,
Israel Olukayode Popoola,
Muhammed Mubashir Babatunde Uthman,
Ebenezer Obi Daniel*,
Ahmed Mamuda Bello,
Michael Avwerhota,
Adebanke Adetutu Ogun,
Celestine Emeka Ekwuluo,
Michael Olabode Tomori,
Adedamola Amos Ogundeji,
Jacob Adebowale Adewole
Tuberculosis (TB) remains a substantial global health challenge, particularly in Nigeria, which has the highest TB incidence in Africa, with approximately 590,000 new cases annually. Multidrug-resistant TB (MDR-TB) complicates treatment and control efforts, necessitating a deeper understanding of drug resistance patterns. This study used a descriptive cross-sectional design to investigate resistance to first- and second-line anti-TB drugs among TB patients in Kwara State, Nigeria. A multi-stage sampling technique was used to recruit 272 participants from selected Local Government Areas. Data collection included questionnaires and laboratory testing using the Hain Line Probe Assay (LPA) and GeneXpert MTB/Rif system. Results showed that age was a statistically significant factor, with resistance rates notably higher among younger individuals aged 20-29 years (p = 0.044), while no significant associations were found for gender (p = 0.166), ethnicity (p = 0.984 for first-line drugs; p = 0.601 for second-line), or educational level (p = 0.131 for first-line; p = 0.260 for second-line). Notably, 84.7% of participants who adhered to Directly Observed Treatment Short-course (DOTS) were sensitive to anti-TB drugs, but adherence alone did not exhibit a statistically significant association with resistance (p = 0.278), questioning assumptions about DOTS effectiveness in this context. Alcohol consumption emerged as a significant predictor of drug resistance (p = 0.0423), with patients consuming alcohol being six times more likely to develop resistance (OR = 6.025, 95% CI = 4.950 - 13.632) than non-drinkers, underscoring the need to address alcohol-related non-adherence to improve outcomes. Conversely, smoking, incarceration, and contact with TB patients showed no significant association with resistance, challenging global assumptions about these risk factors in the Nigerian context. This study highlights the complex nature of TB drug resistance, influenced by socio-demographic and behavioral factors unique to local contexts. The absence of statistically significant correlations for several known risk factors, such as smoking (p = 0.761) and prior TB contact (p = 0.2165), suggests that interventions in Nigeria should be localized and tailored to specific populations rather than relying on generalized global models. Comprehensive strategies targeting alcohol use and younger age groups, alongside strengthened healthcare delivery, are essential to curbing the spread of drug-resistant TB strains in Nigeria.
| Published in | International Journal of Infectious Diseases and Therapy (Volume 10, Issue 1) |
| DOI | 10.11648/j.ijidt.20251001.12 |
| Page(s) | 11-28 |
| Creative Commons |
This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited. |
| Copyright |
Copyright © The Author(s), 2025. Published by Science Publishing Group |
