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 Table of Contents  
ORIGINAL ARTICLE
Year : 2018  |  Volume : 7  |  Issue : 2  |  Page : 137-142

Evaluation of the tuberculosis control program in South West Cameroon: Factors affecting treatment outcomes


1 Department of Microbiology and Parasitology, Faculty of Science, University of Buea, Banjul, The Gambia
2 Laboratory Services Department, Medical Research Council Unit, The Gambia at London School of Hygiene and Tropical Medicine, Banjul, The Gambia
3 Department of Microbiology and Parasitology, Faculty of Science, University of Buea, Banjul, The Gambia; Department of Microbiology and Parasitology, Laboratory for Emerging Infectious Diseases, University of Buea, Cameroon; Department of Biochemistry and Microbiology, University of Fort Hare, Alice, South Africa
4 Department of Microbiology and Parasitology, Laboratory for Emerging Infectious Diseases, University of Buea; Department of Biomedical Sciences, Faculty of Health Sciences, Buea, Cameroon; Center for Tropical Diseases, University of Texas Medical Branch, Texas, USA

Date of Web Publication13-Jun-2018

Correspondence Address:
Nicoline Fri Tanih
Laboratory Services Department, Medical Research Council Unit, The Gambia at London School of Hygiene and Tropical Medicine, Banjul
The Gambia
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Source of Support: None, Conflict of Interest: None


DOI: 10.4103/ijmy.ijmy_20_18

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  Abstract 


Background: Tuberculosis (TB) has been ranked as one of the leading causes of death worldwide. In Cameroon, the National Tuberculosis Control Program aims to fight TB through the implementation of international directives (Directly Observed Treatment Short course [DOTS]). TB control program must reach global targets for detection (70%) and treatment success (85%) as stated by the United Nations Millennium Development Goals (MDGs). Implementing DOTS in Cameroon has not met the MDGs of 85% success rate. This study aimed at identifying factors affecting treatment success. Methods: A cross-sectional retrospective study was used to collect data from 895 TB registers from January 2011 to December 2012. Out of the seven treatment centers in Fako Division, three were randomly selected following stratification into government, not-for-profit and for-profit structures. Descriptive statistics were used to obtain frequencies. Binomial logistics regression was used to obtain significant values for the various factors. Multinomial logistics was used on significant factors. Results: Of the 895 registered TB patient records obtained, 416 (46.5%) patient were female and 479 (53.5%) patient were male. Characterizing TB patients, 510 (57.0%) were smear-positive pulmonary TB, 225 (25.1%) were smear-negative pulmonary TB, and 160 (17.9%) were extrapulmonary TB patients. Comparing treatment success rate (TSR) across the three centers, Baptist Hospital Mutengene had the highest value 94.97 (38%), followed by Regional Hospital Buea 83.74 (33%), and Central Clinic Tiko the least 73.13 (29%). Conclusion: Patient registration year, treatment center, TB classification, and HIV status were identified to significantly affect TSR, hence, effectiveness of the TB program.

Keywords: Evaluation, treatment outcomes, Tuberculosis Control Program


How to cite this article:
Atekem KA, Tanih NF, Ndip RN, Ndip LM. Evaluation of the tuberculosis control program in South West Cameroon: Factors affecting treatment outcomes. Int J Mycobacteriol 2018;7:137-42

How to cite this URL:
Atekem KA, Tanih NF, Ndip RN, Ndip LM. Evaluation of the tuberculosis control program in South West Cameroon: Factors affecting treatment outcomes. Int J Mycobacteriol [serial online] 2018 [cited 2021 Jul 25];7:137-42. Available from: https://www.ijmyco.org/text.asp?2018/7/2/137/234317




  Introduction Top


Tuberculosis (TB) is an infectious disease and leading cause of death alongside with HIV/AIDS.[1] The World Health Organization in 1993 declared TB a global emergency in response to the steady increase in its incidence, shifting dynamics in TB disease related to HIV/AIDS epidemic, and the emergence of multidrug-resistant TB (MDR TB). This increasing burden of TB has been attributed to many factors including neglect of TB control by governments, poor management of programs, the spread of HIV, poverty, population growth and rapid, uncontrolled urbanization.[2] In response, the Directly Observed, Therapy Short-course (DOTS) was recommended as the global TB control strategy [3] which requires that the patient be treated for 8 months: 2 months intensive phase with treatment given under strict supervision by a trained observer, and 6 months continuation phase. This strategy is believed to be the most effective method of TB control, thus the total number of countries implementing DOTS course DOTS has increased steadily from 1995 to 2003.[4]

