrition: result of PNSI study. Nutr J. 2021;20(1):87.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAbate SM, et al. Prevalence and outcomes of malnutrition among hospitalized COVID-19 patients: A systematic review and meta-analysis. Clin Nutr ESPEN. 2021;43:174\u0026ndash;83.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLiu H, et al. Malnutrition is associated with hyperinflammation and immunosuppression in COVID-19 patients: a prospective observational study. Nutr Clin Pract. 2021;36(4):863\u0026ndash;71.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKaegi-Braun N, et al. Nutritional support after hospital discharge improves long-term mortality in malnourished adult medical patients: Systematic review and meta-analysis. Clin Nutr. 2022;41(11):2431\u0026ndash;41.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAlzahrani SH, Alamri SH. Prevalence of malnutrition and associated factors among hospitalized elderly patients in King Abdulaziz University Hospital, Jeddah, Saudi Arabia. BMC Geriatr. 2017;17(1):136.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNorman K, Ha\u0026szlig; U, Pirlich M. Malnutrition in older adults\u0026mdash;recent advances and remaining challenges. Nutrients. 2021;13(8):2764.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBarcus GC, et al. Nutrition Screening, Reported Dietary Intake, Hospital Foods, and Malnutrition in Critical Care Patients in Malawi. Nutrients. 2021;13(4):1170.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eEmery P. Metabolic changes in malnutrition. Eye. 2005;19(10):1029\u0026ndash;34.\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":"Malnutrition, Prevalence, Nutrition status, Hospitalized patients, Uganda","lastPublishedDoi":"10.21203/rs.3.rs-4330592/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4330592/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground: \u003c/strong\u003eMalnutrition among hospitalized patients is associated with suboptimal recovery, unfavorable prognosis and increased mortality. However, malnutrition in hospitalized patients is often overlooked, underdiagnosed, and frequently inadequately addressed in clinical practice. We determined the prevalence and associated factors of malnutrition in hospitalized adult patients at Lira Regional Referral Hospital (LRRH), Uganda.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods: \u003c/strong\u003eWe conducted a cross-sectional study at medical wards of LRRH during November and December 2023. The study included patients aged ≥18years through consecutive sampling method. We excluded those who were too unwell to respond to the research questions. Socio-demographic and clinical characteristics were obtained through interviewer-administered questionnaires. Malnutrition was assessed using the Malnutrition Universal Screening Tool (MUST), which utilizes body mass index (BMI) scores for classification. Individuals with BMI scores \u0026lt;18.5 kg/m² were categorized as undernourished, those with BMI scores \u0026lt;18.5 kg/m² or ≥25 kg/m² were classified as malnourished, and BMI scores of 18.5-24.9 kg/m² were considered normal. Malnutrition was further categorized based on weight loss percentages, using the Subjective Global Assessment (SGA) tool: normal (weight loss 0-\u0026lt;5%), mild/moderate (weight loss 5-10%), and severe (weight loss \u0026gt;10%). Modified Poisson regression was used to evaluate associations between undernutrition and independent variables.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults: \u003c/strong\u003eIn total, 423 patients were recruited with median age of 40 (inter-quartile range [IQR]: 24-63) years; 223 (53%) were female. Overall, 176 (42%, 95% CI: 37-46%) had malnutrition; 116 (27%) were undernourished, 73 (17%) were mild/moderately undernourished, and 43 (10%) severely undernourished. Being aged \u0026gt;64 years (aPR 1.19, 95% CI: 1.01- 1.39), and having adequate dietary intake (aPR 0.91, 95% CI: 0.82-0.99), were independently associated with under-nutrition.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion: \u003c/strong\u003eApproximately 4 out of every 10 patients screened at LRRH had malnutrition. Patients of advanced age were more likely to be undernourished, while those with adequate dietary intake were less likely to be undernourished. The high prevalence of malnutrition highlights the need for increased attention to nutritional assessment and intervention in clinical practice, particularly for older patients. Adequate dietary intake and post-discharge nutritional interventions could reduce the magnitude of under-nutrition and potentially enhance clinical outcomes in this setting.\u003c/p\u003e","manuscriptTitle":"Malnutrition and associated factors among hospitalized adult patients at a tertiary hospital, Northern Uganda: a cross-sectional study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-05-07 19:13:55","doi":"10.21203/rs.3.rs-4330592/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":"b92f2fc1-f910-4a9a-b7ac-e4dd1f53f55c","owner":[],"postedDate":"May 7th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-05-08T08:43:18+00:00","versionOfRecord":[],"versionCreatedAt":"2024-05-07 19:13:55","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4330592","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4330592","identity":"rs-4330592","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}