ASSESSING THE EFFECT OF COVID-19 ON TUBERCULOSIS TREATMENT OUTCOME IN ADAMAWA AND TARABA STATES, NORTH EASTERN NIGERIA

Tuberculosis is a common, and in many cases lethal, infectious disease caused by various strains of mycobacteria. Successes in treatment of tuberculosis lead to reduction in transmission, complications, and mortality among patients. The outbreak of COVID-19 drew the attention of governments and healthcare practitioners. This study considers the effect of COVID-19 on Tuberculosis treatment. Data were sourced from Taraba and Adamawa States in North-east Nigeria. A total of 8820 patients’, records were used, with 3001 from Adamawa State and 5819 from Taraba State which involved TB patients’ records. At the bivariate level, the Pearson Chi-square test was employed to measure the association between the treatment outcome and the independent variables (local government area, treatment facility ownership, treatment regimen, patients’ supported, disease site, gender, HIV status and year of treatment). Thereafter multinomial Poisson regression analysis was performed on all statistically significant variables identified at the bivariate analysis. Decisions were taken based on p-value and odds ratios. The results of this study shows that the overall treatment success and cure rate across the States was on the average, 92.7% and 49.8% respectively. The highest treatment success rate of 94.5% was achieved in year 2021, while the year 2020 witnessed the highest cure rate of 53.5%. The overall cure rate of 49.8% is below the WHO recommendation. This study recommends that the non-pharmaceutical protocols to curtail the spread of COVID-19 should be strengthened in order to curtail TB spread, and that all TB patients should be tested for HIV.


