INTRODUCTION:

Tuberculosis is an infectious disease caused by the pathogenic bacteria Mycobacterium tuberculosis, primarily affecting the lungs. It remains a significant global and national health challenge due to its high prevalence and mortality rates. In 2022. tuberculosis was the second leading cause of death from infectious diseases worldwide, following COVID-19. with an estimated 1.3 million deaths. In the same year, the World Health Organization (WHO) reported 7.5 million new TB cases globally, bringing the total number of cases to 10.6 million¹.

Indonesia ranks second in the world for the highest number of TB cases, accounting for 10% of global TB cases in 2022¹. Nationally, the estimated incidence and mortality rates for tuberculosis are alarmingly high, with 1.060.000 new cases and 141.000 deaths². Despite ongoing efforts, Indonesia has not met its national and international TB control targets. The country has fallen short of the objectives outlined in the 2020–2024 tuberculosis control strategy and Presidential Regulation Number 67 of 2021 concerning Tuberculosis Control³. On the international front, Indonesia has also failed to meet the targets set by "The End Tuberculosis Strategy," showing a troubling decline from the desired progress^1,2.

Assessing medication adherence is a crucial aspect of medicines optimization. It helps pharmacists identify issues with adherence, understand the factors affecting non-adherence, and implement targeted interventions to improve adherence and optimize medication regimens. It is an important element in achieving optimal clinical outcome of medicines⁴. Conducting research on medication adherence in DSTB patients is crucial for several reasons. Firstly, the average adherence rate for chronic diseases stands at only 50%, a figure presumed to be even lower in developing countries due to limited healthcare resources and restricted access to health services⁵. This low adherence rate poses significant challenges, as it directly affects treatment⁶, and non-adherence can heighten the risk of drug resistance and residual symptoms following TB treatment. Secondly, the level of adherence and the factors influencing it vary considerably across different regions in Indonesia. For instance, a 2018 cross-sectional study on TB drug adherence in Bandung reported an 84% adherence rate⁷. In the same year, a descriptive observational study in Pamekasan indicated a higher adherence rate of 87% for Category I pulmonary TB⁸. Conversely, a cross-sectional study in Bandar Lampung on hospitalized TB patients taking ATD reported a lower adherence rate of 74.4%⁹. Thirdly, previous studies on adherence have reported limitations due to bias in self-reporting, necessitating the adoption of more objective methods, such as the Proportion of Days Covered (PDC) derived from patient medical records. The PDC method, recommended by the Pharmacy Quality Alliance (PQA), a U.S.-based quality assurance organization, is instrumental in improving drug safety, adherence, and appropriate usage. This method mitigates the risk of overestimating adherence and provides detailed insights into adherence patterns, including the duration and treatment phases during which patients were not supplied with medication.

This research was conducted at UIH, an accredited type A teaching hospital located in Depok, West Java, Indonesia that provides TB treatment. Depok continues to grapple with high TB prevalence and mortality rates. In 2022, TB cases in Depok doubled compared to the previous year, reaching the highest number since 2019. TB ranked as the 10th most common outpatient disease in Depok, with 3.692 new cases and 15.018 visits, and was the 11^th leading cause of death, accounting for 70 fatalities^10,11. However, research on adherence to ATD in DSTB patients at UIH remains limited. Therefore, an analysis of adherence to ATD and its factors is essential for considering appropriate interventions to improve adherence and achieve improved treatment outcomes.

METHOD:

Research Design and Data Collection:

This study employed a retrospective cross-sectional design. Data collection was carried out on the research population, which consisted of adult outpatients with drug-sensitive tuberculosis (DSTB) at UIH during the period from 1^st January 2022 to 31^st December 2023. in accordance with predefined inclusion and exclusion criteria. The total sampling technique was used in this study to give deeper insights and a complete picture of targeted population. The inclusion criteria were patients aged 18 years and older, patients with pulmonary DSTB with or without extrapulmonary TB, patients who had initiated their DSTB treatment at UIH and had been undergoing DSTB treatment for at least six months. The exclusion criteria were DSTB patients who were receiving ATD (anti-tuberculosis drugs) beside the first-line ATD regimen.

