{"paper_id":"482cd5ba-d3ad-4c03-875c-85fe8dfd35c6","body_text":"Determinants of Tuberculosis Treatment Adherence Among Patients Receiving Tuberculosis Treatment at Federal University Teaching Hospital, Lafia, Nasarawa State | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Determinants of Tuberculosis Treatment Adherence Among Patients Receiving Tuberculosis Treatment at Federal University Teaching Hospital, Lafia, Nasarawa State Esther Anzaku, Saheed Lawal This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6566218/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background Tuberculosis (TB) remains a public health challenge in Nigeria, ranking first in Africa and sixth globally. In Nasarawa State, rising cases and suboptimal treatment outcomes highlight adherence gaps, with over 7,000 cases reported in recent years. Poor adherence contributes to treatment failure, drug-resistance, and transmission. This study investigated determinants of TB treatment adherence among patients at Federal University Teaching Hospital (FUTH) Lafia, using the PRECEDE model to assess predisposing, reinforcing, and enabling factors. Methods A descriptive cross-sectional design was used, surveying 156 TB patients via systematic random sampling from a population of 216. Data were collected using a structured questionnaire covering demographics, knowledge, social support, quality of care, and adherence. Reliability was confirmed with a Cronbach’s alpha of 0.793. A 97.44% response rate (152/156) was achieved. Data was analyzed using SPSS version 27.0, with descriptive statistics, Pearson’s correlation, and logistic regression (significance at p ≤ 0.05). Ethical approvals were obtained from Babcock University and FUTH Lafia. Results Findings revealed moderate TB knowledge (mean = 7.76 ± 2.058). Emotional support was strong (92.8% received medication reminders), but practical support was weak (23.6%). Quality of care scored highest (mean = 17.32 ± 1.836). Treatment adherence was 71.5% (mean = 8.58 ± 1.811), below WHO’s 90% target. Logistic regression showed quality of care significantly predicted adherence (AOR = 2.386, 95% CI = 1.564–3.640, p < 0.001); knowledge and social support were non-significant. Conclusion The study concluded that quality of care is the strongest determinant of adherence, recommending tailored education, practical support, and further research. Enabling factor PRECEDE model Predisposing factor Reinforcing factor Tuberculosis treatment adherence BACKGROUND Tuberculosis (TB) is a virulent disease caused by the bacterium Mycobacterium tuberculosis , predominantly affecting the lungs but can also impact other parts of the body [ 11 ]. It is a major cause of illness and death worldwide, surpassing HIV/AIDS as the most common infectious agent-related cause of mortality prior to the COVID-19 pandemic [ 15 ]. Albeit largely curable with affordable and readily available treatment, TB remains a significant public health concern. Adherence to TB treatment is crucial for successful outcomes. It is viewed as the extent to which a patient takes medications as prescribed by healthcare service providers [ 27 , 7 ]. However, the long duration of anti-TB treatment poses significant challenges, especially non-adherence to medication [ 22 ]. Poor treatment adherence contributes to lower treatment completion rates, higher rates of loss to follow-up, increased relapse, and the development of drug resistance [ 8 , 31 ]. In the year 2022, 2.5 million Africans contracted tuberculosis, making up a quarter of all new cases of tuberculosis cases globally [ 29 ]. An estimated 424,000 people died from the disease in Africa, with over 33% of TB deaths occurring in the region [ 29 ]. Also, multidrug-resistant TB (MDR-TB) remains a public health dilemma and a health security threat, with the World Health Organization (WHO) estimating 62,000 cases in the Africa. A study in South Africa estimated that treatment loss mainly occurred at treatment completion, with only 53% of overall TB cases completing their treatment (Sazali et al., 2023). Nigeria ranks number one in Africa and sixth globally among the 30 high Tuberculosis burden countries [ 3 ]. In 2021, Nigeria held about 4.4% (467,000) of the global burden and 8.0% (125,000) of the global death cases, with a treatment coverage of 44.0%, fatality ratio of 28.0%, and 2.5% new cases of drug resistance [ 29 ]. Previous studies have linked the development of Multidrug-resistant TB to treatment non-adherence and loss to follow-up [ 23 , 28 ]. Studies conducted in different regions have identified various factors contributing to anti-TB medication non-adherence [ 12 , 16 ]. These include patients' forgetfulness, poor knowledge about TB and anti-TB therapy, poor patient-provider relationship, patients' experience of side effects, distance from home to health facility, and long waiting time before access to treatment. In Nigeria, factors such as distance of patients from treatment sites, lack of knowledge of the duration of treatment, low quality of support from treatment supporters, and smoking habits have been found to contribute to non-adherence and increase the likelihood of treatment interruption [ 2 , 4 ]. Identifying and promptly treating individuals affected by TB, while ensuring they have optimal conditions to complete their treatment, has the potential to save millions of lives and eliminate the transmission of TB [ 20 ]. Therefore, this study aimed to investigate the determinants of tuberculosis treatment adherence at Federal University Teaching Hospital (FUTH) Lafia in Nasarawa State, using the PRECEDE model as a framework. This model helped to identify predisposing, reinforcing, and enabling factors influencing TB treatment adherence, providing deeper insight into patient behavior in this locality. METHOD Research Design This study adopted a descriptive cross-sectional design to assess the determinants of tuberculosis (TB) treatment adherence among patients receiving care at the Federal University Teaching Hospital (FUTH) Lafia, Nasarawa State, Nigeria. The PRECEDE model was used to evaluate predisposing (knowledge), reinforcing (social support), and enabling (quality of care) factors influencing adherence. Study Population and Setting The study was conducted at the Directly Observed Therapy (DOT) Clinic of FUTH Lafia, the largest tertiary healthcare facility in Nasarawa State, serving as a referral center for TB diagnosis and treatment. The study population comprised adult male and female patients undergoing tuberculosis treatment at the Directly Observed Therapy (DOT) Clinic of the Federal University Teaching Hospital (FUTH), Lafia. Inclusion criteria were: patients aged 18 years and above, who had been on treatment for at least one month, and who provided informed consent. Critically ill individuals or those unable to participate were excluded. Sampling Technique and Sample Size Systematic random sampling technique was employed to select participants from a sampling frame of 216 TB patients registered in the hospital’s TB unit. The sample size was determined using Slovin’s formula, $$\\:n=\\frac{N}{{1+N\\left(e\\right)}^{2}}$$ Where; n = Sample size N = Total population (216) e = Margin of error (5%) yielding 141 participants. To account for potential non-responses, an additional 10% was added, resulting in a final sample size of 156. The sampling interval (k) was calculated as the ratio of the total population to the sample size, rounded to the nearest whole number. Every eligible patient attending the clinic was enrolled consecutively until the required sample size was achieved. Measures Data was collected using a structured interviewer-administered questionnaire consisting of five sections. Section A captured socio-demographic characteristics. Section B assessed knowledge of tuberculosis (predisposing factor) using both dichotomous and multiple-choice items (max score = 13). Section C examined social support (reinforcing factor) via six items on a 4-point Likert scale (max score = 18). Section D evaluated the quality of care (enabling factor) using eight items on a 4-point Likert scale (max score = 24). Section E measured tuberculosis treatment adherence through four items (max score = 12). Categorization of responses followed the scoring system established in the dissertation. Instrument Reliability Reliability was established through a pilot study involving 10% of the target sample. The Cronbach’s alpha coefficients for each construct were: knowledge (0.711), social support (0.821), quality of care (0.775), and treatment adherence (0.864), indicating good internal consistency. Validity was confirmed through expert review and alignment with the constructs of the PRECEDE model. Data Analysis Data was analyzed using SPSS version 27. Descriptive statistics were used to summarize participant characteristics and construct scores. Pearson’s correlation and logistic regression were conducted to test the relationship between the independent variables (knowledge, social support, quality of care) and the dependent variable (treatment adherence). Statistical significance was set at p ≤ 0.05. RESULTS Socio-Demographic Characteristics of Respondents Table 1 presents key socio-demographic characteristics of the study sample (n = 152), with a nearly balanced gender distribution: 46.1% (70) were male, while 53.9% (82) were female. The participants' ages ranged from 18 to 70 years, with a mean age of 36.16 years (± 12.087). The majority of respondents (32.9%) were between 31 and 40 years old, followed by 26.3% in the 21–30 age group, 18.4% in the 41–50 category, and smaller proportions in the younger and older brackets. Regarding marital status, nearly half (48.7%) were single, while 41.4% were married. A small percentage were either widowed (5.9%) or divorced (3.9%). In terms of educational attainment, the majority (52.6%) had a secondary education, while 21.1% had tertiary education. Additionally, 20.4% had completed primary education, and 5.9% had no formal education. Income levels varied, with 48.7% earning between 20,000 and 50,000, while 25% had a monthly income of 51,000–100,000. About 23% earned less than 20,000, and only 3.3% reported earning more than 100,000. Religious affiliation was predominantly Islamic, with 66.4% of participants identifying as Muslims, while 33.6% were Christians. Ethnic distribution showed that 94.1% of respondents identified as belonging to ethnic groups other than Hausa (3.9%) and Igbo (2.0%), while no respondents identified as Yoruba. Lastly, the majority of respondents (48%) resided in semi-urban areas, followed by 42.1% in rural settings and 9.9% in urban areas. Table 1 Socio-Demographic