Abstract
Background: Weight gain during tuberculosis treatment is an important marker of the restoration of health. The relationship between change in weight among patients during anti-TB treatment in the intensive phase of follow-up and factors has not been well documented in Ethiopia, particularly in the southern Gedeo zone, despite the high burden of TB in the zone. Therefore, this study investigates the determined variables related to weight gain among adults of tuberculosis patients during the intensive phase in the Wonago District public health facility. Method: A Health Institutional-based, unmatched case-control study was conducted from December 1, 2023, to March 30, 2024, among all adult tuberculosis patients who were under follow-up in the Tuberculosis Unit of Wonago District Public Health Facilities. A non-probability convenience sampling method was employed to include participants. A total of 258 tuberculosis patients were included (86 cases and 172 controls). Both Case and Control were selected consecutively with proportional allocation and used all public health facilities in the Wonago District. In order to investigate determinants of weight gain, both bivariate and multivariable analyses were used. Those variables’ p-values of less than or equal to 0.25 in the bivariate were selected as candidate variables for multivariable logistic regression analysis. The adjusted odds ratio (AOR) along with a 95% confidence interval was estimated to assess the strength of the association, and a P value < 0.05 was considered to declare statistical significance in the multivariable analysis in this study. Result: Duration of illness [(AOR 2.0, 95% CI (1.10–4.01)], Types of TB [AOR 6.6, 95% CI (2.24–19.62)], Adherence during the intensive phase partially [AOR 7.4, 95% CI (3.08–18.20)], Material support from family [AOR 4.2, 95% CI (1.32–13.77)], [AOR 0.44, 95% CI (0.24-0.88)] were independently associated with weight gain. Conclusion: Duration of illness, Types of TB, Adherence during intensive, Material support from family and Alcohol consumption were determinants of weight gain on TB patients.
Keywords: Weight gain, Tuberculosis, Wonago
Subject terms: Health care, Medical research
Introduction
Tuberculosis (TB) has probably returned to being the world’s leading cause of death from a single infectious agent, following three years in which it was replaced by coronavirus disease (COVID-19). In 2023, an estimated 10.8 million people fell ill with TB worldwide, including 6.0 million men, 3.6 million women, and 1.3 million children, and A total of 1.25 million people died from tuberculosis in 2023 worldwide1.
Malnutrition is a state of nutrition characterized by a deficiency in nutrients2. TB can lead underweight3. Patients with tuberculosis often experience weight loss or malnourishment due to inadequate protein consumption, muscle breakdown brought on by infection-related inflammation, and gastrointestinal symptoms brought on by acute-phase proteins, such as elevated TNF levels4. Weight loss in tuberculosis patients can result from reduced appetite, decreased consumption of food, loss of nutrients due to vomiting and diarrhea, and metabolic changes induced by the disease5.
The World Health Organization (WHO) emphasizes the importance of nutritional assessment and appropriate care for all tuberculosis patients6. The internationally approved Directly Observed Therapy Short Course (DOTS) is a tuberculosis control strategy that has been implemented with tuberculosis programs with cost-effective results and a high treatment success rate7. Weight gain is a crucial indicator of recovery from TB and significantly influences the overall prognosis of the disease8. During treatment, regaining lost body mass, often accompanied by additional weight gain, is typically seen as a positive sign of health restoration9. Patients with TB who achieve weight gain tend to experience better recovery outcomes and reduced mortality rates compared to those who maintain normal nutritional levels10.
TB programs exist that deliver treatment services to patients freely in order to control the spread of disease. Despite these extensive measures, many adult TB patients in Ethiopia continue to struggle with gaining of weight11. According to a recent meta-analysis conducted in Ethiopia, only 49.2% of TB patients were at a healthy weight, highlighting the significant challenge of achieving weight gain among TB patients in the country12. The WHO’s End TB Strategy, endorsed by the World Health Assembly and aligned with the SDGs, outlines a framework for nations to achieve a 90% reduction in TB mortality and an 80% decrease in new TB infections from 2015 to 203013–15. The purpose of this study is to determinants of weight gain among adult tuberculosis patients during the intensive phase in Wonago district, Gedeo zone, southern Ethiopia, in 2024.