INTRODUCTION
Tuberculosis (TB) is present in all countries and age group, it is a global public health problem especially in low and middle-income countries (Amiri et al., 2021;WHO, 2021a).Recent World Health Organization (WHO,2021b) report has it that: a total of 1.5 million people died from TB in 2020 (including 214 000 people with HIV), 10 million people are estimated to be infected with tuberculosis (TB) worldwide.Furthermore, 5.6 million men, 3.3 million women, and 1.1 million children.One of the United Nations Sustainable Development Goals (SDGs) health targets, is ending the TB epidemic by 2030 (WHO, 2017).The control of tuberculosis is dependent on early identification of cases and timely notification to public health facilities to ensure appropriate treatment of cases and control.Surveillance is an important public health function in the prevention and control of tuberculosis.Accurate and complete timely information improves the quality of surveillance data and supports public health decision-making.In Nigeria, the national TB control activities are coordinated by the National Tuberculosis and Leprosy Control Programme (NTBLCP), NTBLCP is structured along the three tiers of Nigerian government thus: The Federal, State, and Local government areas (LGAs).Each level provides technical and management support to the one directly below it.The NTBLCP is saddled with the responsibility of policy development, tertiary patient care, mobilization and development to human and material resources.The States' TB programmes are responsible for coordinating TB control activities within the States, and provision of secondary patients' care.The operational level of the national TB control programme is the LGAs and it is based on the principles of Primary health-care (PHC) (NTBLCP. (n. d.)) Governments, the world over is facing a torturous path, navigating between the imminent disaster of COVID-19 and the long-running plague of TB.Also, COVID-19 pandemic has disruptive tendencies on routine health services and progress towards Sustainable Development Goals (SDGs).An analysis of survey responses conducted by Global Partnership for Zero Leprosy (GPZL, 2020) indicates that COVID-19 was having a direct impact on the majority of countries.Seventy-six percent of respondents (26 countries) said the outbreak was impacting their program.Their responses varied from clinics and offices being completely closed, to open clinics with limited case finding and community-based activities.This research is aimed at evaluating the effect of COVID-19 on tuberculosis treatment outcomes in Adamawa and Taraba States, Northeast Nigeria.Tuberculosis (TB), caused by Mycobacterium tuberculosis, continues to be the leading source of mortality and morbidity across the world (Ahmed &Hussain, 2011).TB is a preventable and curable disease, and its control is a highly cost-effective health intervention.However, diagnostic delay and inadequate treatment contribute to the severity and mortality of the disease as well as the risk of transmission and development of drug resistance (Alagna et al., 2020).The World Health Organization (WHO, 2021a) estimates that there are nearly 2 million deaths worldwide from tuberculosis annually, with the disease ranking second only to human immunodeficiency virus (HIV) as an infectious cause of death.Nearly one third of the world's population is infected with Mycobacterium tuberculosis, and the rate continues to increase.Oshi et al. (2017) conducted a retrospective evaluation of an active case-finding intervention utilizing community-based approaches and targeted systematic TB screening in Ebonyi State, Nigeria.John et al. (2015) carried out an active case finding for TB among nomadic populations over a 2-year period in Adamawa State and they found that Nomads in Nigeria have high TB rates, and active case-finding approaches may be useful in identifying and successfully treating them.Large-scale interventions in vulnerable populations can improve TB case detection.In their work, Ukwaja et al. (2013) discovered that patient and household costs for TB care were potentially catastrophic even where services are provided free-of-charge and suggested a change in strategy.In their work, Tadolini et al. (2020) raised two important issues, namely the possible association between tuberculosis (TB) and coronavirus disease 2019 (COVID-19); whether infection by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) can re-activate TB, and the effects of TB on early mortality in co-infected patients.Boffan et al. (2020) noted that regardless of HIV status, people with undiagnosed pulmonary TB (PTB), those with drug-resistant TB or complex presentations such as disseminated forms, and those who have only just begun PTB treatment may be at increased risk for severe responses if they become infected with COVID-19.With same symptoms, aside HIV/AIDS, the emergence of COVID-19 added another probable coinfection with TB, (Visca et al., 2021).Izudi et al. (2020) constructed a retrospective cohort of persons with BC-PTB from a routine tuberculosis clinic database in eastern Uganda and performed bivariate and multivariate analysis at the 5% level of significance.The results revealed that, out of the 1123 records retrieved, 81.1% of the 987 persons with BC-PTB that had treatment outcome, were successfully treated.Successful treatment of tuberculosis was less likely to occur among those with HIV infection.They concluded that, treatment success rate among adult persons with BC-PTB in rural eastern Uganda is suboptimal and mortality rate is high.HIV infection and older age reduce chances of treatment success, and increase mortality rate.Older and HIV infected persons with BC-PTB will require special consideration to optimize treatment success rate and reduce mortality rate.Jain et al. (2020) assessed the challenges due to COVID-19 pandemic on management of Tuberculosis and current strategies adopted to mitigate them.The study revealed the disruption in Tuberculosis service provisions both in the primary care and hospital settings.That the COVID-19 protocols; lockdown, social distancing, and isolation strategies impacted the delivery of all aspects of Tuberculosis care.Also, Udwadia et al. (2020) and Cilloni et al. (2020) posited that the consequences of the COVID-19 pandemic, and the global response to it with lockdowns, are likely to leave a profound and long-lasting impact on TB diagnosis and control.Others too, held the same position, that as resources are diverted and the public has been asked to shelter-in-place, the surveillance for and diagnosis of other communicable diseases of public health importance could become more challenging.For tuberculosis (TB), with an untreated case fatality rate of approximately 10%, there could be potential consequences of delayed or missed diagnoses which can lead to increase in TB related hospitalizations and death (Louie et al., 2020;Liu et al., 2021;Togun et al., 2020) Nath et al. (2021) examined the effect of COVID-19 pandemic on tuberculosis notification in India.To understand the potential effect of the COVID-19 response on TB epidemiology, they indicated that modelling studies published by Stop TB Partnership showed that for every month of Lockdown, 232,665 excess Cases and 71,290 Deaths were added in India.They submitted that the first decline in TB notification was in 2020 during the lockdown across the country due to COVID-19.Due to certain similarities in the behavior of TB and severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) generally called COVID-19, There have been inevitable consequences.On one hand, administrative measures to contain SARS-CoV-2 have simultaneously led to a breaking in the chain of tuberculosis (TB) management (Nath et al., 2021;Soko et al., 2021;Madhukar et al., 2022;CDC March, 2022).Kant and Tyagi (2021) opined that, in order to contain severe acute respiratory syndrome coronavirus 2 (SARS-Cov-2), lockdowns were imposed by countries worldwide, people were forced to stay indoors, resulting in a number of effects.Furthermore, that the symptom similarity between TB and COVID-19 probably resulted in a delay in suspecting TB, as most people could have attributed similar symptoms to COVID-19 and preferred to wait it out.Also, pre-existing stigma around TB and the added stigma of COVID-19 might have discouraged people from getting tested, even after experiencing symptoms common to both diseases.They stated that, already diagnosed patients might have also suffered during COVID-19 lockdown; outpatient departments have not been functional, while laboratories have mostly been dedicated to the processing of samples of COVID-19 patients; the follow-up and response evaluation of pulmonary TB patients is chiefly done by sputum microscopy and culture growth, this assessment was lost during lockdown.Those who had treatment failure, relapse, or who had developed drug resistance could not be timely identified and may have continued to deteriorate.Furthermore, extensive counseling and motivation is needed for patients to deal with this disease, its side effects, the stigma associated with it, and the long duration of treatment.The entire process came to a standstill with the implementation of lockdown.Aggarwal et al. (2022) reported that India contributes about a quarter of world tuberculosis burden.However, COVID-19 outshined it leading to several gaps which have caused significant setback in the National Tuberculosis Elimination Program (NTEP).The consequences include reduced and delayed notification of newly detected cases.They applied Winters additive time series model on data obtained for 2018 and 2019 to forecast for 2020 and 2021.They concluded that the decline in recognition of new cases can lead to long-term upsurge in tuberculosis incidence and mortality.