Data Analysis:

The data obtained were processed using Microsoft Excel and SPSS 25.0. Patient adherence with anti-tuberculosis drug (ATD) usage was calculated using the Proportion of Days Covered (PDC) formula^12,13. The degree of patient comorbidity was assessed using the Charlson’s Comorbidity Index¹⁴. Adverse drug reactions (ADRs) were identified through analysis using the Naranjo algorithm, where adverse events scoring above 0 were classified as ADRs. Additionally, the factors related to adherence were analysed and grouped according to the WHO 5-dimensional adherence model⁵.

Duration of treatment (in days)

PDC (%) = --------------------------------------- x 100

Total days in the analysis period

Data analysis was conducted using univariate, bivariate, and multivariate methods. Univariate analysis was employed to examine the sociodemographic and clinical characteristics, tuberculosis treatment, and treatment adherence of DSTB patients at UIH. Bivariate analysis was performed using Fisher's Exact Test to assess the relationship between adherence and sociodemographic characteristics, conditions, and patient treatment. A p-value of <0.05 was considered statistically significant. Factors with a p-value ≤0.2 were further analysed using multivariate logistic regression to control for the influence of various research variables. The results were deemed significant if a p-value of <0.05 was achieved. The findings from the analysis are presented in tables and percentages.

Figure 1. Flowchart of study sample selection

RESULT:

Of the 623 adult outpatients diagnosed with DSTB at UIH during the study period, 126 patients were excluded from the research because their first outpatient episode occurred after August 2023 hence, they had not undergone DSTB treatment for at least six months by the end of December 2023. Additionally, 181 patients were excluded because they did not receive DSTB treatment at UIH, 70 patients were excluded because they did not initiate their DSTB treatment at UIH, and 19 patients were excluded for receiving treatment involving ATD outside of the first-line ATD regimen. Consequently, the total sample obtained for this research was 103 patients.

Sociodemographic and clinical characteristics, tuberculosis treatment, and treatment adherence of DSTB patients at UIH:

Table 1 provides an overview of the characteristics of DSTB outpatients at UIH from January 1. 2022. to December 31. 2023. The research presents various sociodemographic and clinical characteristics, revealing that the majority fall within the 18–64-year age group (91.3%), with a smaller proportion belonging to the elderly group (8.7%).

Table 2 illustrates the ATD dosage forms administered to patients during their TB treatment. The findings indicate that the proportion of patients who received at least one type of loose ATD during treatment (75.7%) was comparable to those who received at least one type of FDC (fixed-dose combination) during treatment (68.0%). The most commonly prescribed ATD dosage forms for DSTB outpatients were Rifampicin (75.7%) and Isoniazid (62.1%).

Table 3 details the distribution of ADR (adverse drug reactions) based on the results of the Naranjo algorithm for determining adverse events. The research found that majority of patients (73.8%) experienced ADR, with nausea being the most frequently recorded adverse reaction occurring in 49.5% of the cases.

Table 1. Sociodemographic and clinical characteristics, tuberculosis treatment, and treatment adherence of drug-resistant TB patients

