Effects of integrating traditional and modern healthcare system on tuberculosis diagnosis delay in Ethiopia: a clustered randomized controlled study

doi:10.21203/rs.3.rs-4703858/v1

Download PDF

Research Article

Effects of integrating traditional and modern healthcare system on tuberculosis diagnosis delay in Ethiopia: a clustered randomized controlled study

https://doi.org/10.21203/rs.3.rs-4703858/v1

This work is licensed under a CC BY 4.0 License

You are reading this latest preprint version

Background: Delay in tuberculosis (TB) diagnosis and treatment is a major challenge in low- and middle-income countries. We aimed to assess the effectiveness of a new approach of integrating traditional care with modern TB control programs in reducing delays in TB diagnosis and treatment.

Methods: We conducted a cluster randomized control trial involving 23 health facilities across four districts and two town administrations in northwest Ethiopia. The clusters were randomly allocated with a 1:1 ratio to intervention or control groups. We provided training for traditional and modern healthcare providers in three different rounds to enhance their knowledge, attitude, and skills towards referral systems. We used shared frailty parametric survival analysis to investigate the relationship between the outcome and exposure variables.

Results: A total of 510 participants (255 in each group) were included in the study. Delay was significantly decreased following the intervention (mean difference=23.678, P=0.008). The effect size of the intervention on patient delay, diagnosis delay, and total delays were 0.281, 0.211, and 0.213, respectively. The total delay was 4.578 per 1000 person-days. The delay in the intervention group was 4.185 per 1000 person-days and 5.031 per 1000 person-days in the control group. The median time to delay was 135 days (95% CI: 102, 223) and the total follow-up period was 55, 026 person-days of observation, with an average follow-up time of 107.894 days. The time to delay who had higher education was significantly decreased by 22.7% (δ=0.773; 95% CI: 0.617, 0.967) compared to the illiterates. Patients who travelled a far distance saw an increase of 1.026 units in delays as distance increased by one kilometer (δ =1.026; 95% CI: 1.007,1.046) compared to their counter parts. Participants with adequate knowledge significantly decreased the time to diagnosis delay by 30.4% compared to those with poor knowledge.

Conclusion: The involvement of traditional care providers in the TB control program has led to a significant decrease in patient and diagnosis delays. Higher education, distance, and knowledge about TB were found to be significantly associated with diagnosis delay. These findings underscore the importance of integrating traditional and modern healthcare systems to effectively combat TB.

Clinical trial registration

· ClinicalTrials.gov ID: NCT05236452.

· The date recruitment began: July 1, 2022.

· Registration date: July 22, 2022.

Tuberculosis

diagnosis delay

patient delay

health system delay

integration

traditional care providers

modern healthcare providers

TB is the leading cause of infectious disease worldwide, in spite of the fact that effective treatment has existed for many years. It accounted for an estimated 10.6 million new cases and 1.3 million deaths in 2022, with over 80% of cases reported in low- and middle-income countries(1). The Southeast Asia and Africa regions collectively account for 67% of the global TB burden, with more than 87% of new TB cases reported in 30 high-TB-burden countries, including Ethiopia (2). Delays in TB diagnosis and treatment pose a significant challenge to achieving the global end-TB targets, which aim to reduce TB incidence by 90% and deaths by 95% associated with TB between 2015 and 2035.

Diagnosis and treatment delays are common in TB control programs in low- and middle-income countries(3–5), where TB is more prevalent. Usually, in the existing settings, individuals with TB infections are not diagnosed and treated within the expected timeframe since many patients visit the traditional healthcare system for the first time, as well as due to the inaccessibility of health facilities, poverty, a lack of awareness, and poor knowledge about TB(6).

In Ethiopia, traditional medicinal practices have been used for many years. The use of traditional medicine and holy water is not only focused on curing diseases but also on protecting and promoting the physical, mental, spiritual, social, and emotional well-being of patients(7, 8). However, this ancient medicinal practice is not well developed and has been overshadowed by Western medicine. There has been no consideration of indigenous knowledge, practices, and cultural perspectives of the community due to weak legal enforcement, a lack of government commitment and support, resource constraints, and inadequate regulatory tools. These are the main challenges that need to be addressed in order for traditional medicine to thrive (7, 8).

