Demographic Predictors of Inpatients’ Postdischarge Stay in Referral Hospitals in Kisumu County, Kenya

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Awuor, Mary A. Ochieng, Charles O. Olang’o, Silas O. Awuor This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6042253/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 Introduction Globally, inpatients continue to unnecessarily prolong their stay in referral hospital wards upon their medical discharge. This causes congestion in wards, hospital reinfection, relapse, death of PDS inpatients, and financial burden to hospital management. The literature links postdischarge stay (PDS) to economic reasons. This study aimed to investigate the demographic predictors of inpatients’ postdischarge stay in Jaramogi Oginga Odinga Teaching and Referral Hospitals (JOOTRH) and Kisumu County Referral Hospital (KCRH) in Kisumu County, Kenya. Methodology This study adopted a correlational cross-sectional research design. A stratified sampling technique was used to select inpatients in the 14 wards, after which systematic random sampling was used to reach the individual PDS inpatients for interviews. To establish the predictors of PDS, binary logistic regression analysis was used, where p values < 0.05 were considered statistically significant, and odds ratios (ORs) and 95% confidence intervals (CIs) were reported to show the magnitude and influence of PDS, resulting in a total sample size of 133 participants. Results The majority 72 (54.13%) of the respondents in this study were female. In the age cohort, 43 (32.33%) were aged 20–29 years, 24 (18.05%) were aged 30–39 years, 20 (15.04%) were aged 0–9 years, 17 (12.78%) were aged 40–49 years, 11 (8.27%) were aged over 60 years, and 7 (5.26%) were aged 50–59 years. In terms of marital status, 40 (30.08%) of the respondents were married, 22 (16.54%) were single, 20 (15.04%) were divorced/separated, 16 (12.03%) were widowed, 9 (45.00%) were partial orphans, 8 (40.00%) were both parents, and 3 (15.00%) were total orphans. Most of the respondents 74 (55.64%) had chronic diseases, while 59 (44.36%) had acute illnesses, among which the majority 88 (66.17%) had reached the primary level; hence, 86 (64.66%) unemployed respondents Conclusion Individual demographic factors associated with the vulnerability of PDS inpatients to becoming PDS victims in referral hospitals. Elderly individuals, males, unmarried individuals, and chronically ill individuals were more at risk of experiencing episodes of PDS. Older people with complex health needs are particularly vulnerable to PDS. Additionally, males, especially middle-aged individuals, are associated with deviance, rendering them social misfits who face neglect due to their social relationships in hospital wards and thus PDS. The chronically ill were perceived as burdens by their family members, whereas the unmarried experienced PDS due to insufficient social support. Whereas the parental status of the children, religiosity, education, and employment status of the PDS inpatients were insignificant to the study, they were not dismissed because they determined the general health outcomes of the PDS. Postdischarge stay PDS inpatients demographic predictor Introduction The Postdischarge stay (PDS), also known as prolonged hospital utilization upon medical discharge of inpatients from hospital wards, is costly and may be a marker of poor quality of care [1, 2]. Other scholars refer to this sociological phenomenon as the detention of insolvent patients [3], discharge delay [4], hospitals as debtor prisons [5], medical hostages [6], and hospital detention practices [7]. Since there is no systematic or official terminology to describe this phenomenon by the World Health Organization (WHO) or any other authority [8], this study employed PDS. Globally, PDS is a healthcare system problem [9], and this episode has been prevalent since the 1990s [3, 10]. Postdischarge stay (PDS) is widespread in Eastern Europe, Latin America, Asia, and Africa [7]. In Latin America, it is common, particularly in Venezuela, Haiti, and middle-income Mexico and Colombia [11]. The consequences of PDS are far-reaching for hospital management, patients, and their families [12, 1]. The United States of America-based study by Cai et al. (2020) [13] revealed that PDS results in unnecessary risk for hospital-acquired infections, loss of revenue for the hospital, and reduced hospital capacity to treat other patients due to bed blocking. According to Silva et al. (2014) [4], 60% and 58% of inpatients in Hospital das Clínicas and Hospital Odilon Behrens, respectively, suffer from a shortage of beds and long waiting lists for hospital admission in Brazil. In a metropolitan teaching hospital in London, inpatients unnecessarily utilize £97,432 annually because of PDS [14]. According to Toh et al. (2017) [15], 20% of older inpatients suffer PDS at Khoo Teck Puat Hospital in Singapore, and the study revealed that PDS disrupts patient flow and access to care due to bed shortages, as discharged inpatients occupy beds that newly admitted inpatients badly require. Devakumar and Yates (2016) [6] argued that additional days spent in the hospital due to PDS imply more medical bills, which the majority of inpatients often fail to pay, resulting in cases of PDS [16]. Despite the above statistics on the occurrence of a PDS and its consequences, its magnitude is unknown [11, 9]. Many studies have attributed the introduction of user fees in many low- and middle-income countries, such as India, Pakistan, Cambodia, and Vietnam, to the cause of PDS [17-19]. Owing to the high cost of hospitalization, inpatients in public referral hospital wards often fail to pay hospital bills, which in turn leads to PDS [20, 21, 3]. For example, inpatients suffer from PDS in a tertiary referral hospital in New South Wales, Australia, because they need financial help [22]. Waiver systems were then introduced to cushion the poor from health shocks [20]. However, non-reimbursement of foregone waiver costs weakens hospital operations. The costs that are foregone by waivers are rarely reimbursed, and the waiver process is burdensome, demeaning, and dangerous for the health of inpatients [23]. As a result, many developed and developing countries have promoted social health insurance (SHI) to eliminate unmet health needs [24, 25]. Despite these efforts, the WHO and the World Bank Group (2015) reported that at least 400 million people globally cannot access health services, as 40% of the world’s population lacks medical coverage [26]. For example, according to the Kaiser Family Foundation American Community Survey (2017), 27.4 million people were uninsured in the USA as of 2017 because of the high cost involved [27]. According to Pryor et al. (2003) [28], uninsured individuals are likely to delay or forego treatment. The delay is until the condition is urgent and more complex, requiring emergency and/or admission to hospitals, which is costly in the long run, resulting in PDS [29]. Although strategies have been put in place by many countries around the world, the emphasis has been to curb financial difficulties that bar people from accessing health services with less attention to what transpires after inpatients are medically discharged but cannot leave referral hospital wards immediately. Demographic factors associated with PDS. Demographic factors have been attributed to PDS episodes. A study by Glasby et al. (2004) [30] reviewed PDS among older people in England and reported that a lack of rehabilitation services and internal hospital factors contributed to the occurrence of PDS. Other studies suggest that inpatients’ demographic characteristics, including age, sex, racial/ethnic identity, marital status, and the nature of illness, are factors that contribute to cases of PDS [31, 32, 15, 16]. According to Little et al. (2019) [33], more males aged over 44 years and indigenous language speakers than their female counterparts in Canada suffer from PDS. An Australian study by Ou et al. (2009) [22] revealed that elderly individuals aged 65 years and above who were single, widowed, or divorced were more affected by PDS than married individuals were. This concurs with the findings of Little et al. (2019) [33]. Moreover, two studies conducted in the UK reported that PDS affects mainly elderly individuals, with a mean age of 84 years [34], and 75 years, of which 9 PDS inpatients were women [35]. This finding was supported by a study by Lim et al. (2006) [36], which revealed that 150 elderly inpatients with a mean age of 84 years experienced PDS at Changi General Hospital-Singapore for more than 28 days. However, the study was performed retrospectively in the Department of Geriatric Medicine only. A study in India by Nath et al. (2015) [37] reported contradictory findings. They argued that young inpatients, mainly males aged 16--20 years (67%), are more prone to PDS due to their unknown identity as a result of severe head injury from a road traffic accident (RTA). In Ontario, Canada, the mental status of inpatients was negatively correlated with the PDS [33]. However, according to Arora et al. (2017) [38] and [16], unidentified inpatients prolong their stay in the hospital both before and after discharge due to the presence of dementia and the severity of acute illness. Although these studies demonstrate that inpatients’ demographic factors predict PDS, the findings are contradictory; thus, the current study is needed. Occurrence of PDS PDS has been reported in numerous parts of Sub-Saharan African countries, including Burundi [39], Ghana [40], Cameroon [41] and Zimbabwe [42]. However, these reports linked user fees to the causes of PDS. For example, Wekesa (2016) [43] in the Ugandan Daily Monitor revealed that PDS inpatients prolonged their stay in the hospital for more than a week over bills in the International Hospital of Kampala (IHK). In addition to user fees, two findings by Mumba (2017) [44] and Phiri (2018) [45] suggest that inpatients in Zambia have been suffering from PDS because they are left in the hospital by their relatives due to the nature of the illness. In addition, a study in Nigeria revealed that women with Vesico Vaginal Fistula (VVF) syndrome suffer from PDS due to this condition [46]. As many PDS cases have been reported, there is a mixed opinion, with much emphasis placed on the inability to make medical bill payments. In Kenya, PDS cases in referral hospitals are widespread [46, 47]. For example, Karongo (2011) [48] reported that inpatients stayed longer at Kenyatta National Hospital (KNH) upon their medical discharge because of their inability to pay medical bills. The literature indicates that before the free maternal health care policy was decreed, inpatients in Pumwani Hospital, KNH, and other public hospitals experienced PDS due to their inability to pay medical bills [21, 49, 50], and the bill continues to increase [7, 10]. With the decree of free maternal health care, which came into effect on 1st June 2013, it was expected that the PDS would decrease [51]. However, emerging reports illustrate that cases of PDS persist [52, 53]. For example, Oketch (2020b) [54] revealed that 20 women who went to deliver at KNH experienced PDS for more than six months because of failure to clear the medical bill, yet there was free maternal health care. Although it has been assumed that economic factors lead to PDS, reports have indicated that 22 of 258 PDS inpatients released from KNHs cannot exit health facilities immediately [55]. Moreover, Meru District Hospital expressed concern over the increasing number of elderly PDS inpatients left and neglected in the hospital by their families [56]. A report in Kenyan Daily Nation further revealed that 32 mental patients suffered from PDS in Gilgil Sub-County Hospital for ten years because of the stigma associated with psychiatric conditions [57]. Maina (2014) [58] established that some of the determinants of PDS in Kenya include the medical insurance status of PDS inpatients. Kisumu is one of the counties in Kenya with relatively high levels of unemployment and poverty [59]. Owing to the high poverty level of 46%, inpatients in Kisumu County referral hospitals are faced with the inability to pay medical bills [60]. Poverty levels are exacerbated by communicable diseases such as malaria, HIV/AIDS, and tuberculosis, which are among the factors that make Kisumu County a UHC pilot study site [61]. Jaramogi Oginga Odinga Teaching and Referral Hospital (JOOTRH) and Kisumu County Referral Hospital (KCRH) are the two major referral hospitals in Kisumu County [62]. Inyanji and Lungai (2016) [63] argued that these key referral hospitals in Kisumu County are in deplorable condition because of congestion, which causes bed shortages. This is partly due to inpatients who have been medically discharged but could not leave the hospital. The main objective of this study was to investigate the demographic predictors of inpatients’ postdischarge stay in referral hospitals in Kisumu County. Methodology Research Design This study adopted a correlational cross-sectional study design with both quantitative and qualitative methods of data collection. The correlational research design was used to describe, explain, and predict how the variables under study influenced the PDS. The design, therefore, enabled the researcher to establish whether and to what degree a relationship existed between variables. A cross-sectional dimension was useful in obtaining an overall ‘picture’ of PDS episodes, as it was at the time of the study [64]. It was quick and helped to collect vast amounts of information in a brief period. The researcher collected and analysed the data, integrated the findings, and drew inferences via both qualitative and quantitative approaches [65]. The quantitative approach was used to obtain numerical data, whereas the qualitative research method produced descriptive data from respondents’ own spoken words to understand their views on PDS [66]. The mixed approach was useful for triangulating the data. This increased the credibility and validity of the research findings [67]. Sample size The population was derived from participants who were medically discharged but were still in the hospital wards at JOOTRH and KCRH during the two-month study. At the end of the sample collection period, 133 inpatient participants, 13 participant in-depth interviews, and 10 participant key informants were included, resulting in a total of 156 participants. Study Area This study was performed at JOOTRH and KCRH, the two major referral hospitals in Kisumu County (Oketch, 2016). According to a report by Inyanji and Lungai (2016) [63], these key referral hospitals in Kisumu County were in deplorable condition because congestion led to bed shortages. Cases of inpatients sharing beds or sleeping on the floor were common [62, 68], which was partly due to cases of PDS arising from social issues such as lack of fare, delay or lack of family members to pick them up from the hospital [69 and patient abandonment [67]. Moreover, the congestion was fuelled by referrals from other neighbouring county hospitals such as Siaya [70] and self-referrals due to poor referral systems, as walk-in patients bypassed lower-tier health facilities. Therefore, studies need to be conducted in Kisumu County. Structured interviews/questionnaires The researcher and the three research assistants used a structured questionnaire that was computerized and administered via the Commcare App installed on the phone. Both open- and closed-ended questions were used to obtain quantitative data from a randomly selected sample of 133 respondents. When consent was obtained, questionnaires were administered by the interviewer, and the interviews continued until the desired sample size was achieved per ward in both referral hospitals. The study variables included the sociodemographic profile of the respondents and facility characteristics such as the nature of the illness, age, and gender. Key informant interview/key informant schedule This study conducted a key informant interview via a key informant interview schedule to gather qualitative data from 10 key informants. The researcher used face‒to-face interviews to collect data about institutional, social support, and inpatients’ demographic characteristics that predict the occurrence of inpatient PDS. The key informants were booked in advance for the interview because they were busy and because the hospital was not fully operational owing to the doctor’s and clinician’s strikes. The consent form was read in English, and the participants were allowed to append their signature as confirmation of consent. The questions from the interview guide were read, the responses were recorded via an audio recorder, and the interviews lasted 25–30 minutes per interviewee. In-depth interviews/in-depth interviews schedule The in-depth interviews were used to probe and elicit detailed answers from 13 PDS inpatients and caregivers on sociodemographic such as marital status, the nature of the illness, religiosity, education level, employment status, and living arrangements before admission, who brought them to the hospital, visited them, and received any aid during hospitalization and at the destination upon hospital discharge. The questions included social reasons and hospital system-related factors that barred them from leaving the facility immediately after being medically discharged. The responses were recorded via a phone audio recorder, which took a maximum of 25–30 minutes per interviewee. Reliability and validity of the instruments To ensure the reliability of the tools, a pilot survey was conducted in Siaya County at Siaya County Referral Hospital (SCRH) using 10% of the 133 (13 PDS inpatients) in the sample [71]. The questionnaire was computerized and administered via a Commcare app installed on the phone. Data analysis and presentation In this study, both quantitative and qualitative methods of data analysis and presentation were utilized. Qualitative Data Analysis: In this study, qualitative data analysis adopted an exploratory and inductive approach. The study involved 23 respondents, of whom 10 were key informants and 13 were PDS inpatients. The responses were recorded on a phone audio recorder and then transcribed verbatim into the transcription template of Ms. Word via Expresscribe software. Themes were first generated from the interview guide, and later, codes were developed from the responses. A code sheet was developed from the first few source documents, and a master code sheet was subsequently developed. The coding of the responses was then performed via NVIVO Version 12 software. For the quantitative data