Epidemiology, Drug Resistance Patterns, Treatment Outcomes, and Predictors of Death among Mycobacterium tuberculosis patients in Northern Malawi

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Chisale, Richard Moyo, Paul Uchizi Kaseka, Frank Watson Sinyiza, and 6 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8818367/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 10 You are reading this latest preprint version Abstract Background The World Health Organisation (WHO) recognises Tuberculosis (TB) as one of the most lethal infectious diseases worldwide. TB is a disease associated with poverty, disproportionately impacting the poorest, most vulnerable, and marginalised populations across various regions. This study was conducted to thoroughly examine the current trends, patterns of drug resistance, treatment outcomes, and predictors of mortality related to TB in Malawi. Methods This was a retrospective cross-sectional study conducted in Malawi's northern region. All laboratory-, radiology-, and clinically confirmed TB patients were enrolled in treatment across all five districts, including Karonga, Rumphi, Nkhatabay, and Chitipa. The study employed a census sampling method. Records with missing key variables, such as diagnosis, gender, age, and outcome, were excluded. Data were collected using the KOBOToolbox application and analysed with SPSS, STATA, and R. Results A total of 3,439 tuberculosis (TB) cases were analysed, revealing that over half of the patients (50.4%) were HIV positive, while 29.2% had an unknown HIV status. The RHZE regimen was the most prescribed treatment, accounting for 98.37% (3,378 of 3,434) of cases. The trend in TB cases in Northern Malawi from 2019 to 2023 demonstrates a fluctuating pattern with notable peaks and declines. Most patients (3,354 cases; 98.36%) exhibited no drug resistance. Mzuzu Central Hospital accounted for the highest proportion of TB-related mortality and loss to follow-up (LTFU), at 33.94% and 71.21%, respectively. The findings suggest that mortality was significantly associated with various sociodemographic, clinical, and facility-level factors. Conclusions This study offers comprehensive evidence regarding the epidemiology, treatment outcomes, drug resistance patterns, and predictors of mortality among patients with TB in Northern Malawi. Overall, the findings demonstrate a relatively high success rate in treatment but also indicate a persistently significant mortality burden, with fatalities strongly influenced by sociodemographic, clinical, and health system factors. These results emphasise the ongoing vulnerability of population subgroups and underscore structural challenges within Malawi’s tuberculosis care delivery system. Tuberculosis TB Mycobacterium Predictors Factors Trend Malawi Figures Figure 1 1.0 Background The World Health Organisation (WHO) recognises Tuberculosis (TB) as one of the most lethal infectious diseases globally, with 1.6 million casualties in 2021 and 10.3 million individuals contracting this preventable and curable illness( 1 ). The bacterium “Mycobacterium tuberculosis” causes this disease ( 2 ). Although a declining trend in tuberculosis (TB) incidence, prevalence, and mortality has been observed over the past decade, the eradication of the disease at the global level remains unattainable, and substantial resource investment is still required( 3 ). Tuberculosis (TB) is a disease associated with poverty that disproportionately impacts the poorest, most vulnerable, and marginalised population groups worldwide. Enhancing access to diagnosis and treatment, which are fundamental components in the fight against TB, presents significant challenges for these populations ( 3 , 4 ). Globally, an estimated 10.6 million individuals (95% UI: 9.9–11.4 million) contracted tuberculosis in 2022, representing an increase from estimates of 10.3 million in 2021 and 10.0 million in 2020. This resurgence exceeds the pre-COVID figure of 7.1 million in 2019, representing a 16% increase over 2021 and a 28% rise over 2020. Additionally, it constitutes the highest annual total recorded since the World Health Organisation commenced global tuberculosis surveillance in the mid-1990s ( 5 ). The WHO 2023 report indicates that trends in the Asian region remain the most affected, followed by those in the African region. Global and national surveys, along with routine data, indicate that tuberculosis (TB) can impact individuals across all age groups and demographics ( 4 – 6 ). Nevertheless, the greatest burden is observed among adult males (aged ≥ 15 years), with a global estimate of 5.8 million cases (95% UI: 5.4–6.2 million) in 2022, constituting approximately 55% of the total estimated cases ( 5 ). Furthermore, a high burden of tuberculosis has been associated with individuals living with HIV, various occupations, geographical locations, socioeconomic factors, and numerous other determinants ( 5 , 4 , 7 , 8 , 6 , 9 , 2 , 3 , 10 ). The severity of national tuberculosis epidemics varies significantly across countries, ranging from fewer than 10 to more than 500 cases ( 5 ). This trend has fluctuated due to various factors, including the effectiveness of interventions being implemented. According to the WHO's 2023 Global TB Report, Malawi is classified among countries with a moderately high incidence, with a rate of 125 cases per 100,000 inhabitants annually. A significant factor contributing to the sustained high prevalence is the issue of TB drug resistance. The ongoing and emerging rise in the incidence of drug-resistant TB constitutes a major challenge for effective tuberculosis control ( 11 ). Resistance to rifampicin, the most effective first-line medication, is a major concern ( 5 ). TB that is resistant to rifampicin and isoniazid is defined as multidrug-resistant TB (MDR-TB). As efforts persist in combating tuberculosis (TB) infections, there exists a limited body of evidence concerning the epidemiology, pattern, and associated factors linked to poor treatment outcomes, as well as predictors of mortality. Additionally, existing studies predominantly focus on prevalence and common determinants contributing to disease incidence, while also attempting to identify predictors of unsuccessful treatment outcomes by consolidating death, treatment failure, and loss to follow-up into a single category ( 12 ). However, this could potentially obscure the true predictors of mortality and treatment failure. In the Malawian context, no investigation has been conducted to analyse five-year trends in the epidemiology, drug resistance patterns, treatment outcomes, and determinants of mortality associated with Mycobacterium tuberculosis in Northern Malawi. Consequently, this signifies a meaningful gap in the existing evidence. Furthermore, only one study has been carried out within Malawi to determine the prevalence and risk factors for death among TB patient cases( 13 ). Regrettably, this research was carried out among paediatric patients receiving treatment for tuberculosis at a solitary institution in Malawi, which substantially limits its capacity to provide insights applicable to the broader context of addressing suboptimal TB outcomes in Malawi( 14 ). This study aimed to comprehensively establish current trends, drug resistance patterns, treatment outcomes, and predictors of death due to Mycobacterium tuberculosis in Northern Malawi. The findings from this study will best inform stakeholders and policies to address TB mortality, thereby contributing to the goal of ending tuberculosis by 2035. 2.0 Methodology 2.1 Study design and settings This was a retrospective cross-sectional investigation conducted in the northern region of Malawi. All laboratory, radiology, and clinically confirmed tuberculosis (TB) patients enrolled in treatment across five districts, namely Mzimba, Karonga, Rumphi, Nkhatabay, and Chitipa, between January 2019 and December 2024, were incorporated into the study. These districts serve patients across northern Malawi, including Mzuzu City, with data sourced from Mzuzu Central Hospital. The study focused on patients managed at district hospitals and at Mzuzu Central Hospital. 2.2 Population and sample size The study focused on patients who were clinically or laboratory-confirmed and diagnosed with TB, DR, or MDR TB. A census sampling method was employed, encompassing all accessible records. Records with missing essential variables, such as diagnosis (laboratory-confirmed, radiological, or clinical), gender, age, and outcome, were excluded from the analysis. 2.3 Study period and Data collection procedures Data collection was carried out in January 2025. We utilised well-trained data collectors employing the KOBOToolbox application on tablets to gather high-quality data. Our data collection checklist included demographic characteristics, other available epidemiological information, clinical data (such as presenting signs and symptoms), diagnoses (laboratory, clinical, radiological), drug prescriptions, and clinical outcomes. Additionally, data on drug susceptibility testing, conducted either phenotypically or genotypically, were documented. Results of drug susceptibility testing (DST) for both first-line and second-line drugs in cases suspected of multidrug resistance (MDR) and extensively drug-resistant (XDR) strains were also recorded. 2.4 Identification and Antimicrobial Susceptibility Testing All drug-resistant tuberculosis (DR TB) samples within the region are consistently collected under the supervision of qualified professionals at the TB control centre. Typically, two samples are obtained. The initial sample is divided equally into two portions: one designated for the Xpert MTB/RIF assay (Cepheid, USA) and smear microscopy, and the other allocated for culture, employing Mycobacteria Growth Indicator Tube (MGIT) liquid culture medium and Lowenstein–Jensen culture medium, as well as drug susceptibility testing (DST). Sputum samples from patients exhibiting positive Xpert MTB/RIF and Ziehl–Neelsen stain results are transmitted to the regional TB culture reference laboratory or the National TB reference laboratory for subsequent culture and drug susceptibility testing (DST) analysis. DST against rifampicin (RIF), ethambutol (EMB), isoniazid (INH), capreomycin (CM), streptomycin (SM), ofloxacin (OFX), amikacin (AM), kanamycin (KM), and ethionamide is conducted using the agar proportion method in Middlebrook 7H10 medium. The tested concentrations include rifampicin (1 µg/mL), EMB (5 µg/mL), KM (5 µg/mL), INH (0.2 µg/mL), OFX (2 µg/mL), SM (2 µg/mL), ethionamide (5 µg/mL), CM (4 µg/mL), and AMK (4 µg/mL). Conversely, DST for pyrazinamide (PZA) is performed utilising BACTEC MGIT 7H12 radiometric medium (Becton, Dickinson, New Jersey, USA) in accordance with the manufacturer’s instructions. Additionally, DST is administered to all DR TB patients at registration and repeated as needed. Typically, sputum smear and culture tests are conducted during scheduled clinical visits. 2.5 Treatment Protocol Patients identified as resistant, either through Xpert MTB/RIF diagnostic testing or DST culture assessments, are classified as DR TB cases and managed in accordance with World Health Organisation (WHO) guidelines and the Malawi National Tuberculosis and Leprosy Elimination Programme (NTLEP) protocols. Prior to the commencement of treatment, baseline laboratory diagnostic procedures, such as haematology and biochemistry analyses, are conducted to evaluate complete blood count, hepatitis and HIV status, blood glucose levels, and renal and hepatic function. Adherence to treatment is generally maintained through the coordinated efforts of pharmacists, physicians, treatment coordinators, and trained supporters. Patients are duly informed of each scheduled follow-up appointment, and complimentary laboratory tests and medications are provided at each visit in public health facilities. 2.6 Variables, Outcomes, and Definitions Treatment outcomes for study participants were categorised according to World Health Organisation (WHO) definitions: cured, treatment completed, treatment failure, death, and lost to follow-up. These outcomes reflect standard international criteria, in which cured and treatment-completed cases are combined as indicators of successful treatment, while failure, death, and loss to follow-up are considered poor outcomes ( 15 ). The selection of explanatory variables was guided by existing epidemiological and clinical literature concerning risk factors for tuberculosis mortality. Demographic attributes such as age and gender, alongside clinical factors including HIV co-infection, prior TB treatment history, and drug resistance status, have been consistently linked to mortality among TB patients across various settings.( 16 ) 2.7 Model Specification The logistic model was specified as follows: $$\:{\varvec{Y}}_{\varvec{i}}=\sum\:_{\varvec{i}}{\varvec{a}}_{0}+{\varvec{\beta\:}}_{\varvec{i}\:}\:{\varvec{\chi\:}}_{\varvec{i}}+{\varvec{\epsilon\:}}_{\varvec{i}}\dots\:\dots\:\dots\:\dots\:\dots\:\dots\:\dots\:\dots\:\dots\:\dots\:\dots\:\dots\:\dots\:\dots\:\dots\:\dots\:\dots\:..................\left(1\right)$$ Whereby \(\:{Y}_{i}\) represent a binary variable “Death” (i.e, 1 if individual i died, 0 otherwise). The right-hand side of the equation consists of all the independent variables and their respective parameters. Specifically, \(\:{a}_{0}\) is the constant term, \(\:{\chi\:}_{i}\) is a vector of specific variables (e.g age, sex and determinants of death) and \(\:{\epsilon\:}_{i}\) is the error term. The equation was now defined as: \(\:{Y}_{i}={a}_{0}+\:{Age}_{i}+{sex}_{i}+{\text{R}\text{e}\text{s}\text{i}\text{d}\text{e}\text{n}\text{t}\text{i}\text{a}\text{l}\_\text{s}\text{t}\text{a}\text{t}\text{u}\text{s}\:}_{i}+{\text{S}\text{i}\text{t}\text{e}\:\text{o}\text{f}\:\text{T}\text{B}\:\text{i}\text{n}\text{f}\text{e}\text{c}\text{t}\text{i}\text{o}\text{n}}_{i}+{HIV\:Status}_{i}+\dots\:\:\) + error term ………………………………………………………………………………( 2 ) 2.8 Estimation and Inference The model was estimated using maximum likelihood estimation (MLE), the standard method for fitting logistic regression models, which provides consistent and efficient parameter estimates under correct model specification( 17 , 18 ). To account for potential heteroskedasticity and correlation of observations within health facilities, robust standard errors clustered at the health-facility level were used, thereby addressing intra-facility dependence in patient outcomes ( 19 , 20 ). In this study, the findings are showed as odds ratios (ORs) with 95% confidence intervals, consistent with established best practices for reporting effect estimates from binary outcome models in epidemiological research( 21 ). Furthermore, average marginal effects were calculated to help interpret the results in terms of absolute changes in mortality risk, which are often more comprehensible for policy formulation and clinical decision-making than odds ratios alone ( 22 ). 