In Ethiopia, easily accessible and affordable facilities for their illnesses include herbal and religious medicines, including holy water. In addition, these locally available diagnosis and treatment approaches are highly accepted and trusted by the communities. Since diagnosing and treating illnesses using herbal medicine and holy water has a long history in human history, the scientific community should not neglect or undermine the contribution of indigenous diagnosing and treating systems. TB diagnosis and treatment initiation services take a long time due to the inaccessibility and unaffordability of modern healthcare systems. This leads to worse clinical outcomes, continuous disease transmission, and increased TB-related costs(9–11). For instance, previous studies have shown that more than two-thirds of TB patients in Ethiopia experienced delayed diagnoses(12), with nearly 91% of TB patients taking more than 31 days for presentation, diagnosis, and treatment (referred to as total delay)(13). There are multiple factors contributing to these delays, mainly related to patients, providers, and health system factors(14).

Therefore, integrating the traditional health care system with the modern approach is vital to shortening the time it takes from the onset of the illness to diagnosis. In Ethiopia and other low-income countries, individuals often prefer traditional care as their initial choice for TB treatment. However, the modern healthcare system lacks mechanisms to develop and retain culturally relevant TB control programs(8). Integrating traditional care into the modern health system has the potential to reduce diagnosis and treatment delays, thereby contributing to the establishment of a more comprehensive, accessible, and patient-friendly TB care system. This integration can be facilitated through screening, referral linkage, and training to shorten the time from symptom onset to diagnosis confirmation and treatment (15–17). This study aimed to assess the effectiveness of integrating traditional care into the modern health system to decrease TB diagnosis and treatment delays.

Study setting

The study was conducted in the South Gondar Zone, Amhara Region, in northwest Ethiopia. The South Gondar zone comprises town administrations and rural districts. There are over 500 public health facilities at various levels, including health posts, that are directly or indirectly involved in the TB control program. The routine practice of the existing TB control program in the South Gondar Zone is passive case detection. Direct Observation Therapy (DOT) is implemented in all public health facilities, including health posts. In addition to modern healthcare facilities, there are several informal healthcare providers, such as traditional healers and holy water centers. Therefore, we specifically selected this zone due to the widespread traditional healthcare practices.

Study design

A cluster randomized controlled trial was conducted from April 1 to January 30, 2024, across four districts and two town administrations (totaling six districts). These districts were randomly selected from a total of 13 districts and eight town administrations. The six districts were assigned to either the intervention or control groups in a 1:1 ratio. The randomization process was conducted by experts who were not involved in the study as researchers. The number and size of the clusters involved in the intervention and control groups were not considered. All health facilities (clusters) that offer TB diagnostic and DOT programs were included in the study. Out of all the health facilities in the study area, 23 facilities were randomly selected. Of these, thirteen health facilities were located at the intervention site, and ten health facilities were found at the control site.

Intervention group

The intervention group received integrated TB care, which integrated traditional and modern TB services. This approach included: 1) training for health professionals and traditional care providers; 2) TB screening at traditional healthcare sites; and 3) referral linkage from traditional to modern healthcare. The training was conducted in three rounds, aiming to increase knowledge, foster a favorable attitude, and enhance skills in TB screening and patient referral activities. The intervention was designed in four phases. In the initial phase, investigators prepared comprehensive training manuals that underwent review and standardization by invited experts, including physicians, public health experts, nurses, and language specialists. A workshop was conducted to enhance the training manuals, and experts provided valuable feedback on the content, depth, readability, and comprehensibility. The manuals covered various aspects of TB, including causes, symptoms, signs, transmission, screening and referral procedures, diagnostic approaches, case detection techniques, treatment outcomes, advantages of early detection, challenges of late diagnosis, and TB control and prevention mechanisms. The training also included models for integrating traditional and modern healthcare systems, which were approved by senior experts.

