Double Burden: Fatigue and Poor Sleep quality comorbidity and its predictor Among Cancer Patients, Northwest Ethiopia: Institutional based cross- sectional study design

Double Burden: Fatigue and Poor Sleep quality comorbidity and its predictor Among Cancer Patients, Northwest Ethiopia: Institutional based cross- sectional study design | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Double Burden: Fatigue and Poor Sleep quality comorbidity and its predictor Among Cancer Patients, Northwest Ethiopia: Institutional based cross- sectional study design Gebreeyesus Zeleke, Astewil Moges Bazezew, Birtukan Atena Negash, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8065921/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background: Cancer-related fatigue and poor sleep quality are among the most prevalent and distressing symptoms experienced by patients with cancer, significantly impairing physical, emotional, and cognitive functioning. Despite their high prevalence and detrimental impact on quality of life, the comorbidity of fatigue and sleep disturbances remains underexplored, particularly in low-resource settings where access to comprehensive oncology care is limited. Understanding the magnitude, contributing factors, and interrelationship of these symptoms is essential for developing targeted interventions. However, existing research predominantly focuses on either fatigue or sleep quality in isolation, highlighting a critical gap in evidence regarding their concurrent occurrence and synergistic effects on cancer patient. Method : Institutional-based quantitative cross-sectional study was conducted among adult cancer patients receiving cancer treatment at an oncology unit from May to June 2025. A systematic random sampling technique was used to select 422 samples. After obtaining consent data were collected using a structured Interviewer-administered questionnaire. Then data were entered into Epi-data version 4.6 and exported to Stata version 14 for analysis. Model fitness was checked by the Hosmer-Lemeshow goodness of fit test. Descriptive statistics including, frequencies and proportions were computed and presented by using tables and texts. Bivariable and multivariable logistic regression analysis was computed considering p<0.05 to be statistically significant at the final model. Result: A total of 405 cancer patients were included in this study, of whom 46.67% experienced comorbid fatigue and poor sleep quality. Age 61-89 years [ AOR = 2.79, 95% CI: [1.02, 7.62]. Rural residency [ AOR = 2.03 95%, CI: [1.02, 4.01], Married & divorced [ AOR = 2.65 95%, CI: (1.01, 6.90)] and [ AOR = 3.54 95% CI: (1.10, 11.40)], Inpatient [ AOR =2.84, 95%, CI: (1.63, 4.95)]. Stage II and Stage IV [ AOR =3.92, 95%, CI: ([1.89, 8.12] and [AOR= 2.52, 95% CI: (1.04, 6.15)] respectively, cancer duration [ AOR =2.70, 95% CI: (1.14, 6.39)]. Anxiety [ AOR = 1.93, 95% CI: (1.06, 3.51)]. depression [ AOR = 2.10, 95% CI: (1.19, 3.70)]. Conclusion and recommendation: Comorbidity of fatigue and poor sleep affected nearly half of cancer patients, representing a substantial and underrecognized clinical burden that necessitates systematic assessment and integrated, multidisciplinary interventions in oncology care. Age, Residence, Marital Status, Cancer Stage, Cancer Duration, Inpatient Admission, And Anxiety and Depression were significant predictors. These findings highlight the need for routine screening and integrated interventions targeting both physical and psychosocial determinants, alongside strengthening supportive and multidisciplinary care to improve patient outcomes. Biological sciences/Cancer Health sciences/Diseases Health sciences/Health care Health sciences/Medical research Health sciences/Oncology Cancer related fatigue sleep quality comorbidity cancer patients Amhara Region Figures Figure 1 Figure 2 Introduction The comorbidity of cancer-related fatigue (CRF) and poor sleep quality constitutes a critical and debilitating symptom cluster, defined by persistent, non-relieving exhaustion and disrupted sleep processes, which together amplify clinical complications, intensify functional impairment, and severely compromise the quality of life in cancer patients. Cancer is major public health challenge, with nearly 20 million new cases and 9.7 million cancer-related deaths reported globally in 2022[1]. Beyond the rising prevalence of cancer, fatigue and sleep disturbances persist as among the most prevalent and distressing symptom clusters, representing a major source of symptom burden and significantly impairing patients’ quality of life[2, 3]. Research from the U.S. Centers for Disease Control and Prevention (CDC) and the World Health Organization (WHO) shows how these symptoms impair psychological health, physical functioning, treatment compliance, and general quality of life.[4, 5]. Longitudinal and systematic reviews repeatedly show that fatigue and poor sleep quality are not only highly prevalent but also commonly co-occur, exacerbating each other's symptoms and making patients' suffering worse[6, 7]. Furthermore, meta-analyses show that, depending on the disease stage and treatment approach, between 40 and 80 percent of patients experience cancer-related fatigue (CRF), and 50 to 70 percent experience clinically significant sleep disturbance [5, 8]. When these conditions co-occur, patients face synergistic declines in quality of life, reduced functional status, and poorer survival outcomes[9]. Additionally, longitudinal data demonstrates that untreated poorer and fatigue can last for years after treatment, severely reducing the chances of surviving. The intensity of this comorbidity has been linked to psychological morbidity, such as anxiety and depression, lower productivity, and higher healthcare consumption. [10]. Consequently, the combined burden of fatigue and poor sleep quality constitutes a significant but little-known aspect of cancer treatment worldwide. Numerous clinical, sociodemographic, and psychological factors have been found to predict the comorbidity of sleep disruption and cancer related fatigue. Higher risks of these symptoms are consistently linked to advanced cancer stage, longer time since diagnosis, and harsh treatment modalities like chemotherapy and radiation[11, 12]. Biological factors, including anemia, pain, systemic inflammation, and poor nutritional status, further exacerbate symptom severity[13]. Psychological factors, particularly depression and anxiety, are strong independent predictors, while low physical activity, poor social support, and rural residency have been implicated in low- and middle-income country (LMIC) settings[13, 14]. This complex etiology emphasizes the necessity of integrated, situation-specific management strategies. The comorbidity of cancer related fatigue and poor sleep quality is not well studied in sub-Saharan Africa, especially Ethiopia, despite its increasing international attention. The majority of Ethiopia's current study has focused on these symptoms separately. Studies carried out in Ethiopia's Amhara area and elsewhere, for instance, have found a high frequency of CRF and poor sleep quality independently, and correlations between these outcomes and variables like depression, advanced cancer stage, anemia, pain, and inpatient status have been found[15–17]. However, to our knowledge, no prior study in northwest Amhara has quantified the combined burden of fatigue and poor sleep quality as a binary comorbidity outcome. This represents a critical knowledge gap, as simultaneous evaluation provides more comprehensive insights into patient symptom clusters and may better inform integrated care strategies. In light of this evidence gap, there is an urgent need for studies that establish both the magnitude and determinants of fatigue–sleep comorbidity in Ethiopian oncology settings. Such evidence is particularly relevant to Ethiopia, where late-stage presentation, limited access to specialized care, and high psychosocial burden may amplify the prevalence and impact of these symptoms. Therefore, this study aimed to estimate the magnitude of comorbid fatigue and poor sleep quality and its predictors among adult cancer patients attending oncology unit in the northwest Amhara region, Ethiopia. Method and material Study design, and period Institutional-based quantitative cross-sectional study was conducted among adult cancer patients receiving cancer treatment at an oncology unit and Data collection was take place over a month from May to June 2025. Study Area The study was carried out in the cancer treatment hospitals in the Amhara region, Northwest Ethiopia. There are eight comprehensive hospitals in the in Amhara region, of which only four hospital have a cancer treatment oncology unit. Thus, hospitals were University of Gondar Comprehensive Specialized Hospital (UOGCSH), Felegehiwot Comprehensive Specialized Hospital (FCSH), Tibebegion Comprehensive Specialized Hospital (TCSH), and Dessie Comprehensive Specialized Hospital (DCSH). The distances of these hospitals from Addis Ababa, the capital city of Ethiopia, are 748 km, 564 km, 399 km, and 480 km, respectively. Each comprehensive specialized hospital serves 3.5–5 million people[18]. Each of these hospitals operates an oncology clinic or treatment center that provides both inpatient and outpatient services. Specifically, the oncology units of FCSH = 28, UoGCSH = 32, DCSH = 20, and TCSH = 25 beds are equipped respectively. These services are delivered by a multidisciplinary team comprising nurses, oncologists, and general practitioners. Study Population All adult cancer patients (aged ≥ 18 years) attending the oncology clinics during the data collection period was study population and study unit consisted of adult cancer patients who were chosen at random during the data collection period. Eligibility Criteria Inclusion Criteria Eligible participants were including adult patients (aged 18 years or older) with a confirmed diagnosis of any type of cancer. Patients must be either currently receiving active cancer treatment (such as chemotherapy, radiotherapy, immunotherapy, or surgery) or undergoing follow-up care. Additionally, participants must be willing and able to provide informed consent and demonstrate sufficient cognitive and communicative ability to complete the study questionnaire reliably. Exclusion Criteria Patients who are critically ill or unable to communicate, as well as those diagnosed with psychiatric disorders or cognitive impairments that interfere with reliable self-reporting, should be excluded. Additionally, patients with previously diagnosed primary sleep disorders that are not attributable to cancer or its treatment should be considered separately, as their sleep disturbances are independent of oncologic processes. Sample Size Determination The sample size was calculated using the single population proportion formula, considering: Estimated prevalence (P) of comorbid poor sleep quality and fatigue among cancer patients 50% (due to lack of local data). Prevalence (P) = 50% Confidence level = 95% (Z = 1.96). Margin of error (d) = 5% (0.05). Formula n= (Zα/2) 2 p (1-p) ------------------------------- d 2 n = 1.96*0.5(1-0.5)/ 0. 05 2 =384 Adding 10% non-response rate 422. Final sample size was 422. Sampling Technique Systematic random sampling technique was used to select study participants from each comprehensive specialized hospital and proportional allocation for each hospital was properly calculated based on number of cancer patients they served per month. The sampling interval was determined by dividing the total study population who had follow-up and on treatment during one typical month (1500) by total sample size (422). Therefore, the sampling fraction was calculated to be 1500/422 ≈ 3. The first participant was selected randomly by a lottery method from 1–3 and the next respondent was chosen at regular intervals (every 3) by data collectors and patient register follow up log book, patient MRN No. and the patient itself should strictly use to avoid repeated data collection ( Fig. 1 ) FHCSH : Felege Hiwot Comprehensive Specialized Hospital TGCSH : Tibebe Gion Comprehensive Specialized Hospital DCSH : Dessie Comprehensive Specialized Hospital UoGCSH : University of Gondar Comprehensive Specialized Hospital Data Collection Tools and Procedures Data were collected using a structured, interviewer-administered questionnaire with open-ended and closed-ended questions. There are eight parts to the data collection tool. Part one contains sociodemographic data, part two disease and treatment-related signs and symptoms, part three sleep quality assessment, part four Brief Fatigue Inventory. The tools for socio-demographic and clinical factors were adapted from the review of different pieces of literature. Anxiety and depression were assessed using the Hospital Anxiety and Depression Scale (HADS) [19] which was also validated in Ethiopian cancer patients [20]. Performance status was assessed by the single item Eastern Cooperative Oncology Group (ECOG) performance status scale [21]. Social support was assessed by the three-item Oslo social support scale (OSSS-3) [22]. Sleep quality was assessed by a standardized and validated Pittsburgh Sleep Quality Index (PSQI). The PSQI was designed to evaluate the subjective quality of sleep in the past month. It contains 18 self-rated questions, including seven subscale components (subjective sleep quality, sleep latency, sleep duration, habitual sleep efficiency, sleep disturbance, use of sleep medication, and daytime dysfunction). The global PSQI score ranges from 0 (no difficulty) to 21 (severe difficulties in all areas). Each component score ranges from 0 (no difficulty) to 3 (severe difficulty). Higher global and component scores indicate more severe complaints and a higher level of poor sleep quality. A global PSQI score greater than 5 yielded a diagnostic sensitivity of 89% and specificity of 86.5% (kappa = 0.75, P ≤ 0:001) in distinguishing “poor” from “good” sleepers [23]. The construct validity and internal consistency are further evaluated and supported in cancer patients with a Cronbach’s α value of 0.81 [24]. For present study, the internal consistency measurement of the PSQI subscales found a Cronbach’s alpha coefficient of 0.761 from the pretest data which was acceptable for this study. Brief Fatigue Inventory scale (BFI): Cancer patients who scored greater than or equal to four (≥ 4) moderate to severe in BFI measurement scale was considers fatigue whereas, < 4 in BFI scale was considers not fatigue[25–29]. Data Collection Procedure Data collectors (trained BSc nurses) conducted face-to-face interviews in a private setting to ensure confidentiality and comfort. Medical records were reviewed to obtain clinical data. The questionnaires were first translated into the local language (Amharic) and then back-translated into English to ensure accuracy. A pretest of the instruments was carried out on 5% of the sample at Debre Berihan Comprehensive Specialized Hospital in a similar population to identify and address potential issues Outcome variable Outcome variable was a binary variable representing the co-occurrence (comorbidity) of poor sleep quality and cancer-related fatigue. Sleep quality was assessed using the Pittsburgh Sleep Quality Index (PSQI). The global PSQI score ranges from 0 to 