Characteristics of Respondents Socio-Demographic characteristics (n = 152) Frequency (n) Percentage (%) Gender Male 70 46.1 Female 82 53.9 Age 18–20 14 9.2 21–30 40 26.3 31–40 50 32.9 41–50 28 18.4 51–60 15 9.9 61–70 5 3.3 = 36.16 ± 12.087 Marital Status Single 74 48.7 Married 63 41.4 Divorced 6 3.9 Widowed 9 5.9 Education No formal education 9 5.9 Primary 31 20.4 Secondary 80 52.6 Tertiary 32 21.1 Monthly Income Less than 20k 35 23 20k-50k 74 48.7 51k- 100k 38 25 More than 100k 5 3.3 Religion Christianity 51 33.6 Islam 101 66.4 Ethnicity Hausa 6 3.9 Igbo 3 2.0 Yoruba 0 0 Others 143 94.1 Settlement type Urban 15 9.9 Semi-Urban 73 48 Rural 64 42.1 Summary of Descriptive Statistics Table 2 summarizes the descriptive statistics, showing that TB knowledge had a mean percentage of 59.69% (7.76 ± 2.058), social support was 56.17% (10.11 ± 12.206), and TB treatment adherence was 71.5% (8.58 ± 1.811). Quality of care from the health facility was the highest at 72.54% (17.41 ± 1.734). Table 2 Summary of Descriptive Statistics Variable Reference scale Mean ± SD Level of Variable (%) Knowledge 13 7.76 ± 2.058 59.69% Social Support 18 10.11 ± 2.206 56.17% Quality of Care from Health Facility 24 17.32 ± 1.836 72.17% TB treatment adherence 12 8.58 ± 1.811 71.5% Test of Hypotheses Three hypotheses were tested for this study. In testing the hypotheses, binary logistics and multiple regression analysis was conducted at 0.05 level of significant. The decision rule applied was that if the p-value computed was less than or equal to the cut off p-value of 0.05, the null hypothesis will be rejected in favour of the alternative hypothesis and vice versa. Binary logistic regression analysis (Table 3 ) showed that the predisposing factor (knowledge), was not significantly associated with the outcome. At both bivariate and multivariate levels, knowledge did not show a significant relationship with treatment adherence (COR = 1.087, 95% CI = 0.88–1.328, p = 0.416; AOR = 1.067, 95% CI = 0.804–1.417, p = 0.653). This indicated that changes in knowledge levels did not significantly influence the odds of adhering to treatment among the respondents. The odds of adherence were 6.7% ((1.067 − 1) ×100) higher for every unit increase in knowledge at the multivariate level, but this was not statistically significant. Therefore, the researcher failed to reject the null hypothesis. Again, binary logistic regression analysis (Table 3 ) indicated that the reinforcing factor, (social support), was not significantly associated with the outcome at both bivariate and multivariate levels. At the bivariate level, social support showed a borderline association (COR = 1.212, 95% CI = 0.966–1.473, p = 0.054), but this was not sustained at the multivariate level (AOR = 1.346, 95% CI = 0.990–1.830, p = 0.058). Thus, social support did not significantly increase the odds of engaging in tb treatment adherence among the respondents. The odds of adherence were exactly 34.6% ((1.346 − 1) ×100%) higher for every unit increase in social support at the multivariate level, but this was not statistically significant. Therefore, the researcher failed to reject the null hypothesis. Binary logistic regression analysis (Table 3 ) revealed that the enabling factor (quality of care from the health facility), was significantly associated with the outcome. At the bivariate level, quality of care was positively associated with adherence (COR = 1.914, 95% CI = 1.402–2.612, p < 0.001). This association remained significant at the multivariate level (AOR = 2.386, 95% CI = 1.564–3.640, p < 0.001). This means that for every unit increase in the quality of care from the health facility, the odds of adhering to tb medication increased by exactly 138.6%; (2.386 − 1) ×100% Therefore, as the quality of care increased by a unit, the odds of adhering to treatment were significantly higher. Hence, the null hypothesis was rejected. Table 3 Bivariate and Multivariate Logistic Regression Analysis of Determinants of TB Treatment Adherence Variable No of respondent = 152 COR (95% CI) P value AOR (95% CI) P value Knowledge 1.087 (0.88–1.328) 0.416 1.067 (0.804–1.417) 0.653 Social Support 1.212 (0.966–1.473) 0.054 1.346 (0.990–1.830) 0.058 Quality of care 1.914 (1.402–2.612) < 0.001 2.386 (1.564–3.640) < 0.001 DISCUSSION Socio-demographic characteristics of the respondents The study revealed a slightly higher proportion of female TB patients (53.9%) compared to males (46.1%), which aligns with findings by [ 13 ] in the same health facility, where female TB patients constituted 52.6% and males 47.4%. However, this contrasts with other studies in Nigeria. For instance, [ 1 ] reported a significantly higher proportion of male TB patients (71.8%) compared to females (28.2%) in Kaduna State, and [ 3 ], observed a predominance of males (55%) over females (45%) among ambulatory drug-sensitive TB patients in southwest Nigeria. Additionally, national data from the World Health Organization [ 29 ] consistently highlights a greater TB burden among males in Nigeria, often attributed to occupational exposure and healthcare-seeking behavior differences. These variations suggest that gender distribution among TB patients may differ based on geographic location, study population, or study period. The educational attainment of the respondents shows a notable concentration in secondary education (52.6%), with smaller proportions having primary (20.4%) or tertiary education (21.1%) and a low percentage with no formal education (5.9%). When compared to other Nigerian studies, the north-western sample in [ 14 ] had a higher percentage in secondary education but similarly low rates of no formal education, while the south-western sample in [ 3 ] presented a contrasting profile with a much larger proportion attaining tertiary education and lower figures in primary and secondary levels. This disparity likely reflects regional differences in access to higher education and socio-economic infrastructure across Nigeria The mean age of TB patients in this study, 36.16 ± 12.087 years, with the majority falling within the 31–40 age group (32.9%). Similarly, [ 18 ] noted that TB prevalence was highest among those aged 30–39 years (35.8%), which is consistent with the observation that TB disproportionately affects the economically productive age group in Nigeria. However, [ 31 ] also highlighted significant cases among older adults aged ≥ 60 years, likely due to factors such as weakened immunity and latent TB reactivation. The predominance of TB among adults aged 31–40 years may be attributed to increased exposure through occupational hazards, urban overcrowding, and lifestyle factors such as alcohol use and smoking, which are significant risk factors for TB mortality and disability-adjusted life years (DALYs) in Nigeria [ 18 ]. Additionally, this age group represents the workforce's backbone, emphasizing the socio-economic burden of TB as infected individuals lose productivity and income during treatment. Addressing these risk factors through targeted interventions can mitigate the disease's impact on this critical demographic. For ethnicity, the overwhelming representation of \"Others\" (94.1%) among TB patients in this study suggests a diverse ethnic composition or a specific concentration of ethnic groups in the study area. This contrasts with national ethnic distributions and may indicate regional variations in TB prevalence. For instance, a study in Enugu reported a high proportion of Igbo ethnicity among TB patients [ 17 ]. Also, the study found that 66.4% of TB patients were Muslims, while 33.6% were Christians. This distribution likely reflects the religious demographics of Nasarawa State rather than any inherent difference in susceptibility to TB. Monthly Income item shows that the majority of the respondents earn between 20k-50k monthly (48.7%), with a smaller proportion earning less than 20k (23%) or more than 100k (3.3%). This income distribution suggests that TB disproportionately affects lower- to middle-income individuals. In Nigeria, TB is recognized as a disease of poverty, affecting low-income populations and often associated with socio-economic challenges [ 17 ]. Similarly, the distribution across semi-urban (48%), rural (42.1%), and urban areas (9.9%) highlights the significant burden of TB in non-urban settings. This finding is consistent with broader trends in Nigeria, where TB is more prevalent in rural and semi-urban areas due to limited access to healthcare services and socio-economic challenges [ 19 ] Knowledge of Tuberculosis and its Treatment The findings on TB knowledge in this study align partially with national studies conducted in Nigeria. For instance, [ 17 ] found that while over 80% of Nigerians had heard about TB, only 26.5% correctly identified its bacterial cause, highlighting significant gaps in understanding the disease's etiology. Similarly, [ 19 ] reported low knowledge scores among urban slum residents, with misconceptions about TB transmission and symptoms being widespread. [ 6 ] further emphasized these knowledge gaps, noting that some participants believed TB was caused by heavy work, alcohol use, tobacco smoking, heredity, HIV positivity, or even witchcraft. These misconceptions reflect a broader issue of inadequate health education campaigns and limited dissemination of accurate information about TB in health facilities. Unlike [ 6 ], who found that individuals with high levels of TB knowledge were four times more likely to adhere to treatment compared to those with poor knowledge, this study did not observe any correlation between TB knowledge and treatment adherence. This discrepancy may be attributed to differences in study populations or methodologies. Social Support for Tb Treatment Social support among TB patients in this study demonstrated mixed outcomes. Emotional support was strong; A significant proportion of respondents (92.8%) reported receiving reminders to take their medication, and 96.7% noted encouragement to complete treatment. However, this contrasts with a study among multidrug-resistant TB (MDR-TB) patients in Zhejiang Province, which reported a low level of social support [ 31 ]. The disparity between these findings may stem from differences in socio-economic conditions and cultural norms between the two regions. In Nigeria, family structures often prioritize emotional support, while in other contexts, urbanization or stigma may limit support. In this study, while emotional support was highly reported, practical assistance such as transportation and financial aid was minimal. Only 23.6% of respondents received help with transportation, and 15.8% received financial assistance. This imbalance