Methods
The research was carried out in Wonago Woreda, which is among the 8 districts of the Gedeo Zone in southern Ethiopia. Located 377 km south of the national capital city, Addis Ababa, and 160 km from the regional city, Wolayita Sodo, the Woreda has 21 kebeles (17 rural and 4 urban) and a total population of 166,491 within 30,541 households. Six health centers, 20 health posts, and 7 private primary clinics, and 5 pharmacies provide the overall health care service in Woreda. The study was conducted from December 1, 2023, to March 30, 2024.
Study design
A health institution based unmatched case-control study was conducted from December 1, 2023, to March 30, 2024.
Sample size determination
The sample size was calculated using the Fleiss formula, calculated by the Epi-Info software version 3.1. The following assumptions are made:
Confidence Level: 95% (Zα/2 = 1.96) Power: 80% (Zβ = 1.28), Control to Case Ratio: 1:2 (r = 2),
Percentage of Controls Exposed (P2): Percentage of Cases Exposed (P1): 91.4% and 76.3% (This represents the proportion of controls with non-weight gain among HIV/AIDS patients.) based on data from a similar study in Debre Markos14.
Odds Ratio: 3.3.
The sample size totaled 270 (90 cases and 180 controls), with a 10% nonresponse rate.
Sampling procedure
All public health facilities within the study area were included. The sample size, as determined by the study’s calculations, was allocated proportionally among all participating public health facilities. A non-probability convenience sampling method was employed to include participants. Participants’ weight data were extracted from the TB register book available at each health facility. Sampling frames were prepared for cases and controls for each health facility, and then case and control selection was performed consecutively across all health facilities during the data collection period until the predetermined sample size was achieved. The selection was based on the adherence to predefined inclusion and exclusion criteria for the study. Adult pulmonary TB patients who are in follow up during 2 months of treatment were included; and.
Data collection instruments and procedures
Data was collected through the use of a structured questionnaire. Two healthcare workers, a public health officer and a nurse, were assigned for data collection for each health facility. From all eligible tuberculosis patients using in-person interviews with data collectors and under the careful supervision of the supervisors, anthropometric measurements were carried out. Using a digital scale, weight was recorded at the time of data collection to the closest ± 0.1 kg precision. The weight was recorded while the individuals wore minimal or lightweight clothing, and the scale was consistently recalibrated to zero following each measurement to ensure accuracy. The same instruments for measuring weight were utilized, and they were calibrated each morning to maintain the validity of the results. Both height and weight were recorded to two decimal places.
Operational definitions
Weight gain: Increment of the patient’s weight by ≥ 5% from the time treatment begins, following the intensive phase of TB treatment16,17.
Intensive phase: Is the first two months of a tuberculosis patient’s anti-TB treatment.
Category of TB: The revised definitions given by WHO was used in this study to define the cases and treatment outcomes. Patients were categorized as follows:
Cat I- New sputum smear positive.
Cat II- Smear positive relapse, smear positive failure and Smear positive treatment after default (previously treated)18.
Dietary counseling: Is a process by which a health professional with special training in nutrition helps people make healthy food choices and form healthy eating habits19.
Nutritional care and support: Is food safety the provision of adequate quality/quantity of food and food aid by any organization to increase weight gain19.
Comorbidity: Diseases are associated with another disease, in addition to tuberculosis in this case.
A directly observed short-course treatment (DOT) patient delivery of TB drugs under the close supervision of trained health professionals18.
Functional status
Working: Able to carry on normal activity and no special care needed.
Ambulatory: Unable to work, able to live at home, and able to care for most of personal needs and requires occasional assistance.
Bedridden: Unable to care for self, require institutional or hospital care.
Diet diversity score: A poor diet diversity score was assigned to individuals who consumed fewer than five different food items per day. In contrast, a good diet diversity score was given to respondents who consumed five or more distinct food items daily from a selection of nine food item groups.