21, with higher scores indicating poorer sleep quality. A commonly accepted cut-off score of > 5 was used to classify poor sleep quality. Fatigue was measured using the Brief Fatigue Inventory (BFI). The (BFI) score ranges from 0 to 10. BFI score ≥ 4 were categorized as clinically significant fatigue, while those with scores < 4 were considered not clinically significant fatigued. Participants were classified as having the comorbid condition (coded as 1 ) if they met both criteria simultaneously that is, they had a PSQI score > 5 and a BFI score ≥ 4 . All other participants (i.e. those with either or neither condition) were coded as 0 , indicating absence of the comorbid outcome. Comorbidity of fatigue and poor sleep quality was defined as the presence of both clinically significant fatigue and poor sleep in the same individual. Fatigue was measured using Brief Fatigue Inventory, with a score of ≥ 4, indicating clinically significant fatigue. Sleep quality was measured using the Pittsburgh Sleep Quality Index (PSQI), with a global score > 5 indicating poor sleep. Participants meeting the criteria for both were coded as “1” (comorbidity); all others were coded as “0” (No comorbidity). Independent Variables Sociodemographic such as Age, sex, residence, marital status, education, occupation. Clinical factors: Cancer type, cancer stage, duration since diagnosis, treatment modality, Behavioral and psychosocial factors: depression/anxiety. Operational Definition Comorbidity fatigue and poor sleep quality is defined as the simultaneous presence of poor sleep quality and fatigue[15] Good sleep quality a global PSQI score of ≤ 5[30] Poor sleep quality a global PSQI score of > 5[30] Anxiety and depression : A patient with more than 10 points on the Hospital Anxiety and Depression Scale (HADS) has anxiety and depression problem. Good performance : if Eastern Cooperative Oncology Group performance status which ranges from 0–4 (ECOG-PS). Patient score 0–1. Poor performance : if Eastern Cooperative Oncology Group performance status (ECOG-PS) patient score 2–4. Social support : by using the three-item Oslo social support scale (OSSS-3), a score of 3–8 represents ‘poor support’, 9–11 ‘moderate support’, and 12–14 ‘strong support[31, 32]. Data Quality Assurance Supervisors and data collectors received training on interviewing techniques, ethical considerations, and study objectives. To guarantee accuracy and consistency, completed surveys were supervised and reviewed every day. To reduce entry errors, data entry was done twice. Scales such as the PSQI and BFI were tested for internal consistency using Cronbach's alpha. Data Processing and Analysis Data were coded and entered into Epi Data and then exported to Stata version 14 for analysis. Descriptive statistics were used to summarize sociodemographic and clinical characteristics as well as the prevalence of comorbid fatigue and poor sleep quality. Bivariate analyses using Chi-square tests were conducted to assess associations between independent variables and comorbidity. Variables with a p-value < 0.25 in the bivariate analysis were included in the multivariable logistic regression model to identify independent predictors of comorbidity, and adjusted odds ratios (AORs) with 95% confidence intervals (CIs) were reported. Ethical Considerations Ethical approval for this study was obtained from the Institutional Review Board (IRB) of the University of Gondar. Permission was also granted by the respective hospital administrations. Written informed consent was obtained from each participant after providing a clear explanation of the study’s purpose, potential risks, and anticipated benefits. To ensure confidentiality and anonymity, all data were securely stored and made accessible only to the research team. Results Socio-demographic characteristics of study participants In this study, 405 patients were participated with 97% response rate. nearly two-thirds (60.74%) participants were female. The Mean age of participants were 46.43 with (Std. Dev. 15.43) years. Around one third of the participants (32%) were stage two cancer and in terms of residency, slightly more than half of the participants (62.96%) were from rural area and nearly half of the participants (45.43%) were not educated ( Table 1 ). Table 1: Frequency distribution of the characteristics of study participants attending oncology units in Amhara region comprehensive socialized hospitals, northwest Ethiopia. 2022 (n=405) Variable Fatigue & poor sleep quality comorbidity Frequency percent (%) Age Yes No 18-30 31-43 44-60 61-89 42 50 73 24 31 45 93 47 73 95 166 71 18.2 23.45 4.98 17.53 Sex Male female 75 114 84 132 159 246 39.25 60.74 Residency Urban Rural 82 107 68 148 152 255 37.53 62.96 Marital status Married divorced single widowed 108 20 38 23 147 24 21 24 255 44 59 47 62.96 10.86 14.56 11.60 Educational status Not education primary education secondary education college and above 74 80 20 15 110 64 28 14 184 144 48 29 45.43 35.55 11.85 7.16 Occupational status unemployed Employed farmer Marchant 22 25 95 47 15 15 136 50 37 40 231 97 9.13 9.87 57. 03 23.95 Admission status Inpatient Outpatient 77 112 140 76 217 188 53.58 46.41 Cancer types Breast cancer colorectal cancer cervical cancer Lung cancer skin cancer prostate cancer 46 28 40 21 23 14 46 31 42 28 18 9 92 59 82 49 41 23 22.71 14.56 2.24 12.9 10.12 5.67 Stage of cancer stage I stage II Stage III Stage IV 80 43 29 37 47 88 35 46 127 131 64 83 31.35 32.34 15.8 20.49 Cancer therapy Medical therapy surgical therapy 124 65 160 55 284 120 70. 12 29.62 Metastasis History Yes No 69 120 92 124 161 244 39.75 60.24 Cancer duration < 1 year 1yr & above 43 146 16 200 59 346 14.56 85.43 Cigarette Smoking Yes No 37 152 19 197 56 349 13.82 86.17 Social support status poor Moderate Strong 100 80 9 125 77 14 225 157 23 55.55 38.76 5.67 Performance status Good poor 99 90 77 139 | 176 229 43.45 56.56 Anxiety No Yes 104 85 74 142 178 227 43.95 56. 04 Depression status No Yes 131 58 99 117 230 175 56.70 43.20 Magnitudes of comorbidity fatigue and poor sleep quality among adult cancer patient were 46.67% [95% CI: (41.8–51.6] ( Fig. 2 ). Association factors with comorbidity of fatigue and poor sleep quality among cancer patient Amhara region, northwest, Ethiopia. In the final multivariable logistic regression model, age, marital status, place of residence, admission status, duration of cancer, cancer type, cancer stage, as well as anxiety and depression status were all identified as factors significantly associated with the outcome. The odds of fatigue and poor sleep quality comorbidity were 2.79 times higher among cancer patient aged 61 to 89 years compared to women aged 18 to 30 years [ AOR = 2.79, 95% CI: [1.02, 7.62]. The odds of fatigue and poor sleep quality comorbidity were 2.03 times higher among cancer patient living in rural residency compared to urban resident [ AOR = 2.03 95%, CI: [1.02, 4.01]. The odds of fatigue and poor sleep quality comorbidity were 2.65 and 3.54 times higher among cancer patient who are married and divorced respectively compared to single cancer patient [ AOR = 2.65 95%, CI: (1.01, 6.90)] and [ AOR = 3.54 95% CI: (1.10, 11.40)]. The odds of fatigue and poor sleep quality comorbidity were 2.84 times higher among cancer patient who are inpatient compared to outpatient [ AOR = 2.84, 95%, CI: (1.63, 4.95)]. The odds of fatigue and poor sleep quality comorbidity were 3.92 & 2.52 times higher among Stage II and Stage IV of cancer patient compared to stage I [ AOR = 3.92, 95%, CI: ([1.89, 8.12] and [ AOR = 2.52, 95% CI: (1.04, 6.15)] respectively. The odds of fatigue and poor sleep quality comorbidity were 2.70 times higher among ≥ 1-year cancer duration compared to < l year cancer duration [ AOR = 2.70, 95% CI: (1.14, 6.39)]. The odds of fatigue and poor sleep quality comorbidity were 1.93 times higher among cancer patient who had an anxiety compared to No anxiety [ AOR = 1.93, 95% CI: (1.06, 3.51)]. The odds of fatigue and poor sleep quality comorbidity were 2.10 times higher among cancer patient who had depression compared to No depression [ AOR = 2.10, 95% CI: (1.19, 3.70) ] ( Table 2 ). Table 2: Final bivariable and multivariable logistic regression analysis of factors associated with the comorbidity of fatigue and poor sleep quality among cancer patients, Amhara region, northwest Ethiopia. Variable Fatigue & poor sleep quality comorbidity COR [95% CI] AOR [95% CI] Age 18-30 31-43 44-60 61-89 Yes No 1 1.21 [0.65,2.25] 1.72 [0.98, 3.01] 2.65 [1.34, 5.21] 1 0.88 [0.38, 2.06] 0.99 [0.44, 2.24] 2.79 [1.02, 7.62] * 42 50 73 24 31 45 93 47 Sex Female Male 75 114 84 132 1 0.96 [0.64, 1.44] 1 0.71 [0.37, 1.37] Residency Urban Rural 82 107 68 148 1 1.66 [1.11, 2.50] 2.03 [1.02, 4.01] * Marital status Single Married divorced widowed 108 20 38 23 147 24 21 24 1 2.46 [1.36, 4.43] 2.17 [0.97, 4.82] 1.88 [.86, 4.12] 1 2.65 [1.01, 6.90] * 3.54 [1.10, 11.40] * 1.12 [0.35, 3.61] Educational status No education primary education secondary edu college and above 74 80 20 15 110 64 28 14 1 0.538 [0.34, 0.83] 0.94 [0.49, 1.79] 0.62 [0.28, 1.37] 1 0.85 [0.43, 1.71] 1.88 [0.70, 5.00] 1.81 [0.55, 5.99] Occupational status Employed Unemployed Farmer Marchant 22 25 95 47 15 15 136 50 1 1.13 [0.45, 2.84] 2.38 [1.19, 4.76] 1.77 [ 0.83, 3.76] 1 0.74 [0.19, 2.94] 1.27 [0.43, 3.76] 1.95 [0.64, 5.99] Admission status Outpatient Inpatient 77 112 140 76 1 2.67 [1.79, 4.00] 2.84 [1.63, 4.95] * Cancer types Breast cancer colorectal cancer cervical cancer lung cancer skin cancer prostate cancer 46 28 40 21 23 14 46 31 42 28 18 9 1 0.90 [0.46, 1.73] 0.94 [0.48, 1.85] 1.20[0.56, 2.58] 0.70 [0.31, 1.57] 0.58 [0.21, 1.54] 1 1.80 [0.75, 4.34] 0.91 [0.43, 1.92] 3.15 [1.23, 8.03] * 1.24 [0.48, 3.22] 1.14 [0.33,3.88] Stage of cancer stage 1 stage 2 stage 3 stage 4 80 43 29 37 47 88 35 46 1 3.48 [2.08, 5.81] 2.05 [1.11,3.78] 2.11 [1.20, 3.71] 1 3.92 [1.89, 8.12] * 2.33 [0.91, 5.93] 2.52 [1.04, 6.15] * Cancer therapy Medical therapy surgical therapy 124 65 160 55 1 0.65 [.42, 1.00] 1 0.69 [0.39, 1.24] Metastasis History No yes 69 120 92 124 1 1.29 [ 0.86, 1.92] 1 1.05 [0.58, 1.90] Cancer duration < 1 year ≥1 year & above 43 146 16 200 3.68 [1.99, 6.79] 1 2.70 [1.14, 6.39] * Cigarette Smoking Yes No 37 152 19 197 2.52 [1.39, 4.56] 1 2.06 [0.88, 4.83] Social support poor moderate strong 100 80 9 125 77 14 0.77 [0.50, 1.12] 1.24 [0.19, 7.13 0.55 [0.31, 0.98] 1.45 [0.12, 16.88] performance status Good poor 99 90 77 139 | 1 1.98 [1.33, 2.95] 0.81 [0.45,1.47] Anxiety status No yes 104 85 74 142 1 2.34 [ 1.57, 3.50] 1.93 [1.06, 3.51] * Depression No Yes 131 58 99 117 1 2.66 [1.77, 4.01] 2.10 [1.19, 3.70] * Discussion This study highlights a range of sociodemographic, clinical, and psychological factors associated with the co-occurrence of fatigue and poor sleep quality among individuals with cancer. Findings suggest that certain demographic characteristics, clinical conditions, and psychological states may contribute to the comorbidity of these symptoms. These results offer important insights into the complex interplay of factors that influence symptom burden in cancer populations and provide a foundation for further exploration and targeted interventions. In this study, comorbidity fatigue and poor sleep quality were 46.67% (95% CI: 41.8–51.6) of adult cancer patients in the Northwest Amhara Region. Our findings are in line with the larger body of research showing that both symptoms are quite common and frequently co-occur in oncology populations, even though no prior studies have specifically recorded this comorbidity as a single outcome. For instance, studies conducted in similar settings have reported that cancer-related fatigue affects approximately 50–60% of patients[16, 33], while poor sleep quality has been observed in 53–61% of patients [16, 34]. The strong associations previously reported between fatigue and sleep problems such as correlation coefficients around 0.6, adjusted odds ratios > 2) support the likelihood of a high degree of overlap between the two symptoms[16, 35, 36]. Our finding indicates that around half of cancer patients in this area had both symptoms, which represents a significant symptom burden. Numerous interrelated factors, including the burden of the disease, adverse drug reactions, psychological stress, disease stage, and restricted access to supportive care, may contribute to this. The results highlight the necessity for cancer care teams and doctors to test for sleep disruptions and exhaustion simultaneously, rather than separately, and to think about integrated therapies that treat both symptoms at the same time. However, This magnitude is somewhat lower than reports from Iran 69.3% [37], Arab countries (77.5% and 78%)[15, 38], and Egypt, where fatigue and poor sleep were reported in 99.2% and 87.4% of patients, respectively[39] and America 93% & 77% [40, 41]. Although prior research has repeatedly shown that cancer-related fatigue and poor sleep quality are significantly correlated, most of these studies did not report comorbidity as a single combined prevalence estimate, which makes it difficult to directly compare our findings with those of other studies. Our study's comparatively lower prevalence could be due to variances in healthcare settings, cultural views of symptoms, study populations, or evaluation instruments. Because fatigue and poor sleep quality can significantly decrease quality of life and may necessitate integrated management techniques in cancer therapy, it is clinically important to recognize their coexistence. The research is still lacking in longitudinal data on the long-term interactions between these two symptoms and if addressing both at the same time with focused therapies can enhance patient outcomes. In this study, the odds of comorbidity of fatigue and poor sleep quality were 2.79 times higher among cancer patient aged 61 to 89 years compared to women aged 18 to 30 years. This association was consistent with study conducted in of university of California[37, 42], Brazil[43]. Age-related physiological changes including reduced sleep efficiency and more overnight awakenings, higher susceptibility to treatment-related adverse effects, and the existence of numerous comorbidities could all contribute to this outcome. Anxiety, social isolation, and a lack of coping mechanisms are examples of psychosocial issues that can make fatigue and sleep disturbance worse. The combined impact of these variables emphasizes the necessity of focused treatments to enhance sleep and lessen fatigue in elderly cancer patients. And cancer patients living in rural areas had 2.03 times higher odds of comorbid fatigue and poor sleep quality compared to urban residents. This finding is consistent with prior research indicating that rural residency is associated with poorer symptom management, higher fatigue, and greater sleep disturbances among cancer patients[17, 44]. Lower health literacy, longer travel times to clinics, fewer specialized cancer care resources, and restricted access to healthcare services are some potential causes, all of which could lead to a higher cumulative symptom burden and delayed symptom management. Environmental and social variables may make fatigue and sleep issues worse, such as a lack of social support and higher levels of stress in rural areas [45, 46]. The odds of fatigue and poor sleep