Characteristics	Number of Patients (n = 103) (%)
Age, years
Median (min—max)	39 (18—82)
18—64 years old	94 (91.3)
≥65 years old	9 (8.7)
Sex
Male	47 (45.6)
Female	56 (54.4)
Comorbidities
Comorbidity Severity (Charlson’s Comorbidity Index)
Median (min—max)	0 (0—10)
None (0)	55 (53.4)
Mild (1–2)	22 (21.4)
Moderate (3–4)	15 (14.6)
Severe (≥5)	11 (10.7)
Disease Profile Characteristics and Treatment
Tuberculosis Types Based on Anatomical Location
Pulmonary	72 (66.9)
Pulmonary and extrapulmonary	31 (30.1)
Types of Treatment
Outpatient	65 (63.1)
Outpatient and inpatient	38 (36.9)
Treatment Characteristics
History of Tuberculosis Treatment
New cases	83 (80.6)
Relapse cases	12 (11.7)
Treatment failure cases	8 (7.8)
Treatment Duration (in months)
Median (min—max)	7 (6—15)
6 months	43 (41.7)
>6 months	60 (58.3)
Treatment Regimen
Category 1 (RHZE/RH)	94 (91.3)
Other regimens	9 (8.7)
AT Dosage Form
All Fixed-Dose	25 (24.3)
All Free-Dose	33 (32.0)
Combination of Fixed and Free Doses	45 (43.7)
Polypharmacy^a
Median number of drugs per patient (min-max)	4 (1—9)
Patients without polypharmacy	53 (51.5)
Patients with polypharmacy	50 (48.5)
TB-Related Non-ATD Dispensing
Based on Drug Compounds
Pyridoxine (Vitamin B6)	66 (64.1)
Lansoprazole	48 (46.6)
Cholecalciferol (Vitamin D3)	39 (37.9)
N-Acetylcysteine	28 (27.2)
Cetirizine	25 (24.3)
Based on ATC Therapeutic Subgroups
Vitamins	86 (83.5)
Drugs for acid-related disorders	56 (54.4)
Cough and flu preparations	45 (43.7)
Antihistamines for systemic use	27 (26.2)
Bile and liver therapy	20 (19.4)
ADR data collection
ADR recorded (Table 3)	76 (73.8)
ADR not recorded	27 (26.2)
History of ATD Regimen Changes due to ADR
Yes	9 (8.7)
No	94 (91.3)
Characteristics of Drug Adherence
Patient Treatment Adherence (%)
Median (min—max)	100 (70—100)
High adherence (≥90%)	94 (91.3)
Moderate adherence (80–89%)	8 (7.8)
Non-adherence (<80%)	1 (0.9)
Number of Days Patients Not Supplied with ATD (in days)
Median (min—max)	0 (0—57)
0 days	63 (61.2)
1–7 days	16 (15.5)
8–14 days	8 (7.8)
15–30 days	14 (13.6)
>30 days	2 (1.9)
Number of Days Patients Not Supplied with ATD in Intensive Phase
Median (min—max)	0 (0—30)
0 days	86 (83.5)
At least 1 day	17 (16.5)
Number of Days Patients Not Supplied with ATD in Continuation Phase, days
Median (min—max)	0 (0—57)
0 days	68 (66.0)
At least 1 day	35 (34.0)

ATC: Anatomical Therapeutic Chemical, ICD-10: International Statistical Classification of Diseases and Related Health Problems 10^th revision, FDC: fixed-dose combination, ATD: anti tuberculosis drug, ADR: adverse drug reactions

^a Based on the number of types of anti-tuberculosis drugs (ATD) and non-ATD related to tuberculosis most commonly dispensed at one time at the pharmacy during tuberculosis treatment

Table 2. Patients’ ATD profile based on pharmacy dispensing records

ATD	Number of Patients (n = 103) (%)
Free-dose ATD form	78 (75.7)
Streptomycin	2 (1.9)
Rifampin	78 (75.7)
Isoniazid	64 (62.1)
Pyrazinamide	54 (52.4)
Ethambutol	57 (55.3)
Isoniazid with Pyridoxine (Vitamin B6)	60 (58.4)
Fixed-dose combination ATD	70 (68.0)
FDC RHZE	54 (52.4)
FDC RH	41 (39.8)

ATD: anti-tuberculosis drugs, RHZE: Rifampicin, Isoniazid, Pyrazinamide, Ethambutol, RH: Rifampicin, Isoniazid

Table 3. Results of naranjo algorithm analysis to generate ADR data related to ATD