Drug Resistance, Tuberculosis (TB), Adherence to Treatment, Socio-Demographic Factors, Multidrug-Resistant Tuberculosis (MDR-TB), Alcohol Consumption
Socio-demographics Characteristics | Resistance Pattern | X2 | P-Value | ||
|---|---|---|---|---|---|
Sensitive n (%) | Resistant n (%) | Total n (%) | |||
Age group (Years) | |||||
10-19 | 3 (100) | 0 (0) | 3 (100) | ||
20-29 | 53 (82.8) | 11 (17.2) | 64 (100) | 16.225 | 0.044 |
30-39 | 88 (88.0) | 12 (12.0) | 100 (100) | ||
40-49 | 57 (100.0) | 0 (0) | 57 (100) | ||
50 and above | 17 (89.5) | 2 (10.5) | 19 (100) | ||
Sex | |||||
Male | 115 (92.0) | 10 (8.0) | 125 (100) | 6.487 | 0.166 |
Female | 103 (87.2) | 15 (12.7) | 118 (100) | ||
Ethnic group | |||||
Hausa | 15 (93.8) | 1 (6.2) | 16 (100) | ||
Igbo | 29 (87.9) | 4 (12.1) | 33 (100) | 1.038 | 0.984 |
Yoruba | 166 (89.7) | 19 (10.3) | 185 (100) | ||
Others | 8 (88.9) | 1 (1.1) | 9 (100) | ||
Religion | |||||
Christianity | 45 (95.7) | 2 (4.3) | 47 (100) | 2.791 | 0.248 |
Islam | 173 (88.3) | 23 (11.7) | 196 (100) | ||
Education | |||||
Quranic | 32 (91.4) | 2 (5.9) | 34 (100) | ||
Primary | 21 (77.8) | 6 (22.2) | 27 (100) | 12.473 | 0.131 |
Secondary | 63 (87.5) | 9 (12.5) | 72 (100) | ||
Tertiary | 99 (93.4) | 7 (6.6) | 93 (100) | ||
Others | 3 (75.0) | 1 (25.0) | 4 (100) | ||
Monthly Income (#) | |||||
Less than 20,000 | 73 (82.9) | 15 (17.1) | 88 (100) | ||
20,000-50,000 | 91 (94.8) | 5 (5.2) | 96 (100) | 11.426 | 0.179 |
51,000-100,000 | 29 (90.6) | 3 (9.4) | 32 (100 | ||
Above 100,000 | 25 (92.3) | 2 (7.7) | 27 (100) | ||
Place of Residence | |||||
Urban | 75 (86.2) | 12 (13.8) | 87 (100) | 2.921 | 0.232 |
Rural | 143 (91.6) | 13 (8.4) | 156 (100) | ||
Marital Status | |||||
Single | 39 (88.6) | 4 (11.4) | 44 (100) | ||
Married | 161 (91.0) | 15 (9.0) | 176 (100) | 13.993 | 0.082 |
Divorced | 2 (50.0) | 2 (50.0) | 4 (100) | ||
Separated | 11 (73.3) | 4 (26.7) | 15 (100) | ||
Widowed | 5 (100.0) | 0 (0) | 5 (100) | ||
Socio-demographics Characteristics | Resistance Pattern | X2 | P-Value | ||
|---|---|---|---|---|---|
Sensitive n (%) | Resistant n (%) | Total (%) | |||
Age group (Years) | |||||
10-19 | 3 (100) | 0 (0) | 3 (100) | ||
20-29 | 62 (93.9) | 2 (6.1) | 64 (100) | 17.503 | 0.036 |
30-39 | 95 (95.0) | 5 (5.0) | 100 (100) | ||
40-49 | 55 (96.5) | 2 (3.5) | 57 (100) | ||
50 and above | 18 (94.7) | 1 (5.3) | 19 (100) | ||
Sex | |||||
Male | 119 (96.8) | 4 (3.2) | 123 (100) | 2.526 | 0.640 |
Female | 114 (95.0) | 6 (5.0) | 120 (100) | ||
Ethnic group | |||||
Hausa | 14 (87.5) | 2 (12.5) | 16 (100) | ||
Igbo | 31 (93.9) | 2 (6.1) | 33 (100) | 4.564 | 0.601 |
Yoruba | 180 (97.3) | 5 (2.7) | 185 (100) | ||
Others | 8 (88.9) | 1 (11.1) | 9 (100) | ||
Religion | |||||
Christianity | 46 (97.9) | 1 (2.1) | 47 (100) | 1.066 | 0.587 |
Islam | 187 (94.4) | 9 (5.6) | 196 (100) | ||
Education | |||||
Quranic | 33 (97.1) | 1 (2.9) | 34 (100) | ||
Primary | 26 (96.3) | 1 (3.7) | 27 (100) | 10.068 | 0.260 |
Secondary | 68 (91.9) | 4 (8.1) | 72 (100) | ||
Tertiary | 103 (97.2) | 3 (2.8) | 106 (100) | ||
Others | 3 (75.0) | 1 (25.0) | 4 (100) | ||
Monthly Income | |||||
Less than 20,000 | 84 (93.3) | 4 (6.7) | 88 (100) | ||
20,000-50,000 | 93 (96.9) | 3 (3.1) | 96 (100) | 5.335 | 0.721 |