quality comorbidity were 2.65 and 3.54 times higher among cancer patient who are married and divorced respectively compared to single cancer patient. This finding is consistent with study conducted Rabat, Morocco[47]. Married couples may benefit from psychological and social support, such as emotional support, and encouragement to adhere to treatment, which may have a protective effect on cancer patients' sleep quality. Support of this kind help in the reduction of stress, worry, and depression symptoms, all of which are closely related to fatigue and sleep quality. On the other hand, cancer patients who are widowed, divorced, or single frequently experience higher levels of psychosocial stress and less coping mechanisms, which raises their risk of fatigue and poor sleep quality[48, 49]. The odds of fatigue and poor sleep quality comorbidity were 2.84 times higher among cancer patient who are inpatient compared to outpatient [ AOR = 2.84, 95%, CI: (1.63, 4.95)]. This finding was consistent with study conducted in Ethiopia[50], Iran[37], systematic review and meta-analysis[51]. Comorbid fatigue and sleep disturbances are more common in inpatient cancer patients because of the advanced stage of the disease, unmanaged symptoms, and more intense therapy. Sleep is further disrupted and weariness is increased by pain, dyspnea, nausea, and hospital procedures including noise and midnight monitoring. In addition, polypharmacy and psychological discomfort[34, 37]. The odds of fatigue and poor sleep quality comorbidity were 3.92 & 2.52 times higher among Stage II and Stage IV of cancer patient compared to stage I [ AOR = 3.92, 95%, CI: ([1.89, 8.12] and [ AOR = 2.52, 95% CI: (1.04, 6.15)] respectively. This finding was concurrent in study conducted in Ethiopia[10, 16], systematic review and meta-analysis[17]. The higher disease load and physiological stress in patients with more advanced cancer stages increases the likelihood of fatigue and restless nights. Tumor growth in stage II frequently results in elevated inflammatory activity, which throws off circadian rhythm and energy management, making fatigue and sleep quality worse. In stage IV, metastases and widespread disease increase the burden of symptoms, the necessity for intense therapy, and psychological distress, all of which independently affect sleep and cause fatigue. Extensive reviews consistently demonstrate that fatigue and poor sleep quality are strongly predicted by tumor stage, symptom burden, and treatment severity [34, 52]. The odds of fatigue and poor sleep quality comorbidity were 2.70 times higher among ≥ 1-year cancer duration compared to < l year cancer duration [ AOR = 2.70, 95% CI: (1.14, 6.39)]. This finding was concurrent with study conducted in USA[53], Netherlands[54], Ethiopia[16]. Patients who have had cancer for more than a year are far more likely than those who have had it for less time to suffer from concomitant fatigue and poor sleep quality. Long-term exposure to the disease and its treatments might result in cumulative adverse effects from radiation, chemotherapy, or surgery, which can worsen sleep problems and exhaustion. Furthermore, extended cancer duration is frequently linked to higher levels of psychological distress, such as anxiety and depression, as well as a greater load of symptoms, such as pain and discomfort, all of which worsen energy and sleep quality. The odds of fatigue and poor sleep quality comorbidity were 1.93 times higher among cancer patient who had an anxiety compared to No anxiety [ AOR = 1.93, 95% CI: (1.06, 3.51)]. This associate was similar with study done in China[34], Ethiopia[50], meta-analysis studies in multiple countries[51]. Cancer patients' comorbidity of fatigue and poor sleep quality is greatly influenced by anxiety through both physiological and psychological factors. It causes the hypothalamic-pituitary-adrenal (HPA) axis to become dysregulated and hyperarousal, which interferes with sleep and results in non-restorative slumber. Consequently, this makes cancer-related fatigue worse, which is a prevalent and enduring symptom in oncology populations. Additionally, anxiety exacerbates fatigue and sleep disruptions by reducing adaptive coping mechanisms [55–57]. The odds of fatigue and poor sleep quality comorbidity were 2.10 times higher among cancer patient who had depression compared to No depression [ AOR = 2.10, 95% CI: (1.19, 3.70) ]. This finding concurrent with study conducted in Ethiopia and Egypt [9, 39, 58]. There are several interrelated biological and psychological processes via which depression in cancer patients can worsen fatigue and interfere with sleep. Increased exhaustion results from neurochemical imbalances linked to depression, such as changed serotonin and dopamine levels, which interfere with sleep regulation and decrease restorative sleep. Moreover, depression frequently entails dysregulation of the hypothalamic-pituitary-adrenal (HPA) axis and increased inflammatory responses, which exacerbate fatigue and poor sleep quality. On a psychological level, depression symptoms make people less motivated and energetic, which makes it harder for them to take care of themselves and keep up good sleep habits. This makes the symptoms worse. All of these processes work together to explain why depressed cancer patients have been shown to have higher risks of experiencing fatigue and poor sleep comorbidities [39, 59]. Strengths and Limitations The major strengths of this study its novel focus on comorbidity, addressing a research gap by assessing the combined prevalence of fatigue and poor sleep in a resource-limited setting. Additionally, the use of validated assessment tools for both fatigue and sleep quality enhances the reliability of our findings. The study’s relatively large and diverse sample drawn from multiple facilities in the Northwest Amhara Region improves its representativeness and relevance to clinical practice. However, several limitations should be acknowledged; the reliance on self-reported data may introduce recall bias, particularly in the subjective evaluation of symptoms, the absence of biochemical or clinical staging data limits the ability to assess the influence of disease severity or treatment modality on symptom burden, while we used a binary outcome to capture comorbidity, more nuanced analyses such as symptom cluster modeling or longitudinal tracking may yield deeper insights into symptom interactions over time. Clinical Implications Nearly half of adult cancer patients in the region experience both fatigue and poor sleep quality, highlighting the need for routine screening of concurrent symptoms. These comorbidities intensify each other’s effects, leading to impaired functioning, reduced quality of life, and lower treatment adherence. Integrated management strategies ranging from CBT and physical activity programs to basic sleep hygiene counseling are essential, especially in resource-limited settings. Conclusions and Recommendation Comorbid fatigue and poor sleep quality are highly prevalent among adult cancer patients in the Northwest Amhara Region, significantly affecting functional status and quality of life. These findings underscore the need for routine screening and integrated management of concurrent symptoms within oncology care. Simple interventions such as sleep hygiene counseling, psychoeducation, and targeted therapies can improve patient outcomes, especially in resource-limited settings. Future research should explore longitudinal trajectories and contributing factors to inform tailored, effective interventions. Declarations Declarations Clinical trial number Not applicable Ethical consideration This study was conducted in accordance with the ethical principles of the Declaration of Helsinki. Ethical approval was obtained from the Research and Ethical Review Committee of the University of Gondar, College of Medicine and Health Sciences, School of Nursing (Reference No. S/N 237/2014). The committee approved the study on behalf of the Institutional Ethical Review Committee of the University of Gondar. The objectives and significance of the study were clearly explained to all participants, and written informed consent was obtained from each participant prior to data collection. Competing interest Authors Declared that, there is no competing interest. AMB involved in analysis, and result interpretation BAN involved in data analysis, interpretation, and, and manuscript writing. DGA involved in discussion and drafting proposal and interpretation of result. YGB involved in conceptualization, validation, writing original draft. AEB involved in data collection and writing original draft AFZ involved in designing and preparing manuscript. Figure 1 Schematic presentation of the sampling procedure comorbidity of fatigue and Poor sleep quality and its predictor among adult cancer patients, Northwest, Ethiopia, 2025 Funding Not applicable Author Contribution GAZ : involved data collection, data analysis, interpretation, report and manuscript writing.AMB: involved in analysis, and result interpretationBAN: involved in data analysis, interpretation, and, and manuscript writing.DGA: involved in discussion and drafting proposal and interpretation of result.YGB: involved in conceptualization, validation, writing original draft.AEB: involved in data collection and writing original draftAFZ: involved in designing and preparing manuscript. Data Availability The manuscript contains all of the data that is crucial to our findings. Request for additional information on the data set and questions about data sharing will be treated in accordance with a reasonable request to [email protected] . References 1. 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Rafie, C., et al., Impact of physical activity and sleep quality on quality of life of rural residents with and without a history of cancer: findings of the Day and Night Study. 2018: p. 5525–5535. 47. Echchikhi, Y., et al., Sleep disorders and sleep quality in Moroccan adult patients with cancer during treatment. 2017. 9 (09): p. 637–643. 48. Cui, C. and L.J.F.i.P. Wang, Role of social support in the relationship between resilience and sleep quality among cancer patients. 2024. 15 : p. 1310118. 49. Kirca, N., et al., Perceived social support, fatigue, and sleep quality in women treated for gynecological cancer: a cross-sectional study. 2025. 33 (7): p. 1–14. 50. Abebe, E., B.W. Giru, and A.J.C.C. Boka, Sleep quality and associated factors among adult cancer patients on treatments at tikur anbessa specialized hospital oncology unit, Addis Ababa, Ethiopia, 2021. 2023. 30 : p. 10732748231160129. 51. Hu, Y., et al., Prevalence and risk factors of sleep disturbances among patients with lung cancer: systematic review and meta-analysis. 2024. 32 (9): p. 619. 52. Al-Habsi, Z.R.S., Sleep Quality, Cancer-Related Fatigue and Health-Related Quality of Life Among Hospitalized Patients with Cancer in Oman . 2020, Sultan Qaboos University (Oman). 53. Yarosh, R.A., et al., Sleep disturbances among cancer survivors. 2023. 87 : p. 102471. 54. Burch, J.B., et al., Sleep disorders and cancer incidence: examining duration and severity of diagnosis among veterans. 2024. 14 : p. 1336487. 55. He, C., et al., Relationship of sleep-quality and social-anxiety in patients with breast cancer: a network analysis. 2023. 23 (1): p. 887. 56. Kanter, N.G., et al., Hypothalamic–Pituitary–Adrenal axis dysfunction in people with cancer: A systematic review. 2024. 13 (22): p. e70366. 57. Huang, C.-Y., et al., Resilience, coping styles, sleep disturbances, depression and anxiety in females with breast cancer. 2016. 58. Ayalew, M., et al., Prevalence of depression and anxiety symptoms and their determinant factors among patients with cancer in southern Ethiopia: a cross-sectional study. 2022. 12 (1): p. e051317. 59. Javan Biparva, A., et al., Global depression in breast cancer patients: Systematic review and meta-analysis. 2023. 18 (7): p. e0287372. 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. 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1","display":"","copyAsset":false,"role":"figure","size":78260,"visible":true,"origin":"","legend":"\u003cp\u003eSchematic presentation of the sampling procedure comorbidity of fatigue and Poor sleep quality and its predictor among adult cancer patients, Northwest, Ethiopia, 2025\u003c/p\u003e","description":"","filename":"Onlinefloatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-8065921/v1/fba9a887e6c2e91ba66b39f5.png"},{"id":96788144,"identity":"a9e8530b-a9ee-4e9d-931c-f5af6df5a1c7","added_by":"auto","created_at":"2025-11-26 06:30:54","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":8082,"visible":true,"origin":"","legend":"\u003cp\u003eMagnitude of Comorbidity fatigue and poor sleep quality and its predictors among cancer patient in Amhara region, northwest Ethiopia, 2025\u003c/p\u003e","description":"","filename":"Onlinefloatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-8065921/v1/7882666ce8479ed03e001062.png"},{"id":98369971,"identity":"0b558fde-4f8c-4509-b954-69dc0a459e72","added_by":"auto","created_at":"2025-12-17 05:23:43","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1768128,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8065921/v1/af8d791f-9e4d-4e38-b148-2bb35dfa8880.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Double Burden: Fatigue and Poor Sleep quality comorbidity and its predictor Among Cancer Patients, Northwest Ethiopia: Institutional based cross- sectional study design","fulltext":[{"header":"Introduction","content":"\u003cp\u003eThe comorbidity of cancer-related fatigue (CRF) and poor sleep quality constitutes a critical and debilitating symptom cluster, defined by persistent, non-relieving exhaustion and disrupted sleep processes, which together amplify clinical complications, intensify functional impairment, and severely compromise the quality of life in cancer patients. Cancer is major public health challenge, with nearly 20\u0026nbsp;million new cases and 9.7\u0026nbsp;million cancer-related deaths reported globally in 2022[1]. Beyond the rising prevalence of cancer, fatigue and sleep disturbances persist as among the most prevalent and distressing symptom clusters, representing a major source of symptom burden and significantly impairing patients\u0026rsquo; quality of life[2, 3]. Research from the U.S. Centers for Disease Control and Prevention (CDC) and the World Health Organization (WHO) shows how these symptoms impair psychological health, physical functioning, treatment compliance, and general quality of life.