Data quality management
For purposes of consistency, the questionnaire was initially prepared in English, translated into Amharic, and then back into English by independent translators. The investigator trained data collectors and supervisors for two days prior to the start of the study on the study’s objectives, anthropometric measures, how to interview, and how to handle questions asked by study subjects. Prior to the actual data collection, the data collection tool was pre-tested in 5% of the sample size in the Dilla Zuria district as part of the training. Based on these results, adjustments and corrections were performed. On a daily basis, the principal investigator reviewed the completeness and consistency of data collection and provided feedback to the data collectors. Each questioner was assigned a code prior to entering data in order to make data processing easier. The data input format was created in EPI Data software using the pre-coded questionnaire. To reduce errors during data entry, a check file was created to check if the skip pattern was followed. Before conducting the analysis in SPSS software, data cleaning was performed to ensure the absence of outliers and consistency.
Data processing and analysis
The collected data was initially encoded and entered into a computer via EPI Data software version 3.1, then transferred to SPSS version 25 for statistical analysis. Descriptive statistics, such as frequencies and proportions, were presented using tables and figures. To identify the factors affecting weight gain, both bivariate and multivariable analyses were used. Variables that have a p-value of ≤ 0.25 in the bivariate analysis were chosen as candidates for further regression in the multivariable logistic analysis. The fitness of the model was verified through the Hosmer-Lemeshow test, and the collinearity of the independent variables was assessed using variance inflation factor (VIF) values less than 10. The adjusted odds ratio (AOR), with a 95% confidence interval, was used to determine the association’s strength. A P value of less than 0.05 was considered for declaring statistical significance within the multivariable analysis.
Ethical consideration
The study received ethical approval from the Ethical Review Board of Dilla University’s College of Medicine and Health Science, School of Public Health and Nutrition department. Then, by explaining the study’s significance and objectives, an official letter of cooperation was secured from the Wonago district health office. The study secured written informed consent from patients after thoroughly explaining the study’s purpose. Privacy and confidentiality of the information collected were maintained at every stage. All methods were performed in accordance with the relevant guidelines and regulations.
Result
Socio-demographic characteristics
A total of 274 TB patients were enrolled in the study (90 cases and 180 controls) with 258 respondents for both case and control with respondent rate of 95.5%. The median age of cases was 30 and SD ± 10.40 years and the median age of controls was 32 and SD ± 8.87 years. About 42(48.8) of cases and 94(54.7) controls were male. (Table 1)
Table 1.
Socio-demographic characteristics of TB patients in Wonago district.
| Socio-demographic variable | Categories | Case N (%) |
Control N (%) |
Total N(%) |
|---|---|---|---|---|
| Sex | Males | 42(48.8) | 94(54.7) | 136(52.7) |
| Female | 44(51.2) | 78(46.5) | 122(47.3) | |
| Age | 18–28 | 31(36.0) | 72(41.9) | 103(39.9) |
| 29–38 | 34(39.5) | 47(27.3) | 81(31.0) | |
| 39–48 | 17(19.8) | 33(19.2) | 50(19.4) | |
| 49 and above | 4(11.6) | 20(4.7) | 24(9.7) | |
| Marital status | Single | 20(23.3) | 48(27.9) | 68(26.4) |
| Married | 62(72.1) | 112(65.1) | 174(67.4) | |
| Widowed | 4(7.0) | 12(7.0) | 16(6.2) | |
| Occupation | House wife | 11(12.8) | 24(14.0) | 35(13.6) |
| Merchant | 4(4.7) | 14(8.1) | 18(6.9) | |
| Farmer | 20(23.3) | 49(28.5) | 69(26.7) | |
| Daily laborer | 26(30.2) | 34(19.8) | 60(23.4) | |
| Self-employer | 10(11.6) | 15(8.7) | 25(9.6) | |
| Other | 15(17.4) | 36(20.9) | 51(19.7) | |
| Family size | 0 to 4 | 49(57.0) | 82(47.7) | 131(50.7) |
| 5 and above | 37(43.0) | 90(52.3) | 127(49.3) | |
| Residence | Urban | 34(39.5) | 74(43.0) | 108(41.8) |
| Rural | 52(60.5) | 98(57.0) | 150(58.2) |
A total of 258 TB patients were enrolled in the study (86 cases and 172 controls), with a 95.5% respondent rate. The median age of cases was 30 and SD ± 10.40 years, and the median age of controls was 32 and SD ± 8.87 years. About 42 (48.8) cases and 94 (54.7) controls were male. About 62 (72.1%) of cases and 112 (65.1%) of controls were married. About 52 (60.5) cases and 98 (57.0) controls were rural. Out of 127 TB patients, 37 (43.0%) cases and 90 (52.3%) controls had a family size of 5 and above.