Like in many countries, TB is still a public health problem in Cameroon, especially as Cameroon, has been classified as one of the 41 high-TB/HIV burden in the category of TB/HIV.[1] The National TB Control Program (NTCP) put in place by the State in 1996 aims to fight this disease through the implementation of DOTS, in order to meet the TB-related Millennium Development Goals (MDGs) of halting and reversing the incidence of TB by 2015.[4] To halve the prevalence rate by 2015, TB control programs must reach global targets for detection (70%) and treatment success (85%) and also reduce the incidence rate by at least 2%. To halve the death rate, incidence must decrease more steeply, by at least 5%–6%.[5]

The United Nations MDGs are stimulating more rigorous evaluations of the impact of DOTS and other possible strategies for TB control, and the treatment success rate (TSR) indicates the effectiveness of any TB control program. The objectives of the NTCP are to detect at least 70% of smear-positive pulmonary TB (SPPTB) cases and to successfully treat at least 85% of these patients. Implementing DOTS in the NTCP of Cameroon has not met the MDGs targets of 85% TSR. The TSR for the Southwest Region for 2011 and 2012 were 71.4% and 71.2%, respectively, which were below the 85%. With the low TSR, the study aimed at investigating factors that may be affecting the program from reaching the stated target in a bid to inform policy and improve on the performance of the program.


  Methods Top


Facility selection

The study involved stratifying the seven TB treatment centers in Fako Division as government, not-for-profit (faith-based), and for-profit structures. With these strata, five government treatment centers, one not-for-profit and one for-profit were identified. One out of the five government structures were selected by simple random selection; and the other structures were automatically chosen since only one structure for each was represented. Selected facilities included BHM (not-for-profit), Regional Hospital Buea (RHB) (government), and CCT (for-profit).

Ethical approval

Ethical clearance was obtained from the Institutional Review Board (IRB) of the Faculty of Health Sciences, University of Bureau, and Cameroon Baptist Hospital Health Board IRB. Administrative authorization was obtained from the Regional Delegation of Public Health for the Southwest as well as the directors of the hospitals where the centers were operational.

Data collection and abstraction

This was a cross-sectional retrospective study and information on the profile and treatment outcome of TB patients was obtained from the TB register from January 2011to December 2012. The registers reviewed contained basic information such as patient's age, sex, address, TB type, and treatment outcome. Treatment outcomes were classified according to the National Tuberculosis Control Program guideline as successful (cured/completed) or unsuccessful (default/failure/death). Successful treatment of TB involved taking anti-TB drugs for at least 6 months: cured (finished treatment with negative bacteriology result at the end of treatment) and completed treatment (finished treatment, but without bacteriology result at the end of treatment). Failure cases (those who remained smear-positive at 5 months despite correct intake of medication), defaulted treatment (patients who interrupted their treatment for 2 consecutive months or more after registration), died (patients who died from any cause during treatment), and transferred out (patients whose treatment results are unknown due to transfer to another health facility). Values for these outcomes were used to calculate indicators for the program.

The main outcome of interest was treatment success of all TB patients. Participants were categorized as having successful treatment if their record showed that they were cured, or they had completed treatment. Otherwise, they were categorized as treatment not successful. Demographic data (age and sex) were also collected, and age divided into seven age groups. Data collected were grouped quarterly, making a total of 4 quarters per year.

Data entry and descriptive analysis were carried out using SPSS version 20.0. Descriptive statistical methods were used to generate and to summarize the frequencies for categorical variables. Mean, standard deviation, and median of numerical variable (age) were also computed using descriptive statistics. Binomial and multinomial logistic regression analyses were used to verify statistical significance of risk factors on treatment success. In the study, no specific factor was hypothesized as the main risk factor for treatment success. Hence, patient age, sex, treatment center where patient was receiving treatment, yearly quarter in which patient was registered, year when patient started treatment, treatment category, and type and HIV status of patients were all considered. Results were reported as being statistically significant if P value was <5%.

TSR which was the main indicator for performance was calculated by dividing the number of new SPPTB cases cured plus those that completed treatment by the total number of new SPPTB registered. These values were entered in Excel 2010 and compared across the different centers and the years. Other core indicators for the program were also calculated and comparison done across the center.