analysis, the Commcare app was used to collect quantitative data. The data were automatically sent to a server. Once the target population was reached, the data from the server were downloaded in Excel and then imported into the Statistical Package for Social Sciences (SPSS) Version 21 for analysis. The dependent variable in this study was postdischarge stay, which was defined as the number of days between the date of discharge and the date of interview among inpatients who were still in the facility after medical discharge. Descriptive statistics and frequency distributions were used to analyse objectives 1, 2, and 3 and estimate the mean number of days of postdischarge stay and other variables in a univariate analysis. Categorical variables are presented in terms of percentages and frequencies. To establish the demographic, social support, and institutional factors and their influence on the postdischarge stay, binary logistic regression analysis was used, where p values <0.05 were considered statistically significant. Odds ratios and 95% confidence intervals were reported to show the magnitude and influence of the PDS. The categorical variables are long and short PDSs, and the results are presented in terms of percentages and figures in the tables. Data were collected over two months. The first two weeks were used to train the research assistants, carry out a pilot study at SCRH, and become clear with the JOOTRH and KCRH hospital training committees. Four weeks were used to collect quantitative data, while the remaining two weeks were utilized to collect qualitative data. The researcher thus obtained information regarding the postdischarge stay, as it was at the time of the study, both from the victims of PDS and key informants who were well conversant with PDS cases in the facility. Ethical approval This study was approved by the instructional research and ethical committee of MSU/DRPI/MUERC/00759/19, The National Commission for Science, Technology and Innovation NACOSTI/P/19/1431. The confidentiality of the information obtained from the mother/guardian was maintained. Results And Discussion Influence of inpatients’ demographic characteristics on PDS When the key informants were asked how they referred to the PDS in the facility, they said that ‘we refer to them as discharge ins’. They called them so because ‘…once they are discharged, they do not leave the facility immediately’ (KI 4). At the individual level, according to SEM, age, gender, marital status, nature of the illness, parental status of the child, religion, educational level, and employment status of the respondent were considered individual predictors of PDS. Proponents of the social-ecological model [73, 74] contend that these individual factors can influence every aspect of health. These factors are complex, often interact, and, in some instances, can be both a cause and predictor of health outcomes such as PDS. Influence of Age on the PDS Majority of the respondents were in the age cohort 20--29 43 years (32.33%), followed by 30--39 24 years (18.05%), 0--9 20 years (15.04%), 40--49 17 years (12.78%) and 60 11 years (8.27%), while the fewest were in the cohort 50--59 7 years (5.26%) as in table 1. Table 1: Influence of Age on PDS Variable Short PDS 7days n (%) Total N (%) OR (95% CI) P Value Age 0-9 17(12.78) 3(2.26) 20(15.04) Ref 10-19 10(7.52) 1(0.75) 11(8.27) 0.57(0.05-6.21) 0.642 20-29 26(19.55) 17(12.78) 43(32.33) 3.71(0.94-14.6) 0.061 30-39 16(12.03) 8(6.02) 24(18.05) 2.83(0.64-12.6) 0.171 40-49 11(8.27) 6(4.51) 17(12.78) 3.09(0.64-15) 0.162 50-59 5(3.76) 2(1.50) 7(5.26) 2.27(0.29-17.58) 0.434 over 60 4(3.01) 7(5.26) 11(8.27) 9.92(1.75-56.3) 0.01 Total 89(66.92) 44(33.08) 133(100) The study, with respect to the PDS, further revealed that the majority of respondents aged 20--29 years (26, 19.55%) had a short PDS, whereas a minority (4, 3.01%) of those with a short PDS were over 60 years. In the long PDS section, the majority 17 (12.78%) of the respondents were from the age cohort between 20 and 29 years, whereas the remaining 1 (0.75%) of the respondents was from the age cohort between 10 and 19 years. The regression analysis revealed that the respondents who were over 60 years of age were 9.92 times more likely to experience long PDSs than those who were between 0 and 9 years of age were (P value 0.01, odds ratio 9.92). The following age categories had no statistical significance in this study: 10--19 years (P value 0.642, odds ratio 0.57), 20--29 years (P value 0.061, odds ratio 3.71), 30--39 years (P value 0.171, odds ratio 2.83), 40--49 years (P value 0.162, odds ratio 3.09) and 50--59 years (P value 0.434, odds ratio 2.27). An elderly female in the medical ward said that I had given birth to five children, but God took them (they died). I have no one to take care of me at home. My neighbors brought me here and left me… (Patient 10) When the opinions of healthcare staff were sought about the age cohorts that were prone to PDS, they responded as follows: Inpatients between the ages of 20 and 40 are likely to be the breadwinners of their families; thus, upon medical discharge, their dependents may be unable to obtain money to pay their medical bills and leave the hospital (KI 1). These are people we expect to be very active in employment and so they may happen to be breadwinners in their families and so if the breadwinner is admitted to the hospital ward, then their dependants may be unable to get him/her out of (Aaah you know) hospital environment … (KI 1). Inpatients in the 20–29-year-old cohort engage in ‘robbery’, and most are involved in road traffic accidents, resulting in head injuries; hence, they experience PDS due to their unknown identity (KI 8). Narratives from healthcare workers demonstrated that PDS inpatients between the ages of 20–40 years were actively employed and that most of them were breadwinners. These results are supported by the findings of Sobotka et al. (2019) [74], who reported that once they are in the hospital, their families feel inadequate to aid them out of the facility because of their inability to clear medical bills and to offer posthospital care, especially to those who sustain ailment, such as amputation, and require long-term mechanical ventilation. The presented results are consistent with those of Nath et al. (2015) [37] and Arora et al. (2017) [12], who dealt with PDSs of unknown/unaccompanied patients and concluded that, owing to the unknown identity of PDS inpatients, hospital social workers faced difficulty obtaining tangible information for tracing purposes. The findings of the regression analysis revealed that healthcare workers aged 60 years and above were more likely to experience PDS. According to Hendy et al. (2012), [15] elderly individuals are increasing in number, and they are the major consumers of health care services and are more susceptible to social vulnerability, especially in modern society [76]. Previous studies have shown that elderly individuals prolong their stay in hospital wards due to frailty, sepsis, deconditioning, cardiovascular disorders, and delays in their relocation to community nursing homes [36, 77, 4]. However, Silva et al. (2014) [4] argued that there is no association between patient age and PDS. In Kenya, reports have shown that increasing numbers of elderly PDS inpatients are left and neglected in hospitals by their families [57] as a result of vulnerability factors, high dependency levels, and the need for domiciliary care [36, 35, 78], which many family members are incapable of offering. Influence of gender on the PDS As indicated in Table 2 below, the majority of all respondents who participated in the study were females 72 (54.13%), whereas the remaining respondents were male 61 (45.87%). Table 2: Influence of sex on the PDS Variable Short PDS 7days n (%) Total N (%) OR (95% CI) P Value Gender Male 35(26.32) 26(19.55) 61(45.87) Ref Female 54(40.60) 18(13.53) 72(54.13) 0.44(0.21-0.93) 0.03 Total 89(66.92) 44(33.08) 133(100.00) The present study revealed that more males (26, 19.55%) than their female counterparts (18, 13.53%) had long PDSs. The regression analysis results revealed that females were 0.44 times less likely to experience a long PDS than males were (P value of 0.03, odds ratio of 0.44). Patient 11, a married male in the surgical ward, reported that he was ready to go home but feared the public’s reaction, as he was rescued by the police from a mob beating. This finding was also confirmed through key informant interviews, which revealed that more men experienced PDS in hospital wards upon discharge. For example, KI 10 mentioned that ‘men leave their homes, come, and stay here in town without even some of their relatives’. This implies that when men end up in referral hospitals, their relatives are ‘not around’ to help them leave the hospital once they are discharged by paying hospital bills and taking them home. In any social context, gender plays a role as a predictor of PDS. Whereas previous studies in Singapore [36] and Brazil [4] reported that more women than men experienced PDS, the present study revealed that men are susceptible to PDS. This discrepancy can be attributed to differences in the context and nature of the illness. A Kenyan report revealed that 20 women who went to deliver at KNH experienced PDS for more than six months because of failure to clear the medical bill, yet there was free maternal health care [55]. Despite these anomalies, the results of this study suggested that males were more prone to PDS than their female counterparts were. This finding is in agreement with a Canadian study [33], which revealed that males in the psychiatric ward were 1.4 times more likely to experience PDS than females were. Influence of marital status on PDS The results from Table 3 below show that 40 (30.08%) of the respondents were married, followed closely by 35 (26.32%) single, 22 (16.54%) divorced/separated and 20 (15.04%) children, while 16 (12.03%) were widowed. Table 3: Influence of marital status on PDS Variable Short PDS 7days n (%) Total N (%) OR (95% CI) P Value Marital status Married 31(23.31) 9(6.77) 40(30.08) Ref Single 18(13.54) 17(12.78) 35(26.32) 3.25(1.20-8.79) 0.02 Divorced/Separated 12(9.02) 10(7.52) 22(16.54) 2.87(0.93-8.79) 0.07 Widowed 11(8.27) 5(3.76) 16(12.03) 1.56(0.43-5.69) 0.5 Child 17(12.78) 3(2.26) 20(15.04) 0.60(0.14-2.55) 0.5 Totals 89(66.92) 44(33.08) 133(100.00) The results revealed that many of the respondents whose marital status was ‘married’ had a short PDS of 31 (23.31%), followed by 18 (13.54%), 17 (12.78%), 12 (9.02%) and 11 (8.27%) in that order. Comparatively, more respondents who were single (17, 12.78%) experienced long PDS, closely followed by divorced/separated (10, 7.52%), married (9, 6.77%), widowed (5, 3.76%) and children (3, 2.26%) in that order. The regression analysis revealed that single respondents were 3.25 times more likely to experience PDS than married respondents were (P value 0.02, odds ratio 3.25), whereas the remaining respondents were divorced/separated (P value 0.07, odds ratio 2.87), widowed (P value 0.5, odds ratio 1.56), and child (P value 0.5, odds ratio 0.5) and were not statistically significant. These results agree with the responses from 9 key informants, which indicated that most PDS inpatients tend to be single, followed by divorced, while widowed, separated, and married individuals constitute the same percentage. KI 9 said that most PDS inpatients “… are not married and if they are married…it is not a proper marriage (cohabitation) that somebody will stand with them” to mean that they have insufficient social support and hence PDS. Most PDS inpatients “…rarely get married, especially males, because mental illness sets in very early in life” (KI 7). Patient 5 , a single mother of 3 in the gynecological ward, said that ‘the ‘baby daddy’ was irresponsible. He left me with 3 children after I was diagnosed with cervical cancer… now am stranded here, waiting for the hospital to help me.’ These results concur with those of an Australian study by Ou et al. (2009) [22], which revealed that those who were single, widowed, or divorced were more affected by PDS than were those who were married since being married was associated with a decrease in PDS episodes. Although Little et al. (2019) [33] reported that the marital status of PDS inpatients was not statistically significant in their study, the presence of a spouse and their relatives translated into a resource for supporting medical discharge. Following the social‒ecological framework paradigm, marital status is an individual-level factor that determines the social support received from social networks [79]. Influence of parental status of children on the PDS Table 4 below indicates that the majority of those who participated in the study were partial orphans (9, 45.00%). These were closely followed by children with both parents (8, 40.00%), while the lowest percentage was total orphans (3, 15.00%). Table 4: Influence of parental status of children on the PDS Variable Short PDS 7days n (%) Total N (%) OR (95% CI) P Value Parental Status of Children Total orphan 2(10.00) 1(5.00) 3(15.00) Ref Partial orphan 7(35.00) 2(10.00) 9(45.00) 0.22(0.03-1.17) 0.15 Have both parents 8(40.00) 0(0.00) 8(40.00) 3.40(0.07-159.52) 0.53 Total 17(85.00) 3(15.00) 20(100) According to the results of the present study, none of the children with both parents had a long PDS, whereas 8 (40.00%) of them had a short PDS, with only 2 (10.00%) total or those who reported having a short PDS. Concerning long PDSs, 2 (10.00%) partial orphans had long PDSs, whereas only 1 (5.00%) total orphan had long PDSs. The logistic regression results further indicate that the parental status of the children was not statistically significant in this study. However, data from key informants show that most of the children who experienced PDS ‘are from single mothers, some are parentless, while some are street children’. When asked why they suffered PDS yet under five were exempt from bill payment, one key informant said that ‘…one way or the other they are abandoned because of the type of illness and stigma associated with it’. These results are in agreement with previous findings that argued that children who are abused or abandoned and those with medical complexity experienced PDS as they were awaiting placement in appropriate homes [81, 80]. The results were also in tandem with those of the Kenyan previous report by Odiwuor (2016) [82], which revealed that a baby girl of approximately 4 years of age experienced long PDS since she had stunted growth. Influence of the Nature of the Illness on the PDS The results in Table 5 below show that most of the respondents had chronic diseases (74 (55.64%)), whereas 59 (44.36%) of them had acute illnesses. Table 5: Influence of the Nature of Illness on the PDS Variable Short PDS 7days n (%) Total N (%) OR (95% CI) P Value Nature of illness Chronic 39(29.32) 35(26.32) 74(55.64) Ref Acute 50(37.59) 9(6.77) 59(44.36) 0.20(0.08-0.46 <0.0001 Totals 89(66.91) 44(33.09) 133(100.00) Furthermore, of the respondents with a short PDS, 50 (37.59%) had acute illness, while 39 (29.32%) were chronically ill at admission. Comparatively, more respondents with a chronic illness had a long PDS 35 (26.32%), whereas 9 (6.77%) had an acute illness. The results further revealed that the respondents who had an acute illness were 0.20 times less likely to experience long PDS than those who had a chronic illness were (P value <0.0001, odds ratio 0.20). Therefore, Fisher’s exact test (p<0.001) revealed that there was a significant association between PDS and hospital wards. KI 9 supported the contention that men suffer from long PDSs because they are more involved in criminal activities such as robbery than women are; thus, due to mob beating, they sustain surgical injuries, leading them to be admitted to the male surgical ward, where they are stranded upon being medically discharged because of social misfits. As a result, they are neither visited nor picked from the hospital by their social networks. The key informants agreed with the results. The KI 2 said that Chronically ill who will stay in the hospital setting for two or more years, two months … will amount to a higher financial responsibility and so they would take a longer time to look for this money. This implies that once the inpatient was medically discharged, due to long hospitalization, the bill would be very large. This was in line with the study done by Mostert et al. (2015) [7] and Rice (2009) [10] reported that relatives of said PDS inpatients would not afford hospital bills, yet the longer the hospital stay was, the greater the hospital bill. According to the healthcare workers’ narratives, most patients are admitted to surgical wards. For example, (KI 8) reported that “…are involved in a road traffic accident, motor vehicle accidents, some are assaulted and some end up coming with a head injury, so they stay in a coma for so long.” This implies that during hospitalization, no tangible information can be extracted by the hospital social worker for contact tracing purposes. KI 4 also responded by saying that ‘…most patients who get stuck here are suffering from surgical issues because some of them are amputees…’ This pattern of results is consistent with the previous literature that concluded that surgical ailments such as amputation and mental cases require additional support, which families are incompetent in providing at home, thereby leading to PDS [9, 2, 83]. Others, especially the chronically ill PDS inpatients, wanted to go home, but they found the hospital ward to be homely as ‘they feel in their heart that they have not fully recovered’ (KI 2), whereas others had self-stigma by stating, ‘the moment you lose your limb and now you think to an extent you are going to be relying on people then you feel you are understood and accepted within the hospital’ (Patient 3, amputee who had been waived but still staying in the male medical ward). Chronic illnesses such as mental illness and the unknown identity of inpatients were also rampant predictors of PDS. According to KI 8, unknown patients usually ‘… engage in ‘robbery’ and most are involved in road traffic accidents resulting in head injuries...’ Head injuries result in long hospitalization and getting tangible information from such kinds of PDS inpatients proves difficult. On admission, some patients are brought to the facility by social relationships and left in the facility due to the nature of the illness. A key informant said: When mentally ill patients are brought here by their relatives, they eventually regain consciousness and are discharged, but getting their relatives to come and pick them up is usually hectic. We sometimes have to use medical social workers to help locate them, and occasionally, we give them a vehicle to take them back to their homes. This, however, still poses security challenges. For instance, there was a day when my staff was chased away by the relatives of a mentally ill patient and the chief had to intervene (KI 10). The findings are in agreement with the Zambian [44, 45] and Kenyan [84, 69] reports, which state that the nature of illness contributes to PDS in most health facilities. SEM, as a theoretical framework, has been employed to explain biological processes at the individual level, underpinning determinants of health outcomes [85], such as PDSs. Mental illness and surgery are among the stigmatizing ailments that result in PDS [86, 3]. Inpatients who undergo surgery and amputation require additional support upon discharge and hence PDS [2]. This is because they feel safe in the hospital upon discharge, incur enormous bills, and fear the stigma they face when taken back home from hospital wards [6]. Patients brought in as ‘unknown’ whose identities were not established on admission through medical discharge also experienced PDS. Influence of Religion on the PDS As shown in Table 6 below, the majority of the respondents were Christians (128, 96.24%), whereas the minority were Muslims (5, 3.76%). Table 6: Influence of Religion on the PDS Variable Short PDS 7days n (%) Total N (%) OR (95% CI) P Value Religion Christian 86(64.66) 42(31.58) 128(96.24) Ref Muslim 3(2.26) 2(1.50) 5(3.76) 1.36(0.21-8.48) 0.74 Total 89(66.92) 44(33.08) 133(100.00) Participation in religious activities Regularly 23(17.29) 10(7.52) 33(24.81) Ref Irregularly 66(49.63) 34(25.56) 100(75.19) 2.09(0.98-4.47) 0.06 Total 89(66.92) 44(33.08) 133(100.00) Attendance of religious services Regularly 44(33.08) 14(10.53) 58(43.61) Ref Irregularly 45(33.83) 30(22.56) 75(56.39) 1.18(0.50-2.77) 0.7 Total 89(66.91) 44(33.09) 133(100.00) The results further revealed that 42 (31.58%) Christians had a long PDS, whereas only 2 (1.50%) Muslims experienced a long PDS. When the respondents were asked how often they participated in religious activities, the majority responded 'Irregularly' 100 (75.19%), while 33 (24.81%) of them reported that they regularly participated. A total of 34 (25.56%) of those who responded that they irregularly participated in religious activities had a long PDS, whereas the other 10 (7.52%) of those who regularly participated in religious activities had a long PDS. Moreover, the majority of the respondents (75, 56.39%) did not attend religious services regularly, while 58 (43.61%) attended them regularly. The results indicate that 30 (22.56%) respondents who did not attend religious services regularly had a long PDS, whereas those who regularly attended religious services numbered 14 (10.53%). The regression results demonstrate that religion, involvement in religious activities, and attendance of religious services were not significantly different across categories. Theoretically, McLeroy et al. (1988) [87] contend that religiosity is an individual-level perspective that affects health outcomes. Scholars of religion [88, 89] have argued that medical discharge may be faster if inpatients are well connected to their religious/spiritual community, where they obtain a social network that can provide social support, as in the current study, and that religion has no influence on the PDS. Influence of the educational level of the respondents on the PDS The results in Table 7 below indicate that the majority of the respondents (88 (66.17%)) had reached the primary level. There were 24 (18.04%) high school dropouts, 12 (9.02%) high school dropouts included children, and the remaining 9 (6.77%) had a tertiary level of education. Table 7: Influence of the educational level of the respondents on the PDS Variable Short PDS 7days n (%) Total N (%) OR (95% CI) P Value Education Level None 8(6.02) 4(3.00) 12(9.02) ref Primary 58(43.61) 30(22.56) 88(66.17) 1.03(0.28-3.71) 0.96 Secondary 17(12.78) 7(5.26) 24(18.04) 0.82(0.18-3.64) 0.8 Tertiary 6(4.51) 3(2.26) 9(6.77) 1(0.15-6.25) 1 Totals 89(66.92) 44(33.08) 133(100.00) Comparatively, of the respondents who had a long PDS, 30 (22.56%) reached the primary level of education, whereas only 3 (2.26%) reported having a tertiary level of education. The findings of this study are consistent with those of the Kenyan study by Maina (2014) [59], which revealed that the majority of PDS inpatients had a primary school level of education. For example, in many sub-Saharan countries, PDS has been associated with the inability to pay medical bills and hence legal bargaining power [6, 43, 90]. This trend necessitated the expansion of social health insurance to the informal sector [91, 25]. This aim was to cushion the poor from health shocks and subsequent PDS episodes. The regression results, however, indicate that the educational level of the respondents was not statistically significant; therefore, the educational level of the respondents had no influence on their PDS in the hospital ward. Influence of employment status on the PDS Among the respondents, 86 (64.66%) were unemployed, while 47 (35.34%) were employed, as indicated in Table 8 below. Among those employed, the majority were working in the informal sector (46, 97.87%), with only 1 (2.13%) reported as working in the formal sector. Table 8: Influence of the employment status of respondents on the PDS Variable Short PDS 7days n (%) Total N (%) OR (95% CI) P Value Employment Unemployed 56(42.11) 30(22.55) 86(64.66) Ref Employed 33(24.81) 14(10.53) 47(35.34) 0.79(0.36-1.70) 0.55 Total 89(66.92) 44(33.08) 133(100.00) The sector of employment for the employed Informal 32(68.08) 14(29.79) 46(97.87) Ref Formal 1(2.13) 0(0.00) 1(2.13) N/A N/A Total 33(70.21) 14(29.79) 47(100.00) The results of this study further indicate that of the respondents who had a long PDS, 30 (22.55%) were unemployed, whereas 14 (10.53%) were employed. Among the majority of respondents who were employed in the informal sector, 14 (29.79%) had a long PDS. KI 2 mentioned that “the employed PDS inpatients …don’t stay long after medical discharge because, to a great extent, they have strategies of sorting out their bills like enrolling in health insurance coverage”. For example, Patient 11, a male aged 30 who had dropped out of school, responded that “I am a hawker selling things in different vehicles.” Due to his nature of employment, which was hand-to-mouth, he could not raise the medical bill and hence PDS. However, the regression results of this study indicate that employment status and sector of employment were not statistically significant for the PDS; therefore, the employment status of PDS inpatients does not influence the PDS. According to McLeroy et al. (1988) [87], employment status is one of the individual factors that determine health outcomes, such as PDS. It influences their ability to pay medical bills in time, thus reducing the level of PDS. Patients with a lower level of education cannot secure employment and earn better income [92]. Previous reports have demonstrated that Kisumu County, in Kenya, has relatively high levels of unemployment and poverty [60]. Due to their poverty level, inpatients in Kisumu County referral hospitals are faced with the inability to pay medical bills [61] and hence PDS. In conclusion, individual demographic factors associated with the vulnerability of PDS inpatients to becoming PDS victims in referral hospitals. Elderly individuals, males, unmarried individuals, and chronically ill individuals were more at risk of experiencing episodes of PDS. Older people with complex health needs are particularly vulnerable to PDS. Males, especially middle-aged individuals, are associated with deviance, rendering them social misfits who face neglect due to their social relationships in hospital wards and thus PDS. The chronically ill were perceived as burdens by their family members, whereas the unmarried experienced PDS due to insufficient social support. Whereas the parental status of the children, religiosity, education, and employment status of the PDS inpatients were insignificant to the study, they were not dismissed because they determined the general health outcomes of the PDS. Conclusion In conclusion, individual demographic factors associated with the vulnerability of PDS inpatients to becoming PDS victims in referral hospitals. Elderly individuals, males, unmarried individuals, and chronically ill individuals were more at risk of experiencing episodes of PDS. Older people with complex health needs are particularly vulnerable to PDS. Additionally, males, especially middle-aged individuals, are associated with deviance, rendering them social misfits who face neglect due to their social relationships in hospital wards and thus PDS. The chronically ill, on the other hand, were perceived as burdens by their family members, whereas the unmarried experienced PDS due to insufficient social support. Whereas the parental status of children, religiosity, education, and employment status of the PDS inpatients were insignificant to the study, they were not dismissed because they determined the general health outcomes of PDS victims. The general population, especially vulnerable individuals such as elderly individuals, males, unmarried individuals, and those with chronic conditions, are recommended to invest in social networks when they are still healthy. They should also take up government initiatives such as enrollment in UHC and social health insurance seriously. The act increases their level of independence, especially when they become ill and are admitted to referral hospitals, thereby reducing the number of cases of PDS. Referral hospital management and policymakers should invest in the early identification of predictors of PDS reported in this study. This approach helps reduce the number of probable cases of PDS by introducing a PDS tracking mechanism. This is achievable by involving medical social workers in the identification of predictors of PDS and multidisciplinary decision-making during admission, hospitalization, and medical discharge of inpatients. They are also encouraged to sensitize the general population to the harm associated with PDS and psycho-educate them on the importance of enrolling with medical coverage. Declarations Author contributions EAG, MAO, and COO were involved in the conception and design of the study. EAG and SOA supervised the interviews, analysed the data, and prepared the manuscript. MAO, COO, and SOA provided guidance and mentorship during the implementation of the study. All the authors reviewed and approved the final manuscript. Conflict of interest The authors declare that they have no financial, political, religious, intellectual, or personal relationships that may have inappropriately influenced them in writing this article. 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Standard Digital. https://www.standardmedia.co.ke/article/2001304864/county-government-on-spot-over-poor-state-of-hospital-wards. Muyela, R. (2018). Uproar in Kisumu as patients sleep on hospital corridors at Jaramogi Oginga Odinga Referral Hospital. https://www.tuko.co.ke/286101-uproar-kisumu-patients-sleep-hospital-corridors-jaramogi-oginga-odinga-referral-hospital.html#286101. Ochieng', J. (2020, June 29). Kenya: Siaya Public Hospitals in Deplorable Condition - Report. Daily Nation. https://allafrica.com/stories/201803010282.ht Van Teijlingen, E. R., & Hundley, V. (2001). The importance of pilot studies. University of Surrey Stokols, D. (2000). The social-ecological paradigm of wellness promotion. Promoting human wellness: New frontiers for research, practice, and policy, 21-37. Sallis, J. F., Owen, N., & Fisher, E. (2015). Ecological models of health behavior. Health behavior: Theory, research, and practice, 5(43-64). Romero-Ortuno, R., Moore, G., & Hartley, P. (2018). Health and Social Factors Associated with a Delayed Discharge Amongst Inpatients on Acute Geriatric Wards: A Retrospective Observational Study. Andrew, M. K., Mitnitski, A. B., & Rockwood, K. (2008). Social vulnerability, frailty and mortality in elderly people. PloS one , 3 (5), e2232. Asghar Ghods, A., Khabiri, R., Raeisdana, N., Ansari, M., Motlagh, N. H., Sadeghi, M., & Zarei, E. (2015). Predictors of inappropriate hospital stay: Experience from Iran. Global journal of health science, 7(3), 82-89. Challis, D., Hughes, J., Xie, C., & Jolley, D. (2014). An examination of factors influencing delayed discharge of older people from hospital. International Journal of geriatric psychiatry, 29(2), 160-168. Cohen, S. (2004). Social relationships and health. American Psychologist, 59(8), 676 -684. Maynard, R., Christensen, E., Cady, R., Jacob, A., Ouellette, Y., Podgorski, H., ... & Wheeler, W. (2019). Home health care availability and discharge delays in children with medical complexity. Pediatrics, 143(1). Lee, M., Bachim, A., Smith, C., Camp, E. A., Donaruma-Kwoh, M., & Patel, B. (2017). Hospital costs and charges of discharge delays in children hospitalized for abuse and neglect. Hospital pediatrics, 7(10), 572-578. Odiwuor, M. (2016, December 6). Helpless children deserted as patients leave Kisumu Hospital. Standard Digital. https://www.standardmedia.co.ke/article/2000225882/helpless-child-deserted-as-patients-leave-kisumu-hospital Dillon, K., & Thomsen, D. (2019). Delays in Hospital Discharges of Behavioral Health Patients: Results from the Maryland Hospital Association Behavioral Health Data Collection. Wilder Research. Okeyo, V. (2020, July 05). Ours is a society that does not care about the mentally ill. Daily Nation. https://www.nation.co.ke/lifestyle/dn2/kenya-health-kisumu-badly-treats-mentally-ill/957860-3153506-50oqudz/index.html. Berk, L. E. (2000). Ecological systems theory. Child development, 23-38. Young, L. (2017, December 8). Hospitals still illegally detain patients who can't pay – what can be done? The Star-Kenya. https://www.the-star.co.ke/news/2017/12/08/hospitals-still-illegally-detain-patients-who-cant-pay-what-can-be_c1681238. McLeroy, K. R., Bibeau, D., Steckler, A., & Glanz, K. (1988). An ecological perspective on health promotion programs. Health education quarterly, 15(4), 351-377. Zand, S., & Rafiei, M. (2011). The Assessment Of Legal And Religious Care Of Patients Admitted To Hospital In Arak, 2010. Saad, M., & de Medeiros, R. (2016). Programs of religious/spiritual support in hospitals: five “Whies” and five “Hows”. Philosophy, Ethics, and Humanities in Medicine, 11(1), 1-4. Minayo, L., & Odallo, B. (2020, July 4). The detection of patients' bodies is illegal. Kenyan Daily Nation. https://www.nation.co.ke/kenya/blogs-opinion/opinion/detention-of-patients-bodies-illegal-156864 on 1/8/2020. Carrin, G., Desmet, M., & Basaza, R. (2001). Social health insurance development in low-income developing countries: new roles for government and nonprofit health insurance organizations. Building social security: The challenge of privatization. Russell, B. (2006). Research Methods in Anthropology: Qualitative and Quantitative Approaches Walnut Creek. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6042253","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":417922297,"identity":"9d97bad8-9d2d-4d65-9c29-74176b4c68ba","order_by":0,"name":"Eunice G. Awuor","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAw0lEQVRIiWNgGAWjYLCCBAYGOQYGHhK1GJOoBQgSG4jWwj8j/dqDBzU26RuOnz344AODnZxuAwEtEjdyyg0SjqXlbjiTl2w4gyHZ2OwAIWtu56RJJDYczt1wIMdMmofhQOI2QlrkIVr+pxucf0OkFoPb6ceAWg4kGNwg1hbD+2/YJBKOJRvOvPHG2HCGARF+kTtz/Jnkjxo7eb7zOYYPPlTYyRH2PgOPAZhSAKs0IKgcBNgfgCn5BqJUj4JRMApGwUgEAIhvRQ/iH1oRAAAAAElFTkSuQmCC","orcid":"","institution":"Maseno University","correspondingAuthor":true,"prefix":"","firstName":"Eunice","middleName":"G.","lastName":"Awuor","suffix":""},{"id":417922298,"identity":"32d58d59-2676-419a-91cf-2992f2740f0e","order_by":1,"name":"Mary A. Ochieng","email":"","orcid":"","institution":"Maseno University","correspondingAuthor":false,"prefix":"","firstName":"Mary","middleName":"A.","lastName":"Ochieng","suffix":""},{"id":417922299,"identity":"de2b0756-c61d-4b22-8388-e7af5b338c47","order_by":2,"name":"Charles O. Olang’o","email":"","orcid":"","institution":"Maseno University","correspondingAuthor":false,"prefix":"","firstName":"Charles","middleName":"O.","lastName":"Olang’o","suffix":""},{"id":417922300,"identity":"f0ff5b5f-4954-4e28-9ac9-4ebea7c0de38","order_by":3,"name":"Silas O. Awuor","email":"","orcid":"","institution":"Jaramogi Oginga Odinga Teaching and Referral Hospital","correspondingAuthor":false,"prefix":"","firstName":"Silas","middleName":"O.","lastName":"Awuor","suffix":""}],"badges":[],"createdAt":"2025-02-16 16:23:16","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6042253/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6042253/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":76736491,"identity":"97e6b420-1df2-4068-90bf-2002e2189ef5","added_by":"auto","created_at":"2025-02-20 07:31:33","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1675338,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6042253/v1/d4d8de38-7be4-4618-bf98-a32cd658a4c9.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Demographic Predictors of Inpatients’ Postdischarge Stay in Referral Hospitals in Kisumu County, Kenya","fulltext":[{"header":"Introduction","content":"\u003cp\u003eThe Postdischarge stay (PDS), also known as prolonged hospital utilization upon medical discharge of inpatients from hospital wards, is costly and may be a marker of poor quality of care [1, 2]. Other scholars refer to this sociological phenomenon as the detention of insolvent patients [3], discharge delay [4], hospitals as debtor prisons [5], medical hostages [6], and hospital detention practices [7]. Since there is no systematic or official terminology to describe this phenomenon by the World Health Organization (WHO) or any other authority [8], this study employed PDS. Globally, PDS is a healthcare system problem [9], and this episode has been prevalent since the 1990s [3, 10]. Postdischarge stay (PDS) is widespread in Eastern Europe, Latin America, Asia, and Africa [7]. In Latin America, it is common, particularly in Venezuela, Haiti, and middle-income Mexico and Colombia [11].\u003c/p\u003e\n\u003cp\u003eThe consequences of PDS are far-reaching for hospital management, patients,\u0026nbsp;and their families [12, 1]. The United States of America-based study by Cai et al. (2020) [13] revealed that PDS results in unnecessary\u0026nbsp;risk for hospital-acquired infections, loss of revenue for the hospital, and reduced hospital capacity to treat other patients due to bed blocking. According to Silva et al. (2014) [4], 60% and 58% of inpatients in Hospital das Cl\u0026iacute;nicas and Hospital Odilon Behrens, respectively, suffer from a shortage of beds and long waiting lists for hospital admission in Brazil. In a\u0026nbsp;metropolitan\u0026nbsp;teaching\u0026nbsp;hospital\u0026nbsp;in London, inpatients\u0026nbsp;unnecessarily\u0026nbsp;utilize\u0026nbsp;\u0026pound;97,432\u0026nbsp;annually\u0026nbsp;because of PDS [14].