51,000-100,000 | 31 (96.9) | 1 (3.1) | 32 (100 | ||
Above 100,000 | 25 (92.3) | 2 (7.7) | 27 (100) | ||
Place of Residence | |||||
Urban | 83 (95.4) | 4 (4.6) | 87 (100) | 1.191 | 0.551 |
Rural | 150 (96.1) | 6 (4.9) | 156 (100) | ||
Marital Status | |||||
Single | 42 (95.5) | 2 (4.5) | 44 (100) | ||
Married | 167 (94.4) | 8 (5.6) | 175 (1000 | 3.316 | 0.913 |
Divorced | 4 (100.0) | 0 (0) | 4 (100) | ||
Seperated | 15 (100.0) | (0) | 15 (100) | ||
Widowed | 5 (100.0) | 0 (0) | 5 (100) | ||
Socio-demographics Characteristics | Resistance Pattern | X2 | P-Value | ||
|---|---|---|---|---|---|
Sensitive n (%) | Resistant n (%) | Total (%) | |||
Age group (Years) | |||||
10-19 | 3 (100) | 0 (0) | 3 (100) | ||
20-29 | 54 (82.0) | 10 (18.0) | 64 (100) | ||
30-39 | 82 (82.0) | 18 (12.0) | 100 (100) | ||
40-49 | 55 (96.5) | 2 (3.5) | 57 (100) | ||
50 and above | 16 (84.2) | 3 (15.8) | 19 (100) | 15.139 | 0.127 |
Sex | |||||
Male | 107 (87.0) | 16 (13.0) | 123 (100) | 4.650 | 0.325 |
Female | 103 (85.8) | 17 (14.2) | 120 (100) | ||
Ethnic group | |||||
Hausa | 14 (87.5) | 2 (12.5) | 16 (100) | ||
Igbo | 28 (84.8) | 5 (15.2) | 33 (100) | 0.759 | 0.993 |
Yoruba | 160 (86.5) | 25 (13.5) | 185 (100) | ||
Others | 8 (88.9) | 1 (11.1) | 9 (100) | ||
Religion | |||||
Christianity | 44 (93.6) | 3 (6.4) | 47 (100) | 3.067 | 0.216 |
Islam | 166 (84.7) | 30 (15.3) | 196 (100) | ||
Education | |||||
Quranic | 30 (88.2) | 4 (11.8) | 34 (100) | ||
Primary | 22 (81.5) | 5 (18.5) | 27 (100) | 8.541 | 0.383 |
Secondary | 59 (81.9) | 13 (18.1) | 72 (100) | ||
Tertiary | 96 (90.6) | 10 (9.4) | 106 (100) | ||
Others | 3 (75.0) | 1 (25.0) | 4 (100) | ||
Monthly Income | |||||
Less than 20,000 | 72 (81.0) | 16 (19.0) | 88 (100) | ||
20,000-50,000 | 87 (90.6) | 9 (9.4) | 96 (100) | 6.616 | 0.579 |
51,000-100,000 | 28 (87.5) | 4 (12.5) | 32 (100 | ||
Above 100,000 | 23 (85.1) | 4 (14.9) | 27 (100) | ||
Place of Residence | |||||
Urban | 72 (82.8) | 15 (17.2) | 87 (100) | 2.664 | 0.264 |
Rural | 138 (88.5) | 18 (11.5) | 156 (100) | ||
Marital Status | |||||
Single | 38 (84.1) | 6 (15.9) | 44 (100) | ||
Married | 153 (94.4) | 22 (5.6) | 175 (1000 | 7.550 | 0.479 |
Divorced | 2 (50.0) | 2 (50.0) | 4 (100) | ||
Seperated | 12 (73.3) | 3 (26.7) | 15 (100) | ||
Widowed | 5 (100.0) | 0 (0) | 5 (100) | ||
Patient-related factors | Resistance Pattern | X2 | P-Value | ||
|---|---|---|---|---|---|
Sensitive n (%) | Resistant n (%) | Total n (%) | |||
HIV Status Yes | 10 (100.0) | 0 (0) | 10 (100) | 1.650 | 0.949 |
BCG Scar Yes | 86 (84.3) | 16 (15.7) | 102 (100) | 9.232 | 0.161 |
Alcohol Consumption Yes | 40 (95.2) | 2 (4.2) | 42 (100) | 2.901 | 0.574 |
Smoke cigarette Yes | 33 (84.6) | 6 (13.4) | 39 (100) | 2.190 | 0.901 |
History of Incarceration Yes | 64 (94.1) | 4 (5.9) | 68 (100) | 5.008 | 0.543 |
History of Hospital admission Yes | 85 (87.6) | 12 (12.3) | 97 (100) | 0.843 | 0.656 |
Contact with Chronic Cough Yes | 18 (90.0) | 2 (10.0) | 20 (100) | 0.825 | 0.820 |
Contact with TB patient Yes | 14 (87.5 | 2 (12.5) | 16 (100) | 3.614 | 0.461 |
Ever diagnosed with TB Yes | 26 (89.7) | 3 (10.3) | 29 (100) | 0.271 | 0.873 |
Use drug as prescribed Yes | 57 (90.5) | 6 (9.5) | 63 (100) | 0.752 | 0.687 |