likely reflects the economic challenges faced by families in Nasarawa state, where providing emotional encouragement is more feasible than offering material support. In spite of the emotional support, this study did not find a statistically significant association between social support and TB treatment adherence. This finding differs from [ 24 ] who reported that a lack of social support is a significant obstacle to TB treatment adherence. They noted that participants without family support or those who did not disclose their TB status to their family were more likely to exhibit non-adherence compared to those who disclosed their status. The difference in findings may be due to differences in socio-economic conditions or cultural norms between the study populations. Quality of care from the Health Facility The evaluation of the quality of care received by TB patients in this study revealed high-quality care for TB patients FUTH Lafia, across multiple domains, including staff friendliness, accessibility, medication availability, and privacy standards. These findings align with studies reporting high-quality TB care in other settings. A systematic review by [ 10 ] found that patient satisfaction with TB healthcare services was generally high globally when facilities ensured medication availability, privacy, and respectful treatment by staff. However patient education was the lowest rated item in this section and it is similar to results reported in a mixed-methods assessment of inpatient TB services in Armenia which identified gaps in communication [ 26 ]. In contrast, a process evaluation in Mongolia using the Zero TB Indicator Framework also highlighted issues such as long waiting times and inconsistent medication supplies [ 21 ]. These disparities may be attributed to differences in healthcare infrastructure, funding, and staff training between regions. There was also a strong positive correlation between the enabling factor in this study and treatment adherence. Tuberculosis Treatment Adherence The TB treatment adherence rate in this study was 71.5%, indicating moderate adherence among patients at the Federal University Teaching Hospital Lafia, Nasarawa State. This rate aligns with findings by [ 6 ], who reported a similar adherence rate of 73.5% among TB patients in a comparable setting. Furthermore, [ 1 ] observed that adherence was higher among patients receiving treatment through hospital-based models compared to community-based models, underscoring the importance of structured healthcare environments in enhancing adherence. Conversely, in Kebbi State, [ 9 ] documented a significantly higher treatment success rate of 91.7% in public hospitals, which they attributed to strong adherence under the Directly Observed Treatment Short-Course (DOTS) framework. This contrasts with the current study's findings and highlights the effectiveness of DOTS in improving adherence rates. Although the TB treatment adherence rate in this study falls short of the WHO-recommended treatment success rate of at least 90%, it represents a notable 10% improvement over the 61.5% success rate reported by [ 5 ]in the same health facility. CONCLUSION In conclusion, this study found that TB treatment adherence among patients at FUTH Lafia was significantly influenced by the quality of care from the health facility, while knowledge and social support showed no significant relationship. Patients reported high satisfaction with healthcare services but gaps in practical assistance from their support networks. Misconceptions about TB transmission and symptoms persisted despite moderate overall knowledge levels. While the 71.5% adherence rate remains below WHO's 90% target, it marks an improvement over prior results. The findings align with the PRECEDE model, showing the importance of the enabling factor in adherence behavior. These insights contribute to understanding the complex dynamics shaping TB treatment outcomes in high-burden settings. Based on the findings, the study recommends enhancing patient education through locally relevant methods, as TB knowledge levels were low and patient education scored poorly in quality-of-care assessments. Health workers should use simple stories, visual aids, and brief discussions during medication pick-ups to reinforce key messages. Training in effective communication and counseling is also essential. To address financial barriers, a hospital-based fund supported by corporate partnerships could help cover transport costs. A “Food for Adherence” program, offering nutritional support tied to treatment milestones, may further encourage adherence. Future research should adopt longitudinal mixed-methods designs to explore adherence throughout the full treatment course. Given the near-significant link between social support and adherence, further studies should examine the types and impact of support and ways to strengthen them. Declarations Availability of data and materials The datasets analyzed for this study are available from the corresponding author upon reasonable request. Funding The research was funded by the researchers. Corresponding Authors email [email protected] , [email protected] Clinical Trial Number: Not Applicable Ethical consideration Ethical approval was obtained from Babcock Health Research Ethics Committee, as well as the Ministry of Health Nasarawa State and FUTH Lafia Ethics Committee. The respondents were given a written consent form, and assurance was given that the study is for educational purposes only. Information and explanation were given, of the confidentiality and anonymity of their information and identities respectively. Consent to Publish declaration: not applicable References Adagba, K., Aliyu, A., Ejembi, C., Olorukooba, A., & Joshua, I. (2023). 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Tuberculosis disease burden and attributable risk factors in Nigeria, 1990–2016. Tropical Medicine and Health , 46 (1). https://doi.org/10.1186/s41182-018-0114-9 Ogunniyi, T. J., Abdulganiyu, M. O., Issa, J. B., Abdulhameed, I., & Batisani, K. (2024). Ending tuberculosis in Nigeria: a priority by 2030. BMJ Global Health , 9 (12), e016820. https://doi.org/10.1136/bmjgh-2024-016820 Ryckman, T., Robsky, K., Cilloni, L., Zawedde-Muyanja, S., Ananthakrishnan, R., Kendall, E. A., Shrestha, S., Turyahabwe, S., Katamba, A., & Dowdy, D. W. (2022). Ending tuberculosis in a post-COVID-19 world: a person-centred, equity-oriented approach. The Lancet Infectious Diseases , 23 (2), e59–e66. https://doi.org/10.1016/s1473- 3099(22)00500-x Saranjav, A., Parisi, C., Zhou, X., Dorjnamjil, K., Samdan, T., Erdenebaatar, S., Chuluun, A., Dalkh, T., Ganbaatar, G., Brooks, M. B., Spiegelman, D., Ganmaa, D., & Davis, J. L. (2022). Assessing the quality of tuberculosis care using routine surveillance data: a process evaluation employing the Zero TB Indicator Framework in Mongolia. BMJ Open , 12 (8), e061229. https://doi.org/10.1136/bmjopen-2022-061229 Sazali, M. F., Rahim, S. S. S. A., Mohammad, A. H., Kadir, F., Payus, A. O., Avoi, R., Jeffree, M. S., Omar, A., Ibrahim, M. Y., Atil, A., Tuah, N. M., Dapari, R., Lansing, M. G., Rahim, A. a. A., & Azhar, Z. I. (2023). Improving Tuberculosis Medication Adherence: The Potential of Integrating Digital Technology and Health Belief Model. Tuberculosis & Respiratory Diseases , 86 (2), 82–93. https://doi.org/10.4046/trd.2022.0148 Shibabaw, A., Gelaw, B., Gebreyes, W., Robinson, R., Wang, S., & Tessema, B. (2020). The burden of pre-extensively and extensively drug-resistant tuberculosis among MDR-TB patients in the Amhara region, Ethiopia. PLoS ONE , 15 (2), e0229040. https://doi.org/10.1371/journal.pone.0229040 Tirore, L. L., Ersido, T., Handiso, T. B., & Areba, A. S. (2024). Non-adherence to anti- tuberculosis treatment and associated factors among TB patients in public health facilities of Hossana town, Southern Ethiopia, 2022. Frontiers in Medicine , 11 . https://doi.org/10.3389/fmed.2024.1360351 Torres, N. M. C., Rodríguez, J. J. Q., Andrade, P. S. P., Arriaga, M. B., & Netto, E. M. (2019). Factors predictive of the success of tuberculosis treatment: A systematic review with meta-analysis. PLoS ONE , 14 (12), e0226507. https://doi.org/10.1371/journal.pone.0226507 Truzyan, N., Grigoryan, Z., Musheghyan, L., Crape, B., & Petrosyan, V. (2019). Quality of inpatient tuberculosis Health Care in High-Burden Resource-Limited Settings: Protocol for a comprehensive Mixed Methods Assessment study. JMIR Research Protocols , 9 (1), e13903. https://doi.org/10.2196/13903 Valencia, S., León, M., Losada, I., Sequera, V. G., Quevedo, M. F., & García-Basteiro, A. L. (2016). How do we measure adherence to anti-tuberculosis treatment? Expert Review of Anti-infective Therapy , 15 (2), 157–165. https://doi.org/10.1080/14787210.2017.1264270 World Health Organization. (2019). Global tuberculosis report . https://www.who.int/teams/global-tuberculosis-programme/tb-reports/global-tuberculosis-report-2022 World Health Organization. (2023). Tuberculosis profile: Nigeria. Global Tuberculosis Report Country, regional and global profiles, 2022. https://worldhealthorg.shinyapps.io/tb_profiles/?_inputs_&lan=%22EN%22&entity_type=%22country%22&iso2=%22NG%22 Wu, Y., Huang, M., Wang, X., Li, Y., Jiang, L., & Yuan, Y. (2020). The prevention and control of tuberculosis: an analysis based on a tuberculosis dynamic model derived from the cases of Americans. BMC Public Health , 20 (1). https://doi.org/10.1186/s12889-020- 09260-wWurie, F. B., Cooper, V., Horne, R., & Hayward, A. C. (2018). Determinants of non-adherence to treatment for tuberculosis in high-income and middle-income settings: a systematic review protocol. BMJ Open , 8 (1), e019287. https://doi.org/10.1136/bmjopen-2017 Wurie, F. B., Cooper, V., Horne, R., & Hayward, A. C. (2018). Determinants of non-adherence to treatment for tuberculosis in high-income and middle-income settings: a systematic review protocol. BMJ Open , 8 (1), e019287. https://doi.org/10.1136/bmjopen-2017- 01 Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {\"props\":{\"pageProps\":{\"initialData\":{\"identity\":\"rs-6566218\",\"acceptedTermsAndConditions\":true,\"allowDirectSubmit\":true,\"archivedVersions\":[],\"articleType\":\"Research Article\",\"associatedPublications\":[],\"authors\":[{\"id\":463303148,\"identity\":\"fce0fcfe-47a4-4a29-a92d-63a55bd54b26\",\"order_by\":0,\"name\":\"Esther Anzaku\",\"email\":\"data:image/png;base64,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\",\"orcid\":\"\",\"institution\":\"Babcock University\",\"correspondingAuthor\":true,\"prefix\":\"\",\"firstName\":\"Esther\",\"middleName\":\"\",\"lastName\":\"Anzaku\",\"suffix\":\"\"},{\"id\":463303149,\"identity\":\"40478fd3-1dac-41f4-9202-c6d465e37ed8\",\"order_by\":1,\"name\":\"Saheed Lawal\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Babcock University\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Saheed\",\"middleName\":\"\",\"lastName\":\"Lawal\",\"suffix\":\"\"}],\"badges\":[],\"createdAt\":\"2025-04-30 15:08:26\",\"currentVersionCode\":1,\"declarations\":\"\",\"doi\":\"10.21203/rs.3.rs-6566218/v1\",\"doiUrl\":\"https://doi.org/10.21203/rs.3.rs-6566218/v1\",\"draftVersion\":[],\"editorialEvents\":[],\"editorialNote\":\"\",\"failedWorkflow\":false,\"files\":[{\"id\":89584079,\"identity\":\"8b7a994f-0520-451e-ac85-f6fd6884e3f7\",\"added_by\":\"auto\",\"created_at\":\"2025-08-21 14:39:03\",\"extension\":\"pdf\",\"order_by\":0,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"manuscript-pdf\",\"size\":921623,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"manuscript.pdf\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-6566218/v1/8614d5da-2659-4fc1-a5af-b5a7830b903d.pdf\"}],\"financialInterests\":\"No competing interests reported.