2.9 TB Trends Most trend analyses employ time-series data; however, this study used cross-sectional data and observed that individuals were diagnosed with tuberculosis (TB) in different years and initiated treatment at different healthcare facilities. The investigation considered the year of diagnosis for each patient and analysed the frequency distributions across all districts in Northern Malawi for that particular year, including the relevant hospitals such as Chitipa District Health Office, Karonga District Health Office, Mzimba District Health Office, Mzuzu Central Hospital, Nkhatabay District Health Office, and Rumphi DHO. 2.10 Data management and analysis The collected data were subsequently cleaned and exported into various formats, including Excel, SPSS, STATA, and R. The analysis included descriptive statistics, such as measures of central tendency and dispersion, with results presented in tables to illustrate the data distribution and facilitate further analysis. Crosstabs were conducted to explore associations among categorical variables, and the findings were presented in tables. Significant factors identified were further subjected to regression analysis and other relevant statistical procedures tests. 2.11 Research Ethics Considerations The study was reviewed and approved by MZUNIREC (Ref#: MZUNIREC/DOR/24/210). Since it was a retrospective study, there was no need for participant consent. 3.0 Results 3.1 Descriptive statistics A total of 3,439 tuberculosis (TB) cases were analysed from northern Malawi. The findings indicate that more than half of the patients (50.4%) were HIV positive, while 20.4% were HIV negative, and 29.2% had an unknown HIV status (see Table 1 ). Concerning the history of previous TB treatment, most patients (85.4%) were newly diagnosed cases, followed by relapse cases (11.7%) and cases of treatment failure (0.9%). Table 1 Descriptive Statistics Variable Obs Percentages (%) HIV Status HIV +ve 3439 50.4% HIV -ve 3439 20.4% Unknown 3439 29.2% History of prev ~ t Failure 3421 0.9% New case 3421 85.4% Other 3421 1% Previously treated 3421 1% Relapse 3421 11.7% Return after lost up 3421 1.9% Site of TB infection Extra-pulmonary 3428 42.1% Pulmonary 3428 57.9% Residence Peri Urban 3318 12.8% Rural 3318 61.2% Urban 3318 25.9% Admissions 3423 1.64% District hospital Chitipa 3429 14.4% Chitipa DHO 3429 1% Karonga 3429 3.1% Karonga DHO 3429 18.8% Mzimba 3429 1% Mzimba DHO 3429 14.6% Mzuzu Central Hospital 3429 28.5% Nkhatabay DHO 3429 10% Rumphi DHO 3429 10.6% Method of Diagnosis Missing/other 80 2.33 GeneXP 931 27.07 TB-Culture 10 0.29 Clinical 868 25.24 Medical Imagery 192 5.58 Microscopy 444 12.91 LM-LAM 914 26.58 Gender Female 3433 37.9% Male 3433 62.1% Age (mean and SD) 3436 38.6, 17.5 Regarding the site of infection, 57.9% of patients had pulmonary TB, while 42.1% had extrapulmonary TB. Most patients (61.2%) resided in rural areas, and Mzuzu Central Hospital (28.5%) had the largest proportion of participants, while Nkhatabay DHO (10%) had the least. The distribution of diagnostic methods for tuberculosis cases shows considerable variability in the approaches used across the sample. GeneXpert was the most used diagnostic tool, accounting for 27.07% of all diagnoses, followed by the Lipoarabinomannan assay (LM-LAM) at 26.58% (see Table 1 ). Clinical diagnosis accounted for 25.24%, and TB culture was the least used method at 0.29%. Most patients were male (62.1%). 3.2 Prescribed Medication Table 2 Tabulation of Prescribed Treatment Regimen Prescribed Treatment Regimen Freq. Percent Cum. (H)REZ + Lfx 10 0.29 0.29 ITR 1 0.03 0.32 LAOTR-A 26 0.76 1.08 LAOTR-P 3 0.09 1.16 RHZE 3378 98.37 99.53 STR-A 12 0.35 99.88 STR-P 4 0.12 100.00 The results show that the RHZE regimen is the most commonly prescribed treatment, accounting for 98.37% (3,378 of 3,434) of all cases (see Table 2 ). All other treatment regimens are rarely used; collectively, they account for less than 2% of total prescriptions. 3.4 Year of Diagnosis In 2019, most diagnoses were documented at Karonga District Hospital (33.61%) and Mzimba District Hospital (25.21%), with Mzuzu Central Hospital contributing minimally (0.42%) (refer to Table 3 ). This pattern changed significantly in 2020 and 2022, when Mzuzu Central Hospital accounted for the highest proportions of diagnoses at 43.05% and 41.94%, respectively. These shifts suggest intervals of increased referral activity or enhancements in diagnostic capacity at the central facility. Conversely, in 2021, Mzuzu Central Hospital recorded nearly no diagnoses (0.44%), whereas facilities such as Karonga, Mzimba, and Nkhatabay managed most diagnostic procedures. The distribution changed again in 2023, with Mzuzu Central Hospital diagnosing approximately one-third of all cases (33.17%) (see Table 3 ). Table 3 Year of Diagnosis Year of Diagnosis Name of facility Chitipa DHO Karonga DHO Mzimba DHO Mzuzu Central Hospital Nkhatabay DHO Rumphi DHO 2019 74 (15.6) 160(33.6) 120(25.2) 2(0.42) 37(7.8) 83(17.4) 2020 78(9.6) 139(17.1) 88(10.8) 350(43.1) 75(9.2) 83(10.2) 2021 89(19.3) 109(24.2) 96(21.3) 2(0.4) 82(18.2) 73(16.2) 2022 104(12.7) 127(15.6) 96(11.8) 341(41.9) 70(8.6) 75(9.2) 2023 147(17.7) 181(21.8) 100(12.1) 275(33.2) 78(9.4) 48(5.8) Pearson Chi2 = 805.30 Prob = 0.0000 3.5 Epidemiological Trends for TB Cases in Northern Malawi The trend in tuberculosis (TB) cases in Northern Malawi from 2019 to 2023 exhibits fluctuations characterised by significant peaks and declines. In 2019, TB cases accounted for 13.94%, indicating a moderate level. This was followed by a considerable increase in 2020 to 23.9%, while in 2021 the percentage declined sharply to 13.24%, nearly halving the previous year's figure (see Fig. 1 ). The upward trend resumed in 2022, with cases rising again to 23.81%, peaking at 24.4% in 2023. 3.6 Drug Resistance in Northern Malawi Table 4 Drugs Reported Resistance Drugs Reported Resistance Freq. Percent Isoniazid 14 0.41 Isoniazid Rifampicin 1 0.03 None 3354 98.36 Rifampicin 41 1.20 The distribution of TB cases by specific drug resistance shows that most patients (3,354 cases; 98.36%) had no resistance. Rifampicin resistance was observed in 41 cases (1.20%), making it the most common form of single-drug resistance (see Table 4 ). Isoniazid resistance alone was reported in 13 cases (0.38%), while resistance to both isoniazid and rifampicin, representing multidrug-resistant TB (MDR-TB), was rare, with only 1 case (0.03%). 3.7 The Treatment Outcome of TB cases in northern Malawi. The treatment outcomes show that most TB patients successfully completed therapy: 65.12%. An additional 23.53% were documented as cured, bringing the overall success rate to nearly 89%. However, some unfavourable outcomes were also observed. About 8.04% of patients died during treatment, which is a significant proportion. Table 5 Tabulation of Treatment outcome Treatment outcome Freq. Percent Completed 2220 65.12 Cured 802 23.53 Died 274 8.04 Lost to follow up 67 1.97 Not evaluated 7 0.21 Others 10 0.29 Treatment failure 29 0.85 A smaller proportion of patients were lost to follow-up (1.97%), suggesting challenges with treatment adherence or patient tracking (see Table 5 ). Treatment failure was recorded in 0.85% of cases, indicating persistent disease despite treatment, which may point to drug resistance or inadequate treatment regimens. Only a very small proportion of patients were not evaluated (0.21%) or classified as other (0.29%), suggesting that documentation and program monitoring were largely consistent. 3.8 Treatment outcome by District Hospital Facility The distribution of treatment outcomes across health facilities in Northern Malawi shows substantial variation, reflecting differences in patient load, case severity, and each facility's capacity to manage and follow up TB patients. Overall, Mzuzu Central Hospital accounts for the largest share of treatment outcomes across almost all categories. It accounted for 30.52% of all patients who completed treatment and 18.65% of those who were cured, underscoring its central role as the region's main referral hospital (see Table 6 ). Karonga DHO also contributed significantly to successful outcomes, accounting for 19.95% of completed treatments and the highest proportion of cured patients at 28.16%. In contrast, facilities like Chitipa and Rumphi reported lower contributions to treatment completion, suggesting lower caseloads or challenges with patient retention and treatment support. Table 6 Treatment outcome by District Hospital Facility Treatment outcome Name of facility Chitipa DHO Karonga DHO Mzimba DHO Mzuzu Central Hospital Nkhatabay DHO Rumphi DHO Completed 293 (13.2) 442(20.0) 379 (17.1) 676(30.5) 226(10.20) 198(8.9) Cured 148 (18.5) 225(28.2) 102(12.8) 149(18.7) 64(8.0) 111(13.9) Died 23(8.4) 56(20.4) 16(5.8) 93(33.9) 42(15.3) 44(16.1) Lost to follow up 3(4.6) 9(13.6) 0(0.0) 47(71.2) 4(6.1) 3(4.6) Not evaluated 1(14.3) 1(14.3) 0(0.0) 0(0.0) 0(0.0) 5(71.4) Others 0(0.0) 7(70.0) 0(0.0) 2(20.0) 1(10.0) 0(0.0) Treatment failure 3(10.3) 8(27.6) 2(6.9) 9(31.0) 5(17.2) 2(6.9) Pearson Chi2 = 261.90 Prob = 0.0000 The distribution of deaths reveals important facility-level differences. Mzuzu Central Hospital accounted for the highest proportion of TB-related deaths at 33.94%, and Mzimba and Chitipa reported comparatively fewer deaths, at 5.84% and 8.39%, respectively. Loss to follow-up (LTFU) was highly concentrated at Mzuzu Central Hospital, which accounted for 71.21% of all LTFU cases, while some facilities, such as Mzimba, recorded no LTFU cases. Treatment failure was most common in Mzuzu Central Hospital (31.03%) and Karonga DHO (27.59%). 3.9 Predictors of Death among Mycobacterium tuberculosis patients in Northern Malawi Table 7 presents the results of the multivariable logistic regression analysis identifying predictors of death among tuberculosis (TB) patients in Northern Malawi. The findings indicate that mortality was significantly associated with a range of sociodemographic, clinical, and facility-level factors. Table 7 Predictors of Death among Mycobacterium tuberculosis patients in Northern Malawi Death Odds Coef. St.Err. t-value p-value Sig Dy/dx Gender .909 .105 -0.82 .411 -0.006 Residence status ~U 1 . . . Rural 1.791 .455 2.30 .022 ** 0.033 Urban 1.678 .37 2.35 .019 ** 0.029 Occupation/Risk Gr ~ s 1 . . . Child 1.451 .545 0.99 .321 0.028 Farmer .903 .108 -0.85 .394 -0.006 Health worker 1.785 .85 1.22 .223 0.047 1 . . . Other civil servants 1.431 .705 0.73 .468 0.027 Others 1.159 .199 0.86 .39 Prisoner .666 .766 -0.35 .724 -0.023 Student 1.596 .334 2.23 .026 ** 0.037 Teacher 3.642 1.225 3.84 0 *** 0.136 Unemployed .675 .699 -0.38 .704 -0.022 Site of TB infecti ~ r 1 . . . Pulmonary .697 .119 -2.11 .035 ** -0.025 Comorbidities : ~V 1 . . . Unknown vs hiv .705 .124 -1.99 .046 ** -0.022 2019b 1 . . . 2020 .459 .197 -1.81 .07 * -0.040 2021 1.104 .73 0.15 .882 0.007 2022 1.437 1.19 0.44 .662 0.029 2023 1.53 1.462 0.45 .656 0.034 2025 3.11 4.036 0.87 .382 0.116 Name of facility ~a 1 . . . Karonga DHO 1.506 .378 1.63 .103 0.022 Mzimba DHO .613 .064 -4.69 0 *** -0.018 Mzuzu Central Hosp ~ l 2.76 .402 6.97 0 *** 0.072 Nkhatabay DHO 2.399 .274 7.65 0 *** 0.059 Rumphi DHO 2.255 .244 7.50 0 *** 0.053 Drugs Reported Res ~ e 1 . . . None .332 .218 -1.68 .093 * 0.03 5o 1 . . . Age 1.025 .006 4.23 0 *** 0.002 Dateforinitialtrea ~ d .999 .001 -1.22 .222 History of previou~: 1 . . . New case 1.536 1.444 0.46 .648 0.43 Relapse 1.873 1.671 0.70 .482 0.37 Constant 28524057 4.893e + 08 1.00 .317 Mean dependent var 0.077 Pseudo r-squared 0.081 Chi-square . Akaike crit. (AIC) 1251.406 Residential location emerged as a significant predictor of mortality. Compared to peri-urban residents, individuals residing in rural areas exhibited considerably higher odds of mortality (OR = 1.79; 95% CI: 1.09–2.95; p = 0.022), as did those living in urban areas (OR = 1.68; 95% CI: 1.09–2.59; p = 0.019) (see Table 7 ). Occupational status was also notably associated with mortality. Students demonstrated a 1.60-fold increase in the likelihood of death relative to the reference occupational group (OR = 1.60; 95% CI: 1.06–2.41; p = 0.026), whereas teachers faced a substantially higher risk, with more than three times the odds of death (OR = 3.64; 95% CI: 1.88–7.04; p < 0.001) (see Table 7 ). Clinical characteristics further influenced mortality risk. Patients diagnosed with pulmonary TB had significantly lower odds of death compared to those with extrapulmonary TB (OR = 0.70; 95% CI: 0.50–0.97; p = 0.035). In relation to HIV status, patients with unknown HIV status had lower odds of death compared to HIV-positive patients (OR = 0.71; 95% CI: 0.50–0.99; p = 0.046). Facility-level differences were pronounced. Treatment at Mzimba District Health Office (DHO) was associated with reduced odds of mortality (OR = 0.61; 95% CI: 0.50–0.75; p < 0.001). In contrast, patients treated at Mzuzu Central Hospital, Nkhatabay DHO, and Rumphi DHO experienced substantially higher odds of death, with increases of 2.76-fold (OR = 2.76; 95% CI: 2.07–3.67; p < 0.001), 2.40-fold (OR = 2.40; 95% CI: 1.92–3.00; p < 0.001), and 2.26-fold (OR = 2.26; 95% CI: 1.82–2.79; p < 0.001), respectively(see Table 7 ).. Age was also a significant determinant of mortality. Each additional year of age was associated with a 2.5% increase in the odds of death (OR = 1.03; 95% CI: 1.01–1.04; p < 0.001), underscoring the heightened vulnerability of older TB patients. 