Patient-related factors | Resistance Pattern | X2 | P-Value | ||
|---|---|---|---|---|---|
Sensitive n (%) | Resistant n (%) | Total n (%) | |||
HIV Status Yes | 8 (80.0) | 2 (20.0) | 10 (100) | 6.981 | 0.323 |
BCG Scar Yes | 99 (96.1) | 3 (3.9) | 102 (100) | 5.039 | 0.539 |
Alcohol Consumption Yes | 38 (90.5) | 4 (9.5) | 42 (100) | 15.072 | 0.023 |
Smoke cigarette Yes | 35 (89.7) | 4 (10.3) | 39 (100) | 4.584 | 0.598 |
History of Incarceration Yes | 63 (89.6) | 5 (10.4) | 68 (100) | 2.119 | 0.9920 |
History of Hospital admission Yes | 94 (97.0) | 3 (3.0) | 97 (100) | 0.511 | 0.774 |
Contact with Chronic Cough Yes | 17 (85.0) | 3 (15.0) | 20 (100) | 10.533 | 0.032 |
Contact with TB patient Yes | 15 (93.8) | 1 (6.2) | 16 (100) | 13.455 | 0.063 |
Ever diagnosed with TB Yes | 27 (93.1) | 2 (6.9 | 29 (100) | 0.921 | 0.631 |
Use drug as prescribed Yes | 60 (95.2) | 3 (4.8) | 63 (100) | 0.789 | 0.674 |
Patient-related factors | Resistance Pattern | X2 | P-Value | ||
|---|---|---|---|---|---|
Sensitive n (%) | Resistant n (%) | Total n (%) | |||
HIV Status Yes | 8 (80.0) | 2 (20.0) | 10 (100) | 1.485 | 0.960 |
BCG Scar Yes | 83 (84.3) | 19 (15.7) | 102 (100) | 8.003 | 0.238 |
Alcohol Consumption Yes | 36 (85.7) | (14.3) | 42 (100) | 11.295 | 0.026 |
Smoke cigarette Yes | 36 (92.3) | 3 (7.7) | 39 (100) | 3.368 | 0.761 |
History of Incarceration Yes | 60 (88.2) | 8 (11.8) | 68 (100) | 2.793 | 0.834 |
History of Hospital admission Yes | 82 (84.2) | 15 (15.8) | 97 (100) | 0.572 | 0.751 |
Contact with Chronic Cough Yes | 15 (75.0) | 5 (25.0) | 20 (100) | 12.912 | 0.043 |
Contact with TB patient Yes | 13 (81.3) | 3 (18.7) | 16 (100) | 2.844 | 0.584 |
Ever diagnosed with TB Yes | 24 (82.8) | 5 (17.2) | 29 (100) | 0.650 | 0.723 |
Use drug as prescribed Yes | 55 (87.3) | 8 (12.7) | 63 (100) | 0.755 | 0.686 |
Healthcare-giver related factors | Resistance Pattern | X2 | P-Value | ||
|---|---|---|---|---|---|
Sensitive n (%) | Resistant n (%) | Total n (%) | |||
Delay in treatment commencement after diagnosis | |||||
Yes | 10 (100.0) | 0 (0) | 10 (100) | 0.336 | 0.845 |
Compliance with DOTS | |||||
Yes | 108 (88.3) | 16 (11.7) | 124 (100) | 3.781 | 0.151 |
Provision of Health talk and awareness | |||||
Yes | 108 (86.0) | 14 (14.0) | 122 (100) | 2.620 | 0.623 |
Provision of Prompt attention by health worker | |||||
Yes | 106 (88.3) | 14 (11.7) | 120 (100) | 2.699 | 0.609 |
Provision of Information about long duration of treatment | |||||
Yes | 189 (88.7) | 24 (11.3) | 213 (100) | 0.500 | 0.779 |
Provision of Information on reasons for daily intake of drugs | |||||
Yes | 189 (88.7) | 24 (11.3) | 213 (100) | 0.500 | 0.779 |
Provision of Information on protection of others | |||||
Yes | 189 (88.7) | 25 (11.3) | 213 (100) | 0.500 | 0.779 |
Healthcare-giver related factors | Resistance Pattern | X2 | P-Value | ||
|---|---|---|---|---|---|
Sensitive n (%) | Resistant n (%) | Total n (%) | |||
Delay in treatment commencement after diagnosis | |||||
Yes | 10 (100.0) | 0 (0) | 10 (100) | 0.104 | 0.949 |
Compliance with DOTS | |||||
Yes | 121 (97.6) | 3 (2.4) | 124 (100) | 3.751 | 0.153 |