\",\"formattedTitle\":\"\\u003cp\\u003eDeterminants of Tuberculosis Treatment Adherence Among Patients Receiving Tuberculosis Treatment at Federal University Teaching Hospital, Lafia, Nasarawa State \\u003c/p\\u003e\",\"fulltext\":[{\"header\":\"BACKGROUND\",\"content\":\"\\u003cp\\u003eTuberculosis (TB) is a virulent disease caused by the bacterium \\u003cem\\u003eMycobacterium tuberculosis\\u003c/em\\u003e, predominantly affecting the lungs but can also impact other parts of the body [\\u003cspan citationid=\\\"CR11\\\" class=\\\"CitationRef\\\"\\u003e11\\u003c/span\\u003e]. It is a major cause of illness and death worldwide, surpassing HIV/AIDS as the most common infectious agent-related cause of mortality prior to the COVID-19 pandemic [\\u003cspan citationid=\\\"CR15\\\" class=\\\"CitationRef\\\"\\u003e15\\u003c/span\\u003e]. Albeit largely curable with affordable and readily available treatment, TB remains a significant public health concern.\\u003c/p\\u003e \\u003cp\\u003eAdherence to TB treatment is crucial for successful outcomes. It is viewed as the extent to which a patient takes medications as prescribed by healthcare service providers [\\u003cspan citationid=\\\"CR27\\\" class=\\\"CitationRef\\\"\\u003e27\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR7\\\" class=\\\"CitationRef\\\"\\u003e7\\u003c/span\\u003e]. However, the long duration of anti-TB treatment poses significant challenges, especially non-adherence to medication [\\u003cspan citationid=\\\"CR22\\\" class=\\\"CitationRef\\\"\\u003e22\\u003c/span\\u003e]. Poor treatment adherence contributes to lower treatment completion rates, higher rates of loss to follow-up, increased relapse, and the development of drug resistance [\\u003cspan citationid=\\\"CR8\\\" class=\\\"CitationRef\\\"\\u003e8\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR31\\\" class=\\\"CitationRef\\\"\\u003e31\\u003c/span\\u003e].\\u003c/p\\u003e \\u003cp\\u003eIn the year 2022, 2.5\\u0026nbsp;million Africans contracted tuberculosis, making up a quarter of all new cases of tuberculosis cases globally [\\u003cspan citationid=\\\"CR29\\\" class=\\\"CitationRef\\\"\\u003e29\\u003c/span\\u003e]. An estimated 424,000 people died from the disease in Africa, with over 33% of TB deaths occurring in the region [\\u003cspan citationid=\\\"CR29\\\" class=\\\"CitationRef\\\"\\u003e29\\u003c/span\\u003e]. Also, multidrug-resistant TB (MDR-TB) remains a public health dilemma and a health security threat, with the World Health Organization (WHO) estimating 62,000 cases in the Africa. A study in South Africa estimated that treatment loss mainly occurred at treatment completion, with only 53% of overall TB cases completing their treatment (Sazali et al., 2023).\\u003c/p\\u003e \\u003cp\\u003eNigeria ranks number one in Africa and sixth globally among the 30 high Tuberculosis burden countries [\\u003cspan citationid=\\\"CR3\\\" class=\\\"CitationRef\\\"\\u003e3\\u003c/span\\u003e]. In 2021, Nigeria held about 4.4% (467,000) of the global burden and 8.0% (125,000) of the global death cases, with a treatment coverage of 44.0%, fatality ratio of 28.0%, and 2.5% new cases of drug resistance [\\u003cspan citationid=\\\"CR29\\\" class=\\\"CitationRef\\\"\\u003e29\\u003c/span\\u003e]. Previous studies have linked the development of Multidrug-resistant TB to treatment non-adherence and loss to follow-up [\\u003cspan citationid=\\\"CR23\\\" class=\\\"CitationRef\\\"\\u003e23\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR28\\\" class=\\\"CitationRef\\\"\\u003e28\\u003c/span\\u003e].\\u003c/p\\u003e \\u003cp\\u003eStudies conducted in different regions have identified various factors contributing to anti-TB medication non-adherence [\\u003cspan citationid=\\\"CR12\\\" class=\\\"CitationRef\\\"\\u003e12\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR16\\\" class=\\\"CitationRef\\\"\\u003e16\\u003c/span\\u003e]. These include patients' forgetfulness, poor knowledge about TB and anti-TB therapy, poor patient-provider relationship, patients' experience of side effects, distance from home to health facility, and long waiting time before access to treatment. In Nigeria, factors such as distance of patients from treatment sites, lack of knowledge of the duration of treatment, low quality of support from treatment supporters, and smoking habits have been found to contribute to non-adherence and increase the likelihood of treatment interruption [\\u003cspan citationid=\\\"CR2\\\" class=\\\"CitationRef\\\"\\u003e2\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR4\\\" class=\\\"CitationRef\\\"\\u003e4\\u003c/span\\u003e]. Identifying and promptly treating individuals affected by TB, while ensuring they have optimal conditions to complete their treatment, has the potential to save millions of lives and eliminate the transmission of TB [\\u003cspan citationid=\\\"CR20\\\" class=\\\"CitationRef\\\"\\u003e20\\u003c/span\\u003e].\\u003c/p\\u003e \\u003cp\\u003eTherefore, this study aimed to investigate the determinants of tuberculosis treatment adherence at Federal University Teaching Hospital (FUTH) Lafia in Nasarawa State, using the PRECEDE model as a framework. This model helped to identify predisposing, reinforcing, and enabling factors influencing TB treatment adherence, providing deeper insight into patient behavior in this locality.\\u003c/p\\u003e\"},{\"header\":\"METHOD\",\"content\":\"\\u003cdiv id=\\\"Sec3\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003eResearch Design\\u003c/h2\\u003e \\u003cp\\u003e This study adopted a descriptive cross-sectional design to assess the determinants of tuberculosis (TB) treatment adherence among patients receiving care at the Federal University Teaching Hospital (FUTH) Lafia, Nasarawa State, Nigeria. The PRECEDE model was used to evaluate predisposing (knowledge), reinforcing (social support), and enabling (quality of care) factors influencing adherence.\\u003c/p\\u003e \\u003c/div\\u003e\\n\\u003ch3\\u003eStudy Population and Setting\\u003c/h3\\u003e\\n\\u003cp\\u003eThe study was conducted at the Directly Observed Therapy (DOT) Clinic of FUTH Lafia, the largest tertiary healthcare facility in Nasarawa State, serving as a referral center for TB diagnosis and treatment. The study population comprised adult male and female patients undergoing tuberculosis treatment at the Directly Observed Therapy (DOT) Clinic of the Federal University Teaching Hospital (FUTH), Lafia. Inclusion criteria were: patients aged 18 years and above, who had been on treatment for at least one month, and who provided informed consent. Critically ill individuals or those unable to participate were excluded.\\u003c/p\\u003e\\n\\u003ch3\\u003eSampling Technique and Sample Size\\u003c/h3\\u003e\\n\\u003cp\\u003eSystematic random sampling technique was employed to select participants from a sampling frame of 216 TB patients registered in the hospital\\u0026rsquo;s TB unit. The sample size was determined using Slovin\\u0026rsquo;s formula,\\u003cdiv id=\\\"Equa\\\" class=\\\"Equation\\\"\\u003e\\u003cdiv format=\\\"TEX\\\" class=\\\"mathdisplay\\\" id=\\\"FileID_Equa\\\" name=\\\"EquationSource\\\"\\u003e\\n$$\\\\:n=\\\\frac{N}{{1+N\\\\left(e\\\\right)}^{2}}$$\\u003c/div\\u003e\\u003c/div\\u003e\\u003c/p\\u003e \\u003cp\\u003eWhere;\\u003c/p\\u003e \\u003cp\\u003en\\u0026thinsp;=\\u0026thinsp;Sample size\\u003c/p\\u003e \\u003cp\\u003eN\\u0026thinsp;=\\u0026thinsp;Total population (216)\\u003c/p\\u003e \\u003cp\\u003ee\\u0026thinsp;=\\u0026thinsp;Margin of error (5%)\\u003c/p\\u003e \\u003cp\\u003eyielding 141 participants. To account for potential non-responses, an additional 10% was added, resulting in a final sample size of 156. The sampling interval (k) was calculated as the ratio of the total population to the sample size, rounded to the nearest whole number. Every eligible patient attending the clinic was enrolled consecutively until the required sample size was achieved.\\u003c/p\\u003e\\n\\u003ch3\\u003eMeasures\\u003c/h3\\u003e\\n\\u003cp\\u003eData was collected using a structured interviewer-administered questionnaire consisting of five sections. Section A captured socio-demographic characteristics. Section B assessed knowledge of tuberculosis (predisposing factor) using both dichotomous and multiple-choice items (max score\\u0026thinsp;=\\u0026thinsp;13). Section C examined social support (reinforcing factor) via six items on a 4-point Likert scale (max score\\u0026thinsp;=\\u0026thinsp;18). Section D evaluated the quality of care (enabling factor) using eight items on a 4-point Likert scale (max score\\u0026thinsp;=\\u0026thinsp;24). Section E measured tuberculosis treatment adherence through four items (max score\\u0026thinsp;=\\u0026thinsp;12). Categorization of responses followed the scoring system established in the dissertation.\\u003c/p\\u003e\\n\\u003ch3\\u003eInstrument Reliability\\u003c/h3\\u003e\\n\\u003cp\\u003eReliability was established through a pilot study involving 10% of the target sample. The Cronbach\\u0026rsquo;s alpha coefficients for each construct were: knowledge (0.711), social support (0.821), quality of care (0.775), and treatment adherence (0.864), indicating good internal consistency. Validity was confirmed through expert review and alignment with the constructs of the PRECEDE model.\\u003c/p\\u003e \\u003cdiv id=\\\"Sec8\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003eData Analysis\\u003c/h2\\u003e \\u003cp\\u003eData was analyzed using SPSS version 27. Descriptive statistics were used to summarize participant characteristics and construct scores. Pearson\\u0026rsquo;s correlation and logistic regression were conducted to test the relationship between the independent variables (knowledge, social support, quality of care) and the dependent variable (treatment adherence). Statistical significance was set at p\\u0026thinsp;\\u0026le;\\u0026thinsp;0.05.