4.0 Discussion This study provides comprehensive insights into the epidemiology, treatment outcomes, drug resistance patterns, and predictors of mortality among Mycobacterium tuberculosis patients in Northern Malawi. Overall, the findings indicate a relatively high treatment success rate, yet a persistent and significant mortality burden, with deaths predominantly influenced by sociodemographic, clinical, and health system-level factors. These results emphasise the ongoing vulnerability of population subgroups and underscore structural challenges within Malawi’s tuberculosis care delivery system. The study determined that approximately 8% of tuberculosis patients succumbed during treatment, a statistic that remains troubling despite the overall high treatment success rate of nearly 89%. Mortality rates were notably elevated among older individuals, residents of both rural and urban areas (as compared to peri-urban residents), particular occupational groups, specifically students and teachers, as well as patients with extrapulmonary tuberculosis, HIV-positive individuals, and those receiving care at certain district and referral clinics. Age emerged as a strong predictor of death, with each additional year increasing the odds of mortality by 2.5%. This finding reflects the cumulative effects of immunosenescence, higher prevalence of comorbidities, delayed health-seeking behaviour, and poorer treatment tolerance among older adults. Similar age-related gradients in TB mortality have been consistently reported across sub-Saharan Africa( 23 , 24 ) Residential location was also strongly associated with mortality. Patients residing in rural areas had significantly higher odds of death, likely reflecting structural barriers such as long travel distances to health facilities, delayed diagnosis, limited access to diagnostic tools, and poorer continuity of care. Interestingly, urban residents also faced elevated mortality risk compared with peri-urban populations. In the Malawian context, this may reflect the concentration of severe and complicated cases in urban referral centres, particularly Mzuzu Central Hospital, rather than a true protective effect of peri-urban residence. Occupational disparities in mortality were markedly significant. Educators demonstrated over three times higher odds of death, whereas students also encountered increased risk. Although these findings may seem counterintuitive, they likely mirror deferred health-seeking behaviour attributable to professional or academic obligations, under-recognition of symptoms, or delays in referral processes within these populations. From a clinical perspective, patients diagnosed with pulmonary tuberculosis exhibited significantly reduced odds of mortality in comparison to those with extrapulmonary tuberculosis. This observation aligns with the understanding that pulmonary tuberculosis is typically easier to diagnose, particularly with sputum-based tests such as GeneXpert, whereas extrapulmonary tuberculosis frequently presents with nonspecific symptoms and requires more complex diagnostic procedures, resulting in delays in treatment initiation ( 25 , 26 ). Delayed diagnosis is particularly detrimental in high HIV-burden settings such as Malawi. HIV status also played a critical role. HIV-positive patients exhibited significantly higher mortality rates compared to those with unknown HIV status. This observation aligns with well-established evidence indicating that HIV co-infection exacerbates tuberculosis severity, complicates treatment protocols, and elevates the risk of adverse outcomes, particularly in contexts where antiretroviral therapy (ART) initiation is delayed or where integration of TB–HIV services is suboptimal. ( 27 , 28 ). One of the most notable findings of this study is the considerable variability in TB mortality rates across different facilities. Patients receiving treatment at Mzuzu Central Hospital, Nkhatabay DHO, and Rumphi DHO faced markedly higher odds of mortality, whereas treatment at Mzimba DHO was correlated with a protective effect. These differences probably mirror variations in case severity, referral patterns, diagnostic capacity, and health system readiness. As the primary referral hospital in the region, Mzuzu Central Hospital receives a disproportionate number of critically ill patients, including those with advanced disease, drug resistance, or HIV co-infection. This referral bias may partially account for the higher observed mortality rates. Nevertheless, the significant concentration of deaths and loss to follow-up at the referral facility also indicates systemic challenges related to patient tracking. Overcrowding and continuity of care are critical factors. In contrast, the reduced mortality rate observed at Mzimba DHO may be attributable to more robust community-based follow-up, improved patient-provider continuity, or lower caseload pressure. This suggests that decentralised TB care models could provide survival benefits when sufficiently and appropriately resourced. Encouragingly, the prevalence of drug resistance in Northern Malawi was low, with rifampicin resistance observed in only 1.2% of cases and multidrug-resistant tuberculosis being infrequent. These findings are consistent with prior national surveys indicating comparatively low levels of drug resistance in Malawi relative to other countries with high burdens ( 17 ). The predominance of the standard RHZE regimen further exemplifies adherence to national and World Health Organisation (WHO) treatment guidelines. Nonetheless, even minimal levels of drug resistance can significantly influence mortality rates when diagnostic delays and treatment disruptions occur. Continuous surveillance and the expansion of drug susceptibility testing are imperative to prevent future escalation. Overall, the study's findings are in strong agreement with regional and global evidence on tuberculosis (TB) mortality. Previous research conducted in Africa has consistently identified older age, HIV co-infection, extrapulmonary TB, and health system factors as principal contributors to mortality among TB patients ( 13 , 14 , 23 ). However, in contrast to numerous studies that aggregate adverse outcomes into a single category, this research distinctly isolates death as a separate endpoint, thereby offering more explicit insights into factors specifically associated with mortality. Importantly, this study expands the scholarly literature by providing sub-national evidence from Northern Malawi, a region that has historically been underrepresented in tuberculosis outcomes research. Consequently, these findings address a significant gap in the existing evidence base and offer locally pertinent insights for targeted interventions intervention. The findings possess several significant implications for tuberculosis (TB) policy and programming in Malawi. There is an urgent need for targeted interventions directed at older TB patients, which should include early screening, integrated management of comorbidities, and more rigorous clinical monitoring throughout the treatment process. Furthermore, strategies to enhance early diagnosis and treatment of extrapulmonary TB, such as expanding access to imaging, biopsy services, and advanced diagnostic tools, should be implemented and prioritised. Nevertheless, the pronounced disparities across facility levels underscore the necessity to enhance referral systems, optimise patient tracking, and minimise loss to follow-up in high-burden facilities, notably Mzuzu Central Hospital. Enhancing decentralised TB care and community-based follow-up may help mitigate mortality risks. Simultaneously, the ongoing integration of TB and HIV services remains indispensable. Guaranteeing timely HIV testing, swift initiation of antiretroviral therapy, and coordinated TB–HIV management could significantly diminish mortality rates among co-infected patients. Finally, sustained investment in routine surveillance, data quality improvement, and operational research is essential to monitor trends, identify emerging risks, and inform evidence-based decision-making as Malawi works towards the End TB targets for 2035. This study had some limitations. As such, the study's findings should be interpreted with the limitations of a cross-sectional design in mind, including the difficulty of making causal inferences. In addition, the study was conducted in a specific geographic area (i.e., the Northern Region of Malawi), which limits the generalisation of the findings to other regions with different socioeconomic contexts. Abbreviations MTB Mycobacterium tuberculosis TB Tuberculosis LFTU Lost to Follow Up MDR Multidrug-resistant TB RR Rifampicin-resistant TB XDR Extensive drug resistance Declarations Ethics approval and consent to participate in the study The study was reviewed and approved by MZUNIREC (Ref#: MZUNIREC/DOR/24/210). Since it was a retrospective study, there was no need for participant consent. Consent for publication Not applicable Funding The study was funded by Pingtung Christian Hospital, Taiwan, through Luke International Norway (LIN), Malawi, under Grant Numbers PS-IR-111001 and PS-IR-112001. The funder played no role in the design of the study, the collection, analysis, and interpretation of data or the writing of the manuscript. Author Contribution MROC conceptualised and designed the study. TJW provided inputs to the manuscript and funding acquisition. PUK, CSC, MRC, BCM, TJW and KJY refined the study design and contributed to the development of the study protocol. FWS, PUK, BKN and CSC supervised data collection. FWS, PUK, MRC, BCM and YSH devised the data analysis plan. RM analysed and interpreted the data. MROC and RM wrote the full manuscript and reviewed it before sharing it with the wider team for reviews and comments. All authors read and approved the final manuscript. Acknowledgement The authors would like to express their gratitude to Pingtung Christian Hospital, Taiwan, through Luke International Norway. We would also like to thank the hospital's directors and staff, as well as the TB coordinators, for their unwavering support during data collection. Mr Brany Mithi and Dr Yusuf Saidi Availability of data and materials Data can be accessed through the corresponding author (Master R.O. Chisale) References WHO. World Health Organisation (WHO). 2024. WHO DG Flagship Initiative on ending TB. Available from: https://www.who.int/initiatives/find-treat-all-endtb Salari N, Kanjoori AH, Hosseinian-Far A, Hasheminezhad R, Mansouri K, Mohammadi M. Global prevalence of drug-resistant tuberculosis: a systematic review and meta-analysis. Infect Dis Poverty. 2023;12(1):1–12. Sulis G, Roggi A, Matteelli A, Raviglione MC. Tuberculosis: Epidemiology and control. Mediterr J Hematol Infect Dis. 2014;6(1). Thit SS, Aung NM, Htet ZW, Boyd MA, Saw HA, Anstey NM et al. The clinical utility of the urine-based lateral flow lipoarabinomannan assay in HIV-infected adults in Myanmar: An observational study. BMC Med. 2017;15(1). World Health Organisation. Global Tuberculosis Report 2023 [Internet]. Vol. t/malaria/. 2023. Available from: https://iris.who.int/bitstream/handle/10665/373828/9789240083851-eng.pdf?sequence=1 Lewis DK, Peters RPH, Schijffelen MJ, Joaki GRF, Walsh AL, Kublin JG et al. Clinical indicators of mycobacteraemia in adults admitted to hospital in Blantyre, Malawi. Int J Tuberc Lung Dis. 2002. Gesesew HA, Ward P, Woldemichael K, Mwanri L. Prevalence, trend and risk factors for antiretroviral therapy discontinuation among HIV-infected adults in Ethiopia in 2003–2015. Maga G, editor. PLOS ONE. 2017;12(6):e0179533. Hakim JG, Gangaidzo IT, Heyderman RS, Mushangi E, Taziwa A, Robertson VJ et al. Impact of HIV infection on meningitis in Harare, Zimbabwe : a prospective study of 406 predominantly adult patients. 2000;(March):1401–7. Abouyannis M, Dacombe R, Dambe I, Mpunga J, Faragher B, Gausi F, et al. Drug resistance of Mycobacterium tuberculosis in Malawi: a cross-sectional survey. Bull World Health Organ. 2014;92(11):798–806. Chisale MRO, Sinyiza F, Kaseka P, Wu JST, Chimbatata C, Mbakaya BC et al. Cancer obscures extrapulmonary tuberculosis (EPTB) at a tertiary hospital in Northern Malawi. Epidemiol Infect [Internet]. 2020 [cited 2026 Feb 6];149:e3. Available from: https://pmc.ncbi.nlm.nih.gov/articles/PMC8057459/ Riccardi N, Alagna R, Saderi L, Ferrarese M, Castellotti P, Mazzola E, et al. Towards tailored regimens in the treatment of drug-resistant tuberculosis: A retrospective study in two Italian reference Centres. BMC Infect Dis. 2019;19(1):1–9. Cobbinah AI, Idan JS, Boakye K, Enimil A, Mensah NK, Adangabe E et al. Prevalence and predictors of unsuccessful tuberculosis treatment outcomes among persons with TB/HIV co-infection in Ghana: a 10-year retrospective study. BMC Infect Dis [Internet]. 2025 May 6 [cited 2026 Jan 27];25:669. Available from: https://pmc.ncbi.nlm.nih.gov/articles/PMC12057233/ Teferi MY, El-Khatib Z, Boltena MT, Andualem AT, Asamoah BO, Biru M et al. Tuberculosis Treatment Outcome and Predictors in Africa: A Systematic Review and Meta-Analysis. Int J Environ Res Public Health [Internet]. 2021 Jan [cited 2026 Jan 25];18(20):10678. Available from: https://www.mdpi.com/1660-4601/18/20/10678 Flick RJ, Kim MH, Simon K, Munthali A, Hosseinipour MC, Rosenberg NE, et al. Burden of disease and risk factors for death among children treated for tuberculosis in Malawi. Int J Tuberc Lung Dis. 2016;20(8):1046–54. Statistical analysis plan. In: WHO consolidated guidelines on tuberculosis: Module 4: treatment - drug-resistant tuberculosis treatment, 2022 update [Internet] [Internet]. World Health Organization. 2022 [cited 2026 Jan 15]. Available from: https://www.ncbi.nlm.nih.gov/books/NBK588552/ Alemu A, Bitew ZW, Worku T, Gamtesa DF, Alebel A. Predictors of mortality in patients with drug-resistant tuberculosis: A systematic review and meta-analysis. PLoS ONE. 2021;16(6):e0253848. Abouyannis M, Dacombe R, Dambe I, Mpunga J, Faragher B, Gausi F et al. Drug resistance of Mycobacterium tuberculosis in Malawi: a cross-sectional survey. Bull World Health Organ [Internet]. 2014 Nov 1 [cited 2026 Jan 27];92(11):798–806. Available from: https://pmc.ncbi.nlm.nih.gov/articles/PMC4221759/ Cameron AC, Trivedi PK. Microeconometrics: Methods and Applications. 2005. Cameron AC, Miller DL. A Practitioner’s Guide to Cluster-Robust Inference. J Hum Resour [Internet]. 2015 Mar 31 [cited 2026 Jan 27];50(2):317–72. Available from: https://jhr.uwpress.org/content/50/2/317 White HA, Heteroskedasticity-Consistent. Covariance Matrix Estimator and a Direct Test for Heteroskedasticity. Econometrica [Internet]. 1980 [cited 2026 Jan 27];48(4):817–38. Available from: https://www.jstor.org/stable/1912934 Hosmer DW, Lemeshow S. Applied Logistic Regression [Internet]. 1st ed. Wiley; 2000 [cited 2026 Jan 27]. Available from: https://onlinelibrary.wiley.com/doi/book/10.1002/0471722146 Norton D, Maciejewski. Marginal Effects—Quantifying the Effect of Changes in Risk Factors in Logistic Regression Models | JAMA Guide to Statistics and Methods | JAMAevidence | McGraw Hill Medical [Internet]. 2019 [cited 2026 Jan 27]. Available from: https://jamaevidence.mhmedical.com/content.aspx?bookid=2742§ionid=233568067 Alemu A, Bitew ZW, Worku T, Gamtesa DF, Alebel A. Predictors of mortality in patients with drug-resistant tuberculosis: A systematic review and meta-analysis. PLOS ONE [Internet]. 2021 Jun 28 [cited 2026 Jan 27];16(6):e0253848. Available from: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0253848 Teferi MY, El-Khatib Z, Boltena MT, Andualem AT, Asamoah BO, Biru M et al. Tuberculosis Treatment Outcome and Predictors in Africa: A Systematic Review and Meta-Analysis. Int J Environ Res Public Health [Internet]. 2021 [cited 2026 Jan 27];18(20):10678. Available from: https://www.mdpi.com/1660-4601/18/20/10678 Lewis DK, Peters RP, H, Schijffelen MJ, Joaki GR, F, Walsh AL, Kublin JG, et al. Clinical indicators of mycobacteraemia in adults admitted to hospital in Blantyre, Malawi. Int J Tuberc Lung Dis. 2002;6(12):1067–74. Sulis G, Roggi A, Matteelli A, Raviglione MC. TUBERCULOSIS: EPIDEMIOLOGY AND CONTROL., Mediterr J, Hematol Infect Dis [Internet]. 2014 Oct 27 [cited 2026 Jan 27];6(1):e2014070–e2014070. Available from: https://www.mjhid.org/mjhid/article/view/2014.070 Gesesew HA, Ward P, Woldemichael K, Mwanri L. Prevalence, trend and risk factors for antiretroviral therapy discontinuation among HIV-infected adults in Ethiopia in 2003–2015. PLOS ONE [Internet]. 2017 Jun 16 [cited 2026 Jan 27];12(6):e0179533. Available from: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0179533 Hakim JG, Gangaidzo IT, Heyderman RS, Mielke J, Mushangi E, Taziwa A, et al. Impact of HIV infection on meningitis in Harare, Zimbabwe: a prospective study of 406 predominantly adult patients. AIDS. 2000;14(10):1401–7. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 10 Apr, 2026 Reviews received at journal 22 Mar, 2026 Reviewers agreed at journal 22 Mar, 2026 Reviews received at journal 21 Mar, 2026 Reviewers agreed at journal 13 Mar, 2026 Reviewers invited by journal 05 Mar, 2026 Editor assigned by journal 04 Mar, 2026 Editor invited by journal 16 Feb, 2026 Submission checks completed at journal 13 Feb, 2026 First submitted to journal 13 Feb, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8818367","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":603830973,"identity":"ca4c11ff-e783-4c21-9be4-33b625a401c4","order_by":0,"name":"Master R.O. Chisale","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA9klEQVRIiWNgGAWjYBCDBDYGBsYDDBVAJjNzAyHVjA1QLQwHGM6AtDASqQXEOsDYBhfADXTbjz9/8HGPXR4f/+EHBz7Oq43mbwdq+VGxDacWszM5ho0zniUXs0mkGRycue147ozDjA2MPWdu49ZyIIexmefAgcQ2CQaDw7zbjuU2ALUwM7bh0XL++cPmPyAt/Mc/HP4751jufIJabiQYNjOAtDDkGAAV1+RuIKzljeHMngPJQIflFBzsOXYgdyNQy0G8fjmf/uDDjwN2ifP7j2988KOmLnfe+cMHH/yowK0FHRwGkweIVg8EdaQoHgWjYBSMghECAKT5aIup4wlTAAAAAElFTkSuQmCC","orcid":"","institution":"Mzuzu University","correspondingAuthor":true,"prefix":"","firstName":"Master","middleName":"R.O.","lastName":"Chisale","suffix":""},{"id":603830977,"identity":"968fbc3d-6c6d-4e06-a687-f0011e69339e","order_by":1,"name":"Richard Moyo","email":"","orcid":"","institution":"ASPIRE Consulting Associates 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International","correspondingAuthor":false,"prefix":"","firstName":"Tsung-Shu","middleName":"Joseph","lastName":"Wu","suffix":""},{"id":603831009,"identity":"114808dd-f3b6-4ad5-afda-680ed2f6d272","order_by":8,"name":"Kwong-Leung Joseph Yu","email":"","orcid":"","institution":"Pingtung Christian Hospital","correspondingAuthor":false,"prefix":"","firstName":"Kwong-Leung","middleName":"Joseph","lastName":"Yu","suffix":""},{"id":603831011,"identity":"67031651-6d39-4bd1-a86d-df6f154b4f47","order_by":9,"name":"Balwani Chingatichifwe Mbakaya","email":"","orcid":"","institution":"University of Livingstonia","correspondingAuthor":false,"prefix":"","firstName":"Balwani","middleName":"Chingatichifwe","lastName":"Mbakaya","suffix":""}],"badges":[],"createdAt":"2026-02-07 23:53:14","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8818367/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8818367/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":104471969,"identity":"15bb1616-cfa5-416b-81f0-3712e350c4ab","added_by":"auto","created_at":"2026-03-12 07:28:29","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":187696,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eEpidemiological Trends for TB Cases in Northern Malawi\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"Picture1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8818367/v1/cb6735eddfc496496d061161.jpg"},{"id":104471991,"identity":"eeb433eb-df2c-4296-9cbb-ff4a38125ade","added_by":"auto","created_at":"2026-03-12 07:28:38","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1586211,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8818367/v1/e576696b-4430-452f-b076-e5f4bceb3435.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Epidemiology, Drug Resistance Patterns, Treatment Outcomes, and Predictors of Death among Mycobacterium tuberculosis patients in Northern Malawi","fulltext":[{"header":"1.0 Background","content":"\u003cp\u003eThe World Health Organisation (WHO) recognises Tuberculosis (TB) as one of the most lethal infectious diseases globally, with 1.6\u0026nbsp;million casualties in 2021 and 10.3\u0026nbsp;million individuals contracting this preventable and curable illness(\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e). The bacterium \u0026ldquo;Mycobacterium tuberculosis\u0026rdquo; causes this disease (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e). Although a declining trend in tuberculosis (TB) incidence, prevalence, and mortality has been observed over the past decade, the eradication of the disease at the global level remains unattainable, and substantial resource investment is still required(\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eTuberculosis (TB) is a disease associated with poverty that disproportionately impacts the poorest, most vulnerable, and marginalised population groups worldwide. Enhancing access to diagnosis and treatment, which are fundamental components in the fight against TB, presents significant challenges for these populations (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e). Globally, an estimated 10.6\u0026nbsp;million individuals (95% UI: 9.9\u0026ndash;11.4\u0026nbsp;million) contracted tuberculosis in 2022, representing an increase from estimates of 10.3\u0026nbsp;million in 2021 and 10.0\u0026nbsp;million in 2020. This resurgence exceeds the pre-COVID figure of 7.1\u0026nbsp;million in 2019, representing a 16% increase over 2021 and a 28% rise over 2020. Additionally, it constitutes the highest annual total recorded since the World Health Organisation commenced global tuberculosis surveillance in the mid-1990s (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e). The WHO 2023 report indicates that trends in the Asian region remain the most affected, followed by those in the African region.\u003c/p\u003e \u003cp\u003eGlobal and national surveys, along with routine data, indicate that tuberculosis (TB) can impact individuals across all age groups and demographics (\u003cspan additionalcitationids=\"CR5\" citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e). Nevertheless, the greatest burden is observed among adult males (aged\u0026thinsp;\u0026ge;\u0026thinsp;15 years), with a global estimate of 5.8\u0026nbsp;million cases (95% UI: 5.4\u0026ndash;6.2\u0026nbsp;million) in 2022, constituting approximately 55% of the total estimated cases (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e). Furthermore, a high burden of tuberculosis has been associated with individuals living with HIV, various occupations, geographical locations, socioeconomic factors, and numerous other determinants (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e). The severity of national tuberculosis epidemics varies significantly across countries, ranging from fewer than 10 to more than 500 cases (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e). This trend has fluctuated due to various factors, including the effectiveness of interventions being implemented. According to the WHO's 2023 Global TB Report, Malawi is classified among countries with a moderately high incidence, with a rate of 125 cases per 100,000 inhabitants annually. A significant factor contributing to the sustained high prevalence is the issue of TB drug resistance. The ongoing and emerging rise in the incidence of drug-resistant TB constitutes a major challenge for effective tuberculosis control (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e). Resistance to rifampicin, the most effective first-line medication, is a major concern (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e). TB that is resistant to rifampicin and isoniazid is defined as multidrug-resistant TB (MDR-TB).\u003c/p\u003e \u003cp\u003eAs efforts persist in combating tuberculosis (TB) infections, there exists a limited body of evidence concerning the epidemiology, pattern, and associated factors linked to poor treatment outcomes, as well as predictors of mortality. Additionally, existing studies predominantly focus on prevalence and common determinants contributing to disease incidence, while also attempting to identify predictors of unsuccessful treatment outcomes by consolidating death, treatment failure, and loss to follow-up into a single category (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e). However, this could potentially obscure the true predictors of mortality and treatment failure. In the Malawian context, no investigation has been conducted to analyse five-year trends in the epidemiology, drug resistance patterns, treatment outcomes, and determinants of mortality associated with Mycobacterium tuberculosis in Northern Malawi. Consequently, this signifies a meaningful gap in the existing evidence. Furthermore, only one study has been carried out within Malawi to determine the prevalence and risk factors for death among TB patient cases(\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e). Regrettably, this research was carried out among paediatric patients receiving treatment for tuberculosis at a solitary institution in Malawi, which substantially limits its capacity to provide insights applicable to the broader context of addressing suboptimal TB outcomes in Malawi(\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e). This study aimed to comprehensively establish current trends, drug resistance patterns, treatment outcomes, and predictors of death due to Mycobacterium tuberculosis in Northern Malawi. The findings from this study will best inform stakeholders and policies to address TB mortality, thereby contributing to the goal of ending tuberculosis by 2035.\u003c/p\u003e"},{"header":"2.0 Methodology","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Study design and settings\u003c/h2\u003e \u003cp\u003eThis was a retrospective cross-sectional investigation conducted in the northern region of Malawi. All laboratory, radiology, and clinically confirmed tuberculosis (TB) patients enrolled in treatment across five districts, namely Mzimba, Karonga, Rumphi, Nkhatabay, and Chitipa, between January 2019 and December 2024, were incorporated into the study. These districts serve patients across northern Malawi, including Mzuzu City, with data sourced from Mzuzu Central Hospital. The study focused on patients managed at district hospitals and at Mzuzu Central Hospital.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Population and sample size\u003c/h2\u003e \u003cp\u003eThe study focused on patients who were clinically or laboratory-confirmed and diagnosed with TB, DR, or MDR TB. A census sampling method was employed, encompassing all accessible records. Records with missing essential variables, such as diagnosis (laboratory-confirmed, radiological, or clinical), gender, age, and outcome, were excluded from the analysis.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Study period and Data collection procedures\u003c/h2\u003e \u003cp\u003eData collection was carried out in January 2025. We utilised well-trained data collectors employing the KOBOToolbox application on tablets to gather high-quality data. Our data collection checklist included demographic characteristics, other available epidemiological information, clinical data (such as presenting signs and symptoms), diagnoses (laboratory, clinical, radiological), drug prescriptions, and clinical outcomes. Additionally, data on drug susceptibility testing, conducted either phenotypically or genotypically, were documented. Results of drug susceptibility testing (DST) for both first-line and second-line drugs in cases suspected of multidrug resistance (MDR) and extensively drug-resistant (XDR) strains were also recorded.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Identification and Antimicrobial Susceptibility Testing\u003c/h2\u003e \u003cp\u003eAll drug-resistant tuberculosis (DR TB) samples within the region are consistently collected under the supervision of qualified professionals at the TB control centre. Typically, two samples are obtained. The initial sample is divided equally into two portions: one designated for the Xpert MTB/RIF assay (Cepheid, USA) and smear microscopy, and the other allocated for culture, employing Mycobacteria Growth Indicator Tube (MGIT) liquid culture medium and Lowenstein\u0026ndash;Jensen culture medium, as well as drug susceptibility testing (DST). Sputum samples from patients exhibiting positive Xpert MTB/RIF and Ziehl\u0026ndash;Neelsen stain results are transmitted to the regional TB culture reference laboratory or the National TB reference laboratory for subsequent culture and drug susceptibility testing (DST) analysis. DST against rifampicin (RIF), ethambutol (EMB), isoniazid (INH), capreomycin (CM), streptomycin (SM), ofloxacin (OFX), amikacin (AM), kanamycin (KM), and ethionamide is conducted using the agar proportion method in Middlebrook 7H10 medium. The tested concentrations include rifampicin (1 \u0026micro;g/mL), EMB (5 \u0026micro;g/mL), KM (5 \u0026micro;g/mL), INH (0.2 \u0026micro;g/mL), OFX (2 \u0026micro;g/mL), SM (2 \u0026micro;g/mL), ethionamide (5 \u0026micro;g/mL), CM (4 \u0026micro;g/mL), and AMK (4 \u0026micro;g/mL). Conversely, DST for pyrazinamide (PZA) is performed utilising BACTEC MGIT 7H12 radiometric medium (Becton, Dickinson, New Jersey, USA) in accordance with the manufacturer\u0026rsquo;s instructions. Additionally, DST is administered to all DR TB patients at registration and repeated as needed. Typically, sputum smear and culture tests are conducted during scheduled clinical visits.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5 Treatment Protocol\u003c/h2\u003e \u003cp\u003e Patients identified as resistant, either through Xpert MTB/RIF diagnostic testing or DST culture assessments, are classified as DR TB cases and managed in accordance with World Health Organisation (WHO) guidelines and the Malawi National Tuberculosis and Leprosy Elimination Programme (NTLEP) protocols. Prior to the commencement of treatment, baseline laboratory diagnostic procedures, such as haematology and biochemistry analyses, are conducted to evaluate complete blood count, hepatitis and HIV status, blood glucose levels, and renal and hepatic function. Adherence to treatment is generally maintained through the coordinated efforts of pharmacists, physicians, treatment coordinators, and trained supporters. Patients are duly informed of each scheduled follow-up appointment, and complimentary laboratory tests and medications are provided at each visit in public health facilities.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e2.6 Variables, Outcomes, and Definitions\u003c/h2\u003e \u003cp\u003eTreatment outcomes for study participants were categorised according to World Health Organisation (WHO) definitions: cured, treatment completed, treatment failure, death, and lost to follow-up. These outcomes reflect standard international criteria, in which cured and treatment-completed cases are combined as indicators of successful treatment, while failure, death, and loss to follow-up are considered poor outcomes (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe selection of explanatory variables was guided by existing epidemiological and clinical literature concerning risk factors for tuberculosis mortality. Demographic attributes such as age and gender, alongside clinical factors including HIV co-infection, prior TB treatment history, and drug resistance status, have been consistently linked to mortality among TB patients across various settings.(\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e)\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e2.7 Model Specification\u003c/h2\u003e \u003cp\u003eThe logistic model was specified as follows:\u003cdiv id=\"Equa\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\n$$\\:{\\varvec{Y}}_{\\varvec{i}}=\\sum\\:_{\\varvec{i}}{\\varvec{a}}_{0}+{\\varvec{\\beta\\:}}_{\\varvec{i}\\:}\\:{\\varvec{\\chi\\:}}_{\\varvec{i}}+{\\varvec{\\epsilon\\:}}_{\\varvec{i}}\\dots\\:\\dots\\:\\dots\\:\\dots\\:\\dots\\:\\dots\\:\\dots\\:\\dots\\:\\dots\\:\\dots\\:\\dots\\:\\dots\\:\\dots\\:\\dots\\:\\dots\\:\\dots\\:\\dots\\:..................