\u003c/p\u003e\n\u003cp\u003eAccording to Toh et al. (2017) [15], 20% of older inpatients suffer PDS at Khoo Teck Puat Hospital in Singapore, and the study revealed that PDS disrupts patient flow and access to care due to bed shortages, as discharged inpatients occupy beds that newly admitted inpatients badly require. Devakumar and Yates (2016) [6] argued that additional days spent in the hospital due to PDS imply more medical bills, which the majority of inpatients often fail to pay, resulting in cases of PDS [16]. Despite the above statistics on the occurrence of a PDS and its consequences, its magnitude is unknown [11, 9].\u003c/p\u003e\n\u003cp\u003eMany studies have attributed the introduction of user fees in many low- and middle-income\u0026nbsp;countries,\u0026nbsp;such\u0026nbsp;as India,\u0026nbsp;Pakistan,\u0026nbsp;Cambodia,\u0026nbsp;and\u0026nbsp;Vietnam,\u0026nbsp;to\u0026nbsp;the\u0026nbsp;cause\u0026nbsp;of\u0026nbsp;PDS\u0026nbsp;[17-19]. Owing to the high cost of hospitalization, inpatients in public referral hospital wards often fail to pay hospital bills, which in turn leads to PDS [20, 21, 3]. For example, inpatients suffer from PDS in a tertiary referral hospital in New South Wales, Australia, because\u0026nbsp;they\u0026nbsp;need financial help [22]. Waiver systems were then introduced to cushion the poor\u0026nbsp;from\u0026nbsp;health\u0026nbsp;shocks\u0026nbsp;[20].\u0026nbsp;However,\u0026nbsp;non-reimbursement\u0026nbsp;of foregone waiver costs weakens hospital operations. The costs that are foregone by waivers are rarely reimbursed, and the waiver process is burdensome, demeaning, and dangerous for the health of inpatients [23].\u003c/p\u003e\n\u003cp\u003eAs a result, many developed and developing countries have promoted social health insurance (SHI) to eliminate unmet health needs [24, 25]. Despite these efforts, the WHO and the World Bank Group (2015) reported that at least 400 million people\u0026nbsp;globally\u0026nbsp;cannot access health services, as 40%\u0026nbsp;of the\u0026nbsp;world\u0026rsquo;s population lacks medical coverage [26]. For example, according to the Kaiser Family Foundation American Community Survey (2017), 27.4 million people were uninsured in the USA as of 2017 because of the high cost involved [27]. According to Pryor et al. (2003) [28], uninsured individuals are likely\u0026nbsp;to delay\u0026nbsp;or forego treatment. The delay\u0026nbsp;is until the condition is urgent and more complex, requiring emergency and/or admission to hospitals, which is costly in the long run, resulting in PDS [29]. Although strategies have been put in place by many countries around the world, the emphasis has been to curb financial difficulties that bar people from accessing health services with less attention to what transpires after inpatients are medically discharged but cannot leave referral hospital wards immediately.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDemographic factors associated with PDS.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDemographic factors have been attributed to PDS episodes. A study by Glasby et al. (2004) [30] reviewed PDS among older people in England and reported that a lack of rehabilitation services and internal hospital factors contributed to the occurrence of PDS. Other studies suggest that inpatients\u0026rsquo; demographic characteristics, including age, sex, racial/ethnic identity, marital status, and the nature of illness, are factors that contribute to cases of PDS [31, 32, 15, 16]. According to Little et al. (2019) [33], more males aged over 44 years and indigenous language speakers than their female counterparts in Canada suffer from PDS. An Australian study by Ou et al. (2009) [22] revealed that elderly individuals aged 65 years and above who were single, widowed, or divorced were more affected by PDS than married individuals were. This concurs with the findings of Little et al. (2019) [33]. Moreover, two studies conducted in the UK reported that PDS affects mainly elderly individuals, with a mean age of 84 years [34], and 75 years, of which 9 PDS inpatients were women [35]. This finding was supported by a study by Lim et al. (2006) [36], which revealed that 150 elderly inpatients with a mean age of 84 years experienced PDS at Changi General Hospital-Singapore for more than 28 days. However, the study was performed retrospectively in the Department of Geriatric Medicine only. A study in India by Nath et al. (2015) [37] reported contradictory findings. They argued that young inpatients, mainly males aged 16--20 years (67%), are more prone to PDS due to their unknown identity as a result of severe head injury from a road traffic accident (RTA). In Ontario, Canada, the mental status of inpatients was negatively correlated with the PDS\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e[33].\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eHowever, according to Arora et al. (2017) [38] and [16], unidentified inpatients prolong their stay in the hospital both before and after discharge due to the presence of dementia and the severity of acute illness. Although these studies demonstrate that inpatients\u0026rsquo; demographic factors predict PDS, the findings are contradictory; thus, the current study is needed.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eOccurrence of PDS\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePDS has been reported in numerous parts of Sub-Saharan African countries, including Burundi [39], Ghana [40], Cameroon [41] and Zimbabwe [42]. However, these reports linked user fees to the causes of PDS. For example,\u0026nbsp;Wekesa\u0026nbsp;(2016) [43] in\u0026nbsp;the\u0026nbsp;Ugandan Daily\u0026nbsp;Monitor revealed\u0026nbsp;that\u0026nbsp;PDS\u0026nbsp;inpatients\u0026nbsp;prolonged\u0026nbsp;their stay in the hospital for more than a week over bills in the International Hospital of Kampala (IHK). In addition to user fees, two findings by Mumba (2017) [44] and Phiri (2018) [45] suggest that inpatients in Zambia have been suffering from PDS because they are left in the hospital by their relatives due to the nature of the illness. In addition, a study\u0026nbsp;in Nigeria revealed that women with Vesico\u0026nbsp;Vaginal\u0026nbsp;Fistula\u0026nbsp;(VVF)\u0026nbsp;syndrome suffer\u0026nbsp;from\u0026nbsp;PDS\u0026nbsp;due\u0026nbsp;to\u0026nbsp;this\u0026nbsp;condition\u0026nbsp;[46].\u0026nbsp;As\u0026nbsp;many PDS cases have been reported, there is a mixed opinion, with much emphasis placed on the inability to make medical bill payments.\u003c/p\u003e\n\u003cp\u003eIn Kenya, PDS cases in referral hospitals are widespread [46, 47]. For example, Karongo (2011) [48] reported that inpatients stayed longer at Kenyatta National Hospital (KNH) upon their medical discharge because of their inability to pay medical bills. The literature indicates that before the free maternal health care policy was decreed, inpatients in Pumwani Hospital, KNH, and other public hospitals experienced PDS due to their inability to pay medical bills [21, 49, 50], and the bill continues to increase [7, 10]. With the decree of free maternal health care, which came into effect on 1st June 2013, it was expected that the PDS would decrease [51]. However, emerging reports illustrate that cases of PDS persist [52, 53]. For example, Oketch (2020b) [54] revealed that 20 women who went to deliver at KNH experienced PDS for more than six months because of failure to clear the medical bill, yet there was free maternal health care.\u003c/p\u003e\n\u003cp\u003eAlthough it has been assumed that economic factors lead to PDS, reports have indicated that 22 of 258 PDS inpatients released from KNHs cannot exit health facilities immediately [55]. Moreover, Meru District Hospital expressed concern over the increasing number of elderly PDS inpatients left and neglected in the hospital by their families [56]. A report in Kenyan Daily Nation further revealed that 32 mental patients suffered from PDS in Gilgil Sub-County Hospital for ten years because of the stigma associated with psychiatric conditions [57]. Maina (2014) [58] established that some of the determinants of PDS in Kenya include the medical insurance status of PDS inpatients. Kisumu is one of the counties in Kenya with relatively high levels of unemployment and poverty [59]. Owing to the high poverty level of 46%, inpatients in Kisumu County referral hospitals are faced with the inability to pay medical bills [60]. Poverty levels are exacerbated by communicable diseases such as malaria, HIV/AIDS, and tuberculosis, which are among the factors that make Kisumu County a UHC pilot study site [61]. Jaramogi Oginga Odinga Teaching and Referral Hospital (JOOTRH) and Kisumu County Referral Hospital (KCRH) are the two major referral hospitals in Kisumu County [62]. Inyanji and Lungai (2016) [63] argued that these key referral hospitals in Kisumu County are in deplorable condition because of congestion, which causes bed shortages. This is partly due to inpatients who have been medically discharged but could not leave the hospital. The main objective of this study was to investigate the demographic predictors of inpatients\u0026rsquo; postdischarge stay in referral hospitals in Kisumu County.\u003c/p\u003e"},{"header":"Methodology","content":"\u003cp\u003e\u003cstrong\u003eResearch Design\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study adopted a correlational cross-sectional study design with both quantitative and qualitative methods of data collection.\u0026nbsp;The correlational research design was used to describe, explain, and predict how the variables under study influenced the PDS. The design, therefore, enabled the researcher to establish whether and to what degree a relationship existed between variables.\u0026nbsp;A cross-sectional dimension\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003ewas useful in obtaining an overall \u0026lsquo;picture\u0026rsquo; of PDS episodes, as it was at the time of the study [64]. It was quick and helped to collect vast amounts of information in a brief period. The researcher collected and analysed the data, integrated the findings, and drew inferences via both qualitative and quantitative approaches [65]. The quantitative approach was used to obtain numerical data, whereas the qualitative research method produced descriptive data from respondents\u0026rsquo; own spoken words to understand their views on PDS [66]. The mixed approach was useful for triangulating the data. This increased the credibility and validity of the research findings [67].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSample size\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe population was derived from participants who were medically discharged but were still in the hospital wards at JOOTRH and KCRH during the two-month study. At the end of the sample collection period, 133 inpatient participants, 13 participant in-depth interviews, and 10 participant key informants were included, resulting in a total of 156 participants.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStudy Area\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was performed at JOOTRH and KCRH, the two major referral hospitals in Kisumu County (Oketch, 2016). According to a report by Inyanji and Lungai (2016) [63], these key referral hospitals in Kisumu County were in deplorable condition because congestion led to bed shortages. Cases of inpatients sharing beds or sleeping on the floor were common [62, 68], which was partly due to cases of PDS arising from social issues such as lack of fare, delay or lack of family members to pick them up from the hospital [69 and patient abandonment [67]. Moreover, the congestion was fuelled by referrals from other neighbouring county hospitals such as Siaya [70] and self-referrals due to poor referral systems, as walk-in patients bypassed lower-tier health facilities. Therefore, studies need to be conducted in Kisumu County.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStructured interviews/questionnaires\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe researcher and the three research assistants used a structured questionnaire that was computerized and administered via\u0026nbsp;the Commcare App installed on the phone. Both open- and closed-ended questions were used to obtain quantitative data from\u0026nbsp;a randomly selected sample\u0026nbsp;of 133 respondents.\u0026nbsp;When consent was obtained, questionnaires were administered by the interviewer, and the interviews continued until the desired sample size was achieved per ward in both referral hospitals.\u0026nbsp;The study variables included the sociodemographic profile of the respondents and facility characteristics such as the nature of the illness, age, and gender.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eKey informant interview/key informant schedule\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study conducted a key informant interview via a key informant interview schedule to gather qualitative data from 10 key informants. The researcher used face‒to-face interviews to collect data about institutional, social support, and inpatients\u0026rsquo; demographic characteristics that predict the occurrence of inpatient PDS. The key informants were booked in advance for the interview because they were busy and because the hospital was not fully operational owing to the doctor\u0026rsquo;s and clinician\u0026rsquo;s strikes. The consent form was read in English, and the participants were allowed to append their signature as confirmation of consent. The questions from the interview guide were read, the responses were recorded via an audio recorder, and the interviews lasted 25\u0026ndash;30 minutes per interviewee.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eIn-depth interviews/in-depth interviews schedule\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe in-depth interviews were used to probe and elicit detailed answers from 13 PDS inpatients and caregivers on sociodemographic such as marital status, the nature of the illness, religiosity, education level, employment status, and living arrangements before admission, who brought them to the hospital, visited them, and received any aid during hospitalization and at the destination upon hospital discharge. The questions included social reasons and hospital system-related factors that barred them from leaving the facility immediately after being medically discharged. The responses were recorded via a phone audio recorder, which took a maximum of 25\u0026ndash;30 minutes per interviewee.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eReliability and validity of the instruments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo ensure the reliability of the tools, a pilot survey was conducted in Siaya County at Siaya County Referral Hospital (SCRH) using 10% of the 133 (13 PDS inpatients) in the sample [71]. The questionnaire was computerized and administered via a Commcare app installed on the phone.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData analysis and presentation\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn this study, both quantitative and qualitative methods of data analysis and presentation were utilized.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eQualitative Data Analysis:\u0026nbsp;\u003c/strong\u003eIn this study, qualitative data analysis adopted an exploratory and inductive approach.\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eThe study involved 23 respondents, of whom 10 were key informants and 13 were PDS inpatients. The responses were recorded on a phone audio recorder and then transcribed verbatim into the transcription template of Ms. Word via Expresscribe software. Themes were first generated from the interview guide, and later, codes were developed from the responses. A code sheet was developed from the first few source documents, and a master code sheet was subsequently developed. The coding of the responses was then performed via NVIVO Version 12 software.\u003c/p\u003e\n\u003cp\u003eFor the quantitative data analysis, the Commcare app was used to collect quantitative data. The data were automatically sent to a server. Once the target population was reached, the data from the server were downloaded in Excel and then imported into the Statistical Package for Social Sciences (SPSS) Version 21 for analysis. The dependent variable in this study was postdischarge stay, which was defined as the number of days between the date of discharge and the date of interview among inpatients who were still in the facility after medical discharge. Descriptive statistics and frequency distributions were used to analyse objectives 1, 2, and 3 and estimate the mean number of days of postdischarge stay and other variables in a univariate analysis. Categorical variables are presented in terms of percentages and frequencies.\u003c/p\u003e\n\u003cp\u003eTo establish the demographic, social support, and institutional factors and their influence on the postdischarge stay,\u0026nbsp;binary logistic regression analysis was used, where p values \u0026lt;0.05 were considered statistically significant. Odds ratios and 95% confidence intervals were reported to show the magnitude and influence of the PDS. The categorical variables\u0026nbsp;are long and short PDSs, and the results are presented in terms of percentages and figures in the tables.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;Data were collected over two months. The first two weeks were used to train the research assistants, carry out a pilot study at SCRH, and become clear with the JOOTRH and KCRH hospital training committees. Four weeks were used to collect quantitative data, while the remaining two weeks were utilized to collect qualitative data. The researcher thus obtained information regarding the postdischarge stay, as it was at the time of the study, both from the victims of PDS and key informants who were well conversant with PDS cases in the facility.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthical approval\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was approved by the instructional research and ethical committee of MSU/DRPI/MUERC/00759/19, The National Commission for Science, Technology and Innovation NACOSTI/P/19/1431. The confidentiality of the information obtained from the mother/guardian was maintained.