[4, 5]. Longitudinal and systematic reviews repeatedly show that fatigue and poor sleep quality are not only highly prevalent but also commonly co-occur, exacerbating each other's symptoms and making patients' suffering worse[6, 7]. Furthermore, meta-analyses show that, depending on the disease stage and treatment approach, between 40 and 80 percent of patients experience cancer-related fatigue (CRF), and 50 to 70 percent experience clinically significant sleep disturbance [5, 8]. When these conditions co-occur, patients face synergistic declines in quality of life, reduced functional status, and poorer survival outcomes[9]. Additionally, longitudinal data demonstrates that untreated poorer and fatigue can last for years after treatment, severely reducing the chances of surviving. The intensity of this comorbidity has been linked to psychological morbidity, such as anxiety and depression, lower productivity, and higher healthcare consumption. [10]. Consequently, the combined burden of fatigue and poor sleep quality constitutes a significant but little-known aspect of cancer treatment worldwide. Numerous clinical, sociodemographic, and psychological factors have been found to predict the comorbidity of sleep disruption and cancer related fatigue. Higher risks of these symptoms are consistently linked to advanced cancer stage, longer time since diagnosis, and harsh treatment modalities like chemotherapy and radiation[11, 12]. Biological factors, including anemia, pain, systemic inflammation, and poor nutritional status, further exacerbate symptom severity[13]. Psychological factors, particularly depression and anxiety, are strong independent predictors, while low physical activity, poor social support, and rural residency have been implicated in low- and middle-income country (LMIC) settings[13, 14]. This complex etiology emphasizes the necessity of integrated, situation-specific management strategies. The comorbidity of cancer related fatigue and poor sleep quality is not well studied in sub-Saharan Africa, especially Ethiopia, despite its increasing international attention. The majority of Ethiopia's current study has focused on these symptoms separately. Studies carried out in Ethiopia's Amhara area and elsewhere, for instance, have found a high frequency of CRF and poor sleep quality independently, and correlations between these outcomes and variables like depression, advanced cancer stage, anemia, pain, and inpatient status have been found[15\u0026ndash;17]. However, to our knowledge, no prior study in northwest Amhara has quantified the combined burden of fatigue and poor sleep quality as a binary comorbidity outcome. This represents a critical knowledge gap, as simultaneous evaluation provides more comprehensive insights into patient symptom clusters and may better inform integrated care strategies. In light of this evidence gap, there is an urgent need for studies that establish both the magnitude and determinants of fatigue\u0026ndash;sleep comorbidity in Ethiopian oncology settings. Such evidence is particularly relevant to Ethiopia, where late-stage presentation, limited access to specialized care, and high psychosocial burden may amplify the prevalence and impact of these symptoms. Therefore, this study aimed to estimate the magnitude of comorbid fatigue and poor sleep quality and its predictors among adult cancer patients attending oncology unit in the northwest Amhara region, Ethiopia.\u003c/p\u003e"},{"header":"Method and material","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eStudy design, and period\u003c/h2\u003e\u003cp\u003eInstitutional-based quantitative cross-sectional study was conducted among adult cancer patients receiving cancer treatment at an oncology unit and Data collection was take place over a month from May to June 2025.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eStudy Area\u003c/h3\u003e\n\u003cp\u003eThe study was carried out in the cancer treatment hospitals in the Amhara region, Northwest Ethiopia. There are eight comprehensive hospitals in the in Amhara region, of which only four hospital have a cancer treatment oncology unit. Thus, hospitals were University of Gondar Comprehensive Specialized Hospital (UOGCSH), Felegehiwot Comprehensive Specialized Hospital (FCSH), Tibebegion Comprehensive Specialized Hospital (TCSH), and Dessie Comprehensive Specialized Hospital (DCSH). The distances of these hospitals from Addis Ababa, the capital city of Ethiopia, are 748 km, 564 km, 399 km, and 480 km, respectively. Each comprehensive specialized hospital serves 3.5\u0026ndash;5\u0026nbsp;million people[18]. Each of these hospitals operates an oncology clinic or treatment center that provides both inpatient and outpatient services. Specifically, the oncology units of FCSH\u0026thinsp;=\u0026thinsp;28, UoGCSH\u0026thinsp;=\u0026thinsp;32, DCSH\u0026thinsp;=\u0026thinsp;20, and TCSH\u0026thinsp;=\u0026thinsp;25 beds are equipped respectively. These services are delivered by a multidisciplinary team comprising nurses, oncologists, and general practitioners.\u003c/p\u003e\n\u003ch3\u003eStudy Population\u003c/h3\u003e\n\u003cp\u003eAll adult cancer patients (aged\u0026thinsp;\u0026ge;\u0026thinsp;18 years) attending the oncology clinics during the data collection period was study population and study unit consisted of adult cancer patients who were chosen at random during the data collection period.\u003c/p\u003e\n\u003ch3\u003eEligibility Criteria\u003c/h3\u003e\n\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\u003ch2\u003eInclusion Criteria\u003c/h2\u003e\u003cp\u003eEligible participants were including adult patients (aged 18 years or older) with a confirmed diagnosis of any type of cancer. Patients must be either currently receiving active cancer treatment (such as chemotherapy, radiotherapy, immunotherapy, or surgery) or undergoing follow-up care. Additionally, participants must be willing and able to provide informed consent and demonstrate sufficient cognitive and communicative ability to complete the study questionnaire reliably.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003eExclusion Criteria\u003c/h2\u003e\u003cp\u003ePatients who are critically ill or unable to communicate, as well as those diagnosed with psychiatric disorders or cognitive impairments that interfere with reliable self-reporting, should be excluded. Additionally, patients with previously diagnosed primary sleep disorders that are not attributable to cancer or its treatment should be considered separately, as their sleep disturbances are independent of oncologic processes.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eSample Size Determination\u003c/h3\u003e\n\u003cp\u003eThe sample size was calculated using the single population proportion formula, considering: Estimated prevalence (P) of comorbid poor sleep quality and fatigue among cancer patients 50% (due to lack of local data).\u003c/p\u003e\u003cp\u003ePrevalence (P)\u0026thinsp;=\u0026thinsp;50%\u003c/p\u003e\u003cp\u003eConfidence level\u0026thinsp;=\u0026thinsp;95% (Z\u0026thinsp;=\u0026thinsp;1.96).\u003c/p\u003e\u003cp\u003eMargin of error (d)\u0026thinsp;=\u0026thinsp;5% (0.05).\u003c/p\u003e\u003cp\u003e\u003cb\u003eFormula n=\u003c/b\u003e \u003cspan type=\"BoldUnderline\" class=\"BoldUnderline\" name=\"Emphasis\"\u003e(Zα/2)\u003c/span\u003e\u003csup\u003e\u003cspan type=\"BoldUnderline\" class=\"BoldUnderline\" name=\"Emphasis\"\u003e2\u003c/span\u003e\u003c/sup\u003e \u003cspan type=\"BoldUnderline\" class=\"BoldUnderline\" name=\"Emphasis\"\u003ep (1-p)\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003e-------------------------------\u003c/p\u003e\n\u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;d\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n\u003cp\u003en\u0026thinsp;=\u0026thinsp;1.96*0.5(1-0.5)/ 0. 05\u003csup\u003e2\u003c/sup\u003e=384\u003c/p\u003e\u003cp\u003eAdding 10% non-response rate 422.\u003c/p\u003e\u003cp\u003eFinal sample size was 422.\u003c/p\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003eSampling Technique\u003c/h2\u003e\u003cp\u003e Systematic random sampling technique was used to select study participants from each comprehensive specialized hospital and proportional allocation for each hospital was properly calculated based on number of cancer patients they served per month. The sampling interval was determined by dividing the total study population who had follow-up and on treatment during one typical month (1500) by total sample size (422). Therefore, the sampling fraction was calculated to be 1500/422\u0026thinsp;\u0026asymp;\u0026thinsp;3. The first participant was selected randomly by a lottery method from 1\u0026ndash;3 and the next respondent was chosen at regular intervals (every 3) by data collectors and patient register follow up log book, patient MRN No. and the patient itself should strictly use to avoid repeated data collection \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e\u003cb\u003e)\u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eFHCSH\u003c/b\u003e: Felege Hiwot Comprehensive Specialized Hospital\u003c/p\u003e\u003cp\u003e\u003cb\u003eTGCSH\u003c/b\u003e: Tibebe Gion Comprehensive Specialized Hospital\u003c/p\u003e\u003cp\u003e\u003cb\u003eDCSH\u003c/b\u003e: Dessie Comprehensive Specialized Hospital\u003c/p\u003e\u003cp\u003e\u003cb\u003eUoGCSH\u003c/b\u003e: University of Gondar Comprehensive Specialized Hospital\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003eData Collection Tools and Procedures\u003c/h2\u003e\u003cp\u003eData were collected using a structured, interviewer-administered questionnaire with open-ended and closed-ended questions. There are eight parts to the data collection tool. Part one contains sociodemographic data, part two disease and treatment-related signs and symptoms, part three sleep quality assessment, part four Brief Fatigue Inventory. The tools for socio-demographic and clinical factors were adapted from the review of different pieces of literature. Anxiety and depression were assessed using the Hospital Anxiety and Depression Scale (HADS) [19] which was also validated in Ethiopian cancer patients [20]. Performance status was assessed by the single item Eastern Cooperative Oncology Group (ECOG) performance status scale [21]. Social support was assessed by the three-item Oslo social support scale (OSSS-3) [22]. Sleep quality was assessed by a standardized and validated Pittsburgh Sleep Quality Index (PSQI). The PSQI was designed to evaluate the subjective quality of sleep in the past month. It contains 18 self-rated questions, including seven subscale components (subjective sleep quality, sleep latency, sleep duration, habitual sleep efficiency, sleep disturbance, use of sleep medication, and daytime dysfunction). The global PSQI score ranges from 0 (no difficulty) to 21 (severe difficulties in all areas). Each component score ranges from 0 (no difficulty) to 3 (severe difficulty). Higher global and component scores indicate more severe complaints and a higher level of poor sleep quality. A global PSQI score greater than 5 yielded a diagnostic sensitivity of 89% and specificity of 86.5% (kappa\u0026thinsp;=\u0026thinsp;0.75, P\u0026thinsp;\u0026le;\u0026thinsp;0:001) in distinguishing \u0026ldquo;poor\u0026rdquo; from \u0026ldquo;good\u0026rdquo; sleepers [23]. The construct validity and internal consistency are further evaluated and supported in cancer patients with a Cronbach\u0026rsquo;s α value of 0.81 [24]. For present study, the internal consistency measurement of the PSQI subscales found a Cronbach\u0026rsquo;s alpha coefficient of 0.761 from the pretest data which was acceptable for this study. Brief Fatigue Inventory scale (BFI): Cancer patients who scored greater than or equal to four (\u0026ge;\u0026thinsp;4) moderate to severe in BFI measurement scale was considers fatigue whereas, \u0026lt; 4 in BFI scale was considers not fatigue[25\u0026ndash;29].\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003eData Collection Procedure\u003c/h2\u003e\u003cp\u003eData collectors (trained BSc nurses) conducted face-to-face interviews in a private setting to ensure confidentiality and comfort. Medical records were reviewed to obtain clinical data. The questionnaires were first translated into the local language (Amharic) and then back-translated into English to ensure accuracy. A pretest of the instruments was carried out on 5% of the sample at Debre Berihan Comprehensive Specialized Hospital in a similar population to identify and address potential issues\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\u003ch2\u003eOutcome variable\u003c/h2\u003e\u003cp\u003eOutcome variable was a binary variable representing the co-occurrence (comorbidity) of poor sleep quality and cancer-related fatigue. Sleep quality was assessed using the Pittsburgh Sleep Quality Index (PSQI). The global PSQI score ranges from 0 to 21, with higher scores indicating poorer sleep quality. A commonly accepted cut-off score of \u0026gt;\u0026thinsp;5 was used to classify poor sleep quality. Fatigue was measured using the Brief Fatigue Inventory (BFI). The (BFI) score ranges from 0 to 10. BFI score\u0026thinsp;\u0026ge;\u0026thinsp;4 were categorized as clinically significant fatigue, while those with scores\u0026thinsp;\u0026lt;\u0026thinsp;4 were considered not clinically significant fatigued. Participants were classified as having the comorbid condition (coded as \u003cb\u003e1\u003c/b\u003e) if they met both criteria simultaneously that is, they had a PSQI \u003cb\u003escore\u0026thinsp;\u0026gt;\u0026thinsp;5\u003c/b\u003e and a BFI score\u0026thinsp;\u0026ge;\u0026thinsp;\u003cb\u003e4\u003c/b\u003e. All other participants (i.e. those with either or neither condition) were coded as \u003cb\u003e0\u003c/b\u003e, indicating absence of the comorbid outcome.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eComorbidity of fatigue and poor sleep quality\u003c/strong\u003e\u003cp\u003ewas defined as the presence of both clinically significant fatigue and poor sleep in the same individual. Fatigue was measured using Brief Fatigue Inventory, with a score of \u0026ge;\u0026thinsp;4, indicating clinically significant fatigue. Sleep quality was measured using the Pittsburgh Sleep Quality Index (PSQI), with a global score\u0026thinsp;\u0026gt;\u0026thinsp;5 indicating poor sleep. Participants meeting the criteria for both were coded as \u0026ldquo;1\u0026rdquo; (comorbidity); all others were coded as \u0026ldquo;0\u0026rdquo; (No comorbidity).\u003c/p\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\u003ch2\u003eIndependent Variables\u003c/h2\u003e\u003cp\u003eSociodemographic such as Age, sex, residence, marital status, education, occupation. Clinical factors: Cancer type, cancer stage, duration since diagnosis, treatment modality, Behavioral and psychosocial factors: depression/anxiety.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\u003ch2\u003eOperational Definition\u003c/h2\u003e\u003cp\u003e\u003cstrong\u003eComorbidity fatigue and poor sleep quality\u003c/strong\u003e\u003cp\u003eis defined as the simultaneous presence of poor sleep quality and fatigue[15]\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eGood sleep quality\u003c/strong\u003e\u003cp\u003ea global PSQI score of \u0026le;\u0026thinsp;5[30]\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003ePoor sleep quality\u003c/strong\u003e\u003cp\u003ea global PSQI score of \u0026gt;\u0026thinsp;5[30]\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eAnxiety and depression\u003c/b\u003e: A patient with more than 10 points on the Hospital Anxiety and Depression Scale (HADS) has anxiety and depression problem. \u003cb\u003eGood performance\u003c/b\u003e: if Eastern Cooperative Oncology Group performance status which ranges from 0\u0026ndash;4 (ECOG-PS). Patient score 0\u0026ndash;1. \u003cb\u003ePoor performance\u003c/b\u003e: if Eastern Cooperative Oncology Group performance status (ECOG-PS) patient score 2\u0026ndash;4. \u003cb\u003eSocial support\u003c/b\u003e: by using the three-item Oslo social support scale (OSSS-3), a score of 3\u0026ndash;8 represents \u0026lsquo;poor support\u0026rsquo;, 9\u0026ndash;11 \u0026lsquo;moderate support\u0026rsquo;, and 12\u0026ndash;14 \u0026lsquo;strong support[31, 32].