Co-morbidity and clinical profile of TB patient
Among the clinical factors examined, 66 cases (76.7%) and 160 controls (93.0%) were identified as having pulmonary positive tuberculosis. Additionally, 78 cases (88.4%) and 140 controls (81.4%) were newly diagnosed individuals. Out of the total 258 cases, 118 (68.6%) of the cases and 37 (43.0%) of the controls began treatment more than one month after the onset of their illness. Furthermore, 11 cases (12.8%) and 10 controls (5.8%) tested positive for HIV. Among all participants, a total of 138 individuals (101 controls and 37 cases) had a history of previous intestinal parasite infections.(Table 2).
Table 2.
Shows Co-morbidity and clinical profile of TB patient in Wonago district.
| Factory | Category | Case N(%) |
Control N(%) |
Total (%) |
|---|---|---|---|---|
| Types of TB | Smear positive TB | 66(76.7) | 160(93.0) | 226(87.6) |
| Smear negative TB | 1(1.2) | 2(1.2) | 3(1.2) | |
| Extra-pulmonary TB | 19(22.1) | 10(5.8) | 29(11.2) | |
| Category of TB | New | 78(88.4) | 140(81.4) | 218(84.4) |
| Relapse | 3(3.5) | 19(11.0) | 22(8.5) | |
| Defaulter | 7(8.1) | 13(7.6) | 20(7.1) | |
| Duration of illness before treatment | < 30 days | 54(31.4) | 49(57.0) | 103(40.0) |
| > 30 days | 118(68.6) | 37(43.0) | 155(60.0) | |
| Baseline status BMI | 16 and below | 15(17.4) | 31(18.0) | 46(17.9) |
| 16.01–18.49 | 44(51.2) | 91(52.9) | 135(52.4) | |
| 18.5–24.9 | 27(31.4) | 48(27.9) | 75(29.0) | |
| 24.91 and above | 0(0.0) | 2(1.2) | 2(0.7) | |
| HIV/AIDS | HIV negative | 75(87.2) | 162(94.2) | 237(91.8) |
| HIV positive | 11(12.8) | 10(5.8) | 21(8.2) | |
| History of previous intestinal parasite | Yes | 37(43.0) | 101(58.7) | 138(53.5) |
| No | 49(57.0) | 71(41.3) | 120(46.5) |
Supervision and support-related factors
Out of the total 258 participants, 68 cases (79.1%) and 153 controls (89.0%) received health education as part of their tuberculosis treatment. Among those, 126 controls (73.3%) and 69 cases (80.2%) were closely monitored by healthcare professionals. Additionally, a significant majority138 controls (80.2%) and 83 cases (96.5%) received material support from their families. Furthermore, 200 individuals, comprising 72 cases and 128 controls, benefited from community psychological support.
Nutritional related and health status of patient
Among the participants, 71 cases (82.6%) and 136 controls (79.1%) reported consuming meals less than three times a day. In terms of micronutrient supplementation during treatment, 60 cases (69.8%) and 108 controls (62.8%) received these supplements. Dietary diversity was assessed, revealing that 43 cases (50.0%) and 80 controls (46.5%) had good dietary diversity. Furthermore, among the 140 individuals who reported alcohol consumption, 37 were cases (43.0%) and 103 were controls (59.9%). Additionally, out of the 144 participants experiencing feeding problems such as loss of appetite, nausea, or vomiting 41 (47.7%) were cases and 103 (59.9%) were controls during the intensive phase of treatment.