\u003c/p\u003e"},{"header":"Results And Discussion","content":"\u003cp\u003e\u003cstrong\u003eInfluence of inpatients\u0026rsquo; demographic characteristics on PDS\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWhen the key informants were asked how they referred to the PDS in the facility, they said that \u0026lsquo;we refer to them as discharge ins\u0026rsquo;. They called them so because \u0026lsquo;\u0026hellip;once they are discharged, they do not leave the facility immediately\u0026rsquo; (KI 4).\u003c/p\u003e\n\u003cp\u003eAt the individual level, according to SEM,\u0026nbsp;age, gender, marital status, nature of the illness, parental status of the child, religion, educational level, and employment status of the respondent were considered individual predictors of PDS. Proponents of the social-ecological model [73, 74] contend that these\u0026nbsp;individual factors can influence every aspect of health. These factors are complex, often interact, and, in some instances, can be both a cause and predictor\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eof health outcomes such as PDS.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003cstrong\u003eInfluence of Age on the PDS\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMajority of the respondents were in the age cohort 20--29 43 years (32.33%), followed by 30--39 24 years (18.05%), 0--9 20 years (15.04%), 40--49 17 years (12.78%) and 60 11 years (8.27%), while the fewest were in the cohort 50--59 7 years (5.26%) as in table 1.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 1:\u003c/strong\u003e\u0026nbsp; \u0026nbsp;\u003cstrong\u003e\u003cem\u003eInfluence of Age on PDS\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"571\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 13.2867%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eVariable\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18.5315%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eShort PDS\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;7days\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;n (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.1329%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLong PDS\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026gt;7days\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;n (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.3846%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTotal\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eN (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 23.0769%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eOR (95% CI)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.5874%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eP Value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 13.2867%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAge\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 18.5315%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 17.1329%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.3846%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 23.0769%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12.5874%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 13.2867%;\"\u003e\n \u003cp\u003e0-9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 18.5315%;\"\u003e\n \u003cp\u003e17(12.78)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 17.1329%;\"\u003e\n \u003cp\u003e3(2.26)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15.3846%;\"\u003e\n \u003cp\u003e20(15.04)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 23.0769%;\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12.5874%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 13.2867%;\"\u003e\n \u003cp\u003e10-19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 18.5315%;\"\u003e\n \u003cp\u003e10(7.52)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 17.1329%;\"\u003e\n \u003cp\u003e1(0.75)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15.3846%;\"\u003e\n \u003cp\u003e11(8.27)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 23.0769%;\"\u003e\n \u003cp\u003e0.57(0.05-6.21)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12.5874%;\"\u003e\n \u003cp\u003e0.642\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 13.2867%;\"\u003e\n \u003cp\u003e20-29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18.5315%;\"\u003e\n \u003cp\u003e26(19.55)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.1329%;\"\u003e\n \u003cp\u003e17(12.78)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.3846%;\"\u003e\n \u003cp\u003e43(32.33)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 23.0769%;\"\u003e\n \u003cp\u003e3.71(0.94-14.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12.5874%;\"\u003e\n \u003cp\u003e0.061\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 13.2867%;\"\u003e\n \u003cp\u003e30-39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18.5315%;\"\u003e\n \u003cp\u003e16(12.03)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.1329%;\"\u003e\n \u003cp\u003e8(6.02)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.3846%;\"\u003e\n \u003cp\u003e24(18.05)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 23.0769%;\"\u003e\n \u003cp\u003e2.83(0.64-12.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12.5874%;\"\u003e\n \u003cp\u003e0.171\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 13.2867%;\"\u003e\n \u003cp\u003e40-49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18.5315%;\"\u003e\n \u003cp\u003e11(8.27)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.1329%;\"\u003e\n \u003cp\u003e6(4.51)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.3846%;\"\u003e\n \u003cp\u003e17(12.78)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 23.0769%;\"\u003e\n \u003cp\u003e3.09(0.64-15)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12.5874%;\"\u003e\n \u003cp\u003e0.162\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 13.2867%;\"\u003e\n \u003cp\u003e50-59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18.5315%;\"\u003e\n \u003cp\u003e5(3.76)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.1329%;\"\u003e\n \u003cp\u003e2(1.50)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.3846%;\"\u003e\n \u003cp\u003e7(5.26)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 23.0769%;\"\u003e\n \u003cp\u003e2.27(0.29-17.58)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12.5874%;\"\u003e\n \u003cp\u003e0.434\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 13.2867%;\"\u003e\n \u003cp\u003eover 60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18.5315%;\"\u003e\n \u003cp\u003e4(3.01)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.1329%;\"\u003e\n \u003cp\u003e7(5.26)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.3846%;\"\u003e\n \u003cp\u003e11(8.27)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 23.0769%;\"\u003e\n \u003cp\u003e9.92(1.75-56.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12.5874%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.01\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 13.2867%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTotal\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18.5315%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e89(66.92)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.1329%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e44(33.08)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.3846%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e133(100)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 23.0769%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.5874%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003eThe study, with respect to the PDS, further revealed that the majority of respondents aged 20--29 years (26, 19.55%) had a short PDS, whereas a minority (4, 3.01%) of those with a short PDS were over 60 years. In the long PDS section, the majority 17 (12.78%) of the respondents were from the age cohort between 20 and 29 years, whereas the remaining 1 (0.75%) of the respondents was from the age cohort between 10 and 19 years. The regression analysis revealed that the respondents who were over 60 years of age were 9.92 times more likely to experience long PDSs than those who were between 0 and 9 years of age were\u0026nbsp;(P value 0.01, odds ratio 9.92). The following age categories had no statistical significance in this study: 10--19 years\u0026nbsp;(P value\u0026nbsp;0.642, odds ratio 0.57), 20--29 years\u0026nbsp;(P value\u0026nbsp;0.061, odds ratio 3.71), 30--39 years\u0026nbsp;(P value\u0026nbsp;0.171, odds ratio 2.83), 40--49 years (P value\u0026nbsp;0.162, odds ratio 3.09) and 50--59\u0026nbsp;years\u0026nbsp;(P value\u0026nbsp;0.434, odds ratio 2.27).\u003c/p\u003e\n\u003cp\u003eAn elderly female in the medical ward said that\u003c/p\u003e\n\u003cp\u003eI had given birth to five children, but God took them (they died). I have no one to take care of me at home. My neighbors brought me here and left me\u0026hellip; (Patient 10)\u003c/p\u003e\n\u003cp\u003eWhen the opinions of healthcare staff were sought about the age cohorts that were prone to PDS, they responded as follows:\u003c/p\u003e\n\u003cp\u003eInpatients between the ages of 20 and 40 are likely to be the breadwinners of their families; thus, upon medical discharge, their dependents may be unable to obtain money to pay their medical bills and leave the hospital (KI 1).\u003c/p\u003e\n\u003cp\u003eThese are people we expect to be very active in employment and so they may happen to be breadwinners in their families and so if the breadwinner is admitted to the hospital ward, then their dependants may be unable to get him/her out of (Aaah you know) hospital environment \u0026hellip; (KI 1).\u003c/p\u003e\n\u003cp\u003eInpatients in the 20\u0026ndash;29-year-old cohort engage in \u0026lsquo;robbery\u0026rsquo;, and most are involved in road traffic accidents, resulting in head injuries; hence, they experience PDS due to their unknown identity (KI 8).\u003c/p\u003e\n\u003cp\u003eNarratives from healthcare workers demonstrated that PDS inpatients between the ages of 20\u0026ndash;40 years were actively employed and that most of them were breadwinners. These results are supported by the findings of Sobotka et al. (2019) [74], who reported that once they are in the hospital, their families feel inadequate to aid them out of the facility because of their inability to clear medical bills and to offer posthospital care, especially to those who sustain ailment, such as amputation, and require long-term mechanical ventilation. The presented results are consistent with those of Nath et al. (2015) [37] and Arora et al. (2017) [12], who dealt with PDSs of unknown/unaccompanied patients and concluded that, owing to the unknown identity of PDS inpatients, hospital social workers faced difficulty obtaining tangible information for tracing purposes.\u003c/p\u003e\n\u003cp\u003eThe findings of the regression analysis revealed that healthcare workers aged 60 years and above were more likely to experience PDS.\u0026nbsp;According to Hendy et al. (2012), [15] elderly individuals are increasing in number, and they are the major consumers of health care services and are more susceptible to social vulnerability, especially in modern society [76].\u0026nbsp;Previous studies have shown that elderly individuals prolong their stay in hospital wards due to\u0026nbsp;frailty,\u0026nbsp;sepsis, deconditioning, cardiovascular disorders, and delays in their relocation to community nursing homes [36, 77, 4]. However, Silva et al. (2014) [4] argued that there is no association between patient age and PDS. In Kenya, reports have shown that increasing numbers of elderly PDS inpatients are left and neglected in hospitals by their families [57] as a result of vulnerability factors, high dependency levels, and the need for domiciliary care [36, 35, 78],\u0026nbsp;which many family members are incapable of offering.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eInfluence of gender on the PDS\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAs indicated in Table 2 below, the majority of all respondents who participated in the study were females\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e72 (54.13%), whereas the remaining respondents were male 61 (45.87%).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2: \u003cem\u003eInfluence of sex on the PDS\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"547\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 14.4161%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eVariable\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.2409%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eShort PDS\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;7days\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;n (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.2409%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLong PDS\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026gt;7days\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;n (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.9708%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTotal\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eN (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.0803%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eOR (95% CI)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.0511%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eP Value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 14.4161%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eGender\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 16.2409%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 16.2409%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 16.9708%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22.0803%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14.0511%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 14.4161%;\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.2409%;\"\u003e\n \u003cp\u003e35(26.32)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.2409%;\"\u003e\n \u003cp\u003e26(19.55)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.9708%;\"\u003e\n \u003cp\u003e61(45.87)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 36.1314%;\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 14.4161%;\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.2409%;\"\u003e\n \u003cp\u003e54(40.60)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.2409%;\"\u003e\n \u003cp\u003e18(13.53)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.9708%;\"\u003e\n \u003cp\u003e72(54.13)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.0803%;\"\u003e\n \u003cp\u003e0.44(0.21-0.93)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.0511%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.03\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 14.4161%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTotal\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.2409%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e89(66.92)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.2409%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e44(33.08)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.9708%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e133(100.00)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.0803%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.0511%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003eThe present study revealed that more males (26, 19.55%) than their female counterparts (18, 13.53%) had long PDSs. The regression analysis results revealed that females were 0.44 times less likely to experience a long PDS than males were\u0026nbsp;(P value of 0.03, odds ratio of 0.44). Patient 11, a married male in the surgical ward, reported that he was ready to go home but feared the public\u0026rsquo;s reaction, as he was rescued by the police from a mob beating. This finding was also confirmed through key informant interviews, which revealed that more men experienced PDS in hospital wards upon discharge. For example, KI 10 mentioned that \u0026lsquo;men leave their homes, come, and stay here in town without even some of their relatives\u0026rsquo;. This implies that when men end up in referral hospitals, their relatives are \u0026lsquo;not around\u0026rsquo; to help them leave the hospital once they are discharged by paying hospital bills and taking them home.\u003c/p\u003e\n\u003cp\u003eIn any social context, gender plays a role as a predictor of PDS. Whereas previous studies in Singapore [36] and Brazil [4] reported that more women than men experienced PDS, the present study revealed that men are susceptible to PDS. This discrepancy can be attributed to differences in the context and nature of the illness. A Kenyan report revealed that 20 women who went to deliver at KNH experienced PDS for more than six months because of failure to clear the medical bill, yet there was free maternal health care [55]. Despite these anomalies, the results of this study suggested that males were more prone to PDS than their female counterparts were. This finding is in agreement with a Canadian study [33], which revealed\u0026nbsp;that males in the psychiatric ward were 1.4 times more likely to experience PDS than females were.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eInfluence of marital status on PDS\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe results from Table 3 below show that 40 (30.08%) of the respondents were married, followed closely by 35 (26.32%) single, 22 (16.54%) divorced/separated and 20 (15.04%) children, while 16 (12.03%) were widowed.