\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e\u003ch2\u003eData Quality Assurance\u003c/h2\u003e\u003cp\u003eSupervisors and data collectors received training on interviewing techniques, ethical considerations, and study objectives. To guarantee accuracy and consistency, completed surveys were supervised and reviewed every day. To reduce entry errors, data entry was done twice. Scales such as the PSQI and BFI were tested for internal consistency using Cronbach's alpha.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec18\" class=\"Section2\"\u003e\u003ch2\u003eData Processing and Analysis\u003c/h2\u003e\u003cp\u003eData were coded and entered into Epi Data and then exported to Stata version 14 for analysis. Descriptive statistics were used to summarize sociodemographic and clinical characteristics as well as the prevalence of comorbid fatigue and poor sleep quality. Bivariate analyses using Chi-square tests were conducted to assess associations between independent variables and comorbidity. Variables with a p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.25 in the bivariate analysis were included in the multivariable logistic regression model to identify independent predictors of comorbidity, and adjusted odds ratios (AORs) with 95% confidence intervals (CIs) were reported.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec19\" class=\"Section2\"\u003e\u003ch2\u003eEthical Considerations\u003c/h2\u003e\u003cp\u003e\u003cstrong\u003eEthical approval\u003c/strong\u003e\u003cp\u003e for this study was obtained from the Institutional Review Board (IRB) of the University of Gondar. Permission was also granted by the respective hospital administrations. Written informed consent was obtained from each participant after providing a clear explanation of the study\u0026rsquo;s purpose, potential risks, and anticipated benefits. To ensure confidentiality and anonymity, all data were securely stored and made accessible only to the research team.\u003c/p\u003e\u003c/p\u003e\u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec20\"\u003e\n \u003cdiv id=\"Sec21\"\u003e\n \u003ch2\u003eSocio-demographic characteristics of study participants\u003c/h2\u003e\n \u003cp\u003eIn this study, 405 patients were participated with 97% response rate. nearly two-thirds (60.74%) participants were female. The Mean age of participants were 46.43 with (Std. Dev. 15.43) years. Around one third of the participants (32%) were stage two cancer and in terms of residency, slightly more than half of the participants (62.96%) were from rural area and nearly half of the participants (45.43%) were not educated \u003cstrong\u003e(\u003c/strong\u003eTable\u0026nbsp;1\u003cstrong\u003e).\u003c/strong\u003e\u003c/p\u003e\n \u003cdiv\u003e\n \u003cp\u003eTable\u0026nbsp;1: Frequency distribution of the characteristics of study participants attending oncology units in Amhara region comprehensive socialized hospitals, northwest Ethiopia. 2022 (n=405)\u003c/p\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"672\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 198px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eVariable\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 216px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eFatigue \u0026amp; poor sleep quality comorbidity\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eFrequency\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003e\u003cstrong\u003epercent (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 198px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAge\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eYes\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 126px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNo\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 198px;\"\u003e\n \u003cp\u003e18-30\u003c/p\u003e\n \u003cp\u003e31-43\u003c/p\u003e\n \u003cp\u003e44-60\u003c/p\u003e\n \u003cp\u003e61-89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003e42\u003c/p\u003e\n \u003cp\u003e50 \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e73\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e24 \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 126px;\"\u003e\n \u003cp\u003e31\u003c/p\u003e\n \u003cp\u003e45\u003c/p\u003e\n \u003cp\u003e93\u003c/p\u003e\n \u003cp\u003e47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003e73\u003c/p\u003e\n \u003cp\u003e95\u003c/p\u003e\n \u003cp\u003e166\u003c/p\u003e\n \u003cp\u003e71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003e18.2\u003c/p\u003e\n \u003cp\u003e23.45\u003c/p\u003e\n \u003cp\u003e4.98\u003c/p\u003e\n \u003cp\u003e17.53\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 198px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSex\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003cp\u003efemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e75\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e114 \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 126px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e84\u003c/p\u003e\n \u003cp\u003e132\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e159\u003c/p\u003e\n \u003cp\u003e246\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e39.25\u003c/p\u003e\n \u003cp\u003e60.74\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 198px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eResidency\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eUrban\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eRural\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e82 \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u003c/p\u003e\n \u003cp\u003e107 \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 126px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e68\u003c/p\u003e\n \u003cp\u003e148\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e152\u003c/p\u003e\n \u003cp\u003e255\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e37.53\u003c/p\u003e\n \u003cp\u003e62.96\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 198px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMarital status\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eMarried\u003c/p\u003e\n \u003cp\u003edivorced\u003c/p\u003e\n \u003cp\u003esingle\u0026nbsp;\u003c/p\u003e\n \u003cp\u003ewidowed\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e108\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e20\u003c/p\u003e\n \u003cp\u003e38\u003c/p\u003e\n \u003cp\u003e23 \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 126px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e147\u003c/p\u003e\n \u003cp\u003e24\u003c/p\u003e\n \u003cp\u003e21\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;24 \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e255\u003c/p\u003e\n \u003cp\u003e44\u003c/p\u003e\n \u003cp\u003e59\u003c/p\u003e\n \u003cp\u003e47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e62.96\u003c/p\u003e\n \u003cp\u003e10.86\u003c/p\u003e\n \u003cp\u003e14.56\u003c/p\u003e\n \u003cp\u003e11.60\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 198px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eEducational status\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eNot education\u003c/p\u003e\n \u003cp\u003eprimary education\u0026nbsp;\u003c/p\u003e\n \u003cp\u003esecondary education\u003c/p\u003e\n \u003cp\u003ecollege and above\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e74\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e80 \u0026nbsp;\u003c/p\u003e\n \u003cp\u003e20 \u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;15 \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 126px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e110\u003c/p\u003e\n \u003cp\u003e64\u003c/p\u003e\n \u003cp\u003e28\u003c/p\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e184\u003c/p\u003e\n \u003cp\u003e144\u003c/p\u003e\n \u003cp\u003e48\u003c/p\u003e\n \u003cp\u003e29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e45.43\u003c/p\u003e\n \u003cp\u003e35.55\u003c/p\u003e\n \u003cp\u003e11.85\u003c/p\u003e\n \u003cp\u003e7.16\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 198px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eOccupational status\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eunemployed\u003c/p\u003e\n \u003cp\u003eEmployed\u003c/p\u003e\n \u003cp\u003efarmer\u003c/p\u003e\n \u003cp\u003eMarchant\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e22 \u0026nbsp;\u003c/p\u003e\n \u003cp\u003e25\u003c/p\u003e\n \u003cp\u003e95\u003c/p\u003e\n \u003cp\u003e47 \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 126px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e15\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003cp\u003e136\u003c/p\u003e\n \u003cp\u003e50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e37\u003c/p\u003e\n \u003cp\u003e40\u003c/p\u003e\n \u003cp\u003e231\u003c/p\u003e\n \u003cp\u003e97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e9.13\u003c/p\u003e\n \u003cp\u003e9.87\u003c/p\u003e\n \u003cp\u003e57. 03\u003c/p\u003e\n \u003cp\u003e23.95\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 198px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAdmission status\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eInpatient\u003c/p\u003e\n \u003cp\u003eOutpatient\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e77\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;112 \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 126px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e140\u003c/p\u003e\n \u003cp\u003e76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e217\u003c/p\u003e\n \u003cp\u003e188\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e53.58\u003c/p\u003e\n \u003cp\u003e46.41\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 198px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCancer types\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eBreast cancer\u003c/p\u003e\n \u003cp\u003ecolorectal cancer\u003c/p\u003e\n \u003cp\u003ecervical cancer\u003c/p\u003e\n \u003cp\u003eLung cancer\u003c/p\u003e\n \u003cp\u003eskin cancer\u003c/p\u003e\n \u003cp\u003eprostate cancer\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e46 \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e28\u003c/p\u003e\n \u003cp\u003e40\u003c/p\u003e\n \u003cp\u003e21\u003c/p\u003e\n \u003cp\u003e23\u003c/p\u003e\n \u003cp\u003e14 \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 126px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e46\u003c/p\u003e\n \u003cp\u003e31\u003c/p\u003e\n \u003cp\u003e42\u003c/p\u003e\n \u003cp\u003e28\u003c/p\u003e\n \u003cp\u003e18\u003c/p\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e92\u003c/p\u003e\n \u003cp\u003e59\u003c/p\u003e\n \u003cp\u003e82\u003c/p\u003e\n \u003cp\u003e49\u003c/p\u003e\n \u003cp\u003e41\u003c/p\u003e\n \u003cp\u003e23 \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e22.71\u003c/p\u003e\n \u003cp\u003e14.56\u003c/p\u003e\n \u003cp\u003e2.24\u003c/p\u003e\n \u003cp\u003e12.9\u003c/p\u003e\n \u003cp\u003e10.12\u003c/p\u003e\n \u003cp\u003e5.67\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 198px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eStage of cancer\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003estage I\u003c/p\u003e\n \u003cp\u003estage II\u003c/p\u003e\n \u003cp\u003eStage III\u003c/p\u003e\n \u003cp\u003eStage IV\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e80 \u0026nbsp; \u0026nbsp;\u003c/p\u003e\n \u003cp\u003e43\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;29 \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;37 \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 126px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e47\u003c/p\u003e\n \u003cp\u003e88\u003c/p\u003e\n \u003cp\u003e35\u003c/p\u003e\n \u003cp\u003e46 \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e127\u003c/p\u003e\n \u003cp\u003e131\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; 64\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; 83 \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e31.35\u003c/p\u003e\n \u003cp\u003e32.34\u003c/p\u003e\n \u003cp\u003e15.8\u003c/p\u003e\n \u003cp\u003e20.49\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 198px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCancer therapy\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eMedical therapy\u003c/p\u003e\n \u003cp\u003esurgical therapy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e124\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;65 \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 126px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e160\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e55 \u0026nbsp; \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e284\u003c/p\u003e\n \u003cp\u003e120 \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e70. 12\u003c/p\u003e\n \u003cp\u003e29.62\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 198px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMetastasis History\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eYes\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e69 \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e120 \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 126px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e92\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e124 \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e161\u003c/p\u003e\n \u003cp\u003e244\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e39.75\u003c/p\u003e\n \u003cp\u003e60.24\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 198px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCancer duration\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u0026lt; 1 year\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e1yr \u0026amp; above\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e43\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e146 \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 126px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e16\u003c/p\u003e\n \u003cp\u003e200 \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e59\u003c/p\u003e\n \u003cp\u003e346\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e14.56\u003c/p\u003e\n \u003cp\u003e85.43\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 198px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCigarette Smoking\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e37\u003c/p\u003e\n \u003cp\u003e152 \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 126px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e19\u003c/p\u003e\n \u003cp\u003e197\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e56\u003c/p\u003e\n \u003cp\u003e349\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e13.82\u003c/p\u003e\n \u003cp\u003e86.17\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 198px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSocial support status\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003epoor\u003c/p\u003e\n \u003cp\u003eModerate\u003c/p\u003e\n \u003cp\u003eStrong\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e100\u003c/p\u003e\n \u003cp\u003e80\u003c/p\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 126px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e125\u003c/p\u003e\n \u003cp\u003e77\u003c/p\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e225\u003c/p\u003e\n \u003cp\u003e157\u003c/p\u003e\n \u003cp\u003e23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e55.55\u003c/p\u003e\n \u003cp\u003e38.76\u003c/p\u003e\n \u003cp\u003e5.67\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 198px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePerformance status\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eGood\u0026nbsp;\u003c/p\u003e\n \u003cp\u003epoor\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e99\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;90 \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 126px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e77 \u0026nbsp;\u003c/p\u003e\n \u003cp\u003e139 | \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e176\u003c/p\u003e\n \u003cp\u003e229\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e43.45\u003c/p\u003e\n \u003cp\u003e56.56\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 198px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAnxiety\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eNo\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eYes\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e104\u003c/p\u003e\n \u003cp\u003e85 \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 126px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e74\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e142 \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e178\u003c/p\u003e\n \u003cp\u003e227\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e43.95\u003c/p\u003e\n \u003cp\u003e56. 