Definition of basic terms
The following terms were defined and used based on WHO (2003) criteria; Treatment success rate: Is the percentage of cured TB cases and treatment completed.Mortality rate: Is the percentage of persons with TB who died from any cause during tuberculosis treatment.Cured: Bacteriological positive patient who was sputum negative in the last month of the treatment and on at least one previous occasion.Treatment completed: Patients who have completed treatment but who do not meet criteria to be classified as cured or failed treatment.Successful Treatment: Cured plus Treatment completed cases.Unsuccessful Treatment: Patients whose treatment failed and those who were lost to follow up.

MATERIALS AND METHODS
We retrieved and reviewed records for patients PTB and EPTB, persons with a biological specimen that is positive for Mycobacterium tuberculosis (MTB) on smear microscopy, culture, or molecular test like GeneXpert.We selected 3 LGAs each from Adamawa and Taraba States, North-east Nigeria, between 2017 and 2021.The records are routinely collected by the DOTs centres under the supervision of the LGA TB programme supervisors.The records capture include; DOTS centre, health facility ownership type (private or public health facilities) and LGA where the patients received treatment, gender, age category, site of disease (pulmonary or extrapulmonary), type of drug regimen, HIV status, and availability of a treatment supporter with clinical outcomes as the dependent variable.

Methods of Data Analysis
The data collected was analysed at the univariate, bivariate and multivariate levels.In the univariate analysis, frequencies and percentages were employed to elucidate information on the categorical variables.At the bivariate level, the Pearson Chi-square test was employed to measure the association between the dependent variables and the independent variables using P-value < 0.05 as the criterion for significance.At the multivariate level, multinomial Poisson regression analysis was performed on all statistically significant variables identified at the bivariate level and reported the results as odds ratios.Variables and their levels were deemed significant if the P-value associated with the odds ratio is < 0.05.In constructing the multinomial Poisson regression model, dependent variable is the treatment outcome, this was categorised as; cured, treatment completed, died, lost to follow up, not evaluated, transferred.These were again classified as, successful and unsuccessful for the multinomial Poisson regression.The explanatory variables are; local government area, treatment facility ownership, treatment regimen, patients' supported, disease site, gender, age group, HIV status and year of treatment.Let   denote the probability of an observation falling in the multinomial probability of an observation falling in the j th category, to find the relationship between this probability and the  explanatory variables,  1 ,  2 , . . .,   .The multinomial Poisson regression model used is: Where,  refers to the effect of the independent variables   on the log odds of the occurrence of the dependent variable (treatment outcome).All statistical analyses were performed using IBM SPSS 23.All patients' record with no treatment outcome evaluation namely; those who were transferred out to other health facilities and those whom treatment outcome was not reported as at the time of data extraction, were excluded.

RESULTS AND DISCUSSION
A total of 8820patients record were used, with 3,001 from Adamawa State and 5,819 from Taraba State.Tables 1 and 2 show the results of the bivariate analysis of treatment outcomes versus other patients' variables.Tables 3 and 4 show the cure rate versus treatment success by year and local government.2), the overall treatment success in the State was 92.20% with Wukari LGA having the lowest at 82.4%, the 12 months treatment regimen has treatment success less than 90% so also patients treated at private facilities.Both PTB and EPTB patients had treatment success greater than 90%, and patients with HIV coinfection had 87.40% treatment success.
There was no significant difference in treatment success rate with respect to treatment regimen, site of the disease, patient supporter, age group and gender.Tables 1 and 2 show that treatment success and mortality rates for TB patients with HIV complications were 87.4% and 9.8% respectively in Adamawa State, while in Taraba State, there were 87.4% and 8.1% treatment success and mortality rate respectively.There was also a decrease in treatment success rate in 2019 and an increase in mortality rate in 2020.3 shows that there was significant difference in the treatment outcome across the two States and for all variables except for Gender.That is, the variable, Gender is not significantly associated with treatment success when the data was combined.