\\u003c/p\\u003e \\u003c/div\\u003e\"},{\"header\":\"RESULTS\",\"content\":\"\\u003cdiv id=\\\"Sec10\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003eSocio-Demographic Characteristics of Respondents\\u003c/h2\\u003e \\u003cp\\u003eTable\\u0026nbsp;\\u003cspan refid=\\\"Tab1\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003e presents key socio-demographic characteristics of the study sample (n\\u0026thinsp;=\\u0026thinsp;152), with a nearly balanced gender distribution: 46.1% (70) were male, while 53.9% (82) were female. The participants' ages ranged from 18 to 70 years, with a mean age of 36.16 years (\\u0026plusmn;\\u0026thinsp;12.087). The majority of respondents (32.9%) were between 31 and 40 years old, followed by 26.3% in the 21\\u0026ndash;30 age group, 18.4% in the 41\\u0026ndash;50 category, and smaller proportions in the younger and older brackets. Regarding marital status, nearly half (48.7%) were single, while 41.4% were married. A small percentage were either widowed (5.9%) or divorced (3.9%). In terms of educational attainment, the majority (52.6%) had a secondary education, while 21.1% had tertiary education. Additionally, 20.4% had completed primary education, and 5.9% had no formal education.\\u003c/p\\u003e \\u003cp\\u003eIncome levels varied, with 48.7% earning between 20,000 and 50,000, while 25% had a monthly income of 51,000\\u0026ndash;100,000. About 23% earned less than 20,000, and only 3.3% reported earning more than 100,000. Religious affiliation was predominantly Islamic, with 66.4% of participants identifying as Muslims, while 33.6% were Christians. Ethnic distribution showed that 94.1% of respondents identified as belonging to ethnic groups other than Hausa (3.9%) and Igbo (2.0%), while no respondents identified as Yoruba. Lastly, the majority of respondents (48%) resided in semi-urban areas, followed by 42.1% in rural settings and 9.9% in urban areas.\\u003c/p\\u003e \\u003cp\\u003e \\u003cdiv class=\\\"gridtable\\\"\\u003e\\u003ctable float=\\\"Yes\\\" id=\\\"Tab1\\\" border=\\\"1\\\"\\u003e \\u003ccaption language=\\\"En\\\"\\u003e \\u003cdiv class=\\\"CaptionNumber\\\"\\u003eTable 1\\u003c/div\\u003e \\u003cdiv class=\\\"CaptionContent\\\"\\u003e \\u003cp\\u003eSocio-Demographic Characteristics of Respondents\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/caption\\u003e \\u003ccolgroup cols=\\\"3\\\"\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c1\\\" colnum=\\\"1\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c2\\\" colnum=\\\"2\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c3\\\" colnum=\\\"3\\\"\\u003e\\u003c/div\\u003e \\u003cthead\\u003e \\u003ctr\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eSocio-Demographic characteristics (n\\u0026thinsp;=\\u0026thinsp;152)\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eFrequency (n)\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003ePercentage (%)\\u003c/p\\u003e \\u003c/th\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eGender\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/th\\u003e \\u003c/tr\\u003e \\u003c/thead\\u003e \\u003ctbody\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eMale\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e70\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e46.1\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eFemale\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e82\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e53.9\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eAge\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e18\\u0026ndash;20\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e14\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e9.2\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e21\\u0026ndash;30\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e40\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e26.3\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e31\\u0026ndash;40\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e50\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e32.9\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e41\\u0026ndash;50\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e28\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e18.4\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e51\\u0026ndash;60\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e15\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e9.9\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e61\\u0026ndash;70\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e5\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e3.3\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e = 36.16\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;12.087\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eMarital Status\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eSingle\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e74\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e48.7\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eMarried\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e63\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e41.4\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eDivorced\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e6\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e3.9\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eWidowed\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e9\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e5.9\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eEducation\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eNo formal education\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e9\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e5.9\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003ePrimary\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e31\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e20.4\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eSecondary\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e80\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e52.6\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eTertiary\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e32\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e21.1\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eMonthly Income\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eLess than 20k\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e35\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e23\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e20k-50k\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e74\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e48.7\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e51k- 100k\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e38\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e25\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eMore than 100k\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e5\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e3.3\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eReligion\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eChristianity\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e51\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e33.6\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eIslam\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e101\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e66.4\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eEthnicity\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eHausa\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e6\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e3.9\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eIgbo\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e3\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e2.0\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eYoruba\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e0\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eOthers\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e143\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e94.1\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eSettlement type\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eUrban\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e15\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e9.9\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eSemi-Urban\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e73\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e48\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eRural\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e64\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e42.1\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003c/tbody\\u003e \\u003c/colgroup\\u003e \\u003c/table\\u003e\\u003c/div\\u003e \\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec11\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003eSummary of Descriptive Statistics\\u003c/h2\\u003e \\u003cp\\u003eTable\\u0026nbsp;\\u003cspan refid=\\\"Tab2\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003e summarizes the descriptive statistics, showing that TB knowledge had a mean percentage of 59.69% (7.76\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;2.058), social support was 56.17% (10.11\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;12.206), and TB treatment adherence was 71.5% (8.58\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;1.811). Quality of care from the health facility was the highest at 72.54% (17.41\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;1.734).