\\left(1\\right)$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eWhereby \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{Y}_{i}\\)\u003c/span\u003e\u003c/span\u003e represent a binary variable \u0026ldquo;Death\u0026rdquo; (i.e, 1 if individual i died, 0 otherwise). The right-hand side of the equation consists of all the independent variables and their respective parameters. Specifically, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{a}_{0}\\)\u003c/span\u003e\u003c/span\u003e is the constant term, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\chi\\:}_{i}\\)\u003c/span\u003e\u003c/span\u003e is a vector of specific variables (e.g age, sex and determinants of death) and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\epsilon\\:}_{i}\\)\u003c/span\u003e\u003c/span\u003e is the error term. The equation was now defined as:\u003c/p\u003e \u003cp\u003e \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\(\\:{Y}_{i}={a}_{0}+\\:{Age}_{i}+{sex}_{i}+{\\text{R}\\text{e}\\text{s}\\text{i}\\text{d}\\text{e}\\text{n}\\text{t}\\text{i}\\text{a}\\text{l}\\_\\text{s}\\text{t}\\text{a}\\text{t}\\text{u}\\text{s}\\:}_{i}+{\\text{S}\\text{i}\\text{t}\\text{e}\\:\\text{o}\\text{f}\\:\\text{T}\\text{B}\\:\\text{i}\\text{n}\\text{f}\\text{e}\\text{c}\\text{t}\\text{i}\\text{o}\\text{n}}_{i}+{HIV\\:Status}_{i}+\\dots\\:\\:\\)\u003c/span\u003e \u003c/span\u003e+ error term \u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;(\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e)\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e2.8 Estimation and Inference\u003c/h2\u003e \u003cp\u003eThe model was estimated using maximum likelihood estimation (MLE), the standard method for fitting logistic regression models, which provides consistent and efficient parameter estimates under correct model specification(\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e). To account for potential heteroskedasticity and correlation of observations within health facilities, robust standard errors clustered at the health-facility level were used, thereby addressing intra-facility dependence in patient outcomes (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e). In this study, the findings are showed as odds ratios (ORs) with 95% confidence intervals, consistent with established best practices for reporting effect estimates from binary outcome models in epidemiological research(\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e). Furthermore, average marginal effects were calculated to help interpret the results in terms of absolute changes in mortality risk, which are often more comprehensible for policy formulation and clinical decision-making than odds ratios alone (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e2.9 TB Trends\u003c/h2\u003e \u003cp\u003eMost trend analyses employ time-series data; however, this study used cross-sectional data and observed that individuals were diagnosed with tuberculosis (TB) in different years and initiated treatment at different healthcare facilities. The investigation considered the year of diagnosis for each patient and analysed the frequency distributions across all districts in Northern Malawi for that particular year, including the relevant hospitals such as Chitipa District Health Office, Karonga District Health Office, Mzimba District Health Office, Mzuzu Central Hospital, Nkhatabay District Health Office, and Rumphi DHO.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e2.10 Data management and analysis\u003c/h2\u003e \u003cp\u003eThe collected data were subsequently cleaned and exported into various formats, including Excel, SPSS, STATA, and R. The analysis included descriptive statistics, such as measures of central tendency and dispersion, with results presented in tables to illustrate the data distribution and facilitate further analysis. Crosstabs were conducted to explore associations among categorical variables, and the findings were presented in tables. Significant factors identified were further subjected to regression analysis and other relevant statistical procedures tests.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e2.11 Research Ethics Considerations\u003c/h2\u003e \u003cp\u003eThe study was reviewed and approved by MZUNIREC (Ref#: MZUNIREC/DOR/24/210). Since it was a retrospective study, there was no need for participant consent.\u003c/p\u003e \u003c/div\u003e"},{"header":"3.0 Results","content":"\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Descriptive statistics\u003c/h2\u003e \u003cp\u003eA total of 3,439 tuberculosis (TB) cases were analysed from northern Malawi. The findings indicate that more than half of the patients (50.4%) were HIV positive, while 20.4% were HIV negative, and 29.2% had an unknown HIV status (see Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Concerning the history of previous TB treatment, most patients (85.4%) were newly diagnosed cases, followed by relapse cases (11.7%) and cases of treatment failure (0.9%).\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\u003eDescriptive Statistics\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\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eObs\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePercentages (%)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHIV Status\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\u003eHIV +ve\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3439\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e50.4%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHIV -ve\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3439\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\u003eUnknown\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3439\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e29.2%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHistory of prev\u0026thinsp;~\u0026thinsp;t\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\u003eFailure\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3421\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.9%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNew case\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3421\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e85.4%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOther\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3421\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePreviously treated\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3421\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRelapse\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3421\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11.7%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eReturn after lost up\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3421\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.9%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSite of TB infection\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\u003eExtra-pulmonary\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3428\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e42.1%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePulmonary\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3428\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e57.9%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eResidence\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\u003ePeri Urban\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3318\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12.8%\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\u003e3318\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e61.2%\u003c/p\u003e \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\u003e3318\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e25.9%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAdmissions\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3423\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.64%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDistrict hospital\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\u003eChitipa\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3429\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14.4%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChitipa DHO\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3429\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKaronga\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3429\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.1%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKaronga DHO\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3429\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e18.8%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMzimba\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3429\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMzimba DHO\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3429\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14.6%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMzuzu Central Hospital\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3429\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e28.5%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNkhatabay DHO\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3429\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRumphi DHO\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3429\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10.6%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMethod of Diagnosis\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\u003eMissing/other\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\u003e2.33\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGeneXP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e931\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e27.07\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTB-Culture\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.29\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eClinical\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e868\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e25.24\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMedical Imagery\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e192\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.58\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMicroscopy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e444\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12.91\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLM-LAM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e914\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e26.58\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGender\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\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3433\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e37.9%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3433\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e62.1%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge (mean and SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3436\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e38.6, 17.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eRegarding the site of infection, 57.9% of patients had pulmonary TB, while 42.1% had extrapulmonary TB. Most patients (61.2%) resided in rural areas, and Mzuzu Central Hospital (28.5%) had the largest proportion of participants, while Nkhatabay DHO (10%) had the least. The distribution of diagnostic methods for tuberculosis cases shows considerable variability in the approaches used across the sample. GeneXpert was the most used diagnostic tool, accounting for 27.07% of all diagnoses, followed by the Lipoarabinomannan assay (LM-LAM) at 26.58% (see Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Clinical diagnosis accounted for 25.24%, and TB culture was the least used method at 0.29%. Most patients were male (62.1%).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Prescribed Medication\u003c/h2\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\u003eTabulation of Prescribed Treatment Regimen\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=\".\" 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\u003ePrescribed Treatment Regimen\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFreq.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePercent\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCum.\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e(H)REZ\u0026thinsp;+\u0026thinsp;Lfx\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.29\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eITR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.32\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLAOTR-A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.08\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLAOTR-P\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.16\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRHZE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3378\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e98.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e99.53\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSTR-A\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=\".\" colname=\"c3\"\u003e \u003cp\u003e0.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e99.88\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSTR-P\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e100.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe results show that the RHZE regimen is the most commonly prescribed treatment, accounting for 98.37% (3,378 of 3,434) of all cases (see Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). All other treatment regimens are rarely used; collectively, they account for less than 2% of total prescriptions.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003e3.4 Year of Diagnosis\u003c/h2\u003e \u003cp\u003eIn 2019, most diagnoses were documented at Karonga District Hospital (33.61%) and Mzimba District Hospital (25.21%), with Mzuzu Central Hospital contributing minimally (0.42%) (refer to Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). This pattern changed significantly in 2020 and 2022, when Mzuzu Central Hospital accounted for the highest proportions of diagnoses at 43.05% and 41.94%, respectively. These shifts suggest intervals of increased referral activity or enhancements in diagnostic capacity at the central facility. Conversely, in 2021, Mzuzu Central Hospital recorded nearly no diagnoses (0.44%), whereas facilities such as Karonga, Mzimba, and Nkhatabay managed most diagnostic procedures. The distribution changed again in 2023, with Mzuzu Central Hospital diagnosing approximately one-third of all cases (33.17%) (see Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\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\u003eYear of Diagnosis\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYear of Diagnosis\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"6\" nameend=\"c7\" namest=\"c2\"\u003e \u003cp\u003eName of facility\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eChitipa DHO\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eKaronga DHO\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMzimba DHO\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMzuzu Central Hospital\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNkhatabay DHO\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eRumphi DHO\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2019\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e74 (15.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e160(33.