  Results Top


Demographic characteristics of patients

A total of 895 registered records of TB patients were obtained from the three treatment centers, RHB, Baptist Hospital Mutengene (BHM), and Central Clinic Tiko (CCT) from January 2011 to December 2012. Of this, 416 (46.5%) were females while 479 (53.5%) were males [Table 1]. These patients had a mean, standard deviation, and median age of 34.55, 12.104, and 33.00 years, respectively, and minimum and maximum age of 3 and 73. In total, 510 (57.0%) were pulmonary positive, 225 (25.1%) were pulmonary negative, and 160 (17.9%) were extrapulmonary (EP) TB patients [Table 1]. Of the 895 TB-registered cases, 726 (81.1%) were cured and 169 (18.9%) were considered not cured based on the definition of treatment success in this study [Table 1].
Table 1: Characteristics and treatment success for registered tuberculosis patients in Baptist Hospital Mutengene, Regional Hospital Buea and Central Clinic Tiko from January to December 2011-2012

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Treatment outcomes

A total of 10 core indicators were calculated and rates compared across the centers for the 2 years. Indicators were calculated following the calculations stated in the WHO compendium for indicators [Figure 1]. Indicators revealed TB case detection was highest in BHM, RHB, and CCT in that descending order. The same trend applies to indicators such as TSR, cure rate, and smear conversion rate. Default rates and death rates were higher in CCT and RHB while Treatment failure rate was not recorded for all three treatment centers [Figure 1].
Figure 1: Indicators compared across the centers for 2011–2012

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TSR was compared across the three treatment centers under investigation [Figure 2]. As per treatment center, BHM recorded the highest TSR 94.97 (38%) compared to RHB 83.74 (33%) and CCT 73.13 (29%); this difference was statistically significant (P< 0.0001). To identify factors affecting TSRs, a number of factors were taken into consideration. These were year, sex, age, HIV status, treatment center, category of TB, type of TB, and yearly quarter in which patient was cured. Binomial logistic regression was used to determine if these factors significantly affected treatment success. Treatment centers (P = 0.001), TB classification (P = 0.001), and HIV status (P = 0.003) were found to significantly affect TSR [Table 2].
Figure 2: Treatment success rate for the treatment centers for the years 2011 and 2012

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Table 2: Factors affecting treatment success rate and their statistical values

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Multinomial logistic regression analysis was carried out on factors that were significant. As shown in [Table 3], TB patients registered in the BHM were 3.94 times more likely to be treated successfully than patients from RHB, and this difference was significant (P< 0.0001; confidence interval [CI] = 2.60–6.00). CCT had some much higher odds (4.40) of patients not being cured than BHM, which was also statistically significant (P< 0.0001; CI = 2.73–7.11). Patients registered in 2012 had significantly higher TSR compared to those registered in 2011 with P = 0.015 (1.09–2.17) and odds ratio of 1.54. Furthermore, patients in the TB category SPPTB were significantly more likely to be treated successfully compared to smear-negative pulmonary TB (SNPTB) and EPTB (P< 0.0001; CI = 0.24–0.58 and 0.52–1.30 respectively). In addition, HIV-negative patients had a significantly higher TSR than HIV-positive patients (P = 0.003; 0.41–0.84). HIV-negative TB patients had 0.59 odds of being cured than HIV-positive TB patients.
Table 3: Multinomial logistic regression on factors significant to treatment success rate

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  Discussion Top


The evaluation of TB control program for Fako Division of the South West Region of Cameroon has demonstrated a fairly strong performing system and the tenacity of factors affecting the program's effectiveness. There was, however, little evidence that adverse events were properly monitored or addressed given the data used for the study was reviewed from registers; this shortcoming is disconcerting and warrants attention.

Overall, TSR of registered TB patients in this study was 83.4% which is slightly higher than 82.7%, and 80.9% obtained in a similar study in Addis Ababa, Ethiopia, and Ebonyi in Southeastern Nigeria;[6],[7] and even higher compared to a previous finding in the Gondar University Teaching hospitals, Ethiopia (29.5%).[8] However, it was lower than 90.9% obtained in the Debre Tabor General Hospital, northwest Ethiopia [9] and 86.5% in Afar Regional State, still in Ethiopia.[10] Although high, this treatment success did not attain the WHO stated target of 85%. This could be attributed to the data recording systems of the different centers, which may not have been accurate enough.