\\u003c/p\\u003e \\u003cp\\u003e \\u003cdiv class=\\\"gridtable\\\"\\u003e\\u003ctable float=\\\"Yes\\\" id=\\\"Tab2\\\" border=\\\"1\\\"\\u003e \\u003ccaption language=\\\"En\\\"\\u003e \\u003cdiv class=\\\"CaptionNumber\\\"\\u003eTable 2\\u003c/div\\u003e \\u003cdiv class=\\\"CaptionContent\\\"\\u003e \\u003cp\\u003eSummary of Descriptive Statistics\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/caption\\u003e \\u003ccolgroup cols=\\\"4\\\"\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c1\\\" colnum=\\\"1\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c2\\\" colnum=\\\"2\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\" class=\\\"colspec\\\" colname=\\\"c3\\\" colnum=\\\"3\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c4\\\" colnum=\\\"4\\\"\\u003e\\u003c/div\\u003e \\u003cthead\\u003e \\u003ctr\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eVariable\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eReference scale\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eMean\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;SD\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003eLevel of Variable (%)\\u003c/p\\u003e \\u003c/th\\u003e \\u003c/tr\\u003e \\u003c/thead\\u003e \\u003ctbody\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eKnowledge\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e13\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e7.76\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;2.058\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e59.69%\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eSocial Support\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e18\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e10.11\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;2.206\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e56.17%\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eQuality of Care from Health Facility\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e24\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e17.32\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;1.836\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e72.17%\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eTB treatment adherence\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e12\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e8.58\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;1.811\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e71.5%\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003c/tbody\\u003e \\u003c/colgroup\\u003e \\u003c/table\\u003e\\u003c/div\\u003e \\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec12\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003eTest of Hypotheses\\u003c/h2\\u003e \\u003cp\\u003eThree hypotheses were tested for this study. In testing the hypotheses, binary logistics and multiple regression analysis was conducted at 0.05 level of significant. The decision rule applied was that if the p-value computed was less than or equal to the cut off p-value of 0.05, the null hypothesis will be rejected in favour of the alternative hypothesis and vice versa.\\u003c/p\\u003e \\u003cp\\u003eBinary logistic regression analysis (Table\\u0026nbsp;\\u003cspan refid=\\\"Tab3\\\" class=\\\"InternalRef\\\"\\u003e3\\u003c/span\\u003e) showed that the predisposing factor (knowledge), was not significantly associated with the outcome. At both bivariate and multivariate levels, knowledge did not show a significant relationship with treatment adherence (COR\\u0026thinsp;=\\u0026thinsp;1.087, 95% CI\\u0026thinsp;=\\u0026thinsp;0.88\\u0026ndash;1.328, p\\u0026thinsp;=\\u0026thinsp;0.416; AOR\\u0026thinsp;=\\u0026thinsp;1.067, 95% CI\\u0026thinsp;=\\u0026thinsp;0.804\\u0026ndash;1.417, p\\u0026thinsp;=\\u0026thinsp;0.653). This indicated that changes in knowledge levels did not significantly influence the odds of adhering to treatment among the respondents. The odds of adherence were 6.7% ((1.067\\u0026thinsp;\\u0026minus;\\u0026thinsp;1) \\u0026times;100) higher for every unit increase in knowledge at the multivariate level, but this was not statistically significant. Therefore, the researcher failed to reject the null hypothesis.\\u003c/p\\u003e \\u003cp\\u003eAgain, binary logistic regression analysis (Table\\u0026nbsp;\\u003cspan refid=\\\"Tab3\\\" class=\\\"InternalRef\\\"\\u003e3\\u003c/span\\u003e) indicated that the reinforcing factor, (social support), was not significantly associated with the outcome at both bivariate and multivariate levels. At the bivariate level, social support showed a borderline association (COR\\u0026thinsp;=\\u0026thinsp;1.212, 95% CI\\u0026thinsp;=\\u0026thinsp;0.966\\u0026ndash;1.473, p\\u0026thinsp;=\\u0026thinsp;0.054), but this was not sustained at the multivariate level (AOR\\u0026thinsp;=\\u0026thinsp;1.346, 95% CI\\u0026thinsp;=\\u0026thinsp;0.990\\u0026ndash;1.830, p\\u0026thinsp;=\\u0026thinsp;0.058). Thus, social support did not significantly increase the odds of engaging in tb treatment adherence among the respondents. The odds of adherence were exactly 34.6% ((1.346\\u0026thinsp;\\u0026minus;\\u0026thinsp;1) \\u0026times;100%) higher for every unit increase in social support at the multivariate level, but this was not statistically significant. Therefore, the researcher failed to reject the null hypothesis.\\u003c/p\\u003e \\u003cp\\u003eBinary logistic regression analysis (Table\\u0026nbsp;\\u003cspan refid=\\\"Tab3\\\" class=\\\"InternalRef\\\"\\u003e3\\u003c/span\\u003e) revealed that the enabling factor (quality of care from the health facility), was significantly associated with the outcome. At the bivariate level, quality of care was positively associated with adherence (COR\\u0026thinsp;=\\u0026thinsp;1.914, 95% CI\\u0026thinsp;=\\u0026thinsp;1.402\\u0026ndash;2.612, p\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.001). This association remained significant at the multivariate level (AOR\\u0026thinsp;=\\u0026thinsp;2.386, 95% CI\\u0026thinsp;=\\u0026thinsp;1.564\\u0026ndash;3.640, p\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.001). This means that for every unit increase in the quality of care from the health facility, the odds of adhering to tb medication increased by exactly 138.6%; (2.386\\u0026thinsp;\\u0026minus;\\u0026thinsp;1) \\u0026times;100% Therefore, as the quality of care increased by a unit, the odds of adhering to treatment were significantly higher. Hence, the null hypothesis was rejected.\\u003c/p\\u003e \\u003cp\\u003e \\u003cdiv class=\\\"gridtable\\\"\\u003e\\u003ctable float=\\\"Yes\\\" id=\\\"Tab3\\\" border=\\\"1\\\"\\u003e \\u003ccaption language=\\\"En\\\"\\u003e \\u003cdiv class=\\\"CaptionNumber\\\"\\u003eTable 3\\u003c/div\\u003e \\u003cdiv class=\\\"CaptionContent\\\"\\u003e \\u003cp\\u003eBivariate and Multivariate Logistic Regression Analysis of Determinants of TB Treatment Adherence\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/caption\\u003e \\u003ccolgroup cols=\\\"5\\\"\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c1\\\" colnum=\\\"1\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c2\\\" colnum=\\\"2\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c3\\\" colnum=\\\"3\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c4\\\" colnum=\\\"4\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c5\\\" colnum=\\\"5\\\"\\u003e\\u003c/div\\u003e \\u003cthead\\u003e \\u003ctr\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eVariable\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eNo of respondent\\u0026thinsp;=\\u0026thinsp;152\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/th\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u0026nbsp;\\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eCOR (95% CI)\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eP value\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003eAOR (95% CI)\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003eP value\\u003c/p\\u003e \\u003c/th\\u003e \\u003c/tr\\u003e \\u003c/thead\\u003e \\u003ctbody\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eKnowledge\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e1.087 (0.88\\u0026ndash;1.328)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0.416\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e1.067 (0.804\\u0026ndash;1.417)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0.653\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eSocial Support\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e1.212 (0.966\\u0026ndash;1.473)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0.054\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e1.346 (0.990\\u0026ndash;1.830)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0.058\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eQuality of care\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e1.914 (1.402\\u0026ndash;2.612)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e2.386 (1.564\\u0026ndash;3.640)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003c/tbody\\u003e \\u003c/colgroup\\u003e \\u003c/table\\u003e\\u003c/div\\u003e \\u003c/p\\u003e \\u003c/div\\u003e\"},{\"header\":\"DISCUSSION\",\"content\":\"\\u003cdiv id=\\\"Sec14\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003eSocio-demographic characteristics of the respondents\\u003c/h2\\u003e \\u003cp\\u003eThe study revealed a slightly higher proportion of female TB patients (53.9%) compared to males (46.1%), which aligns with findings by [\\u003cspan citationid=\\\"CR13\\\" class=\\\"CitationRef\\\"\\u003e13\\u003c/span\\u003e] in the same health facility, where female TB patients constituted 52.6% and males 47.4%. However, this contrasts with other studies in Nigeria. For instance, [\\u003cspan citationid=\\\"CR1\\\" class=\\\"CitationRef\\\"\\u003e1\\u003c/span\\u003e] reported a significantly higher proportion of male TB patients (71.8%) compared to females (28.2%) in Kaduna State, and [\\u003cspan citationid=\\\"CR3\\\" class=\\\"CitationRef\\\"\\u003e3\\u003c/span\\u003e], observed a predominance of males (55%) over females (45%) among ambulatory drug-sensitive TB patients in southwest Nigeria. Additionally, national data from the World Health Organization [\\u003cspan citationid=\\\"CR29\\\" class=\\\"CitationRef\\\"\\u003e29\\u003c/span\\u003e] consistently highlights a greater TB burden among males in Nigeria, often attributed to occupational exposure and healthcare-seeking behavior differences. These variations suggest that gender distribution among TB patients may differ based on geographic location, study population, or study period.