04\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 198px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDepression status\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eNo\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e131\u003c/p\u003e\n \u003cp\u003e58 \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 126px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e99\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e117 \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e230\u003c/p\u003e\n \u003cp\u003e175\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e56.70\u003c/p\u003e\n \u003cp\u003e43.20\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/div\u003e\n \u003cp\u003eMagnitudes of comorbidity fatigue and poor sleep quality among adult cancer patient were 46.67% \u003cstrong\u003e[95% CI: (41.8\u0026ndash;51.6] (\u003c/strong\u003eFig. 2\u003cstrong\u003e).\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eAssociation factors with comorbidity of fatigue and poor sleep quality among cancer patient Amhara region, northwest, Ethiopia.\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eIn the final multivariable logistic regression model, age, marital status, place of residence, admission status, duration of cancer, cancer type, cancer stage, as well as anxiety and depression status were all identified as factors significantly associated with the outcome. The odds of fatigue and poor sleep quality comorbidity were 2.79 times higher among cancer patient aged 61 to 89 years compared to women aged 18 to 30 years [\u003cstrong\u003eAOR\u003c/strong\u003e\u0026thinsp;=\u0026thinsp;2.79, 95% CI: [1.02, 7.62]. The odds of fatigue and poor sleep quality comorbidity were 2.03 times higher among cancer patient living in rural residency compared to urban resident [\u003cstrong\u003eAOR\u003c/strong\u003e\u0026thinsp;=\u0026thinsp;2.03 95%, CI: [1.02, 4.01]. The odds of fatigue and poor sleep quality comorbidity were 2.65 and 3.54 times higher among cancer patient who are married and divorced respectively compared to single cancer patient [\u003cstrong\u003eAOR\u003c/strong\u003e\u0026thinsp;=\u0026thinsp;2.65 95%, CI: (1.01, 6.90)] and [\u003cstrong\u003eAOR\u003c/strong\u003e\u0026thinsp;=\u0026thinsp;3.54 95% CI: (1.10, 11.40)]. The odds of fatigue and poor sleep quality comorbidity were 2.84 times higher among cancer patient who are inpatient compared to outpatient [\u003cstrong\u003eAOR\u003c/strong\u003e\u0026thinsp;=\u0026thinsp;2.84, 95%, CI: (1.63, 4.95)]. The odds of fatigue and poor sleep quality comorbidity were 3.92 \u0026amp; 2.52 times higher among Stage II and Stage IV of cancer patient compared to stage I [\u003cstrong\u003eAOR\u003c/strong\u003e\u0026thinsp;=\u0026thinsp;3.92, 95%, CI: ([1.89, 8.12] and [\u003cstrong\u003eAOR\u003c/strong\u003e\u0026thinsp;=\u0026thinsp;2.52, 95% CI: (1.04, 6.15)] respectively. The odds of fatigue and poor sleep quality comorbidity were 2.70 times higher among \u0026ge;\u0026thinsp;1-year cancer duration compared to \u0026lt;\u0026thinsp;l year cancer duration [\u003cstrong\u003eAOR\u003c/strong\u003e\u0026thinsp;=\u0026thinsp;2.70, 95% CI: (1.14, 6.39)]. The odds of fatigue and poor sleep quality comorbidity were 1.93 times higher among cancer patient who had an anxiety compared to No anxiety [\u003cstrong\u003eAOR\u003c/strong\u003e\u0026thinsp;=\u0026thinsp;1.93, 95% CI: (1.06, 3.51)]. The odds of fatigue and poor sleep quality comorbidity were 2.10 times higher among cancer patient who had depression compared to No depression [\u003cstrong\u003eAOR\u003c/strong\u003e\u0026thinsp;=\u0026thinsp;2.10, 95% CI: (1.19, 3.70)\u003cstrong\u003e] (\u003c/strong\u003eTable 2\u003cstrong\u003e).\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eTable 2:\u003c/strong\u003e Final bivariable and multivariable logistic regression analysis of factors associated with the comorbidity of fatigue and poor sleep quality among cancer patients, Amhara region, northwest Ethiopia. \u0026nbsp;\u003c/p\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"1344\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 18.6454%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eVariable\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 19.4096%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eFatigue \u0026amp; poor sleep quality comorbidity\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.6718%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eCOR [95% CI]\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 5.693%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAOR [95% CI]\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"bottom\" style=\"width: 18.6454%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAge\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e18-30\u003c/p\u003e\n \u003cp\u003e31-43\u003c/p\u003e\n \u003cp\u003e44-60\u003c/p\u003e\n \u003cp\u003e61-89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.4755%;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.934%;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 14.6718%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003cp\u003e1.21 [0.65,2.25]\u003c/p\u003e\n \u003cp\u003e1.72 [0.98, 3.01]\u003c/p\u003e\n \u003cp\u003e2.65 [1.34, 5.21]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"bottom\" style=\"width: 45.8494%;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003cp\u003e0.88 [0.38, 2.06]\u003c/p\u003e\n \u003cp\u003e0.99 [0.44, 2.24]\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e2.79 [1.02, 7.62]\u003c/strong\u003e *\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 9.4755%;\"\u003e\n \u003cp\u003e42\u003c/p\u003e\n \u003cp\u003e50 \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e73\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e24 \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.934%;\"\u003e\n \u003cp\u003e31\u003c/p\u003e\n \u003cp\u003e45\u003c/p\u003e\n \u003cp\u003e93\u003c/p\u003e\n \u003cp\u003e47\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 18.6454%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSex\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.4755%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e75\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e114 \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.934%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e84\u003c/p\u003e\n \u003cp\u003e132\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.6718%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003cp\u003e0.96 [0.64, 1.44]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 45.8494%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003cp\u003e0.71 [0.37, 1.37]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 18.6454%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eResidency\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eUrban\u003c/p\u003e\n \u003cp\u003eRural\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.4755%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e82 \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e107 \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.934%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e68\u003c/p\u003e\n \u003cp\u003e148\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.6718%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003cp\u003e1.66 [1.11, 2.50]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 45.8494%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e2.03 [1.02, 4.01]\u003c/strong\u003e *\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 18.6454%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMarital status\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eSingle\u003c/p\u003e\n \u003cp\u003eMarried\u003c/p\u003e\n \u003cp\u003edivorced\u003c/p\u003e\n \u003cp\u003ewidowed\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.4755%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e108\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e20\u003c/p\u003e\n \u003cp\u003e38\u003c/p\u003e\n \u003cp\u003e23 \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.934%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e147\u003c/p\u003e\n \u003cp\u003e24\u003c/p\u003e\n \u003cp\u003e21\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;24 \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.6718%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003cp\u003e2.46 [1.36, 4.43]\u003c/p\u003e\n \u003cp\u003e2.17 [0.97, 4.82]\u003c/p\u003e\n \u003cp\u003e1.88 [.86, 4.12]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 45.8494%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e2.65 [1.01, 6.90] *\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e3.54\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e[1.10,\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e11.40] *\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e1.12 [0.35, 3.61]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 18.6454%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eEducational status\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eNo education\u003c/p\u003e\n \u003cp\u003eprimary education\u003c/p\u003e\n \u003cp\u003esecondary edu\u003c/p\u003e\n \u003cp\u003ecollege and above\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.4755%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e74\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e80 \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e20 \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;15 \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.934%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e110\u003c/p\u003e\n \u003cp\u003e64\u003c/p\u003e\n \u003cp\u003e28\u003c/p\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.6718%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003cp\u003e0.538 [0.34, 0.83]\u003c/p\u003e\n \u003cp\u003e0.94 [0.49, 1.79]\u003c/p\u003e\n \u003cp\u003e0.62 [0.28, 1.37]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 45.8494%;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003cp\u003e0.85 [0.43, 1.71]\u003c/p\u003e\n \u003cp\u003e1.88 [0.70, 5.00]\u003c/p\u003e\n \u003cp\u003e1.81 [0.55, 5.99]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 18.6454%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eOccupational status\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eEmployed\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eUnemployed\u003c/p\u003e\n \u003cp\u003eFarmer\u003c/p\u003e\n \u003cp\u003eMarchant\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.4755%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e22 \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e25\u003c/p\u003e\n \u003cp\u003e95\u003c/p\u003e\n \u003cp\u003e47 \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.934%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e15\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003cp\u003e136\u003c/p\u003e\n \u003cp\u003e50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.6718%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003cp\u003e1.13 [0.45, 2.84]\u003c/p\u003e\n \u003cp\u003e2.38 [1.19, 4.76]\u003c/p\u003e\n \u003cp\u003e1.77 [ 0.83, 3.76]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 45.8494%;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003cp\u003e0.74 [0.19, 2.94]\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e1.27 [0.43, 3.76]\u003c/p\u003e\n \u003cp\u003e1.95 [0.64, 5.99]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 18.6454%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAdmission status\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eOutpatient\u003c/p\u003e\n \u003cp\u003eInpatient\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.4755%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e77\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;112 \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.934%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e140\u003c/p\u003e\n \u003cp\u003e76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.6718%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003cp\u003e2.67 [1.79, 4.00]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 45.8494%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e2.84 [1.63, 4.95] *\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 18.6454%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCancer types\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eBreast cancer\u003c/p\u003e\n \u003cp\u003ecolorectal cancer\u003c/p\u003e\n \u003cp\u003ecervical cancer\u003c/p\u003e\n \u003cp\u003elung cancer\u003c/p\u003e\n \u003cp\u003eskin cancer\u003c/p\u003e\n \u003cp\u003eprostate cancer\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.4755%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e46 \u0026nbsp; \u0026nbsp; \u0026nbsp;\u003c/p\u003e\n \u003cp\u003e28\u003c/p\u003e\n \u003cp\u003e40\u003c/p\u003e\n \u003cp\u003e21\u003c/p\u003e\n \u003cp\u003e23\u003c/p\u003e\n \u003cp\u003e14 \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.934%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e46\u003c/p\u003e\n \u003cp\u003e31\u003c/p\u003e\n \u003cp\u003e42\u003c/p\u003e\n \u003cp\u003e28\u003c/p\u003e\n \u003cp\u003e18\u003c/p\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.6718%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003cp\u003e0.90 [0.46, 1.73]\u003c/p\u003e\n \u003cp\u003e0.94 [0.48, 1.85]\u003c/p\u003e\n \u003cp\u003e1.20[0.56, 2.58]\u003c/p\u003e\n \u003cp\u003e0.70 [0.31, 1.57]\u003c/p\u003e\n \u003cp\u003e0.58 [0.21, 1.54]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 45.8494%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003cp\u003e1.80 [0.75, 4.34]\u003c/p\u003e\n \u003cp\u003e0.91 [0.43, 1.92]\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e3.15 [1.23, 8.03]\u003c/strong\u003e *\u003c/p\u003e\n \u003cp\u003e1.24 [0.48, 3.22]\u003c/p\u003e\n \u003cp\u003e1.14 [0.33,3.88]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 18.6454%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eStage of cancer\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003estage 1\u003c/p\u003e\n \u003cp\u003estage 2\u003c/p\u003e\n \u003cp\u003estage 3\u003c/p\u003e\n \u003cp\u003estage 4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.4755%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e80 \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e43\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;29 \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;37 \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.934%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e47\u003c/p\u003e\n \u003cp\u003e88\u003c/p\u003e\n \u003cp\u003e35\u003c/p\u003e\n \u003cp\u003e46 \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.6718%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003cp\u003e3.48 [2.08, 5.81]\u003c/p\u003e\n \u003cp\u003e2.05 [1.11,3.78]\u003c/p\u003e\n \u003cp\u003e2.11 [1.20, 3.71]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 45.8494%;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e3.92 [1.89, 8.12] *\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e2.33 [0.91, 5.93]\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e2.52 [1.04,\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e6.15] *\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 18.6454%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCancer therapy\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eMedical therapy\u003c/p\u003e\n \u003cp\u003esurgical therapy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.4755%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e124\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;65 \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.934%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e160\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e55 \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.6718%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003cp\u003e0.65 [.42, 1.00]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 45.8494%;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003cp\u003e0.69 [0.39, 1.24]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 18.6454%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMetastasis History\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003cp\u003eyes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.4755%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e69 \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u003c/p\u003e\n \u003cp\u003e120 \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.934%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e92\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e124 \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.6718%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003cp\u003e1.29 [ 0.86, 1.92]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 45.8494%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003cp\u003e1.05 [0.58, 1.90]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 18.6454%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCancer duration\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u0026lt; 1 year\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026ge;1 year \u0026amp; above\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.4755%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e43\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e146 \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.934%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e16\u003c/p\u003e\n \u003cp\u003e200 \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.6718%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e3.68 [1.99, 6.79]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 45.8494%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e2.70 [1.14,\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e6.39] *\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 18.6454%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCigarette Smoking\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.4755%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e37\u003c/p\u003e\n \u003cp\u003e152 \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.934%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e19\u003c/p\u003e\n \u003cp\u003e197\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.6718%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e2.52 [1.39, 4.56]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 45.8494%;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003cp\u003e2.06 [0.88, 4.83]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 18.6454%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSocial support\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003epoor\u003c/p\u003e\n \u003cp\u003emoderate\u003c/p\u003e\n \u003cp\u003estrong\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.4755%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e100\u003c/p\u003e\n \u003cp\u003e80\u003c/p\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.934%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e125\u003c/p\u003e\n \u003cp\u003e77\u003c/p\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.6718%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.77 [0.50, 1.12]\u003c/p\u003e\n \u003cp\u003e1.24 [0.19, 7.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 45.8494%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.55 [0.31, 0.98]\u003c/p\u003e\n \u003cp\u003e1.45 [0.12, 16.88]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 18.6454%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eperformance status\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eGood\u0026nbsp;\u003c/p\u003e\n \u003cp\u003epoor\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.4755%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e99\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;90 \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.934%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e77 \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e139 | \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.6718%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003cp\u003e1.98 [1.33, 2.95]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 45.8494%;\"\u003e\n \u003cp\u003e0.81 [0.45,1.47]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 18.6454%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAnxiety status\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003cp\u003eyes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.4755%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e104\u003c/p\u003e\n \u003cp\u003e85 \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.934%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e74\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e142 \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.6718%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003cp\u003e2.34 [\u0026nbsp;1.57, 3.50]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 45.8494%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e1.93 [1.06,\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e3.51] *\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 18.6454%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDepression\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.4755%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e131\u003c/p\u003e\n \u003cp\u003e58 \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.934%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e99\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e117 \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.6718%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003cp\u003e2.66 [1.77, 4.01]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 45.8494%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e2.10 [1.19, 3.70] *\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study highlights a range of sociodemographic, clinical, and psychological factors associated with the co-occurrence of fatigue and poor sleep quality among individuals with cancer. Findings suggest that certain demographic characteristics, clinical conditions, and psychological states may contribute to the comorbidity of these symptoms. These results offer important insights into the complex interplay of factors that influence symptom burden in cancer populations and provide a foundation for further exploration and targeted interventions.\u003c/p\u003e\u003cp\u003eIn this study, comorbidity fatigue and poor sleep quality were 46.67% (95% CI: 41.8\u0026ndash;51.6) of adult cancer patients in the Northwest Amhara Region. Our findings are in line with the larger body of research showing that both symptoms are quite common and frequently co-occur in oncology populations, even though no prior studies have specifically recorded this comorbidity as a single outcome. For instance, studies conducted in similar settings have reported that cancer-related fatigue affects approximately 50\u0026ndash;60% of patients[16, 33], while poor sleep quality has been observed in 53\u0026ndash;61% of patients [16, 34]. The strong associations previously reported between fatigue and sleep problems such as correlation coefficients around 0.6, adjusted odds ratios\u0026thinsp;\u0026gt;\u0026thinsp;2) support the likelihood of a high degree of overlap between the two symptoms[16, 35, 36]. Our finding indicates that around half of cancer patients in this area had both symptoms, which represents a significant symptom burden. Numerous interrelated factors, including the burden of the disease, adverse drug reactions, psychological stress, disease stage, and restricted access to supportive care, may contribute to this. The results highlight the necessity for cancer care teams and doctors to test for sleep disruptions and exhaustion simultaneously, rather than separately, and to think about integrated therapies that treat both symptoms at the same time. However, This magnitude is somewhat lower than reports from Iran 69.3% [37], Arab countries (77.5% and 78%)[15, 38], and Egypt, where fatigue and poor sleep were reported in 99.2% and 87.4% of patients, respectively[39] and America 93% \u0026amp; 77% [40, 41]. Although prior research has repeatedly shown that cancer-related fatigue and poor sleep quality are significantly correlated, most of these studies did not report comorbidity as a single combined prevalence estimate, which makes it difficult to directly compare our findings with those of other studies. Our study's comparatively lower prevalence could be due to variances in healthcare settings, cultural views of symptoms, study populations, or evaluation instruments. Because fatigue and poor sleep quality can significantly decrease quality of life and may necessitate integrated management techniques in cancer therapy, it is clinically important to recognize their coexistence. The research is still lacking in longitudinal data on the long-term interactions between these two symptoms and if addressing both at the same time with focused therapies can enhance patient outcomes.\u003c/p\u003e\u003cp\u003eIn this study, the odds of comorbidity of fatigue and poor sleep quality were 2.79 times higher among cancer patient aged 61 to 89 years compared to women aged 18 to 30 years. This association was consistent with study conducted in of university of California[37, 42], Brazil[43]. Age-related physiological changes including reduced sleep efficiency and more overnight awakenings, higher susceptibility to treatment-related adverse effects, and the existence of numerous comorbidities could all contribute to this outcome. Anxiety, social isolation, and a lack of coping mechanisms are examples of psychosocial issues that can make fatigue and sleep disturbance worse. The combined impact of these variables emphasizes the necessity of focused treatments to enhance sleep and lessen fatigue in elderly cancer patients. And cancer patients living in rural areas had 2.03 times higher odds of comorbid fatigue and poor sleep quality compared to urban residents. This finding is consistent with prior research indicating that rural residency is associated with poorer symptom management, higher fatigue, and greater sleep disturbances among cancer patients[17, 44]. Lower health literacy, longer travel times to clinics, fewer specialized cancer care resources, and restricted access to healthcare services are some potential causes, all of which could lead to a higher cumulative symptom burden and delayed symptom management. Environmental and social variables may make fatigue and sleep issues worse, such as a lack of social support and higher levels of stress in rural areas [45, 46]. The odds of fatigue and poor sleep quality comorbidity were 2.65 and 3.54 times higher among cancer patient who are married and divorced respectively compared to single cancer patient. This finding is consistent with study conducted Rabat, Morocco[47]. Married couples may benefit from psychological and social support, such as emotional support, and encouragement to adhere to treatment, which may have a protective effect on cancer patients' sleep quality. Support of this kind help in the reduction of stress, worry, and depression symptoms, all of which are closely related to fatigue and sleep quality. On the other hand, cancer patients who are widowed, divorced, or single frequently experience higher levels of psychosocial stress and less coping mechanisms, which raises their risk of fatigue and poor sleep quality[48, 49]. The odds of fatigue and poor sleep quality comorbidity were 2.84 times higher among cancer patient who are inpatient compared to outpatient [\u003cb\u003eAOR\u003c/b\u003e\u0026thinsp;=\u0026thinsp;2.84, 95%, CI: (1.63, 4.95)]. This finding was consistent with study conducted in Ethiopia[50], Iran[37], systematic review and meta-analysis[51]. Comorbid fatigue and sleep disturbances are more common in inpatient cancer patients because of the advanced stage of the disease, unmanaged symptoms, and more intense therapy. Sleep is further disrupted and weariness is increased by pain, dyspnea, nausea, and hospital procedures including noise and midnight monitoring. In addition, polypharmacy and psychological discomfort[34, 37]. The odds of fatigue and poor sleep quality comorbidity were 3.92 \u0026amp; 2.52 times higher among Stage II and Stage IV of cancer patient compared to stage I [\u003cb\u003eAOR\u003c/b\u003e\u0026thinsp;=\u0026thinsp;3.92, 95%, CI: ([1.89, 8.12] and [\u003cb\u003eAOR\u003c/b\u003e\u0026thinsp;=\u0026thinsp;2.52, 95% CI: (1.04, 6.15)] respectively. This finding was concurrent