ADR	Number of Patients			Total patient (n = 103) (%)
ADR	*Possible* (score 1—4)	*Probable* (score 5—8)	*Definite* (score ≥9)	Total patient (n = 103) (%)
Nausea	39	12	0	51 (49.5)
Vomiting	12	3	0	15 (14.6)
Dyspepsia	14	3	0	17 (16.5)
Peripheral neuropathy	8	8	0	16 (15.5)
Hyperuricemia	3	8	0	11 (10.7)
DILI	7	4	0	11 (10.7)
Menstrual changes	1	0	0	1 (1.0)
ATD allergy symptoms	8	10	1	19 (18.4)

DILI: Drug induced liver injury; ATD: anti-tuberculosis drugs; ADR: adverse drug reaction

Table 4. Results of analysis of the relationship between patient characteristics and adherence using fisher's exact test

Patients’ Characteristics	Adherence (n = 103)			P value
Patients’ Characteristics	High Adherence (n = 94) (%)	Moderate Adherence (n = 8) (%)	Non-Adherence (n = 1) (%)	P value
Sociodemographic Characteristics
Age				0.576
18—64 years old	86 (91.5)	7 (87.5)	1 (100.0)
≥65 years old	8 (8.5)	1 (12.5)	0 (0.0)
Sex				0.044^b
Male	45 (47.9)	1 (12.5)	1 (100.0)
Female	49 (52.1)	7 (87.5)	0 (0.0)
Comorbidity Severity (Charlson’s Comorbidity Index)				0.066
None (0)	51 (54.3)	3 (37.5)	1 (100.0)
Mild (1–2)	22 (23.4)	0 (0.0)	0 (0.0)
Moderate (3–4)	13 (13.8)	2 (25.0)	0 (0.0)
Severe (≥5)	8 (8.5)	3 (37.5)	0 (0.0)
Treatment Characteristics
History of Tuberculosis Treatment				0.138
New cases	75 (79.8)	8 (100.0)	0 (0.0)
Relapse cases	12 (12.8)	0 (0.0)	0 (0.0)
Treatment failure cases	7 (7.4)	0 (0.0)	1 (100.0)
Treatment Duration				0.687
6 months	39 (41.5)	3 (37.5)	1 (100.0)
>6 months	55 (58.5)	5 (62.5)	0 (0.0)
ATD Dosage Form				0.753
Full FDC	24 (25.5)	1 (12.5)	0 (0.0)
Full Free-dosage	41 (43.6)	4 (50.0)	0 (0.0)
Combination of FDC and free dosage	29 (30.9)	3 (37.5)	1 (100.0)
Polypharmacy^a				0.716
Patients without polypharmacy	47 (50.0)	5 (62.5)	1 (100.0)
Patients with polypharmacy	47 (50.0)	3 (37.5)	0 (0.0)
ADR Record				0.327
Recorded	71 (75.5)	4 (50.0)	1 (100.0)
Not Recorded	23 (24.5)	4 (50.0)	0 (0.0)
ADR - Nausea				0.060
Recorded	49 (52.1)	1 (12.5)	1 (100.0)
Not Recorded	45 (47.9)	7 (87.5)	0 (0.0)
History of Regimen Changes Due to ADR				0.221
Yes	7 (7.4)	2 (25.0)	0 (0.0)
No	87 (92.6)	6 (75.0)	1 (100.0)

FDC: fixed dose combination, ATD: anti-tuberculosis drug, ADR: adverse drug reaction

^aBased on the number of types of ATD and non-ATD related to tuberculosis in a single dispensing at the pharmacy during tuberculosis treatment

^bResult is statistically significant

Table 5. Results of logistic regression analysis of factors influencing high and moderate adherence

Research Variable	P value	Crude OR (95% CI)	Adjusted OR (95% CI)^a
Sex
Male	0.069	6.429 (0.761–54.313)	8.951 (0.842–95.175)
Female		1.00 (Reference)	1.00 (Reference)
Comorbidity Severity
None (0)	0.044*	6.375(1.091–37.253)	8.305 (1.056–65.286)
Mild (1–2)	0.998	>1000 (overflow)	>1000 (overflow)
Moderate (3–4)	0.975	2.437(0.332–17.907)	0.960 (0.079–11.613)
Severe (≥5)		1.00 (Reference)	1.00 (Reference)
History of Tuberculosis Treatment
New Cases	0.999	<0.001	<0.001
Relapse Cases	1.000	0.996 (overflow)	0.179 (overflow)
Treatment Failure Cases		1.00 (Reference)	1.00 (Reference)
ADR Record - Nausea
Not Recorded	0.081	0.062(0.131–0.016)	0.121 (0.011–1.302)
Recorded		1.00 (Reference)	1.00 (Reference)