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3: \u003cem\u003eInfluence of marital status on PDS\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"614\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 23.6156%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eVariable\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.4951%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eShort PDS\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;7days\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;n (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.4951%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLong PDS\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026gt;7days\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;n (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.1466%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTotal\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eN (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20.684%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eOR (95% CI)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.5635%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eP Value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"6\" style=\"width: 100%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMarital status\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 23.6156%;\"\u003e\n \u003cp\u003eMarried\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.4951%;\"\u003e\n \u003cp\u003e31(23.31)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.4951%;\"\u003e\n \u003cp\u003e9(6.77)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.1466%;\"\u003e\n \u003cp\u003e40(30.08)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20.684%;\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 11.5635%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 23.6156%;\"\u003e\n \u003cp\u003eSingle\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.4951%;\"\u003e\n \u003cp\u003e18(13.54)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.4951%;\"\u003e\n \u003cp\u003e17(12.78)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.1466%;\"\u003e\n \u003cp\u003e35(26.32)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20.684%;\"\u003e\n \u003cp\u003e3.25(1.20-8.79)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.5635%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.02\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 23.6156%;\"\u003e\n \u003cp\u003eDivorced/Separated\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.4951%;\"\u003e\n \u003cp\u003e12(9.02)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.4951%;\"\u003e\n \u003cp\u003e10(7.52)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.1466%;\"\u003e\n \u003cp\u003e22(16.54)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20.684%;\"\u003e\n \u003cp\u003e2.87(0.93-8.79)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.5635%;\"\u003e\n \u003cp\u003e0.07\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 23.6156%;\"\u003e\n \u003cp\u003eWidowed\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.4951%;\"\u003e\n \u003cp\u003e11(8.27)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.4951%;\"\u003e\n \u003cp\u003e5(3.76)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.1466%;\"\u003e\n \u003cp\u003e16(12.03)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20.684%;\"\u003e\n \u003cp\u003e1.56(0.43-5.69)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.5635%;\"\u003e\n \u003cp\u003e0.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 23.6156%;\"\u003e\n \u003cp\u003eChild\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.4951%;\"\u003e\n \u003cp\u003e17(12.78)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.4951%;\"\u003e\n \u003cp\u003e3(2.26)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.1466%;\"\u003e\n \u003cp\u003e20(15.04)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20.684%;\"\u003e\n \u003cp\u003e0.60(0.14-2.55)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.5635%;\"\u003e\n \u003cp\u003e0.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 23.6156%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTotals\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.4951%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e89(66.92)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.4951%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e44(33.08)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.1466%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e133(100.00)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20.684%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.5635%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003eThe results revealed that many of the respondents whose marital status was \u0026lsquo;married\u0026rsquo; had a short PDS of 31 (23.31%), followed by 18 (13.54%), 17 (12.78%), 12 (9.02%) and 11 (8.27%) in that order. Comparatively, more respondents who were single (17, 12.78%) experienced long PDS, closely followed by divorced/separated (10, 7.52%), married (9, 6.77%), widowed (5, 3.76%) and children (3, 2.26%) in that order. The regression analysis revealed that single respondents were 3.25 times more likely to experience PDS\u0026nbsp;than married respondents were (P value 0.02, odds ratio 3.25), whereas the remaining respondents were divorced/separated\u0026nbsp;(P value 0.07, odds ratio 2.87),\u0026nbsp;widowed\u0026nbsp;(P value 0.5, odds ratio 1.56), and child\u0026nbsp;(P value 0.5, odds ratio 0.5)\u0026nbsp;and were not statistically significant.\u003c/p\u003e\n\u003cp\u003eThese results agree with the responses from 9 key informants, which indicated that most PDS inpatients tend to be single, followed by divorced, while widowed, separated, and married individuals constitute the same percentage. KI 9 said that most PDS inpatients \u0026ldquo;\u0026hellip; are not married and if they are married\u0026hellip;it is not a proper marriage (cohabitation) that somebody will\u0026nbsp;stand with them\u0026rdquo; to mean that they have insufficient social support and hence PDS. Most PDS inpatients \u0026ldquo;\u0026hellip;rarely get married, especially males, because mental illness sets in very early in life\u0026rdquo; (KI 7).\u003cem\u003e\u0026nbsp;\u003c/em\u003ePatient 5\u003cem\u003e,\u0026nbsp;\u003c/em\u003ea single mother of 3 in the gynecological ward, said that \u0026lsquo;the \u0026lsquo;baby daddy\u0026rsquo; was irresponsible. He left me with 3 children after I was diagnosed with cervical cancer\u0026hellip; now am stranded here, waiting for the hospital to help me.\u0026rsquo;\u003c/p\u003e\n\u003cp\u003eThese results concur with those of an Australian study by Ou et al. (2009) [22], which revealed that those who were single, widowed, or divorced were more affected by PDS than were those who were married since being married was associated with a decrease in PDS episodes. Although Little et al. (2019) [33] reported that the marital status of PDS inpatients was not statistically significant in their study, the presence of a spouse and their relatives translated into a resource for supporting medical discharge. Following the social‒ecological framework paradigm, marital status is an individual-level factor that determines the social support received from social networks [79].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eInfluence of parental status of children on the PDS\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTable 4 below indicates that the majority of those who participated in the study were partial orphans (9, 45.00%). These were closely followed by children with both parents (8, 40.00%), while the lowest percentage was total orphans (3, 15.00%).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 4: \u003cem\u003eInfluence of parental status of children on the PDS\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"596\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 22.3154%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eVariable\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.1007%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eShort PDS\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;7days\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;n (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.1007%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLong PDS\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026gt;7days\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;n (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.0805%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTotal\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eN (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 23.1544%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eOR (95% CI)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.2483%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eP Value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"6\" style=\"width: 100%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eParental Status of Children\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 22.3154%;\"\u003e\n \u003cp\u003eTotal orphan\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.1007%;\"\u003e\n \u003cp\u003e2(10.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.1007%;\"\u003e\n \u003cp\u003e1(5.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.0805%;\"\u003e\n \u003cp\u003e3(15.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 23.1544%;\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12.2483%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 22.3154%;\"\u003e\n \u003cp\u003ePartial orphan\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.1007%;\"\u003e\n \u003cp\u003e7(35.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.1007%;\"\u003e\n \u003cp\u003e2(10.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.0805%;\"\u003e\n \u003cp\u003e9(45.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 23.1544%;\"\u003e\n \u003cp\u003e0.22(0.03-1.17)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.2483%;\"\u003e\n \u003cp\u003e0.15\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 22.3154%;\"\u003e\n \u003cp\u003eHave both parents\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.1007%;\"\u003e\n \u003cp\u003e8(40.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.1007%;\"\u003e\n \u003cp\u003e0(0.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.0805%;\"\u003e\n \u003cp\u003e8(40.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 23.1544%;\"\u003e\n \u003cp\u003e3.40(0.07-159.52)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.2483%;\"\u003e\n \u003cp\u003e0.53\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 22.3154%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTotal\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.1007%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e17(85.00)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.1007%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e3(15.00)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.0805%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e20(100)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 23.1544%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.2483%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\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\u003cp\u003eAccording to the results of the present study, none of the children with both parents had a long PDS, whereas 8 (40.00%) of them had a short PDS, with only 2 (10.00%) total or those who reported having a short PDS. Concerning long PDSs, 2 (10.00%) partial orphans had long PDSs, whereas only 1 (5.00%) total orphan had long PDSs. The logistic regression results further indicate that the parental status of the children was not statistically significant in this study. However, data from key informants show that most of the children who experienced PDS \u0026lsquo;are from single mothers, some are parentless, while some are street children\u0026rsquo;. When asked why they suffered PDS yet under five were exempt from bill payment, one key informant said that \u0026lsquo;\u0026hellip;one way or the other they are abandoned because of the type of illness and stigma associated with it\u0026rsquo;.\u003c/p\u003e\n\u003cp\u003eThese results are in agreement with previous findings that argued that children who are abused or abandoned and those with medical complexity experienced PDS as they were awaiting placement in appropriate homes [81, 80].\u0026nbsp;The results were also in tandem with those of the Kenyan previous report by Odiwuor (2016) [82], which revealed that a baby girl of approximately 4 years of age experienced long PDS since she had stunted growth.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eInfluence of the Nature of the Illness on the PDS\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe results in Table 5 below show that most of the respondents had chronic diseases (74 (55.64%)), whereas 59 (44.36%) of them had acute illnesses.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 5: \u003cem\u003eInfluence of the Nature of Illness on the PDS\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"565\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 15.0442%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eVariable\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.9912%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eShort PDS\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;7days\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;n (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.9912%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLong PDS\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026gt;7days\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;n (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.9912%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTotal\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eN (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21.2389%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eOR (95% CI)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.7434%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eP Value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"6\" style=\"width: 100%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNature of illness\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 15.0442%;\"\u003e\n \u003cp\u003eChronic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.9912%;\"\u003e\n \u003cp\u003e39(29.32)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.9912%;\"\u003e\n \u003cp\u003e35(26.32)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.9912%;\"\u003e\n \u003cp\u003e74(55.64)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21.2389%;\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12.7434%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 15.0442%;\"\u003e\n \u003cp\u003eAcute\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.9912%;\"\u003e\n \u003cp\u003e50(37.59)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.9912%;\"\u003e\n \u003cp\u003e9(6.77)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.9912%;\"\u003e\n \u003cp\u003e59(44.36)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21.2389%;\"\u003e\n \u003cp\u003e0.20(0.08-0.46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.7434%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.0001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 15.0442%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTotals\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.9912%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e89(66.91)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.9912%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e44(33.09)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.9912%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e133(100.00)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21.2389%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.7434%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\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\u003cp\u003eFurthermore, of the respondents with a short PDS, 50 (37.59%) had acute illness, while 39 (29.32%) were chronically ill at admission. Comparatively, more respondents with a chronic illness had a long PDS 35 (26.32%), whereas 9 (6.77%) had an acute illness. The results further revealed that the respondents who had an acute illness were 0.20 times less likely to experience long PDS than those who had a chronic illness were\u0026nbsp;(P value \u0026lt;0.0001, odds ratio 0.20). Therefore, Fisher\u0026rsquo;s exact test (p\u0026lt;0.001) revealed that there was a significant association between PDS and hospital wards.\u003c/p\u003e\n\u003cp\u003eKI 9 supported the contention that men suffer from long PDSs because they are more involved in criminal activities such as robbery than women are; thus, due to mob beating, they sustain surgical injuries, leading them to be admitted to the male surgical ward, where they are stranded upon being medically discharged because of social misfits. As a result, they are neither visited nor picked from the hospital by their social networks.\u003c/p\u003e\n\u003cp\u003eThe key informants agreed with the results. The KI 2 said that Chronically ill who will stay in the hospital setting for two or more years, two months \u0026hellip; will amount to a higher financial responsibility and so they would take a longer time to look for this money.\u003c/p\u003e\n\u003cp\u003eThis implies that once the inpatient was medically discharged, due to long hospitalization, the bill would be very large. This was in line with the study done by Mostert et al. (2015) [7] and Rice (2009) [10] reported that relatives of said PDS inpatients would not afford hospital bills, yet the longer the hospital stay was, the greater the hospital bill.\u003c/p\u003e\n\u003cp\u003eAccording to the healthcare workers\u0026rsquo; narratives, most patients are admitted to surgical wards. For example, (KI 8) reported that \u0026ldquo;\u0026hellip;are involved in a road traffic accident, motor vehicle accidents, some are assaulted and some end up coming with a head injury, so they stay in a coma for so long.\u0026rdquo; This implies that during hospitalization, no tangible information can be extracted by the hospital social worker for contact tracing purposes. KI 4 also responded by saying that \u0026lsquo;\u0026hellip;most patients who get stuck here are suffering from surgical issues because some of them are amputees\u0026hellip;\u0026rsquo; This pattern of results is consistent with the previous literature that concluded that surgical ailments such as amputation and mental cases require additional support, which families are incompetent in providing at home, thereby leading to PDS [9, 2, 83].\u003c/p\u003e\n\u003cp\u003eOthers, especially the chronically ill PDS inpatients, wanted to go home, but they found the hospital ward to be homely as \u0026lsquo;they feel in their heart that they have not fully recovered\u0026rsquo; (KI 2), whereas others had self-stigma by stating, \u0026lsquo;the moment you lose your limb and now you think to an extent you are going to be relying on people then you feel you are understood and accepted within the hospital\u0026rsquo; (Patient 3, amputee who had been waived but still staying in the male medical ward).