\\u003c/p\\u003e \\u003cp\\u003eThe educational attainment of the respondents shows a notable concentration in secondary education (52.6%), with smaller proportions having primary (20.4%) or tertiary education (21.1%) and a low percentage with no formal education (5.9%). When compared to other Nigerian studies, the north-western sample in [\\u003cspan citationid=\\\"CR14\\\" class=\\\"CitationRef\\\"\\u003e14\\u003c/span\\u003e] had a higher percentage in secondary education but similarly low rates of no formal education, while the south-western sample in [\\u003cspan citationid=\\\"CR3\\\" class=\\\"CitationRef\\\"\\u003e3\\u003c/span\\u003e] presented a contrasting profile with a much larger proportion attaining tertiary education and lower figures in primary and secondary levels. This disparity likely reflects regional differences in access to higher education and socio-economic infrastructure across Nigeria\\u003c/p\\u003e \\u003cp\\u003eThe mean age of TB patients in this study, 36.16\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;12.087 years, with the majority falling within the 31\\u0026ndash;40 age group (32.9%). Similarly, [\\u003cspan citationid=\\\"CR18\\\" class=\\\"CitationRef\\\"\\u003e18\\u003c/span\\u003e] noted that TB prevalence was highest among those aged 30\\u0026ndash;39 years (35.8%), which is consistent with the observation that TB disproportionately affects the economically productive age group in Nigeria. However, [\\u003cspan citationid=\\\"CR31\\\" class=\\\"CitationRef\\\"\\u003e31\\u003c/span\\u003e] also highlighted significant cases among older adults aged\\u0026thinsp;\\u0026ge;\\u0026thinsp;60 years, likely due to factors such as weakened immunity and latent TB reactivation. The predominance of TB among adults aged 31\\u0026ndash;40 years may be attributed to increased exposure through occupational hazards, urban overcrowding, and lifestyle factors such as alcohol use and smoking, which are significant risk factors for TB mortality and disability-adjusted life years (DALYs) in Nigeria [\\u003cspan citationid=\\\"CR18\\\" class=\\\"CitationRef\\\"\\u003e18\\u003c/span\\u003e]. Additionally, this age group represents the workforce's backbone, emphasizing the socio-economic burden of TB as infected individuals lose productivity and income during treatment. Addressing these risk factors through targeted interventions can mitigate the disease's impact on this critical demographic.\\u003c/p\\u003e \\u003cp\\u003eFor ethnicity, the overwhelming representation of \\\"Others\\\" (94.1%) among TB patients in this study suggests a diverse ethnic composition or a specific concentration of ethnic groups in the study area. This contrasts with national ethnic distributions and may indicate regional variations in TB prevalence. For instance, a study in Enugu reported a high proportion of Igbo ethnicity among TB patients [\\u003cspan citationid=\\\"CR17\\\" class=\\\"CitationRef\\\"\\u003e17\\u003c/span\\u003e]. Also, the study found that 66.4% of TB patients were Muslims, while 33.6% were Christians. This distribution likely reflects the religious demographics of Nasarawa State rather than any inherent difference in susceptibility to TB.\\u003c/p\\u003e \\u003cp\\u003eMonthly Income item shows that the majority of the respondents earn between 20k-50k monthly (48.7%), with a smaller proportion earning less than 20k (23%) or more than 100k (3.3%). This income distribution suggests that TB disproportionately affects lower- to middle-income individuals. In Nigeria, TB is recognized as a disease of poverty, affecting low-income populations and often associated with socio-economic challenges [\\u003cspan citationid=\\\"CR17\\\" class=\\\"CitationRef\\\"\\u003e17\\u003c/span\\u003e]. Similarly, the distribution across semi-urban (48%), rural (42.1%), and urban areas (9.9%) highlights the significant burden of TB in non-urban settings. This finding is consistent with broader trends in Nigeria, where TB is more prevalent in rural and semi-urban areas due to limited access to healthcare services and socio-economic challenges [\\u003cspan citationid=\\\"CR19\\\" class=\\\"CitationRef\\\"\\u003e19\\u003c/span\\u003e]\\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec15\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003eKnowledge of Tuberculosis and its Treatment\\u003c/h2\\u003e \\u003cp\\u003eThe findings on TB knowledge in this study align partially with national studies conducted in Nigeria. For instance, [\\u003cspan citationid=\\\"CR17\\\" class=\\\"CitationRef\\\"\\u003e17\\u003c/span\\u003e] found that while over 80% of Nigerians had heard about TB, only 26.5% correctly identified its bacterial cause, highlighting significant gaps in understanding the disease's etiology. Similarly, [\\u003cspan citationid=\\\"CR19\\\" class=\\\"CitationRef\\\"\\u003e19\\u003c/span\\u003e] reported low knowledge scores among urban slum residents, with misconceptions about TB transmission and symptoms being widespread. [\\u003cspan citationid=\\\"CR6\\\" class=\\\"CitationRef\\\"\\u003e6\\u003c/span\\u003e] further emphasized these knowledge gaps, noting that some participants believed TB was caused by heavy work, alcohol use, tobacco smoking, heredity, HIV positivity, or even witchcraft. These misconceptions reflect a broader issue of inadequate health education campaigns and limited dissemination of accurate information about TB in health facilities.\\u003c/p\\u003e \\u003cp\\u003eUnlike [\\u003cspan citationid=\\\"CR6\\\" class=\\\"CitationRef\\\"\\u003e6\\u003c/span\\u003e], who found that individuals with high levels of TB knowledge were four times more likely to adhere to treatment compared to those with poor knowledge, this study did not observe any correlation between TB knowledge and treatment adherence. This discrepancy may be attributed to differences in study populations or methodologies.\\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec16\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003eSocial Support for Tb Treatment\\u003c/h2\\u003e \\u003cp\\u003eSocial support among TB patients in this study demonstrated mixed outcomes. Emotional support was strong; A significant proportion of respondents (92.8%) reported receiving reminders to take their medication, and 96.7% noted encouragement to complete treatment.\\u003c/p\\u003e \\u003cp\\u003eHowever, this contrasts with a study among multidrug-resistant TB (MDR-TB) patients in Zhejiang Province, which reported a low level of social support [\\u003cspan citationid=\\\"CR31\\\" class=\\\"CitationRef\\\"\\u003e31\\u003c/span\\u003e]. The disparity between these findings may stem from differences in socio-economic conditions and cultural norms between the two regions. In Nigeria, family structures often prioritize emotional support, while in other contexts, urbanization or stigma may limit support.\\u003c/p\\u003e \\u003cp\\u003eIn this study, while emotional support was highly reported, practical assistance such as transportation and financial aid was minimal. Only 23.6% of respondents received help with transportation, and 15.8% received financial assistance. This imbalance likely reflects the economic challenges faced by families in Nasarawa state, where providing emotional encouragement is more feasible than offering material support.\\u003c/p\\u003e \\u003cp\\u003eIn spite of the emotional support, this study did not find a statistically significant association between social support and TB treatment adherence. This finding differs from [\\u003cspan citationid=\\\"CR24\\\" class=\\\"CitationRef\\\"\\u003e24\\u003c/span\\u003e] who reported that a lack of social support is a significant obstacle to TB treatment adherence. They noted that participants without family support or those who did not disclose their TB status to their family were more likely to exhibit non-adherence compared to those who disclosed their status. The difference in findings may be due to differences in socio-economic conditions or cultural norms between the study populations.\\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec17\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003eQuality of care from the Health Facility\\u003c/h2\\u003e \\u003cp\\u003eThe evaluation of the quality of care received by TB patients in this study revealed high-quality care for TB patients FUTH Lafia, across multiple domains, including staff friendliness, accessibility, medication availability, and privacy standards. These findings align with studies reporting high-quality TB care in other settings. A systematic review by [\\u003cspan citationid=\\\"CR10\\\" class=\\\"CitationRef\\\"\\u003e10\\u003c/span\\u003e] found that patient satisfaction with TB healthcare services was generally high globally when facilities ensured medication availability, privacy, and respectful treatment by staff.\\u003c/p\\u003e \\u003cp\\u003eHowever patient education was the lowest rated item in this section and it is similar to results reported in a mixed-methods assessment of inpatient TB services in Armenia which identified gaps in communication [\\u003cspan citationid=\\\"CR26\\\" class=\\\"CitationRef\\\"\\u003e26\\u003c/span\\u003e].\\u003c/p\\u003e \\u003cp\\u003eIn contrast, a process evaluation in Mongolia using the Zero TB Indicator Framework also highlighted issues such as long waiting times and inconsistent medication supplies [\\u003cspan citationid=\\\"CR21\\\" class=\\\"CitationRef\\\"\\u003e21\\u003c/span\\u003e]. These disparities may be attributed to differences in healthcare infrastructure, funding, and staff training between regions.\\u003c/p\\u003e \\u003cp\\u003eThere was also a strong positive correlation between the enabling factor in this study and treatment adherence.\\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec18\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003eTuberculosis Treatment Adherence\\u003c/h2\\u003e \\u003cp\\u003eThe TB treatment adherence rate in this study was 71.5%, indicating moderate adherence among patients at the Federal University Teaching Hospital Lafia, Nasarawa State. This rate aligns with findings by [\\u003cspan citationid=\\\"CR6\\\" class=\\\"CitationRef\\\"\\u003e6\\u003c/span\\u003e], who reported a similar adherence rate of 73.5% among TB patients in a comparable setting. Furthermore, [\\u003cspan citationid=\\\"CR1\\\" class=\\\"CitationRef\\\"\\u003e1\\u003c/span\\u003e] observed that adherence was higher among patients receiving treatment through hospital-based models compared to community-based models, underscoring the importance of structured healthcare environments in enhancing adherence.\\u003c/p\\u003e \\u003cp\\u003eConversely, in Kebbi State, [\\u003cspan citationid=\\\"CR9\\\" class=\\\"CitationRef\\\"\\u003e9\\u003c/span\\u003e] documented a significantly higher treatment success rate of 91.7% in public hospitals, which they attributed to strong adherence under the Directly Observed Treatment Short-Course (DOTS) framework. This contrasts with the current study's findings and highlights the effectiveness of DOTS in improving adherence rates.\\u003c/p\\u003e \\u003cp\\u003eAlthough the TB treatment adherence rate in this study falls short of the WHO-recommended treatment success rate of at least 90%, it represents a notable 10% improvement over the 61.5% success rate reported by [\\u003cspan citationid=\\\"CR5\\\" class=\\\"CitationRef\\\"\\u003e5\\u003c/span\\u003e]in the same health facility.