^aaOR was adjusted for the variables sex, severity of comorbidities, history of tuberculosis treatment, and ADR data in the form of nausea

^*Statistically significant relationship

Table 6. Results of logistic regression analysis of factors influencing adherence (low and moderate)

Research Variable	P value	Crude OR (95% CI)	Adjusted OR (95% CI)^a
Sex
Male	1.000	<0.001 (overflow)	150.27 (overflow)
Female		1.00 (Reference)	1.00 (Reference)
Comorbidity Severity
None (0)	1.000	>1000 (overflow)	29.142 (overflow)
Mild (1–2)	0.999	1.394 (1.394—1.394)	>1000 (overflow)
Moderate (3–4)	0.999	1.642 (1.642—1.642)	<0.001 (overflow)
Severe (≥5)		1.00 (Reference)	1.00 (Reference)
History of Tuberculosis Treatment
New Cases	0.998	<0.001 (overflow)	<0.001 (overflow)
Relapse Cases	-	<0.001 (overflow)	<0.001 (overflow)
Treatment Failure Cases	-	1.00 (Reference)	1.00 (Reference)
ADR Record - Nausea
Not Recorded	0.999	<0.001 (overflow)	<0.001 (overflow)
Recorded		1.00 (Reference)	1.00 (Reference)

^aaOR was adjusted for the variables Sex, severity of Comorbidities, history of tuberculosis treatment, and ADR data in the form of nausea

DISCUSSION:

These findings align with previous research indicating that DSTB patients are predominantly adults rather than elderly individuals^15,16. At UIH, female DSTB patients represented a larger proportion (54.4%) compared to male patients (45.6%). The research identified 61 different comorbidities among DSTB outpatients, with the most prevalent being type 2 diabetes mellitus (15.5%) and hypertension (14.6%). Consistent with other research, type 2 diabetes mellitus is frequently associated with tuberculosis^15,17,18.

However, this research revealed that the most common comorbidity group based on the ICD-10 classification was circulatory system diseases (24.3%), followed by respiratory system diseases (21.4%) and endocrine, nutritional, and metabolic diseases (15.5%). The severity of comorbidities, as assessed by the Charlson’s Comorbidity Index (CCI), indicated that 53.4% of patients had health conditions that did not contribute to the severity of their comorbidities (CCI score = 0). This finding is corroborated by other studies¹⁸. Among patients with comorbid conditions affecting the severity of the CCI, most had mild severity (21.4%), while those with severe comorbidities were the least represented (10.7%).

The research also showed a higher prevalence of patients diagnosed solely with pulmonary TB (66.9%) compared to those with both pulmonary and extrapulmonary TB (30.1%). This observation is consistent with other studies that report a smaller proportion of extrapulmonary TB cases among overall TB patients¹⁷. Additionally, a minority of outpatients in the research sample experienced one or more episodes of hospitalization during TB treatment (36.9%).

The majority of DSTB outpatients in the research were classified as new cases (80.6%), meaning they had either no prior history of TB treatment or had received ATD for less than one month previously. Among these patients, a significant proportion (58.3%) underwent treatment for more than six months, surpassing those with the standard six-month treatment duration. The predominant treatment regimen was Category 1 TB treatment (91.3%).

The most frequently administered ATD dosage form was a combination of fixed-dose combinations (FDC) and release forms (43.7%), while patients receiving only FDCs constituted the smallest group (24.3%). These findings are consistent with those of other studies^15,19. Throughout the TB treatment phases, Rifampicin and Isoniazid were commonly used, although Isoniazid was administered in lower quantities than Rifampicin, possibly due to its combination with Pyridoxine (Vitamin B6), which was also given to the majority of patients (58.3%).