Multinomial Poisson regression analysis of the significant variables
In this section, variables that were judged significant at the bivariate levels were further analysed for significance based on their levels and their contributions to the levels of the dependent variable (treatment successful and unsuccessful while Died is the reference category), the results are reported as odds ratio (OR).The multinomial regression results are presented in Tables 6a-6c.Table 6a shows that there was no significant difference in treatment success rate with respect to health facility ownership, treatment regimen, and disease site (though these variables were significant at the bivariate level).The sensitivity analysis presented in Table 6b showed that exclusion of the statistically non-significant variables; health facility ownership, treatment regimen, and disease site resulted in Pseudo R-square (measured by the Nagelkerke statistic) decreasing from 22.5% to 15.6%, which means that these variables contributed in a little way to the variation of the outcome, hence these variables were retained.7a and 7b., 1999;Izudi, et al., 2020).Furthermore, the study revealed that the TB program in the two States performed far below the global milestones and targets for reductions in the number of people who develop TB each year and reductions in the case fatality ratio (CFR) (WHO, 2021b).Thus, we recommend strengthening the collaboration between tuberculosis and HIV control programs to improve the management of HIV infected persons with tuberculosis.A situation where some TB patients have no known HIV status is counterproductive.The results further revealed that the year 2021 witnessed a higher survival rate, this could imply strict patients adherence to pharmaceutical measures of TB and non-pharmaceutical protocols of COVID-19.

CONCLUSION
The results of this work revealed COVID-19 affect TB treatment, there is every need to tackle COVID-19 pandemic quickly to pave way for the rebuilding of tuberculosis services in addition to other essential health services.Since both TB and COVID-19 are infectious diseases that primarily attack the lungs, both spread through droplets, and promoted via overcrowding, the nonpharmaceutical protocols to curtail the spread of COVID-19 should be strengthened among TB patients, relatives and health providers to also curtail TB spread.
The results further revealed that TB cure rate is far below the WHO expectations, thus, this posits a lot of danger for all of us, hence, the worsening tuberculosis epidemic needs to be highlighted.Tuberculosis programs should make available real-time TB dashboard, such that, governments can respond with the needed immediacy.Investments in digital data systems, connected diagnostics, and digital treatment-support tools could make tuberculosis data more visible and accessible, particularly for TB burden regions.Finally, there is need to ensure that all TB patients are tested for HIV, since there is an established relationship between HIV and TB.Also, all stakeholders should take the caution given by LoBue of CDC (March, 2022) with all seriousness, that, "Delayed or missed tuberculosis disease diagnoses are threatening the health of people with TB disease and the communities where they live.A delayed or missed TB diagnosis leads to TB disease progression and can result in hospitalization or death and the risk of transmitting TB to others"

Human subjects' issues and ethics approval
This study was reviewed and approved by the Modibbo Adama University, Yola, Research Ethics Committee.The need for patient consent was waived by the ethics committee because data collection involved retrieval of records from large numbers of TB patients, for whom it would have been logistically impractical to reach and seek individual consent.Data were handled confidentially since names of patients were excluded.

Table 1 : Chi-square analysis of treatment outcome variables Adamawa State patients Variables Level
HIV coinfection have lower treatment success (87.4%) compared to patients without HIV complication (95.20%).Across the Age group and Gender, there was treatment success of over 90% with 100% success for ages 0-4.All the variables are significant with -values < 0.05, which implies that these variables are associated with treatment outcome.

Table 4 .
On the LGA basis, treatment success rate ranges between 98.7% in Mubi South and 82.4% in Wukari, while cure rate ranges between 23.1% in Yola North to 84.7% in Gassol.Mortality rate ranges between 1.0% in Mubi South to 7.3% in Wukari (see Table5).

Table 5 : Chi-square analysis of treatment outcome by LGA
In computing treatment success and mortality rate, patients with incomplete record ( = 1057) and those transferred out were excluded.

Table 7b : Parameters estimates and odds ratio of factors associated with treatment outcome in Taraba State Variables / Levels Successful Unsuccessful p-value OR p-value OR
success rate (OR=1.702).On the other hand, patients with HIV complications are more likely to die with treatment success rate (OR=0.300),than those without HIV complications treatment success rate is (OR=0.576), the results show that the case of patients with unsuccessful treatment is worst, the survival rate is (OR=0.065)and (OR=0.218)respectively for HIV positive and HIV negative patients.On the year of disease notification, treatment success rate is poor across the years compared to the year 2021 except for the year 2018.
The result presented in Table7bshows that patients treated in Jalingo and Gassol are more likely to survive with odds ratio (OR=2.198)and (OR=3.354),with unsuccessful treatment rates of (OR=0.618)and (OR=1.933)respectively, all levels of this variable LGA contributed significantly.Patients treated at public health facilities are more likely to survive with treatment