As per treatment center, BHM recorded the highest TSR (94.97%) compared to RHB (83.74%) and CCT (73.13%); this difference was statistically significant (P< 0.0001) and the results were similar to the study of Getahun et al.[6] where treatment center-affected treatment success. The highest TSR recorded by BHM may be attributed to the fact that it is a faith-based facility, where there are a greater patient care and better case management; there is a better outcome compared with private and government facilities which may be lacking in one of or more aspects.

TSR was significantly better for 2012 than in 2011 (P = 0.015) and patients in 2012 were 1.54 times more likely of being treated successfully than patients in 2011. This significantly higher TSR can be attributed to improved TB control program policy for the year 2012. Grouping quarterly reports (Q1-Q4) into two seasons of Cameroon, with each quarter representing a trimester, it was not surprising that the dry season (Q1 & Q4) recorded a higher proportion of TB cases than the rainy season (Q2 & Q3) as this is usually a dusty and windy period with higher transmission rate. However, seasonal variation did not affect TSR (P = 0.279).

Smear-positive pulmonary-registered TB (SPPTB) cases recorded significant (P< 0.001) highest treatment success (61.02%) and extrapulmonary the least (15.70%). This finding was contrary to the study of Babatunde et al.,[11] in which EPTB had the highest treatment success (60.0% and 42.6%, respectively); and SNPTB patients having the highest TSR s compared to EPTB and SPPTB patients (89.5%, 87.2%, and 84.0%, respectively) in Melese et al. 2016.[12] This may suggest that SPPTB and SNPTB cases are better treated than EPTB cases; the disease is disseminated or affects other organs of the body which may be difficult to treat. From the study, it was observed that males had a higher TSR than females, 53.03% and 46%, respectively, and these results are not in line with those of Tessema et al.[6] However, the difference in treatment success with respect to sex was not statistically significant (P = 0.543), but significant with the study of Worku et al. 2018.[9] New TB cases had the greatest number of cured cases (674, 92.84%) compared to retreatment TB cases (8, 1.10%), a finding like the study by Bhagat and Gattani.[13] New cases are naive to any anti-TB drug, recognizing the drugs for the first time may respond faster to treatment and develop little or no resistance. For retreatment cases, they have been exposed to the treatment more than once and resistance may likely develop affecting treatment success. This variation of TB type with TSR was not statistically significant (P = 0.238) as was the study by Getahun et al.[6]

The study established that treatment success of TB patients coinfected with HIV was lower (45.87%) compared to TB-HIV negative patients (51.38%) and this is in line with Bhagat and Gattani,[13] but contrary to the study in conducted by Mekonnen et al. 2015 in Northeastern Ethiopia;[14] SPPTB having the highest treatment success among the various categories and was highly significant (P = 0.003). This could be attributed to drug-to-drug interactions which may lead to resistance to anti-TB drugs.[15] In addition, HIV patients may not be compliant to their treatment and turn to neglect the regimen as they think having HIV is the end of life to them. Furthermore, death occurs more in HIV-TB patients than in non-HIV TB patients due to weakened immune system.[16] With this, the patients are recorded as dead instead of cured or treatment completed.

This study faced the limitation of hospital records not being complete as some registered cases had no record on HIV status. Thus, analysis did not include missing values. In addition, the quality of the data collected was not checked for authenticity, though after collection, it was rechecked for mathematical errors and omissions. However, the main strength of the study is that it cuts across the three different types of health structure in Cameroon. Hence, the finding can clearly reflect the treatment outcome of TB patients under DOTS program at the government, faith-based and private health facilities.


  Conclusion Top


In summary, the mean TSR of all registered pulmonary TB patients was 83.4% which though high, did not meet the WHO target for TSR; this was significantly affected by the year in which the patient started treatment, HIV status, category of TB, and the treatment center where the patient was receiving treatment. We recommend reinforcing completeness and accuracy of data entry, particularly in the recording of HIV test results and treatment determination. Standard operating procedures could be developed to assist with this activity. In addition, government- and private-based treatment centers should study the managerial strategies for handling TB cases from the faith-based center and try to implement them. Sensitization on the prevention of HIV/AIDS should be reinforced so as to have a lesser number of HIV-infected cases and hence a better TB treatment outcome.

Financial support and sponsorship

Nil.

Conflicts of interest

There are no conflicts of interest.



 
  References Top

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Tessema B, Muche A, Bekele A, Reissig D, Emmrich F, Sack U, et al. Treatment outcome of tuberculosis patients at Gondar University Teaching Hospital, Northwest Ethiopia. A five – Year retrospective study. BMC Public Health 2009;9:371.  Back to cited text no. 8
    
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    Figures

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    Tables

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