Provision of Health talk and awareness Yes | 119 (97.6) | 3 (2.4) | 122 (100) | 3.934 | 0.415 |
Provision of Prompt attention by health worker | |||||
Yes | 117 (97.5) | 3 (2.5) | 120 (100) | 3.769 | 0.438 |
Provision of Information about long duration of treatment | |||||
Yes | 206 (96.7) | 7 (3.3) | 213 (100) | 2.905 | 0.234 |
Provision of Information on reasons for daily intake of drugs | |||||
Yes | 206 (96.7) | 7 (3.3) | 213 (100) | 2.905 | 0.234 |
Provision of Information on the protection of others | |||||
Yes | 206 (96.7) | 7 (3.3) | 213 (100) | 2.905 | 0.234 |
Healthcare-giver related factors | Resistance Pattern | X2 | P-Value | ||
|---|---|---|---|---|---|
Sensitive n (%) | Resistant n (%) | Total n (%) | |||
Delay in treatment commencement after diagnosis Yes | 5 (50.0) | 5 (50.0) | 10 (100) | 0.336 | 0.845 |
Compliance with DOTS Yes | 105 (84.7) | 19 (15.3) | 124 (100) | 2.560 | 0.278 |
Provision of Health talk and awareness Yes | 105 (86.1) | 17 (13.9) | 122 (100) | 2.456 | 0.653 |
Provision of Prompt attention by health worker Yes | 103 (85.8) | 17 (14.2) | 120 (100) | 2.398 | 0.633 |
Provision of Information about long duration of treatment Yes | 187 (87.8) | 26 (12.2) | 213 (100) | 2.479 | 0.290 |
Provision of Information on reasons for daily intake of drugs Yes | 187 (87.8) | 26 (12.2) | 213 (100) | 2.479 | 0.290 |
Provision of Information on protection of others Yes | 187 (87.8) | 26 (12.2) | 213 (100) | 2.479 | 0.290 |
Patient related factors | OR | 95% C. I | P-Value |
|---|---|---|---|
Alcohol Consumption | |||
Yes No* | 6.025 | 4.950 -13.632 | 0.0423** |
Contact with Chronic Cough | |||
Yes No* | 3.118 | 0.549 -18.116 | 0.2165 |
DR-TB | Drug Resistant Tuberculosis |
DOTS | Directly Observed Therapy Short Course |
DST | Drug Susceptibility Testing |
MDR-TB | Multi-Drug Resistant Tuberculosis |
NTP | National Tuberculosis Programme |
P/PZH | Pyrazinamide |
PLHIV | Persons living with HIV/AIDS |
PTB | Pulmonary Tuberculosis |
R | Rifampicin |
RR | Rifampicin Resistance |
WHO | World Health Organization |
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APA Style
Muhammed, O. R., Popoola, I. O., Uthman, M. M. B., Daniel, E. O., Bello, A. M., et al. (2025). Exploring the Pattern of Resistance to Anti-Tuberculosis Drugs Among Tuberculosis Patients in Kwara State, Nigeria. International Journal of Infectious Diseases and Therapy, 10(1), 11-28. https://doi.org/10.11648/j.ijidt.20251001.12
ACS Style
Muhammed, O. R.; Popoola, I. O.; Uthman, M. M. B.; Daniel, E. O.; Bello, A. M., et al. Exploring the Pattern of Resistance to Anti-Tuberculosis Drugs Among Tuberculosis Patients in Kwara State, Nigeria. Int. J. Infect. Dis. Ther. 2025, 10(1), 11-28. doi: 10.11648/j.ijidt.20251001.12
AMA Style
Muhammed OR, Popoola IO, Uthman MMB, Daniel EO, Bello AM, et al. Exploring the Pattern of Resistance to Anti-Tuberculosis Drugs Among Tuberculosis Patients in Kwara State, Nigeria. Int J Infect Dis Ther. 2025;10(1):11-28. doi: 10.11648/j.ijidt.20251001.12