The research observed that DSTB outpatients at UIH were nearly evenly divided concerning polypharmacy, with the polypharmacy group being relatively small (48.5%). This contrasts with findings from other studies¹⁵, likely because this research focused solely on TB-related treatments without accounting for the additional medication burden from other conditions.

The research identified 42 non-ATD drug compounds administered to patients. Commonly used non-ATD drug compounds included Pyridoxine (64.1%), Lansoprazole (46.6%), Cholecalciferol (37.9%), N-Acetylcysteine (27.2%), and Cetirizine (24.3%). Pyridoxine is frequently used in conjunction with TB treatment to mitigate the side effects of Isoniazid, such as peripheral neuropathy¹⁵. These non-ATD drugs were categorized into 15 therapeutic subgroups according to the Anatomical Therapeutic Chemical (ATC) classification, with the most common subgroups being vitamins (83.5%), drugs for acid-related disorders (54.4%), cough and flu preparations (43.7%), antihistamines for systemic use (26.2%), and bile and liver therapy (19.4%).

Vitamins play various roles in TB treatment: Vitamin B6 (Pyridoxine) prevents peripheral neuropathy caused by Isoniazid, Vitamin C helps sterilize TB cultures and prevent persistent bacteria, Vitamin D inhibits Mycobacterium replication in vitro, shorten conversion time and reduce severe TB clinical manifestation²⁰ and Vitamin E helps maintain oxidative balance related to TB pathology. Antihistamines like Cetirizine are used to manage allergic reactions, and bile and liver therapy can address liver enzyme elevation due to ATD use (“Guidelines for The Management of Adverse Drug Effects of Antimycobacterial Agents,” 1998)²¹.

Another research reported that more than half of TB patients experienced regimen-related adverse drug reactions (ADR), aligning with findings from Kiros et al¹⁷. In contrast, this research found that only a small proportion of patients (8.7%) required regimen changes due to ADR. Generally, the ADR related to ATD for DSTB were minor and manageable without altering the therapy regimen²². However, severe ADR, such as severe nausea, drug-induced liver injury (DILI), severe hyperuricemia, peripheral neuropathy, intense allergic reactions, acute kidney injury, and psychosis were noted as reasons for regimen modifications^22,23,24.

Patient medication adherence to ATD was assessed using the Proportion of Days Covered (PDC) method, categorizing adherence into high, moderate, and non-adherence. According to recommendations, high adherence is defined as taking more than 90% of the prescribed TB medication²⁵. The research revealed that the majority of DSTB outpatients at UIH exhibited high adherence (91.3%), with a smaller fraction classified as moderately adherent (7.8%) and only one patient (0.9%) falling into the non-adherent category. This rate of non-adherence is notably lower than in some other studies¹⁷, though it is consistent with findings of high adherence rates reported elsewhere¹⁷.

The high adherence observed among patients at UIH could be influenced by its status as a teaching hospital. A similar research at Jimma University Specialist Hospital also reported high adherence levels²³. The research found that the majority of patients were consistently supplied with ATD throughout both the intensive (83.5%) and continuation phases (66.0%). Notably, patients missing at least one day of ATD were more prevalent in the continuation phase (34.0%) compared to the intensive phase (16.5%), despite the higher ATD dosage in the intensive phase. This suggests a potential decline in adherence as treatment progresses. Other research supports this trend, noting that missed doses are more common in the continuation phase²⁶, though some studies have found a decreasing trend in discontinuation rates during the continuation phase²⁷. In addition, another study has found reasons for discontinuation may be because of patient nonadherent, distance from healthcare facilities, complexity of drug regimen and ADR²⁸.

In this research, the majority of patients missed medication for short period of 1–7 days (15.5%), with only two patients (1.9%) missing ATD for more than 30 days. This aligns with findings from other studies, which also report that most patients missed doses for one week or less²⁶.