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e120(25.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2(0.42)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e37(7.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e83(17.4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e78(9.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e139(17.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e88(10.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e350(43.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e75(9.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e83(10.2)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e89(19.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e109(24.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e96(21.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2(0.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e82(18.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e73(16.2)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e104(12.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e127(15.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e96(11.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e341(41.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e70(8.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e75(9.2)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2023\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e147(17.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e181(21.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e100(12.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e275(33.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e78(9.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e48(5.8)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003ePearson Chi2\u0026thinsp;=\u0026thinsp;805.30 Prob\u0026thinsp;=\u0026thinsp;0.0000\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003e3.5 Epidemiological Trends for TB Cases in Northern Malawi\u003c/h2\u003e \u003cp\u003eThe trend in tuberculosis (TB) cases in Northern Malawi from 2019 to 2023 exhibits fluctuations characterised by significant peaks and declines. In 2019, TB cases accounted for 13.94%, indicating a moderate level. This was followed by a considerable increase in 2020 to 23.9%, while in 2021 the percentage declined sharply to 13.24%, nearly halving the previous year's figure (see Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The upward trend resumed in 2022, with cases rising again to 23.81%, peaking at 24.4% in 2023.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003e3.6 Drug Resistance in Northern Malawi\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDrugs Reported Resistance\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=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDrugs Reported Resistance\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFreq.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePercent\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIsoniazid\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.41\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIsoniazid Rifampicin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNone\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3354\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e98.36\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRifampicin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.20\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe distribution of TB cases by specific drug resistance shows that most patients (3,354 cases; 98.36%) had no resistance. Rifampicin resistance was observed in 41 cases (1.20%), making it the most common form of single-drug resistance (see Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Isoniazid resistance alone was reported in 13 cases (0.38%), while resistance to both isoniazid and rifampicin, representing multidrug-resistant TB (MDR-TB), was rare, with only 1 case (0.03%).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003e3.7 The Treatment Outcome of TB cases in northern Malawi.\u003c/h2\u003e \u003cp\u003eThe treatment outcomes show that most TB patients successfully completed therapy: 65.12%. An additional 23.53% were documented as cured, bringing the overall success rate to nearly 89%. However, some unfavourable outcomes were also observed. About 8.04% of patients died during treatment, which is a significant proportion.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eTabulation of Treatment outcome\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=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTreatment outcome\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFreq.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePercent\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCompleted\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2220\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e65.12\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCured\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e802\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e23.53\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDied\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e274\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e8.04\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLost to follow up\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.97\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNot evaluated\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.21\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\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.29\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTreatment failure\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.85\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eA smaller proportion of patients were lost to follow-up (1.97%), suggesting challenges with treatment adherence or patient tracking (see Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). Treatment failure was recorded in 0.85% of cases, indicating persistent disease despite treatment, which may point to drug resistance or inadequate treatment regimens. Only a very small proportion of patients were not evaluated (0.21%) or classified as other (0.29%), suggesting that documentation and program monitoring were largely consistent.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003e3.8 Treatment outcome by District Hospital Facility\u003c/h2\u003e \u003cp\u003eThe distribution of treatment outcomes across health facilities in Northern Malawi shows substantial variation, reflecting differences in patient load, case severity, and each facility's capacity to manage and follow up TB patients. Overall, Mzuzu Central Hospital accounts for the largest share of treatment outcomes across almost all categories. It accounted for 30.52% of all patients who completed treatment and 18.65% of those who were cured, underscoring its central role as the region's main referral hospital (see Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e). Karonga DHO also contributed significantly to successful outcomes, accounting for 19.95% of completed treatments and the highest proportion of cured patients at 28.16%. In contrast, facilities like Chitipa and Rumphi reported lower contributions to treatment completion, suggesting lower caseloads or challenges with patient retention and treatment support.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eTreatment outcome by District Hospital Facility\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTreatment outcome\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"6\" nameend=\"c7\" namest=\"c2\"\u003e \u003cp\u003eName of facility\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eChitipa DHO\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eKaronga DHO\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMzimba DHO\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMzuzu Central Hospital\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNkhatabay DHO\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eRumphi DHO\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCompleted\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e293 (13.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e442(20.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e379 (17.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e676(30.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e226(10.20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e198(8.9)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCured\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e148 (18.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e225(28.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e102(12.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e149(18.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e64(8.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e111(13.9)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDied\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e23(8.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e56(20.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e16(5.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e93(33.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e42(15.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e44(16.1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLost to follow up\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3(4.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9(13.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0(0.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e47(71.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4(6.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e3(4.6)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNot evaluated\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1(14.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1(14.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0(0.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0(0.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0(0.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e5(71.4)\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=\"left\" colname=\"c2\"\u003e \u003cp\u003e0(0.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7(70.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0(0.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2(20.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1(10.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0(0.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTreatment failure\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3(10.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8(27.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2(6.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e9(31.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5(17.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2(6.9)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003ePearson Chi2\u0026thinsp;=\u0026thinsp;261.90 Prob\u0026thinsp;=\u0026thinsp;0.0000\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe distribution of deaths reveals important facility-level differences. Mzuzu Central Hospital accounted for the highest proportion of TB-related deaths at 33.94%, and Mzimba and Chitipa reported comparatively fewer deaths, at 5.84% and 8.39%, respectively. Loss to follow-up (LTFU) was highly concentrated at Mzuzu Central Hospital, which accounted for 71.21% of all LTFU cases, while some facilities, such as Mzimba, recorded no LTFU cases. Treatment failure was most common in Mzuzu Central Hospital (31.03%) and Karonga DHO (27.59%).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec22\" class=\"Section2\"\u003e \u003ch2\u003e3.9 Predictors of Death among \u003cem\u003eMycobacterium tuberculosis\u003c/em\u003e patients in Northern Malawi\u003c/h2\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e7\u003c/span\u003e presents the results of the multivariable logistic regression analysis identifying predictors of death among tuberculosis (TB) patients in Northern Malawi. The findings indicate that mortality was significantly associated with a range of sociodemographic, clinical, and facility-level factors.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab7\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 7\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ePredictors of Death among \u003cem\u003eMycobacterium tuberculosis\u003c/em\u003e patients in Northern Malawi\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"10\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDeath\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eOdds Coef.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSt.Err.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003et-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eSig\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003eDy/dx\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGender\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e.909\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.105\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003e-0.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e.411\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-0.006\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eResidence status ~U\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003e.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRural\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e1.791\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.455\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003e2.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e.022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.033\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUrban\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e1.678\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003e2.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e.019\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.029\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOccupation/Risk Gr\u0026thinsp;~\u0026thinsp;s\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003e.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChild\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e1.451\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.545\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003e0.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e.321\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.028\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFarmer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e.903\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.108\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003e-0.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e.394\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-0.006\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHealth worker\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e1.785\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003e1.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e.223\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.047\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003e.