\u003c/p\u003e\n\u003cp\u003eChronic illnesses such as mental illness and the unknown identity of inpatients were also rampant predictors of PDS. According to KI 8, unknown patients usually \u0026lsquo;\u0026hellip; engage in \u0026lsquo;robbery\u0026rsquo; and most are involved in road traffic accidents resulting in head injuries...\u0026rsquo; Head injuries result in long hospitalization and getting tangible information from such kinds of PDS inpatients proves difficult. On admission, some patients are brought to the facility by social relationships and left in the facility due to the nature of the illness. A key informant said:\u003c/p\u003e\n\u003cp\u003eWhen mentally ill patients are brought here by their relatives, they eventually regain consciousness and are discharged, but getting their relatives to come and pick them up is usually hectic. We sometimes have to use medical social workers to help locate them, and occasionally, we give them a vehicle to take them back to their homes. This, however, still poses security challenges. For instance, there was a day when my staff was chased away by the relatives of a mentally ill patient and the chief had to intervene\u0026nbsp;(KI 10).\u003c/p\u003e\n\u003cp\u003eThe findings are in agreement with the Zambian [44, 45] and Kenyan [84, 69] reports, which state that the nature of illness contributes to PDS in most health facilities.\u0026nbsp;SEM, as a\u0026nbsp;theoretical framework, has been employed to explain biological processes at the individual level, underpinning determinants of health outcomes [85], such as PDSs. Mental illness and surgery are among the stigmatizing ailments that result in PDS [86, 3]. Inpatients who undergo surgery and amputation require additional support upon discharge and hence PDS [2]. This is because they feel safe in the hospital upon discharge, incur enormous bills, and fear the stigma they face when taken back home from hospital wards [6]. Patients brought in as \u0026lsquo;unknown\u0026rsquo; whose identities were not established on admission through medical discharge also experienced PDS.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eInfluence of Religion on the PDS\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAs shown in Table 6 below, the majority of the respondents were Christians (128, 96.24%), whereas the minority were Muslims (5, 3.76%).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 6: \u003cem\u003eInfluence of Religion on the PDS\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"584\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 17.637%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eVariable\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.2671%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eShort PDS\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;7days\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;n (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.411%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLong PDS\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026gt;7days\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;n (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.9247%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTotal\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eN (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21.4041%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eOR (95% CI)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.3562%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eP Value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"6\" style=\"width: 100%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eReligion\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 17.637%;\"\u003e\n \u003cp\u003eChristian\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.2671%;\"\u003e\n \u003cp\u003e86(64.66)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.411%;\"\u003e\n \u003cp\u003e42(31.58)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.9247%;\"\u003e\n \u003cp\u003e128(96.24)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21.4041%;\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 13.3562%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 17.637%;\"\u003e\n \u003cp\u003eMuslim\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.2671%;\"\u003e\n \u003cp\u003e3(2.26)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.411%;\"\u003e\n \u003cp\u003e2(1.50)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.9247%;\"\u003e\n \u003cp\u003e5(3.76)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21.4041%;\"\u003e\n \u003cp\u003e1.36(0.21-8.48)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.3562%;\"\u003e\n \u003cp\u003e0.74\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 17.637%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTotal\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.2671%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e89(66.92)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.411%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e44(33.08)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.9247%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e133(100.00)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21.4041%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.3562%;\"\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 colspan=\"6\" style=\"width: 100%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eParticipation in religious activities\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 17.637%;\"\u003e\n \u003cp\u003eRegularly\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.2671%;\"\u003e\n \u003cp\u003e23(17.29)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.411%;\"\u003e\n \u003cp\u003e10(7.52)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.9247%;\"\u003e\n \u003cp\u003e33(24.81)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21.4041%;\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 13.3562%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 17.637%;\"\u003e\n \u003cp\u003eIrregularly\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.2671%;\"\u003e\n \u003cp\u003e66(49.63)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.411%;\"\u003e\n \u003cp\u003e34(25.56)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.9247%;\"\u003e\n \u003cp\u003e100(75.19)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21.4041%;\"\u003e\n \u003cp\u003e2.09(0.98-4.47)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.3562%;\"\u003e\n \u003cp\u003e0.06\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 17.637%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTotal\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.2671%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e89(66.92)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.411%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e44(33.08)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.9247%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e133(100.00)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21.4041%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.3562%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"6\" style=\"width: 100%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAttendance of \u0026nbsp;religious services\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 17.637%;\"\u003e\n \u003cp\u003eRegularly\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.2671%;\"\u003e\n \u003cp\u003e44(33.08)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.411%;\"\u003e\n \u003cp\u003e14(10.53)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.9247%;\"\u003e\n \u003cp\u003e58(43.61)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21.4041%;\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 13.3562%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 17.637%;\"\u003e\n \u003cp\u003eIrregularly\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.2671%;\"\u003e\n \u003cp\u003e45(33.83)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.411%;\"\u003e\n \u003cp\u003e30(22.56)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.9247%;\"\u003e\n \u003cp\u003e75(56.39)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21.4041%;\"\u003e\n \u003cp\u003e1.18(0.50-2.77)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.3562%;\"\u003e\n \u003cp\u003e0.7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 17.637%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTotal\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.2671%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e89(66.91)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.411%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e44(33.09)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.9247%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e133(100.00)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21.4041%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.3562%;\"\u003e\n \u003cp\u003e\u0026nbsp;\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\u003cp\u003eThe results further revealed that 42 (31.58%) Christians had a long PDS, whereas only 2 (1.50%) Muslims experienced a long PDS. When the respondents were asked how often they participated in religious activities, the majority responded \u0026apos;Irregularly\u0026apos; 100 (75.19%), while 33 (24.81%) of them reported that they regularly participated. A total of 34 (25.56%) of those who responded that they irregularly participated in religious activities had a long PDS, whereas the other 10 (7.52%) of those who regularly participated in religious activities had a long PDS.\u003c/p\u003e\n\u003cp\u003eMoreover, the majority of the respondents (75, 56.39%) did not attend religious services regularly, while 58 (43.61%) attended them regularly. The results indicate that 30 (22.56%) respondents who did not attend religious services regularly had a long PDS, whereas those who regularly attended religious services numbered 14 (10.53%). The regression results demonstrate that religion, involvement in religious activities, and attendance of religious services were not significantly different across categories. Theoretically, McLeroy et al. (1988) [87] contend that\u0026nbsp;religiosity is an individual-level perspective that affects health outcomes. Scholars of religion [88, 89] have argued that medical discharge may be faster if inpatients are well connected to their religious/spiritual community, where they obtain a social network that can provide social support, as in the current study, and that religion has no influence on the PDS.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eInfluence of the educational level of the respondents on the PDS\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe results in Table 7 below indicate that the majority of the respondents (88 (66.17%)) had reached the primary level. There were 24 (18.04%) high school dropouts, 12 (9.02%) high school dropouts included children, and the remaining 9 (6.77%) had a tertiary level of education.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 7: \u003cem\u003eInfluence of the educational level of the respondents on the PDS\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"589\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 21.0526%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eVariable\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.1104%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eShort PDS\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;7days\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;n (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.1104%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLong PDS\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026gt;7days\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;n (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.7895%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTotal\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eN (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19.8642%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eOR (95% CI)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.073%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eP Value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"6\" style=\"width: 100%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eEducation Level\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 21.0526%;\"\u003e\n \u003cp\u003eNone\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.1104%;\"\u003e\n \u003cp\u003e8(6.02)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.1104%;\"\u003e\n \u003cp\u003e4(3.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.7895%;\"\u003e\n \u003cp\u003e12(9.02)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19.8642%;\"\u003e\n \u003cp\u003eref\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.073%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 21.0526%;\"\u003e\n \u003cp\u003ePrimary\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.1104%;\"\u003e\n \u003cp\u003e58(43.61)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.1104%;\"\u003e\n \u003cp\u003e30(22.56)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.7895%;\"\u003e\n \u003cp\u003e88(66.17)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19.8642%;\"\u003e\n \u003cp\u003e1.03(0.28-3.71)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.073%;\"\u003e\n \u003cp\u003e0.96\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 21.0526%;\"\u003e\n \u003cp\u003eSecondary\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.1104%;\"\u003e\n \u003cp\u003e17(12.78)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.1104%;\"\u003e\n \u003cp\u003e7(5.26)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.7895%;\"\u003e\n \u003cp\u003e24(18.04)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19.8642%;\"\u003e\n \u003cp\u003e0.82(0.18-3.64)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.073%;\"\u003e\n \u003cp\u003e0.8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 21.0526%;\"\u003e\n \u003cp\u003eTertiary\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.1104%;\"\u003e\n \u003cp\u003e6(4.51)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.1104%;\"\u003e\n \u003cp\u003e3(2.26)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.7895%;\"\u003e\n \u003cp\u003e9(6.77)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19.8642%;\"\u003e\n \u003cp\u003e1(0.15-6.25)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.073%;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 21.0526%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTotals\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.1104%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e89(66.92)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.1104%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e44(33.08)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.7895%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e133(100.00)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19.8642%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.073%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\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\u003cp\u003eComparatively, of the respondents who had a long PDS, 30 (22.56%) reached the primary level of education, whereas only 3 (2.26%) reported having a tertiary level of education. The findings of this study are consistent with those of the Kenyan study by Maina (2014) [59], which revealed that the majority of PDS inpatients had a primary school level of education. For example, in many sub-Saharan countries, PDS has been associated with the inability to pay medical bills and hence legal bargaining power [6, 43, 90].\u0026nbsp;This trend necessitated\u0026nbsp;the expansion of social health insurance to the informal sector [91, 25]. This aim was to cushion the poor from health shocks and subsequent PDS episodes. The regression results, however, indicate that the educational level of the respondents was not statistically significant; therefore, the educational level of the respondents had no influence on their PDS in the hospital ward.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003cstrong\u003eInfluence of employment status on the PDS\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAmong the respondents, 86 (64.66%) were unemployed, while 47 (35.34%) were employed, as indicated in Table 8 below. Among those employed, the majority were working in the informal sector (46, 97.87%), with only 1 (2.13%) reported as working in the formal sector.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;Table 8: \u0026nbsp; \u003cem\u003e\u0026nbsp;Influence of the employment status of respondents on the PDS\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"577\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 18.8908%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eVariable\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.5979%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eShort PDS\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;7days\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;n (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.5979%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLong PDS\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026gt;7days\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;n (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.2912%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTotal\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;N (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20.104%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eOR (95% CI)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.5182%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eP Value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"6\" style=\"width: 100%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eEmployment\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 18.8908%;\"\u003e\n \u003cp\u003eUnemployed\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.5979%;\"\u003e\n \u003cp\u003e56(42.11)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.5979%;\"\u003e\n \u003cp\u003e30(22.55)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.2912%;\"\u003e\n \u003cp\u003e86(64.66)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20.104%;\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 13.5182%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 18.8908%;\"\u003e\n \u003cp\u003eEmployed\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.5979%;\"\u003e\n \u003cp\u003e33(24.81)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.5979%;\"\u003e\n \u003cp\u003e14(10.53)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.2912%;\"\u003e\n \u003cp\u003e47(35.34)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20.104%;\"\u003e\n \u003cp\u003e0.79(0.36-1.70)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.5182%;\"\u003e\n \u003cp\u003e0.55\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 18.8908%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTotal\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.5979%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e89(66.92)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.5979%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e44(33.08)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.2912%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e133(100.00)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20.104%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.5182%;\"\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 colspan=\"6\" style=\"width: 100%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eThe sector of employment for the employed\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 18.8908%;\"\u003e\n \u003cp\u003eInformal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.5979%;\"\u003e\n \u003cp\u003e32(68.08)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.5979%;\"\u003e\n \u003cp\u003e14(29.79)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.2912%;\"\u003e\n \u003cp\u003e46(97.87)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20.104%;\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 13.5182%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 18.8908%;\"\u003e\n \u003cp\u003eFormal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.5979%;\"\u003e\n \u003cp\u003e1(2.13)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.5979%;\"\u003e\n \u003cp\u003e0(0.