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author = {Oyeniyi Rasheed Muhammed and Israel Olukayode Popoola and Muhammed Mubashir Babatunde Uthman and Ebenezer Obi Daniel and Ahmed Mamuda Bello and Taiwo Aderemi Popoola and Michael Avwerhota and Adebanke Adetutu Ogun and Celestine Emeka Ekwuluo and Aremu Samuel Danladi and Michael Olabode Tomori and Adedamola Amos Ogundeji and Jacob Adebowale Adewole},
title = {Exploring the Pattern of Resistance to Anti-Tuberculosis Drugs Among Tuberculosis Patients in Kwara State, Nigeria
},
journal = {International Journal of Infectious Diseases and Therapy},
volume = {10},
number = {1},
pages = {11-28},
doi = {10.11648/j.ijidt.20251001.12},
url = {https://doi.org/10.11648/j.ijidt.20251001.12},
eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijidt.20251001.12},
abstract = {Tuberculosis (TB) remains a substantial global health challenge, particularly in Nigeria, which has the highest TB incidence in Africa, with approximately 590,000 new cases annually. Multidrug-resistant TB (MDR-TB) complicates treatment and control efforts, necessitating a deeper understanding of drug resistance patterns. This study used a descriptive cross-sectional design to investigate resistance to first- and second-line anti-TB drugs among TB patients in Kwara State, Nigeria. A multi-stage sampling technique was used to recruit 272 participants from selected Local Government Areas. Data collection included questionnaires and laboratory testing using the Hain Line Probe Assay (LPA) and GeneXpert MTB/Rif system. Results showed that age was a statistically significant factor, with resistance rates notably higher among younger individuals aged 20-29 years (p = 0.044), while no significant associations were found for gender (p = 0.166), ethnicity (p = 0.984 for first-line drugs; p = 0.601 for second-line), or educational level (p = 0.131 for first-line; p = 0.260 for second-line). Notably, 84.7% of participants who adhered to Directly Observed Treatment Short-course (DOTS) were sensitive to anti-TB drugs, but adherence alone did not exhibit a statistically significant association with resistance (p = 0.278), questioning assumptions about DOTS effectiveness in this context. Alcohol consumption emerged as a significant predictor of drug resistance (p = 0.0423), with patients consuming alcohol being six times more likely to develop resistance (OR = 6.025, 95% CI = 4.950 - 13.632) than non-drinkers, underscoring the need to address alcohol-related non-adherence to improve outcomes. Conversely, smoking, incarceration, and contact with TB patients showed no significant association with resistance, challenging global assumptions about these risk factors in the Nigerian context. This study highlights the complex nature of TB drug resistance, influenced by socio-demographic and behavioral factors unique to local contexts. The absence of statistically significant correlations for several known risk factors, such as smoking (p = 0.761) and prior TB contact (p = 0.2165), suggests that interventions in Nigeria should be localized and tailored to specific populations rather than relying on generalized global models. Comprehensive strategies targeting alcohol use and younger age groups, alongside strengthened healthcare delivery, are essential to curbing the spread of drug-resistant TB strains in Nigeria.