Research has consistently shown that age can influence adherence to tuberculosis (TB) treatment, though findings vary. Some studies suggest that younger patients are more likely to adhere to TB treatment regimens, while older patients may be more prone to non-adherence^15,18. Conversely, other research indicates that elderly patients may demonstrate higher adherence rates²⁹.

In this research, gender emerged as a significant factor affecting adherence (p = 0.044). Women were found to be more likely to be in the high adherence (52.1%) and moderate adherence (87.5%) groups compared to men. Non-adherent patients were predominantly male, aligning with other studies that also report higher adherence rates among women¹⁸.

Regarding comorbidities, this research did not find a significant association between the severity of comorbidities and adherence to TB treatment (p = 0.066). The majority of patients in the high adherence group had a Charlson’s Comorbidity Index (CCI) score of 0 (54.3%), and non-adherent patients also predominantly had a CCI score of 0. These findings are consistent with another research indicating that CCI does not significantly correlate with adherence among DSTB patients¹⁸.

The research found no significant relationship between a history of tuberculosis treatment and adherence (p = 0.138), even though non-adherent patients had a history of treatment failure. This contrasts with other studies where relapse or drug-resistant cases were significantly associated with non-adherence^30,31,32. A possible explanation for these differing findings is the enhanced patient education provided at UIH, a teaching hospital. Increased education about drug resistance and TB treatment may improve adherence rates, as suggested by other research indicating that better education can lead to higher adherence¹⁶.

Similarly, the research did not find a significant relationship between the duration of treatment and adherence (p = 0.687). This result diverges from findings by Ali and Prins³², who identified longer treatment durations as a significant factor in non-adherence, and Pinho et al³³, who associated shorter treatment durations with increased adherence. The lack of significance in this research might be attributed to patients' understanding of the importance of completing their treatment, regardless of duration, which can support adherence³⁴.

The research's findings indicate that the use of Fixed Dose Combination (FDC) ATD did not significantly impact treatment adherence compared to free-dose ATD (p = 0.753). This result aligns with various studies suggesting that the form of ATD—whether FDC or free—dose not notably affect patient adherence^15,35,36. The research found that most patients in both high and moderate adherence groups received ATD in release form, while non-adherent patients were more likely to receive a combination of FDC and release ATD.

Several factors influence the choice of ATD form beyond adherence, including the prescribing doctor's professional judgment and the availability of stock at the hospital. The research also reflects concerns documented in other research, such as the difficulty in identifying which drug component in FDC causes adverse reactions, adjusting doses for individual patients, and the potential for increased difficulty in swallowing due to the volume of FDC³⁷. Additionally, FDC may increase the risk of resistance if a dose is missed, compared to missing a dose of a single drug component.

Despite these concerns, FDC ATD can offer some benefits. For instance, FDC has been associated with faster sputum conversion rates in the initial two months of treatment, although this effect does not persist throughout the entire six-month treatment period³⁵. This indicates that while adherence rates may not differ significantly between FDC and loose ATD, FDC could still provide some therapeutic advantages in terms of treatment efficacy in the short term.

The research's findings regarding polypharmacy and adherence highlight a nuanced perspective. Despite polypharmacy not showing a significant relationship with adherence (p = 0.716), it is essential to note that this research specifically focused on polypharmacy related to TB treatment, excluding medications for comorbid conditions. Previous research has indicated that polypharmacy related to chronic diseases often negatively impacts adherence, as seen in studies where patients prescribed ten or more medications had lower adherence⁴⁰. Medical regimen complexity is one of therapy related factors influencing medication adherence³⁸. Another study reports that patients who self-administer their medication and use a purchased adherence pill box had higher adherence³⁹. However, in this research, the absence of a significant effect may reflect the fact that non-ATD medications, which were included in the analysis, were aimed at alleviating symptoms associated with TB treatment rather than adding to the treatment burden.