in study conducted in Ethiopia[10, 16], systematic review and meta-analysis[17]. The higher disease load and physiological stress in patients with more advanced cancer stages increases the likelihood of fatigue and restless nights. Tumor growth in stage II frequently results in elevated inflammatory activity, which throws off circadian rhythm and energy management, making fatigue and sleep quality worse. In stage IV, metastases and widespread disease increase the burden of symptoms, the necessity for intense therapy, and psychological distress, all of which independently affect sleep and cause fatigue. Extensive reviews consistently demonstrate that fatigue and poor sleep quality are strongly predicted by tumor stage, symptom burden, and treatment severity [34, 52]. The odds of fatigue and poor sleep quality comorbidity were 2.70 times higher among \u0026ge;\u0026thinsp;1-year cancer duration compared to \u0026lt;\u0026thinsp;l year cancer duration [\u003cb\u003eAOR\u003c/b\u003e\u0026thinsp;=\u0026thinsp;2.70, 95% CI: (1.14, 6.39)]. This finding was concurrent with study conducted in USA[53], Netherlands[54], Ethiopia[16]. Patients who have had cancer for more than a year are far more likely than those who have had it for less time to suffer from concomitant fatigue and poor sleep quality. Long-term exposure to the disease and its treatments might result in cumulative adverse effects from radiation, chemotherapy, or surgery, which can worsen sleep problems and exhaustion. Furthermore, extended cancer duration is frequently linked to higher levels of psychological distress, such as anxiety and depression, as well as a greater load of symptoms, such as pain and discomfort, all of which worsen energy and sleep quality. The odds of fatigue and poor sleep quality comorbidity were 1.93 times higher among cancer patient who had an anxiety compared to No anxiety [\u003cb\u003eAOR\u003c/b\u003e\u0026thinsp;=\u0026thinsp;1.93, 95% CI: (1.06, 3.51)]. This associate was similar with study done in China[34], Ethiopia[50], meta-analysis studies in multiple countries[51]. Cancer patients' comorbidity of fatigue and poor sleep quality is greatly influenced by anxiety through both physiological and psychological factors. It causes the hypothalamic-pituitary-adrenal (HPA) axis to become dysregulated and hyperarousal, which interferes with sleep and results in non-restorative slumber. Consequently, this makes cancer-related fatigue worse, which is a prevalent and enduring symptom in oncology populations. Additionally, anxiety exacerbates fatigue and sleep disruptions by reducing adaptive coping mechanisms [55\u0026ndash;57].\u003c/p\u003e\u003cp\u003eThe odds of fatigue and poor sleep quality comorbidity were 2.10 times higher among cancer patient who had depression compared to No depression [\u003cb\u003eAOR\u003c/b\u003e\u0026thinsp;=\u0026thinsp;2.10, 95% CI: (1.19, 3.70)\u003cb\u003e].\u003c/b\u003e This finding concurrent with study conducted in Ethiopia and Egypt [9, 39, 58].\u003c/p\u003e\u003cp\u003eThere are several interrelated biological and psychological processes via which depression in cancer patients can worsen fatigue and interfere with sleep. Increased exhaustion results from neurochemical imbalances linked to depression, such as changed serotonin and dopamine levels, which interfere with sleep regulation and decrease restorative sleep. Moreover, depression frequently entails dysregulation of the hypothalamic-pituitary-adrenal (HPA) axis and increased inflammatory responses, which exacerbate fatigue and poor sleep quality. On a psychological level, depression symptoms make people less motivated and energetic, which makes it harder for them to take care of themselves and keep up good sleep habits. This makes the symptoms worse. All of these processes work together to explain why depressed cancer patients have been shown to have higher risks of experiencing fatigue and poor sleep comorbidities [39, 59].\u003c/p\u003e\u003cdiv id=\"Sec23\" class=\"Section2\"\u003e\u003ch2\u003eStrengths and Limitations\u003c/h2\u003e\u003cp\u003eThe major strengths of this study its novel focus on comorbidity, addressing a research gap by assessing the combined prevalence of fatigue and poor sleep in a resource-limited setting. Additionally, the use of validated assessment tools for both fatigue and sleep quality enhances the reliability of our findings. The study\u0026rsquo;s relatively large and diverse sample drawn from multiple facilities in the Northwest Amhara Region improves its representativeness and relevance to clinical practice. However, several limitations should be acknowledged; the reliance on self-reported data may introduce recall bias, particularly in the subjective evaluation of symptoms, the absence of biochemical or clinical staging data limits the ability to assess the influence of disease severity or treatment modality on symptom burden, while we used a binary outcome to capture comorbidity, more nuanced analyses such as symptom cluster modeling or longitudinal tracking may yield deeper insights into symptom interactions over time.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec24\" class=\"Section2\"\u003e\u003ch2\u003eClinical Implications\u003c/h2\u003e\u003cp\u003eNearly half of adult cancer patients in the region experience both fatigue and poor sleep quality, highlighting the need for routine screening of concurrent symptoms. These comorbidities intensify each other\u0026rsquo;s effects, leading to impaired functioning, reduced quality of life, and lower treatment adherence. Integrated management strategies ranging from CBT and physical activity programs to basic sleep hygiene counseling are essential, especially in resource-limited settings.\u003c/p\u003e\u003cdiv id=\"Sec25\" class=\"Section3\"\u003e\u003ch2\u003eConclusions and Recommendation\u003c/h2\u003e\u003cp\u003eComorbid fatigue and poor sleep quality are highly prevalent among adult cancer patients in the Northwest Amhara Region, significantly affecting functional status and quality of life. These findings underscore the need for routine screening and integrated management of concurrent symptoms within oncology care. Simple interventions such as sleep hygiene counseling, psychoeducation, and targeted therapies can improve patient outcomes, especially in resource-limited settings. Future research should explore longitudinal trajectories and contributing factors to inform tailored, effective interventions.\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e"},{"header":"Declarations","content":"\u003ch2\u003eDeclarations\u003c/h2\u003e\u003cp\u003e\u003cb\u003eClinical trial number\u003c/b\u003e\u003c/p\u003e\u003cp\u003eNot applicable\u003c/p\u003e\u003cp\u003e\u003cb\u003eEthical consideration\u003c/b\u003e\u003c/p\u003e\u003cp\u003e This study was conducted in accordance with the ethical principles of the Declaration of Helsinki. Ethical approval was obtained from the Research and Ethical Review Committee of the University of Gondar, College of Medicine and Health Sciences, School of Nursing (Reference No. S/N 237/2014). The committee approved the study on behalf of the Institutional Ethical Review Committee of the University of Gondar. The objectives and significance of the study were clearly explained to all participants, and written informed consent was obtained from each participant prior to data collection.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eCompeting interest\u003c/strong\u003e\u003cp\u003eAuthors Declared that, there is no competing interest.\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003ch2\u003eAMB\u003c/h2\u003e\u003cp\u003einvolved in analysis, and result interpretation\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eBAN\u003c/strong\u003e\u003cp\u003einvolved in data analysis, interpretation, and, and manuscript writing.\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eDGA\u003c/strong\u003e\u003cp\u003einvolved in discussion and drafting proposal and interpretation of result.\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eYGB\u003c/strong\u003e\u003cp\u003einvolved in conceptualization, validation, writing original draft.\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eAEB\u003c/strong\u003e\u003cp\u003einvolved in data collection and writing original draft\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eAFZ\u003c/strong\u003e\u003cp\u003einvolved in designing and preparing manuscript.\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003ch2\u003e\u003cb\u003eFigure\u0026nbsp;1\u003c/b\u003e\u003c/h2\u003e\u003cp\u003eSchematic presentation of the sampling procedure comorbidity of fatigue and Poor sleep quality and its predictor among adult cancer patients, Northwest, Ethiopia, 2025\u003c/p\u003e\u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e\u003cp\u003eNot applicable\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eGAZ : involved data collection, data analysis, interpretation, report and manuscript writing.AMB: involved in analysis, and result interpretationBAN: involved in data analysis, interpretation, and, and manuscript writing.DGA: involved in discussion and drafting proposal and interpretation of result.YGB: involved in conceptualization, validation, writing original draft.AEB: involved in data collection and writing original draftAFZ: involved in designing and preparing manuscript.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe manuscript contains all of the data that is crucial to our findings. Request for additional information on the data set and questions about data sharing will be treated in accordance with a reasonable request to [email protected].\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003e1. Bray, F., et al., \u003cem\u003eGlobal cancer statistics 2022: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries.\u003c/em\u003e 2024. \u003cb\u003e74\u003c/b\u003e(3): p. 229\u0026ndash;263.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003e2. Rutkowski, N.A., \u003cem\u003eWhy Am I Still Tired? Adaptation, Implementation, and Evaluation of an Intervention for Cancer-Related Fatigue\u003c/em\u003e. 2025, Universit\u0026eacute; d'Ottawa| University of Ottawa.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003e3. 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Javan Biparva, A., et al., \u003cem\u003eGlobal depression in breast cancer patients: Systematic review and meta-analysis.\u003c/em\u003e 2023. \u003cb\u003e18\u003c/b\u003e(7): p. e0287372.\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":"Cancer related fatigue, sleep quality, comorbidity, cancer patients, Amhara Region","lastPublishedDoi":"10.21203/rs.3.rs-8065921/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8065921/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground: \u003c/strong\u003eCancer-related fatigue and poor sleep quality are among the most prevalent and distressing symptoms experienced by patients with cancer, significantly impairing physical, emotional, and cognitive functioning. Despite their high prevalence and detrimental impact on quality of life, the comorbidity of fatigue and sleep disturbances remains underexplored, particularly in low-resource settings where access to comprehensive oncology care is limited. Understanding the magnitude, contributing factors, and interrelationship of these symptoms is essential for developing targeted interventions. However, existing research predominantly focuses on either fatigue or sleep quality in isolation, highlighting a critical gap in evidence regarding their concurrent occurrence and synergistic effects on cancer patient.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethod\u003c/strong\u003e: Institutional-based quantitative cross-sectional study was conducted among adult cancer patients receiving cancer treatment at an oncology unit from May to June 2025. A systematic random sampling technique was used to select 422 samples. After obtaining consent data were collected using a structured Interviewer-administered questionnaire. Then data were entered into Epi-data version 4.6 and exported to Stata version 14 for analysis. Model fitness was checked by the Hosmer-Lemeshow goodness of fit test. Descriptive statistics including, frequencies and proportions were computed and presented by using tables and texts. Bivariable and multivariable logistic regression analysis was computed considering p\u0026lt;0.05 to be statistically significant at the final model.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResult:\u003c/strong\u003e A total of 405 cancer patients were included in this study, of whom 46.67% experienced comorbid fatigue and poor sleep quality. Age 61-89 years [\u003cstrong\u003eAOR\u003c/strong\u003e = 2.79, 95% CI: [1.02, 7.62]. Rural residency [\u003cstrong\u003eAOR\u003c/strong\u003e= 2.03 95%, CI: [1.02, 4.01], Married \u0026amp; divorced [\u003cstrong\u003eAOR\u003c/strong\u003e= 2.65 95%, CI: (1.01, 6.90)] and [\u003cstrong\u003eAOR\u003c/strong\u003e= 3.54 95% CI: (1.10, 11.40)], Inpatient [\u003cstrong\u003eAOR\u003c/strong\u003e=2.84, 95%, CI: (1.63, 4.95)]. Stage II and Stage IV [\u003cstrong\u003eAOR\u003c/strong\u003e=3.92, 95%, CI: ([1.89, 8.12] and [AOR= 2.52, 95% CI: (1.04, 6.15)] respectively, cancer duration [\u003cstrong\u003eAOR\u003c/strong\u003e=2.70, 95% CI: (1.14, 6.39)]. Anxiety [\u003cstrong\u003eAOR\u003c/strong\u003e= 1.93, 95% CI: (1.06, 3.51)]. \u0026nbsp;depression [\u003cstrong\u003eAOR\u003c/strong\u003e= 2.10, 95% CI: (1.19, 3.70)].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion and recommendation:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eComorbidity of fatigue and poor sleep affected nearly half of cancer patients, representing a substantial and underrecognized clinical burden that necessitates systematic assessment and integrated, multidisciplinary interventions in oncology care.\u003cstrong\u003e \u003c/strong\u003eAge, Residence, Marital Status, Cancer Stage, Cancer Duration, Inpatient Admission, And Anxiety and Depression were significant predictors. These findings highlight the need for routine screening and integrated interventions targeting both physical and psychosocial determinants, alongside strengthening supportive and multidisciplinary care to improve patient outcomes.\u003c/p\u003e","manuscriptTitle":"Double Burden: Fatigue and Poor Sleep quality comorbidity and its predictor Among Cancer Patients, Northwest Ethiopia: Institutional based cross- sectional study design","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-11-26 06:30:49","doi":"10.21203/rs.3.rs-8065921/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":"6880bbdc-7a55-4085-b0fe-5cd5bf639319","owner":[],"postedDate":"November 26th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":58464046,"name":"Biological sciences/Cancer"},{"id":58464047,"name":"Health sciences/Diseases"},{"id":58464048,"name":"Health sciences/Health care"},{"id":58464049,"name":"Health sciences/Medical research"},{"id":58464050,"name":"Health sciences/Oncology"}],"tags":[],"updatedAt":"2025-12-17T05:23:12+00:00","versionOfRecord":[],"versionCreatedAt":"2025-11-26 06:30:49","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8065921","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8065921","identity":"rs-8065921","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}