Nutritional related and health status of patient
From the nutritionally related factors, about 71 (82.6) cases and 136 (79.1) controls were getting the meal frequency less than 3 times per day. From the respondents, 60 (69.8%) cases and 108 (62.8%) controls were getting micronutrient supplementation during treatment. About 43 (50.0%) cases and 80 (46.5%) controls were getting good dietary diversity. Among 140 who had alcohol consumed TB patient 37(43.0%) were case and 103(59.9%) were control. Out of 144 who had feeding problems (loss of appetite, nausea, vomiting) during the intensive phase, 41 (47.7%) were cases and 103 (59.9%) were controls (Table 3).
Table 3.
Nutritional related and health status of TB patients in Wonago district.
| Variables | Categories of variable | Case N (%) |
Control N (%) |
Total N (%) |
|---|---|---|---|---|
| Meal frequency | Less than 3 times per day | 71(82.6) | 136(79.1) | 207(80.2) |
| 4 and above | 15(17.4) | 36(20.9) | 51(19.8) | |
| Micro nutrient supplementation | Yes | 60(69.8) | 108(62.8) | 168(68.1) |
| No | 26(30.2) | 64(37.2) | 90(31.9) | |
| Dietary diversity | Poor dietary diversity | 43(50.0) | 92(53.5) | 135(52.3) |
| Good dietary diversity | 43(50.0) | 80(46.5) | 123(47.7) | |
| Feeding problems | Yes | 41(47.7) | 103(59.9) | 144(55.8) |
| No | 45(52.3) | 69(40.1) | 114(44.2) | |
| Alcohol consumption | Yes | 37(43.0) | 103(59.9) | 140(54.3) |
| No | 49(57.0) | 69(40.1) | 118(45.7) | |
| Functional status of patient | Yes | 84(97.6) | 165(95.9) | 249(96.5) |
| No | 2(2.6) | 7(4.1) | 9(3.5) |
Bivariate analysis and multivariable analysis
Twelve variables (duration of illness, history of parasite, HIV status of patient, categories of TB, types of TB, nutritional counseling, monitored by the health profession, adherence during the intensive phase, material support from family, alcohol consumption, psychological support from the community, and feeding problem (p-value < 0.25) with weight gain at the bi-variable analysis) were entered into the multivariable model. The model was a good logistic regression fit since the Hosmer-Lemeshow goodness of fit, P – value was 0.28, which is greater than 0.05. There was no co-linearity between the predictor variables. A P value < 0.05 was considered to declare statistical significance in the multivariable analysis in this study. The final predictors of weight gain were duration of illness, types of TB, adherence during the intensive phase, material support from family, and alcohol consumption. (Table 4)
Table 4.
Factors associated with weight gain among TB patients.