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOther civil servants\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e1.431\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.705\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003e0.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e.468\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.027\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=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e1.159\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.199\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003e0.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrisoner\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e.666\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.766\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003e-0.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e.724\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-0.023\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStudent\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e1.596\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.334\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003e2.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e.026\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.037\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTeacher\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e3.642\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.225\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003e3.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.136\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnemployed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e.675\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.699\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003e-0.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e.704\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-0.022\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSite of TB infecti\u0026thinsp;~\u0026thinsp;r\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003e.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePulmonary\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e.697\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.119\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003e-2.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e.035\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-0.025\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eComorbidities : ~V\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003e.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnknown vs hiv\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e.705\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.124\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003e-1.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e.046\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-0.022\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2019b\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003e.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e.459\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.197\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003e-1.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-0.040\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e1.104\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003e0.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e.882\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.007\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e1.437\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003e0.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e.662\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.029\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2023\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e1.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.462\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003e0.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e.656\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.034\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2025\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e3.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.036\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003e0.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e.382\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.116\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eName of facility ~a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003e.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKaronga DHO\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e1.506\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.378\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003e1.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e.103\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.022\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMzimba DHO\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e.613\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.064\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003e-4.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-0.018\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMzuzu Central Hosp\u0026thinsp;~\u0026thinsp;l\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e2.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.402\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003e6.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.072\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNkhatabay DHO\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e2.399\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.274\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003e7.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.059\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRumphi DHO\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e2.255\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.244\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003e7.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.053\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDrugs Reported Res\u0026thinsp;~\u0026thinsp;e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003e.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNone\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e.332\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.218\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003e-1.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e.093\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5o\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003e.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e1.025\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003e4.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDateforinitialtrea\u0026thinsp;~\u0026thinsp;d\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e.999\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003e-1.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e.222\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHistory of previou~:\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003e.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNew case\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e1.536\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.444\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003e0.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e.648\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.43\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRelapse\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e1.873\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.671\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003e0.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e.482\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.37\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eConstant\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e28524057\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.893e\u0026thinsp;+\u0026thinsp;08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e.317\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eMean dependent var\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c5\" namest=\"c3\"\u003e \u003cp\u003e0.077\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c10\" namest=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003ePseudo r-squared\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c5\" namest=\"c3\"\u003e \u003cp\u003e0.081\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c10\" namest=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eChi-square\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c5\" namest=\"c3\"\u003e \u003cp\u003e.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c10\" namest=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eAkaike crit. (AIC)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c5\" namest=\"c3\"\u003e \u003cp\u003e1251.406\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c10\" namest=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eResidential location emerged as a significant predictor of mortality. Compared to peri-urban residents, individuals residing in rural areas exhibited considerably higher odds of mortality (OR\u0026thinsp;=\u0026thinsp;1.79; 95% CI: 1.09\u0026ndash;2.95; p\u0026thinsp;=\u0026thinsp;0.022), as did those living in urban areas (OR\u0026thinsp;=\u0026thinsp;1.68; 95% CI: 1.09\u0026ndash;2.59; p\u0026thinsp;=\u0026thinsp;0.019) (see Table\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e7\u003c/span\u003e). Occupational status was also notably associated with mortality. Students demonstrated a 1.60-fold increase in the likelihood of death relative to the reference occupational group (OR\u0026thinsp;=\u0026thinsp;1.60; 95% CI: 1.06\u0026ndash;2.41; p\u0026thinsp;=\u0026thinsp;0.026), whereas teachers faced a substantially higher risk, with more than three times the odds of death (OR\u0026thinsp;=\u0026thinsp;3.64; 95% CI: 1.88\u0026ndash;7.04; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) (see Table\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e7\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eClinical characteristics further influenced mortality risk. Patients diagnosed with pulmonary TB had significantly lower odds of death compared to those with extrapulmonary TB (OR\u0026thinsp;=\u0026thinsp;0.70; 95% CI: 0.50\u0026ndash;0.97; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.035). In relation to HIV status, patients with unknown HIV status had lower odds of death compared to HIV-positive patients (OR\u0026thinsp;=\u0026thinsp;0.71; 95% CI: 0.50\u0026ndash;0.99; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.046).\u003c/p\u003e \u003cp\u003eFacility-level differences were pronounced. Treatment at Mzimba District Health Office (DHO) was associated with reduced odds of mortality (OR\u0026thinsp;=\u0026thinsp;0.61; 95% CI: 0.50\u0026ndash;0.75; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). In contrast, patients treated at Mzuzu Central Hospital, Nkhatabay DHO, and Rumphi DHO experienced substantially higher odds of death, with increases of 2.76-fold (OR\u0026thinsp;=\u0026thinsp;2.76; 95% CI: 2.07\u0026ndash;3.67; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), 2.40-fold (OR\u0026thinsp;=\u0026thinsp;2.40; 95% CI: 1.92\u0026ndash;3.00; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and 2.26-fold (OR\u0026thinsp;=\u0026thinsp;2.26; 95% CI: 1.82\u0026ndash;2.79; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), respectively(see Table\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e7\u003c/span\u003e)..\u003c/p\u003e \u003cp\u003eAge was also a significant determinant of mortality. Each additional year of age was associated with a 2.5% increase in the odds of death (OR\u0026thinsp;=\u0026thinsp;1.03; 95% CI: 1.01\u0026ndash;1.04; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), underscoring the heightened vulnerability of older TB patients.\u003c/p\u003e \u003c/div\u003e"},{"header":"4.0 Discussion","content":"\u003cp\u003eThis study provides comprehensive insights into the epidemiology, treatment outcomes, drug resistance patterns, and predictors of mortality among Mycobacterium tuberculosis patients in Northern Malawi. Overall, the findings indicate a relatively high treatment success rate, yet a persistent and significant mortality burden, with deaths predominantly influenced by sociodemographic, clinical, and health system-level factors. These results emphasise the ongoing vulnerability of population subgroups and underscore structural challenges within Malawi\u0026rsquo;s tuberculosis care delivery system.\u003c/p\u003e \u003cp\u003eThe study determined that approximately 8% of tuberculosis patients succumbed during treatment, a statistic that remains troubling despite the overall high treatment success rate of nearly 89%. Mortality rates were notably elevated among older individuals, residents of both rural and urban areas (as compared to peri-urban residents), particular occupational groups, specifically students and teachers, as well as patients with extrapulmonary tuberculosis, HIV-positive individuals, and those receiving care at certain district and referral clinics.\u003c/p\u003e \u003cp\u003eAge emerged as a strong predictor of death, with each additional year increasing the odds of mortality by 2.5%. This finding reflects the cumulative effects of immunosenescence, higher prevalence of comorbidities, delayed health-seeking behaviour, and poorer treatment tolerance among older adults. Similar age-related gradients in TB mortality have been consistently reported across sub-Saharan Africa(\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e)\u003c/p\u003e \u003cp\u003eResidential location was also strongly associated with mortality. Patients residing in rural areas had significantly higher odds of death, likely reflecting structural barriers such as long travel distances to health facilities, delayed diagnosis, limited access to diagnostic tools, and poorer continuity of care. Interestingly, urban residents also faced elevated mortality risk compared with peri-urban populations. In the Malawian context, this may reflect the concentration of severe and complicated cases in urban referral centres, particularly Mzuzu Central Hospital, rather than a true protective effect of peri-urban residence.\u003c/p\u003e \u003cp\u003eOccupational disparities in mortality were markedly significant. Educators demonstrated over three times higher odds of death, whereas students also encountered increased risk. Although these findings may seem counterintuitive, they likely mirror deferred health-seeking behaviour attributable to professional or academic obligations, under-recognition of symptoms, or delays in referral processes within these populations.