\\u003c/p\\u003e \\u003c/div\\u003e\"},{\"header\":\"CONCLUSION\",\"content\":\"\\u003cp\\u003eIn conclusion, this study found that TB treatment adherence among patients at FUTH Lafia was significantly influenced by the quality of care from the health facility, while knowledge and social support showed no significant relationship. Patients reported high satisfaction with healthcare services but gaps in practical assistance from their support networks. Misconceptions about TB transmission and symptoms persisted despite moderate overall knowledge levels. While the 71.5% adherence rate remains below WHO's 90% target, it marks an improvement over prior results. The findings align with the PRECEDE model, showing the importance of the enabling factor in adherence behavior. These insights contribute to understanding the complex dynamics shaping TB treatment outcomes in high-burden settings.\\u003c/p\\u003e \\u003cp\\u003eBased on the findings, the study recommends enhancing patient education through locally relevant methods, as TB knowledge levels were low and patient education scored poorly in quality-of-care assessments. Health workers should use simple stories, visual aids, and brief discussions during medication pick-ups to reinforce key messages. Training in effective communication and counseling is also essential.\\u003c/p\\u003e \\u003cp\\u003eTo address financial barriers, a hospital-based fund supported by corporate partnerships could help cover transport costs. A \\u0026ldquo;Food for Adherence\\u0026rdquo; program, offering nutritional support tied to treatment milestones, may further encourage adherence.\\u003c/p\\u003e \\u003cp\\u003eFuture research should adopt longitudinal mixed-methods designs to explore adherence throughout the full treatment course. Given the near-significant link between social support and adherence, further studies should examine the types and impact of support and ways to strengthen them.\\u003c/p\\u003e\"},{\"header\":\"Declarations\",\"content\":\"\\u003cul\\u003e\\n \\u003cli\\u003eAvailability of data and materials\\u003c/li\\u003e\\n\\u003c/ul\\u003e\\n\\u003cp\\u003eThe datasets analyzed for this study are available from the corresponding author upon reasonable request.\\u003c/p\\u003e\\n\\u003cul\\u003e\\n \\u003cli\\u003eFunding\\u003c/li\\u003e\\n\\u003c/ul\\u003e\\n\\u003cp\\u003eThe research was funded by the researchers.\\u003c/p\\u003e\\n\\u003cul\\u003e\\n \\u003cli\\u003e\\u003cstrong\\u003eCorresponding Authors email\\u003c/strong\\u003e\\u003c/li\\u003e\\n\\u003c/ul\\u003e\\n\\u003cp\\u003eaea.anzaku@gmail.com, anzaku0358@pg.babcock.edu.ng\\u003c/p\\u003e\\n\\u003cul\\u003e\\n \\u003cli\\u003e\\u003cstrong\\u003eClinical Trial Number: Not Applicable\\u003c/strong\\u003e\\u003c/li\\u003e\\n \\u003cli\\u003e\\u003cstrong\\u003eEthical consideration\\u0026nbsp;\\u003c/strong\\u003e\\u003c/li\\u003e\\n\\u003c/ul\\u003e\\n\\u003cp\\u003eEthical approval was obtained from Babcock Health Research Ethics Committee, as well as the Ministry of Health Nasarawa State and FUTH Lafia Ethics Committee. The respondents\\u0026nbsp;were\\u0026nbsp;given\\u0026nbsp;a\\u0026nbsp;written\\u0026nbsp;consent\\u0026nbsp;form,\\u0026nbsp;and\\u0026nbsp;assurance\\u0026nbsp;was given\\u0026nbsp;that\\u0026nbsp;the\\u0026nbsp;study\\u0026nbsp;is\\u0026nbsp;for educational purposes only. Information and explanation were given, of the confidentiality and anonymity of their information and identities respectively.\\u003c/p\\u003e\\n\\u003cul\\u003e\\n \\u003cli\\u003eConsent to Publish declaration: not applicable\\u003c/li\\u003e\\n\\u003c/ul\\u003e\\n\"},{\"header\":\"References\",\"content\":\"\\u003col\\u003e\\n\\u003cli\\u003eAdagba, K., Aliyu, A., Ejembi, C., Olorukooba, A., \\u0026amp; Joshua, I. (2023). 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How do we measure adherence to anti-tuberculosis treatment? \\u003cem\\u003eExpert Review of Anti-infective Therapy\\u003c/em\\u003e, \\u003cem\\u003e15\\u003c/em\\u003e(2), 157\\u0026ndash;165. https://doi.org/10.1080/14787210.2017.1264270\\u003c/li\\u003e\\n\\u003cli\\u003eWorld Health Organization. (2019). \\u003cem\\u003eGlobal tuberculosis report\\u003c/em\\u003e. https://www.who.int/teams/global-tuberculosis-programme/tb-reports/global-tuberculosis-report-2022\\u003c/li\\u003e\\n\\u003cli\\u003eWorld Health Organization. (2023). \\u003cem\\u003eTuberculosis profile: Nigeria. Global Tuberculosis Report Country, regional and global profiles, 2022. \\u003c/em\\u003ehttps://worldhealthorg.shinyapps.io/tb_profiles/?_inputs_\\u0026amp;lan=%22EN%22\\u0026amp;entity_type=%22country%22\\u0026amp;iso2=%22NG%22\\u003c/li\\u003e\\n\\u003cli\\u003eWu, Y., Huang, M., Wang, X., Li, Y., Jiang, L., \\u0026amp; Yuan, Y. (2020). The prevention and control of tuberculosis: an analysis based on a tuberculosis dynamic model derived from the cases of Americans. \\u003cem\\u003eBMC Public Health\\u003c/em\\u003e, \\u003cem\\u003e20\\u003c/em\\u003e(1). https://doi.org/10.1186/s12889-020- 09260-wWurie, F. B., Cooper, V., Horne, R., \\u0026amp; Hayward, A. C. (2018). Determinants of non-adherence to treatment for tuberculosis in high-income and middle-income settings: a systematic review protocol. \\u003cem\\u003eBMJ Open\\u003c/em\\u003e, \\u003cem\\u003e8\\u003c/em\\u003e(1), e019287. https://doi.org/10.1136/bmjopen-2017\\u003c/li\\u003e\\n\\u003cli\\u003eWurie, F. B., Cooper, V., Horne, R., \\u0026amp; Hayward, A. C. (2018). Determinants of non-adherence to treatment for tuberculosis in high-income and middle-income settings: a systematic review protocol. \\u003cem\\u003eBMJ Open\\u003c/em\\u003e, \\u003cem\\u003e8\\u003c/em\\u003e(1), e019287. https://doi.org/10.1136/bmjopen-2017- 01\\u003c/li\\u003e\\n\\u003c/ol\\u003e\"}],\"fulltextSource\":\"\",\"fullText\":\"\",\"funders\":[],\"hasAdminPriorityOnWorkflow\":false,\"hasManuscriptDocX\":true,\"hasOptedInToPreprint\":true,\"hasPassedJournalQc\":\"\",\"hasAnyPriority\":false,\"hideJournal\":true,\"highlight\":\"\",\"institution\":\"\",\"isAcceptedByJournal\":false,\"isAuthorSuppliedPdf\":false,\"isDeskRejected\":\"\",\"isHiddenFromSearch\":false,\"isInQc\":false,\"isInWorkflow\":false,\"isPdf\":false,\"isPdfUpToDate\":true,\"isWithdrawnOrRetracted\":false,\"journal\":{\"display\":true,\"email\":\"info@researchsquare.com\",\"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\":\"Enabling factor, PRECEDE model, Predisposing factor, Reinforcing factor, Tuberculosis treatment adherence\",\"lastPublishedDoi\":\"10.21203/rs.3.rs-6566218/v1\",\"lastPublishedDoiUrl\":\"https://doi.org/10.21203/rs.3.rs-6566218/v1\",\"license\":{\"name\":\"CC BY 4.0\",\"url\":\"https://creativecommons.org/licenses/by/4.0/\"},\"manuscriptAbstract\":\"\\u003ch2\\u003eBackground\\u003c/h2\\u003e \\u003cp\\u003eTuberculosis (TB) remains a public health challenge in Nigeria, ranking first in Africa and sixth globally. In Nasarawa State, rising cases and suboptimal treatment outcomes highlight adherence gaps, with over 7,000 cases reported in recent years. Poor adherence contributes to treatment failure, drug-resistance, and transmission. This study investigated determinants of TB treatment adherence among patients at Federal University Teaching Hospital (FUTH) Lafia, using the PRECEDE model to assess predisposing, reinforcing, and enabling factors.\\u003c/p\\u003e\\u003ch2\\u003eMethods\\u003c/h2\\u003e \\u003cp\\u003eA descriptive cross-sectional design was used, surveying 156 TB patients via systematic random sampling from a population of 216. Data were collected using a structured questionnaire covering demographics, knowledge, social support, quality of care, and adherence. Reliability was confirmed with a Cronbach\\u0026rsquo;s alpha of 0.793. A 97.44% response rate (152/156) was achieved. Data was analyzed using SPSS version 27.0, with descriptive statistics, Pearson\\u0026rsquo;s correlation, and logistic regression (significance at p\\u0026thinsp;\\u0026le;\\u0026thinsp;0.05). Ethical approvals were obtained from Babcock University and FUTH Lafia.\\u003c/p\\u003e\\u003ch2\\u003eResults\\u003c/h2\\u003e \\u003cp\\u003eFindings revealed moderate TB knowledge (mean\\u0026thinsp;=\\u0026thinsp;7.76\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;2.058). Emotional support was strong (92.8% received medication reminders), but practical support was weak (23.6%). Quality of care scored highest (mean\\u0026thinsp;=\\u0026thinsp;17.32\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;1.836). Treatment adherence was 71.5% (mean\\u0026thinsp;=\\u0026thinsp;8.58\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;1.811), below WHO\\u0026rsquo;s 90% target. Logistic regression showed quality of care significantly predicted adherence (AOR\\u0026thinsp;=\\u0026thinsp;2.386, 95% CI\\u0026thinsp;=\\u0026thinsp;1.564\\u0026ndash;3.640, p\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.001); knowledge and social support were non-significant.\\u003c/p\\u003e\\u003ch2\\u003eConclusion\\u003c/h2\\u003e \\u003cp\\u003eThe study concluded that quality of care is the strongest determinant of adherence, recommending tailored education, practical support, and further research.\\u003c/p\\u003e\",\"manuscriptTitle\":\"Determinants of Tuberculosis Treatment Adherence Among Patients Receiving Tuberculosis Treatment at Federal University Teaching Hospital, Lafia, Nasarawa State\",\"msid\":\"\",\"msnumber\":\"\",\"nonDraftVersions\":[{\"code\":1,\"date\":\"2025-05-29 13:13:23\",\"doi\":\"10.21203/rs.3.rs-6566218/v1\",\"editorialEvents\":[{\"type\":\"communityComments\",\"content\":0}],\"status\":\"published\",\"journal\":{\"display\":true,\"email\":\"info@researchsquare.com\",\"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\":\"b4cdcdd4-0f25-4d9d-93d8-63d6c6da20c5\",\"owner\":[],\"postedDate\":\"May 29th, 2025\",\"published\":true,\"recentEditorialEvents\":[],\"rejectedJournal\":[],\"revision\":\"\",\"amendment\":\"\",\"status\":\"posted\",\"subjectAreas\":[],\"tags\":[],\"updatedAt\":\"2025-08-21T14:38:42+00:00\",\"versionOfRecord\":[],\"versionCreatedAt\":\"2025-05-29 13:13:23\",\"video\":\"\",\"vorDoi\":\"\",\"vorDoiUrl\":\"\",\"workflowStages\":[]},\"version\":\"v1\",\"identity\":\"rs-6566218\",\"journalConfig\":\"researchsquare\"},\"__N_SSP\":true},\"page\":\"/article/[identity]/[[...version]]\",\"query\":{\"redirect\":\"/article/rs-6566218\",\"identity\":\"rs-6566218\",\"version\":[\"v1\"]},\"buildId\":\"8U1c8b4HqxoKbykW_rLl7\",\"isFallback\":false,\"isExperimentalCompile\":false,\"dynamicIds\":[84888],\"gssp\":true,\"scriptLoader\":[]}","source_license":"CC-BY-4.0","license_restricted":false}