Regarding adverse drug reactions (ADRs), the research found no significant impact on adherence (p = 0.327), which contrasts with other research suggesting a correlation between ADRs and lower adherence⁴¹. Specifically, nausea, a common ADR, was almost significantly related to adherence (p = 0.060). Patients who experienced nausea were predominantly in the high adherence group, which could be attributed to proactive management by healthcare workers who might adjust therapy or prescribe additional medications to mitigate the effects of nausea and maintain adherence²². ADR management greatly facilitates tuberculosis patient adherence⁴².

While changes in treatment regimens due to ADRs were noted in a small proportion of patients (7.4%), this factor did not significantly affect adherence (p = 0.221). This suggests that while ADRs and regimen modifications occur, their direct impact on adherence may be less pronounced, possibly due to effective management strategies that maintain overall patient adherence.

Based on the results of the multivariate multinomial analysis, the only significant variable related to high adherence compared to moderate adherence was comorbidity severity, with a p-value of 0.044. Patients without comorbidity severity, as indicated by a Charlson Comorbidity Index (CCI) score of 0. were 8.3 times more likely to be in the high adherence group (PDC ≥90%) than in the moderate adherence group (PDC 80-89%) compared to those with severe comorbidity (aOR = 8.305; 95% CI 1.056-65.286; p = 0.044). This finding contrasts with previous research by Kwon et al¹⁸, which found no significant relationship between CCI and adherence in DSTB patients. However, other studies, such as the one by Starshinova et al⁴³, have found that higher CCI scores impact treatment success in MDR-TB and XDR-TB patients. This highlights a gap in understanding how CCI scores influence adherence in DSTB patients and suggests that further research is needed.

The relationship between chronic disease comorbidity severity and treatment adherence can be examined using the COM-B model, which posits that human behavior is influenced by physical and psychological capabilities, opportunities provided by the environment, and motivation. Comorbid conditions like hypertension, diabetes, pleural effusion or COPD in tuberculosis patients⁴⁴ can impact physical capabilities and increase the burden of disease management, potentially reducing adherence to TB treatment. This is supported by research showing that diabetes mellitus is a risk factor for treatment failure in TB patients³¹ and that managing multiple comorbidities can affect adherence⁴⁵. Improving patient capability, opportunity, and motivation, particularly in managing their diseases, can help enhance adherence to TB treatment. Involvement of clinical pharmacist in disease management creates patient awareness about the disease and medication which helps in medication adherence and patient quality of life improvement^46,47.

The primary limitation of this study is the reliance on medical records and the proportion of days covered (PDC) method to assess adherence. This method measures adherence to oral anticoagulant therapy (OAT) based on pharmacy records, rather than actual patient adherence to the prescribed regimen. Consequently, it may not accurately reflect true patient adherence. Furthermore, the relatively small sample size of the study may affect the generalizability of the findings regarding overall patient adherence. Evidence suggests that outpatient patients undergoing SO TB treatment for less than six months at the Hospital are at an increased risk of non- adherence, which impacts the availability of comprehensive data for analysis in the SPSS system. Patients who are non-adherent may be more likely to discontinue treatment prematurely, leading to treatment dropout cases, development of resistance and a huge economic burden from provider, societal, and patient perspectives^48,49.

CONCLUSION:

Adherence to ATD in adult DSTB outpatients at UIH, as measured by the Proportion of Days Covered (PDC) was considered high. Fisher’s Exact Test indicated a significant relationship between gender and adherence. Additionally, multivariate multinomial logistic regression analysis showed that patients without severe comorbidities were 8.3 times more likely to exhibit high medication adherence compared to those with severe comorbidities. These findings suggest that adherence is significantly impacted by comorbidity severity, underscoring the need for targeted educational interventions to improve adherence, particularly among patients with severe comorbidities, especially during the advanced phase of treatment.

ACKNOWLEDGMENT:

Heartfelt gratitude is expressed to all units and departments at UIH for their invaluable support and contribution to the research

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Received on 09.09.2024 Revised on 06.01.2025

Accepted on 03.03.2025 Published on 05.09.2025

Available online from September 08, 2025

Research J. Pharmacy and Technology. 2025;18(9):4337-4346.

DOI: 10.52711/0974-360X.2025.00622

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