\u003c/p\u003e \u003cp\u003eFrom a clinical perspective, patients diagnosed with pulmonary tuberculosis exhibited significantly reduced odds of mortality in comparison to those with extrapulmonary tuberculosis. This observation aligns with the understanding that pulmonary tuberculosis is typically easier to diagnose, particularly with sputum-based tests such as GeneXpert, whereas extrapulmonary tuberculosis frequently presents with nonspecific symptoms and requires more complex diagnostic procedures, resulting in delays in treatment initiation (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e). Delayed diagnosis is particularly detrimental in high HIV-burden settings such as Malawi.\u003c/p\u003e \u003cp\u003eHIV status also played a critical role. HIV-positive patients exhibited significantly higher mortality rates compared to those with unknown HIV status. This observation aligns with well-established evidence indicating that HIV co-infection exacerbates tuberculosis severity, complicates treatment protocols, and elevates the risk of adverse outcomes, particularly in contexts where antiretroviral therapy (ART) initiation is delayed or where integration of TB\u0026ndash;HIV services is suboptimal. (\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eOne of the most notable findings of this study is the considerable variability in TB mortality rates across different facilities. Patients receiving treatment at Mzuzu Central Hospital, Nkhatabay DHO, and Rumphi DHO faced markedly higher odds of mortality, whereas treatment at Mzimba DHO was correlated with a protective effect.\u003c/p\u003e \u003cp\u003eThese differences probably mirror variations in case severity, referral patterns, diagnostic capacity, and health system readiness. As the primary referral hospital in the region, Mzuzu Central Hospital receives a disproportionate number of critically ill patients, including those with advanced disease, drug resistance, or HIV co-infection. This referral bias may partially account for the higher observed mortality rates. Nevertheless, the significant concentration of deaths and loss to follow-up at the referral facility also indicates systemic challenges related to patient tracking. Overcrowding and continuity of care are critical factors. In contrast, the reduced mortality rate observed at Mzimba DHO may be attributable to more robust community-based follow-up, improved patient-provider continuity, or lower caseload pressure. This suggests that decentralised TB care models could provide survival benefits when sufficiently and appropriately resourced.\u003c/p\u003e \u003cp\u003eEncouragingly, the prevalence of drug resistance in Northern Malawi was low, with rifampicin resistance observed in only 1.2% of cases and multidrug-resistant tuberculosis being infrequent. These findings are consistent with prior national surveys indicating comparatively low levels of drug resistance in Malawi relative to other countries with high burdens (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e). The predominance of the standard RHZE regimen further exemplifies adherence to national and World Health Organisation (WHO) treatment guidelines. Nonetheless, even minimal levels of drug resistance can significantly influence mortality rates when diagnostic delays and treatment disruptions occur. Continuous surveillance and the expansion of drug susceptibility testing are imperative to prevent future escalation.\u003c/p\u003e \u003cp\u003eOverall, the study's findings are in strong agreement with regional and global evidence on tuberculosis (TB) mortality. Previous research conducted in Africa has consistently identified older age, HIV co-infection, extrapulmonary TB, and health system factors as principal contributors to mortality among TB patients (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e). However, in contrast to numerous studies that aggregate adverse outcomes into a single category, this research distinctly isolates death as a separate endpoint, thereby offering more explicit insights into factors specifically associated with mortality.\u003c/p\u003e \u003cp\u003eImportantly, this study expands the scholarly literature by providing sub-national evidence from Northern Malawi, a region that has historically been underrepresented in tuberculosis outcomes research. Consequently, these findings address a significant gap in the existing evidence base and offer locally pertinent insights for targeted interventions intervention.\u003c/p\u003e \u003cp\u003eThe findings possess several significant implications for tuberculosis (TB) policy and programming in Malawi. There is an urgent need for targeted interventions directed at older TB patients, which should include early screening, integrated management of comorbidities, and more rigorous clinical monitoring throughout the treatment process. Furthermore, strategies to enhance early diagnosis and treatment of extrapulmonary TB, such as expanding access to imaging, biopsy services, and advanced diagnostic tools, should be implemented and prioritised.\u003c/p\u003e \u003cp\u003eNevertheless, the pronounced disparities across facility levels underscore the necessity to enhance referral systems, optimise patient tracking, and minimise loss to follow-up in high-burden facilities, notably Mzuzu Central Hospital. Enhancing decentralised TB care and community-based follow-up may help mitigate mortality risks. Simultaneously, the ongoing integration of TB and HIV services remains indispensable. Guaranteeing timely HIV testing, swift initiation of antiretroviral therapy, and coordinated TB\u0026ndash;HIV management could significantly diminish mortality rates among co-infected patients.\u003c/p\u003e \u003cp\u003eFinally, sustained investment in routine surveillance, data quality improvement, and operational research is essential to monitor trends, identify emerging risks, and inform evidence-based decision-making as Malawi works towards the End TB targets for 2035.\u003c/p\u003e \u003cp\u003eThis study had some limitations. As such, the study's findings should be interpreted with the limitations of a cross-sectional design in mind, including the difficulty of making causal inferences. In addition, the study was conducted in a specific geographic area (i.e., the Northern Region of Malawi), which limits the generalisation of the findings to other regions with different socioeconomic contexts.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eMTB\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eMycobacterium tuberculosis\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eTB\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eTuberculosis\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eLFTU\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eLost to Follow Up\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eMDR\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eMultidrug-resistant TB\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eRR\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eRifampicin-resistant TB\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eXDR\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eExtensive drug resistance\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate in the study\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study was reviewed and approved by MZUNIREC (Ref#: MZUNIREC/DOR/24/210). Since it was a retrospective study, there was no need for participant consent.\u0026nbsp;\u003c/p\u003e\u003cp\u003e \u003ch2\u003eConsent for publication\u003c/h2\u003e \u003cp\u003eNot applicable\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003eThe study was funded by Pingtung Christian Hospital, Taiwan, through Luke International Norway (LIN), Malawi, under Grant Numbers PS-IR-111001 and PS-IR-112001. The funder played no role in the design of the study, the collection, analysis, and interpretation of data or the writing of the manuscript.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eMROC conceptualised and designed the study. TJW provided inputs to the manuscript and funding acquisition. PUK, CSC, MRC, BCM, TJW and KJY refined the study design and contributed to the development of the study protocol. FWS, PUK, BKN and CSC supervised data collection. FWS, PUK, MRC, BCM and YSH devised the data analysis plan. RM analysed and interpreted the data. MROC and RM wrote the full manuscript and reviewed it before sharing it with the wider team for reviews and comments. All authors read and approved the final manuscript.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eThe authors would like to express their gratitude to Pingtung Christian Hospital, Taiwan, through Luke International Norway. We would also like to thank the hospital's directors and staff, as well as the TB coordinators, for their unwavering support during data collection. Mr Brany Mithi and Dr Yusuf Saidi\u003c/p\u003e\u003ch2\u003eAvailability of data and materials\u003c/h2\u003e \u003cp\u003eData can be accessed through the corresponding author (Master R.O. Chisale)\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eWHO. World Health Organisation (WHO). 2024. WHO DG Flagship Initiative on ending TB. Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.who.int/initiatives/find-treat-all-endtb\u003c/span\u003e\u003cspan address=\"https://www.who.int/initiatives/find-treat-all-endtb\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSalari N, Kanjoori AH, Hosseinian-Far A, Hasheminezhad R, Mansouri K, Mohammadi M. Global prevalence of drug-resistant tuberculosis: a systematic review and meta-analysis. Infect Dis Poverty. 2023;12(1):1\u0026ndash;12.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSulis G, Roggi A, Matteelli A, Raviglione MC. Tuberculosis: Epidemiology and control. Mediterr J Hematol Infect Dis. 2014;6(1).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eThit SS, Aung NM, Htet ZW, Boyd MA, Saw HA, Anstey NM et al. 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Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://jamaevidence.mhmedical.com/content.aspx?bookid=2742\u0026amp;sectionid=233568067\u003c/span\u003e\u003cspan address=\"https://jamaevidence.mhmedical.com/content.aspx?bookid=2742\u0026amp;sectionid=233568067\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAlemu A, Bitew ZW, Worku T, Gamtesa DF, Alebel A. Predictors of mortality in patients with drug-resistant tuberculosis: A systematic review and meta-analysis. PLOS ONE [Internet]. 2021 Jun 28 [cited 2026 Jan 27];16(6):e0253848. 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AIDS. 2000;14(10):1401\u0026ndash;7.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-infectious-diseases","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"infd","sideBox":"Learn more about [BMC Infectious Diseases](http://bmcinfectdis.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/infd","title":"BMC Infectious Diseases","twitterHandle":"#bmcinfectdis","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Tuberculosis, TB, Mycobacterium, Predictors, Factors, Trend, Malawi","lastPublishedDoi":"10.21203/rs.3.rs-8818367/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8818367/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eThe World Health Organisation (WHO) recognises Tuberculosis (TB) as one of the most lethal infectious diseases worldwide. TB is a disease associated with poverty, disproportionately impacting the poorest, most vulnerable, and marginalised populations across various regions. This study was conducted to thoroughly examine the current trends, patterns of drug resistance, treatment outcomes, and predictors of mortality related to TB in Malawi.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eThis was a retrospective cross-sectional study conducted in Malawi's northern region. All laboratory-, radiology-, and clinically confirmed TB patients were enrolled in treatment across all five districts, including Karonga, Rumphi, Nkhatabay, and Chitipa. The study employed a census sampling method. Records with missing key variables, such as diagnosis, gender, age, and outcome, were excluded. Data were collected using the KOBOToolbox application and analysed with SPSS, STATA, and R.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eA total of 3,439 tuberculosis (TB) cases were analysed, revealing that over half of the patients (50.4%) were HIV positive, while 29.2% had an unknown HIV status. The RHZE regimen was the most prescribed treatment, accounting for 98.37% (3,378 of 3,434) of cases. The trend in TB cases in Northern Malawi from 2019 to 2023 demonstrates a fluctuating pattern with notable peaks and declines. Most patients (3,354 cases; 98.36%) exhibited no drug resistance. Mzuzu Central Hospital accounted for the highest proportion of TB-related mortality and loss to follow-up (LTFU), at 33.94% and 71.21%, respectively. The findings suggest that mortality was significantly associated with various sociodemographic, clinical, and facility-level factors.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eThis study offers comprehensive evidence regarding the epidemiology, treatment outcomes, drug resistance patterns, and predictors of mortality among patients with TB in Northern Malawi. Overall, the findings demonstrate a relatively high success rate in treatment but also indicate a persistently significant mortality burden, with fatalities strongly influenced by sociodemographic, clinical, and health system factors. These results emphasise the ongoing vulnerability of population subgroups and underscore structural challenges within Malawi\u0026rsquo;s tuberculosis care delivery system.\u003c/p\u003e","manuscriptTitle":"Epidemiology, Drug Resistance Patterns, Treatment Outcomes, and Predictors of Death among Mycobacterium tuberculosis patients in Northern Malawi","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-03-12 07:23:56","doi":"10.21203/rs.3.rs-8818367/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-04-10T09:11:49+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-03-22T18:45:34+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"31047167875201979185656607944967441026","date":"2026-03-22T16:51:59+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-03-21T14:39:02+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"97287642675890247084091165352721937879","date":"2026-03-13T14:11:37+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-03-05T17:50:35+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-03-05T04:52:58+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-02-16T10:42:44+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-02-13T20:54:15+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Infectious Diseases","date":"2026-02-13T20:49:55+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-infectious-diseases","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"infd","sideBox":"Learn more about [BMC Infectious Diseases](http://bmcinfectdis.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/infd","title":"BMC Infectious Diseases","twitterHandle":"#bmcinfectdis","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"5829573b-a830-4236-9197-792209680928","owner":[],"postedDate":"March 12th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-05-11T14:08:33+00:00","versionOfRecord":[],"versionCreatedAt":"2026-03-12 07:23:56","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8818367","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8818367","identity":"rs-8818367","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}