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.2912%;\"\u003e\n \u003cp\u003e1(2.13)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20.104%;\"\u003e\n \u003cp\u003eN/A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.5182%;\"\u003e\n \u003cp\u003eN/A\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 18.8908%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTotal\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.5979%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e33(70.21)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.5979%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e14(29.79)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.2912%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e47(100.00)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20.104%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.5182%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eThe results of this study further indicate that of the respondents who had a long PDS, 30 (22.55%) were unemployed, whereas 14 (10.53%) were employed. Among the majority of respondents who were employed in the informal sector, 14 (29.79%) had a long PDS. KI 2 mentioned that \u0026ldquo;the employed PDS inpatients \u0026hellip;don\u0026rsquo;t stay long after medical discharge because, to a great extent, they have strategies of sorting out their bills like enrolling in health insurance coverage\u0026rdquo;. For example, Patient 11, a male aged 30 who had dropped out of school, responded that \u0026ldquo;I am a hawker selling things in different vehicles.\u0026rdquo; Due to his nature of employment, which was hand-to-mouth, he could not raise the medical bill and hence PDS. However, the regression results of this study indicate that employment status and sector of employment were not statistically significant for the PDS; therefore, the employment status of PDS inpatients does not influence the PDS.\u003c/p\u003e\n\u003cp\u003eAccording to McLeroy et al. (1988) [87], employment status is one of the individual factors that determine health outcomes, such as PDS. It\u0026nbsp;influences their ability to pay medical bills in time, thus reducing the level of PDS.\u0026nbsp;Patients with a lower level of education cannot secure employment and earn better income [92].\u0026nbsp;Previous reports have demonstrated that Kisumu County, in Kenya, has relatively high levels of unemployment and poverty [60]. Due to their poverty level, inpatients in Kisumu County referral hospitals are faced with the inability to pay medical bills [61] and hence PDS.\u003c/p\u003e\n\u003cp\u003eIn conclusion, individual demographic factors associated with the vulnerability of PDS inpatients to becoming PDS victims in referral hospitals. Elderly individuals, males, unmarried individuals, and chronically ill individuals were more at risk of experiencing episodes of PDS. Older people with complex health needs are particularly vulnerable to PDS. Males, especially middle-aged individuals, are associated with deviance, rendering them social misfits who face neglect due to their social relationships in hospital wards and thus PDS. The chronically ill were perceived as burdens by their family members, whereas the unmarried experienced PDS due to insufficient social support. Whereas the parental status of the children, religiosity, education, and employment status of the PDS inpatients were insignificant to the study, they were not dismissed because they determined the general health outcomes of the PDS.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn conclusion, individual demographic factors associated with the vulnerability of PDS inpatients to becoming PDS victims in referral hospitals. Elderly individuals, males, unmarried individuals, and chronically ill individuals were more at risk of experiencing episodes of PDS. Older people with complex health needs are particularly vulnerable to PDS. Additionally, males, especially middle-aged individuals, are associated with deviance, rendering them social misfits who face neglect due to their social relationships in hospital wards and thus PDS. The chronically ill, on the other hand, were perceived as burdens by\u0026nbsp;their family members, whereas the unmarried experienced PDS due to insufficient social support. Whereas the parental status of children, religiosity, education, and employment status of the PDS inpatients were insignificant to the study, they were not dismissed because they determined the general health outcomes of PDS victims.\u003c/p\u003e\n\u003cp\u003eThe general population, especially vulnerable individuals such as elderly individuals, males, unmarried individuals, and\u0026nbsp;those\u0026nbsp;with chronic conditions,\u0026nbsp;are\u0026nbsp;recommended to\u0026nbsp;invest\u0026nbsp;in social\u0026nbsp;networks\u0026nbsp;when they are still healthy. They should also take up government initiatives such as enrollment in UHC and\u0026nbsp;social\u0026nbsp;health\u0026nbsp;insurance\u0026nbsp;seriously. The act increases\u0026nbsp;their\u0026nbsp;level of independence, especially when they become ill and are admitted to referral hospitals, thereby reducing the number of cases of PDS.\u003c/p\u003e\n\u003cp\u003eReferral hospital management and policymakers should invest in the early identification of predictors of PDS reported in this study. This approach helps reduce the number of probable cases of PDS by introducing a PDS tracking mechanism. This is achievable by involving medical social workers in the identification of predictors of PDS and multidisciplinary decision-making during admission, hospitalization, and medical discharge of inpatients. They are also encouraged to sensitize the general population to the harm associated with PDS and psycho-educate them on the importance of enrolling with medical coverage.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eEAG, MAO, and COO were involved in the conception and design of the study. EAG and SOA supervised the interviews, analysed the data, and prepared the manuscript. MAO, COO, and SOA provided guidance and mentorship during the implementation of the study. All the authors reviewed and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no financial, political, religious, intellectual, or personal relationships that may have inappropriately influenced them in writing this article.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding information\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll the data have been shared in the manuscript.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eRojas‐Garc\u0026iacute;a, A., Turner, S., Pizzo, E., Hudson, E., Thomas, J., \u0026amp; Raine, R. (2018). Impact and experiences of delayed discharge: A mixed‐studies systematic review. Health Expectations, 21(1), 41-56.\u003c/li\u003e\n \u003cli\u003eHoughton, J. S. M., Rodriguez, D. U., Weale, A. R., Brooks, M. J., \u0026amp; Mitchell, D. C. (2016). Delayed discharges at a major arterial center: a 4-month cross-sectional study at a single specialist vascular surgery ward. BMJ Open, 6(9), e011193.\u003c/li\u003e\n \u003cli\u003eKippenberg, J., Sahokwasama, J. B., \u0026amp; Amon, J. J. (2007). Detention of insolvent patients in Burundian hospitals. Health Policy and Planning, 23(1), 14-23.\u003c/li\u003e\n \u003cli\u003eSilva, S. A. d., Val\u0026aacute;cio, R. A., Botelho, F. C., \u0026amp; Amaral, C. F. S. (2014). Reasons for discharge delays in teaching hospitals. Revista de saude publica, 48(2), 314-321.\u003c/li\u003e\n \u003cli\u003eOtremba, M., Berland, G., \u0026amp; Amon, J. J. (2015). Hospitals as debtor prisons. The Lancet Global Health, 3(5), e253-e254.\u003c/li\u003e\n \u003cli\u003eDevakumar, D., \u0026amp; Yates, R. (2016). Medical Hostages: Detention of Women and Babies in Hospitals. Health and human rights, 18(1), 277-282.\u003c/li\u003e\n \u003cli\u003eMostert, S., Lam, C. G., Njuguna, F., Patenaude, A. F., Kulkarni, K., Salaverria, C., . . . Buckle, G. C. (2015). Hospital Detention Practices: Position Statement of a SIOP PODC Global Taskforce. Lancet (London, England), 386(9994), 649. doi: 10.1016/S0140-6736(15)61495-7\u003c/li\u003e\n \u003cli\u003eHandayani, K., Sijbranda, T. C., Westenberg, M. A., Rossell, N., Sitaresmi, M. N., Kaspers, G. J., \u0026amp; Mostert, S. (2020). The global problem of hospital detention practices. \u003cem\u003eInternational Journal of Health Policy and Management\u003c/em\u003e, \u003cem\u003e9\u003c/em\u003e(8), 319-326 doi: 10.15171/ijhpm.2020.10\u003c/li\u003e\n \u003cli\u003eRosman, M., Rachminov, O., Segal, O., \u0026amp; Segal, G. (2015). 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Postdischarge stay of unknown/unaccompanied patients: A challenging situation. \u003cem\u003eJournal of Mahatma Gandhi Institute of Medical Sciences, 22\u003c/em\u003e(1), 66-67.\u003c/li\u003e\n \u003cli\u003eCai, C., Lindquist, K., \u0026amp; Bongiovanni, T. (2020). Factors associated with delays in discharge for trauma patients at an urban county hospital. \u003cem\u003eTrauma surgery \u0026amp; acute care open\u003c/em\u003e, \u003cem\u003e5\u003c/em\u003e(1), e000535. http://dx.doi.org/10.1136/tsaco-2020-000535\u003c/li\u003e\n \u003cli\u003eHendy, P., Patel, J., Kordbacheh, T., Laskar, N., \u0026amp; Harbord, M. (2012). In-depth analysis of delays to patient discharge: a metropolitan teaching hospital experience. \u003cem\u003eClinical Medicine, 12\u003c/em\u003e(4), 320-323.\u003c/li\u003e\n \u003cli\u003eToh, H. J., Lim, Z. Y., Yap, P., \u0026amp; Tang, T. (2017). Factors associated with prolonged length of stay in older patients. \u003cem\u003eSingapore Medical Journal, 58\u003c/em\u003e(3), 134-138 doi: 10.11622/smedj.2016158\u003c/li\u003e\n \u003cli\u003eGrusky, S. (2002). Hospitals Become Jails in Ghana. \u003cem\u003eDrop the Debt: Jubilee USA\u003c/em\u003e.\u003c/li\u003e\n \u003cli\u003eJacobs, B., \u0026amp; Price, N. (2004). The impact of the introduction of user fees at a district hospital in Cambodia. \u003cem\u003eHealth policy and planning, 19\u003c/em\u003e(5), 310-321\u003c/li\u003e\n \u003cli\u003eShahrawat, R., \u0026amp; Rao, K. D. (2011). Insured yet vulnerable: out-of-pocket payments and India\u0026lsquo;s poor. \u003cem\u003eHealth Policy and Planning, 27\u003c/em\u003e(3), 213-221.\u003c/li\u003e\n \u003cli\u003eSardan, J.-P. O., \u0026amp; Ridde, V. (2015). Diagnosis of a public policy: an introduction to user fee exemptions for healthcare in the Sahel. 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Discharge delay in acute care: reasons and determinants of delay in general ward patients. \u003cem\u003eAustralian Health Review\u003c/em\u003e, \u003cem\u003e33\u003c/em\u003e(3), 513-521.\u003c/li\u003e\n \u003cli\u003eOpondo, E. (2015, October 29). When Does a Hospital Become a Jail? When You Can\u0026lsquo;t Afford the Bill. https://www.opensocietyfoundations.org/voices/when-does-hospital-become-jail-when- you-can-t-afford-bill.\u003c/li\u003e\n \u003cli\u003eCarrin, G., Desmet, M., \u0026amp; Basaza, R. (2001). Social health insurance development in low-income developing countries: new roles for government and nonprofit health insurance organizations. \u003cem\u003eBuilding social security: The challenge of privatization\u003c/em\u003e.\u003c/li\u003e\n \u003cli\u003eScheil‐Adlung, X., Carrin, G., Juetting, J., \u0026amp; Xu, K. (2006). What is the impact of social health protection on access to health care, health expenditure, and impoverishment? 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Paper presented at the XXV11IUSSP International Population Conference, Busan, Republic of Korea.\\\u003c/li\u003e\n \u003cli\u003eGlasby, J., Littlechild, R., \u0026amp; Pryce, K. (2004). Show Me the Way to go home: a narrative review of the Literature on delayed hospital discharges and older people. British Journal of Social Work, 34(8), 1189-1197.\u003c/li\u003e\n \u003cli\u003eThomas, S. N., McGwin Jr, G., \u0026amp; Rue III, L. W. (2005). The financial impact of delayed discharge at a level I trauma center. Journal of Trauma and Acute Care Surgery, 58(1), 121-12.\u003c/li\u003e\n \u003cli\u003eJasinarachchi, K. H., Ibrahim, I. R., Keegan, B. C., Mathialagan, R., McGourty, J. C., Phillips, J. R., \u0026amp; Myint, P. K. (2009). Delayed transfer of care from NHS secondary care to primary care in England: its determinants, effect on hospital bed days, the prevalence of acute medical conditions and deaths during the delay, in older adults aged 65 years and over. 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Research Methods in Anthropology: Qualitative and Quantitative Approaches Walnut Creek.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"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":"Postdischarge stay, PDS inpatients, demographic, predictor","lastPublishedDoi":"10.21203/rs.3.rs-6042253/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6042253/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eIntroduction\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eGlobally, inpatients continue to unnecessarily prolong their stay in referral hospital wards upon their medical discharge. This causes congestion in wards, hospital reinfection, relapse, death of PDS inpatients, and financial burden to hospital management. The literature links postdischarge stay (PDS) to economic reasons. This study aimed to investigate the demographic predictors of inpatients’ postdischarge stay in Jaramogi Oginga Odinga Teaching and Referral Hospitals (JOOTRH) and Kisumu County Referral Hospital (KCRH) in Kisumu County, Kenya.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethodology\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study adopted a correlational cross-sectional research design. A stratified sampling technique was used to select inpatients in the 14 wards, after which systematic random sampling was used to reach the individual PDS inpatients for interviews. To establish the predictors of PDS, binary logistic regression analysis was used, where p values \u0026lt; 0.05 were considered statistically significant, and odds ratios (ORs) and 95% confidence intervals (CIs) were reported to show the magnitude and influence of PDS, resulting in a total sample size of 133 participants.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe majority 72 (54.13%) of the respondents in this study were female. In the age cohort, 43 (32.33%) were aged 20–29 years, 24 (18.05%) were aged 30–39 years, 20 (15.04%) were aged 0–9 years, 17 (12.78%) were aged 40–49 years, 11 (8.27%) were aged over 60 years, and 7 (5.26%) were aged 50–59 years. In terms of marital status, 40 (30.08%) of the respondents were married, 22 (16.54%) were single, 20 (15.04%) were divorced/separated, 16 (12.03%) were widowed, 9 (45.00%) were partial orphans, 8 (40.00%) were both parents, and 3 (15.00%) were total orphans. Most of the respondents 74 (55.64%) had chronic diseases, while 59 (44.36%) had acute illnesses, among which the majority 88 (66.17%) had reached the primary level; hence, 86 (64.66%) unemployed respondents\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIndividual demographic factors associated with the vulnerability of PDS inpatients to becoming PDS victims in referral hospitals. Elderly individuals, males, unmarried individuals, and chronically ill individuals were more at risk of experiencing episodes of PDS. Older people with complex health needs are particularly vulnerable to PDS. Additionally, males, especially middle-aged individuals, are associated with deviance, rendering them social misfits who face neglect due to their social relationships in hospital wards and thus PDS. The chronically ill were perceived as burdens by their family members, whereas the unmarried experienced PDS due to insufficient social support. Whereas the parental status of the children, religiosity, education, and employment status of the PDS inpatients were insignificant to the study, they were not dismissed because they determined the general health outcomes of the PDS.\u003c/p\u003e","manuscriptTitle":"Demographic Predictors of Inpatients’ Postdischarge Stay in Referral Hospitals in Kisumu County, Kenya","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-02-20 07:15:28","doi":"10.21203/rs.3.rs-6042253/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":"8321a6a5-8d5c-44c8-9df3-914d0b567c3c","owner":[],"postedDate":"February 20th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-03-04T02:53:09+00:00","versionOfRecord":[],"versionCreatedAt":"2025-02-20 07:15:28","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6042253","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6042253","identity":"rs-6042253","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}