},
year = {2025}
}
TY - JOUR T1 - Exploring the Pattern of Resistance to Anti-Tuberculosis Drugs Among Tuberculosis Patients in Kwara State, Nigeria AU - Oyeniyi Rasheed Muhammed AU - Israel Olukayode Popoola AU - Muhammed Mubashir Babatunde Uthman AU - Ebenezer Obi Daniel AU - Ahmed Mamuda Bello AU - Taiwo Aderemi Popoola AU - Michael Avwerhota AU - Adebanke Adetutu Ogun AU - Celestine Emeka Ekwuluo AU - Aremu Samuel Danladi AU - Michael Olabode Tomori AU - Adedamola Amos Ogundeji AU - Jacob Adebowale Adewole Y1 - 2025/01/24 PY - 2025 N1 - https://doi.org/10.11648/j.ijidt.20251001.12 DO - 10.11648/j.ijidt.20251001.12 T2 - International Journal of Infectious Diseases and Therapy JF - International Journal of Infectious Diseases and Therapy JO - International Journal of Infectious Diseases and Therapy SP - 11 EP - 28 PB - Science Publishing Group SN - 2578-966X UR - https://doi.org/10.11648/j.ijidt.20251001.12 AB - Tuberculosis (TB) remains a substantial global health challenge, particularly in Nigeria, which has the highest TB incidence in Africa, with approximately 590,000 new cases annually. Multidrug-resistant TB (MDR-TB) complicates treatment and control efforts, necessitating a deeper understanding of drug resistance patterns. This study used a descriptive cross-sectional design to investigate resistance to first- and second-line anti-TB drugs among TB patients in Kwara State, Nigeria. A multi-stage sampling technique was used to recruit 272 participants from selected Local Government Areas. Data collection included questionnaires and laboratory testing using the Hain Line Probe Assay (LPA) and GeneXpert MTB/Rif system. Results showed that age was a statistically significant factor, with resistance rates notably higher among younger individuals aged 20-29 years (p = 0.044), while no significant associations were found for gender (p = 0.166), ethnicity (p = 0.984 for first-line drugs; p = 0.601 for second-line), or educational level (p = 0.131 for first-line; p = 0.260 for second-line). Notably, 84.7% of participants who adhered to Directly Observed Treatment Short-course (DOTS) were sensitive to anti-TB drugs, but adherence alone did not exhibit a statistically significant association with resistance (p = 0.278), questioning assumptions about DOTS effectiveness in this context. Alcohol consumption emerged as a significant predictor of drug resistance (p = 0.0423), with patients consuming alcohol being six times more likely to develop resistance (OR = 6.025, 95% CI = 4.950 - 13.632) than non-drinkers, underscoring the need to address alcohol-related non-adherence to improve outcomes. Conversely, smoking, incarceration, and contact with TB patients showed no significant association with resistance, challenging global assumptions about these risk factors in the Nigerian context. This study highlights the complex nature of TB drug resistance, influenced by socio-demographic and behavioral factors unique to local contexts. The absence of statistically significant correlations for several known risk factors, such as smoking (p = 0.761) and prior TB contact (p = 0.2165), suggests that interventions in Nigeria should be localized and tailored to specific populations rather than relying on generalized global models. Comprehensive strategies targeting alcohol use and younger age groups, alongside strengthened healthcare delivery, are essential to curbing the spread of drug-resistant TB strains in Nigeria. VL - 10 IS - 1 ER -
Department of Public Health, Ministry of Health, Ilorin, Nigeria
Department of Epidemiology and Community Health, University of Ilorin, Ilorin, Nigeria
Department of Epidemiology and Community Health, College of Health Sciences, University of Ilorin, Ilorin, Nigeria
Department of Public Health, Swansea University, Swansea, United Kingdom; Department of Public Health, Texila American University, Georgetown, Guyana
Department of Public Health, Texila American University, Georgetown, Guyana
Department of Public Health, Atlantic International University, Hawaii, United States of America
Department of Policy Governance Liaison and Support, International Organization for Migration, Abuja, Nigeria
Department of Health and Nutrition Sector, International Medical Corps, Kyiv, Ukraine
Department of Public Health, Texila American University, Georgetown, Guyana
Department of Defense, Walter Reed Army Institute of Research-Africa Embassy of the United States of America, Abuja, Nigeria
Department of Public Health, Brits District Hospital, Bojanala District, Rustenburg, South Africa
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