| Variables | Categories | Weight gain | COR (95% CI) | AOR (95% CI) | p- value <0.05 | |
|---|---|---|---|---|---|---|
| No (%) | Yes (%) | |||||
| Duration of illness | < 30 days | 49(57.0) | 54(31.4) | 2.8(1.69–4.9) | 2.0(1.10–4.01)* | 0.02 |
| > 30 days | 37(43.0) | 118(68.6) | 1 | 1 | ||
| Parasite | Yes | 37(43.0) | 101(58.7) | 1 | 1 | |
| No | 49(57.0) | 71(41.3) | 1.8(1.11–3.18) | 0.8 (0.44-1.56) | ||
| HIV status of patient | Positive | 75(87.2) | 162(94.2) | 1 | 1 | |
| Negative | 11(12.8) | 10(5.8) | 0.4(0.17–1.03) | 0.50 (0.17-1.46) | ||
| Types of TB | Smear positive | 66(76.7) | 160(93.0) | 1 | 1 | |
| Smear negative | 1(1.2) | 2(1.2) | 1.2(0.10-13.59) | 0.152 (0.041-6.65) | ||
| Extra-pulmonary | 19(22.1) | 10(5.8) | 1.6(2.03–10.43) | 6.6(2.24–19.62)** | < 0.001 | |
| Nutritional counseling | Yes | 76(88.4) | 129(70.5) | 2.5 (1.20–5.33) | 1.7(0.68-4.49) | |
| No | 10(11.6) | 43(25.0) | 1 | 1 | ||
| Monitored by health profession | Fully | 69(80.2) | 126(73.3) | 1.4(0.79–2.77) | 0.92 (0.44 -1.96) | |
| Partially | 17(19.8) | 46(26.7) | 1 | 1 | ||
| Adherence during intensive phase | Fully | 77(89.5) | 99(57.6) | 6.8(2.9-13.41) | 7.4(3.08–18.20) | 0.01 |
| Partially | 9(10.5) | 73(42.4) | 1 | 1 | ||
| Material support from family | Fully | 81(94.2) | 135(78.5) | 6.8(2.03–22.89) | 4.2 (1.32–13.77)* | < 0.001 |
| Partially | 5(5.8) | 37(21.5) | 1 | 1 | ||
| Alcohol consumption | Yes | 37(43.0) | 103(59.9) | 0.5(0.29–0.85) | 0.44 (0.23-0.85)* | 0.01 |
| No | 49(57.0) | 69(40.1) | 1 | 1 | ||
| Feeding problem | Yes | 41(47.7) | 103(59.9) | 0.6(0.36–1.02) | 1.0 (0.52 − 1.90) | |
| No | 45(52.3) | 69(40.1) | 1 | 1 | ||
| Psychological support from community | Yes | 72(74.4) | 128(83.7) | 1.76(0.9–3.4) | 0.6(0.24-1.18) | |
| No | 14(25.6) | 44(16.3) | 1 | 1 | ||
Weight gain was significantly associated with the length of the illness. Patients who begin treatment within 30 days of the illness had 2.0 times the odds of gaining weight compared to those who begin treatment later [(AOR 2.0, 95%CI (1.10–4.01)]. Patients with extra pulmonary tuberculosis had 6.6 times the odds of gaining weight compared to those with pulmonary tuberculosis [AOR 6.6, 95%CI (2.24–19.62)]. Patients who adhered fully to the intensive phase had a 7.4-fold greater odd of weight gain compared to those who adhered partially [AOR 7.4, 95% CI (3.08–18.20)]. Patients who received full material support from their families had a 4.2-fold odd of weight gain compared to those who received partial family support [AOR 4.2, 95%CI (1.32–13.77)]. Patients with a history of alcohol consumption had a 56% lower chance of gaining weight than patients who do not [AOR 0.44, 95%CI (0.24-0.88)].
Discussion
Weight gain while receiving tuberculosis treatment is related to a lower chance of death. Gaining 5% weight can lower the chance of tuberculosis death by 61%, especially during the first two months of treatment19. In this study, we aimed to identify the determinants of weight gain in TB patients who had intensive phase follow-up at Gedeo Zone, wonago District, public health facilities. The findings of this study revealed that duration of illness was one of the variables associated with weight gain. This finding is in line with the studies conducted in Debre Markos, Gondar University Hospital, and in India, where patients who started treatment within 30 days had a 2.8-fold higher chance of weight gain than those who started treatment later20–22.
Moreover, adherence to TB treatments during the intensive phase was a predictor of weight gain among TB patients. The findings of this study are in line with studies conducted in Bahir Dar and India22,23. This might be due to poor counseling from health providers, bad patient experiences, and the inaccessibility of health care settings. Poor TB treatment results, the emergence of multidrug-resistant TB (MDR-TB), and extended infectiousness may all be caused by non-adherence to anti-TB medication. This is currently one of the most serious public health challenges in Ethiopia and throughout the world24.
Type of TB was one of the factors associated with weight gain in this study. And this finding contradicts studies conducted in Jimma, Debre Markos, and India, which indicate the weight gain of pulmonary tuberculosis patients was faster than that of EPTB patients14,21,25. The differences in the results of these two studies might be due to the varying socio-economic conditions and levels of food security across the regions, as well as differences in healthcare accessibility and the fact that most participants in this study resided in rural areas.
The odds of weight gain for patients who got fully material support from family were 4.2 times odd to gain weight as compared to patients who got partially family support. This may be due to the fact that they may be more likely to adhere to their TB treatment, allowing patients to focus on their recovery, and reduced stress positively impacts weight gain. Stress can disrupt hormonal balance and appetite, potentially leading to weight fluctuations.
Patients with a history of alcohol consumption had a 56% lower chance of gaining weight than patients who never took alcohol. The findings of this study are in line with cross-sectional studies conducted in the Amahara region and with other studies26–30. The potential link between alcohol consumption and health complications may involve alcohol’s harmful impact on the gastrointestinal tract, leading to mal-absorption issues. This effect can be particularly severe in TB patients, often contributing to weight loss. However, alcohol offers calories to our bodies, but it interferes with their development, maintenance, repair, and recovery from sickness. Alcohol use during anti-TB treatment, particularly excessive episodic drinking, has been linked to delayed culture conversion and greater rates of treatment failure, relapse, and death24.
Limitation of the study
The initial admission weights of patients were not measured, but rather taken from the unit TB register. Non-probability sampling techniques were used for the selection of cases and controls Furthermore, the study is not free of recall bias and sampling bias because the evaluation of dietary diversity and meal frequency was based on memory; nevertheless, attempts were made to minimize this bias through data collector training as well as appropriate probing procedures.
Conclusion
Overall, the determinants of weight gain among adult TB patients during the intensive phase in Wonago district were duration of illness, types of TB, adherence during the intensive phase, material support from family, and alcohol consumption. These results show that TB preventive and control programs should center on these factories and give them sufficient attention. It is imperative that healthcare professionals consider these factors and ensure that patients receive appropriate follow-up treatment. To avoid poor disease outcomes, complications from the disease, and probable development of multidrug-resistant tuberculosis (MDR-TB), patients with risk factors for non-weight gain at the end of the intense phase of anti-TB treatment should be recognized early. Patients with these identified risk factors should receive appropriate and attentive monitoring and treatment, in contrast to current local practice. It should also be decided whether to institute a more regular follow-up schedule.
Acknowledgements
We would like to express my great gratitude to Dilla University College of Medicine and Health Science, Department of Human Nutrition. I would also like to thank supervisors, data collectors, and clients/patients who participated in this research.
Abbreviations
- AFB
Acid fast Bacillus
- AOR
Adjusted odd ratio
- BMI
Body mass index
- CI
Confidence interval
- COR
Crud odd ratio
- DOTS
Directly observed therapy short course
- EPTB
Extra pulmonary tuberculosis
- HIV
Human immune deficiency virus
- MDR-TB
Multidrug resistant tuberculosis
- MTB
Mycobacterium tuberculosis
- NGO
Nongovernmental Organizations
- OR
Odds ratio
- PTB
Pulmonary tuberculosis
- TB
Tuberculosis
- VIF
Variance inflation factor
- WHO
World Health Organization
Author contributions
LE, AT, YA and HA were involved in the design and selection of the articles, analysis, and manuscript writing. All authors were involved in analyses, manuscript preparation, and editing. All authors read and approved the final draft of the manuscript. All authors gave their final approval for the version that would be published, agreed to the journal to which the article would be submitted, and agreed to be responsible for all aspects of this work.
Data availability
Data availability statement Data are available upon reasonable request from the corresponding author: Adanetesfaye2006@gmail.com.
Declarations
Competing interests
The authors declare no competing interests.
Footnotes
Publisher’s note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
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Associated Data
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Data Availability Statement
Data availability statement Data are available upon reasonable request from the corresponding author: Adanetesfaye2006@gmail.com.