During the training provision phase, both traditional and modern care practitioners underwent training in three rounds. The first round focused on traditional practitioners (e.g., traditional healers, religious leaders) for five days, while healthcare providers working at DOT clinics and TB focal persons received training for two days. Subsequent one-day training sessions were conducted three and six months after the initial training. The training was facilitated by researchers and TB experts who held trainers' certificates. Traditional healers and religious leaders who demonstrated proficiency in knowledge, attitude, and skills through a post-test and practical assessment were included in the intervention to screen and refer presumptive cases. Details of the operational procedures for the intervention packages are provided in a published protocol(18) and are available in the supplementary materials (S1-Table 1).

The intervention was fully implemented when patients were screened and referred to the health facilities near them. Traditional healers and religious leaders used standardized screening tools to identify patients showing symptoms of TB, referring all suspected cases to nearby health facilities in the intervention districts. Trained TB focal persons re-screened and diagnosed patients according to national TB treatment guidelines. To ensure effective intervention implementation, regular monthly supervision was conducted by experts. In the final phase, the end-line outcome was assessed by comparing the difference between the end-line and baseline results in both the intervention and control groups. The details of screening and referral formats are provided in the supplementary materials (S2: screening form and S3: referral format).

Control group

In the control group, routine TB care continued without any additional intervention from the research team. Routine TB care in Ethiopia involves identifying individuals with TB when they visit healthcare services on their own due to symptoms or other health concerns. The control group served as a reference comparator for measuring the effectiveness of integrated interventions. Baseline information was collected simultaneously in both the control and intervention groups.

The outcome of interest and its definition

This study focused on the delay in diagnosing TB, which includes patient, health system, and treatment delays. Diagnosis delay is defined as the time interval between the onset of symptoms and the confirmation of TB in the patient. Patient delay refers to the period from the onset of the first symptom to the first medical consultation. Health system delay is also defined as the time from the first consultation to the date of TB diagnosis. Treatment delay refers to the time from diagnosis to the start of anti-TB medication. Detailed definitions of the outcome of interest and other variables are available in the supplementary materials (S4-Table 2). To measure the association between the dependent and independent variables, the median diagnosis time was used as the cutoff time. The time of measurement for this study was during the day. The time exceeding the median was considered a “delayed diagnosis,” while the time from the onset of symptoms to the median was considered "not delayed."

Participant recruitment and randomization

Two districts and one town administration were assigned to the intervention group, while the other two districts and one town administration were assigned to the control group. Twenty-three public health facilities located within the intervention and control districts were included in the study. Additionally, 29 traditional care centers located at the intervention site were selected for the implementation of the intervention. Using a random sampling approach, Dera woreda, Libokemekem woreda, and Worta town were chosen as intervention districts, while Farta, Gunabeyemeder, and Debre-Tabor town were selected as control districts. The districts and town administrations allocated to the intervention and control groups had similar baseline characteristics. In the study area, the level of training for providers, Directly Observed Treatment (DOT) programs, laboratory supplies, diagnostic techniques, and guidelines were consistent across all health facilities. Furthermore, study participants were selected randomly. A buffering zone between the intervention and control groups was implemented to minimize information contamination.

Data safety and adverse effects monitoring

The trial examined the integration of traditional care with modern care without involving invasive procedures or the administration of any drugs. Participant adherence in both intervention and control groups was assessed through self-reports and direct observation by trained field supervisors, with regular communication and feedback maintained between supervisors and researchers. Ethical approval was obtained from the institutional review board (IRB) of Bahir Dar University, College of Medicine and Health Sciences, with ethical review Ref No. 353/2021. Written consent for adult participants and written assent for pediatric participants were obtained from each participant.

Sample size determination

The sample size was determined using a two-sample comparison of the mean, incorporating data from a previous study on diagnosis and treatment delays in southwestern Ethiopia with a total median of 55 (interquartile range (IQR): 32–100) days (median: m1 = 55)(19). Assuming a 14-day reduction in diagnosis delay in the intervention group compared to the control group (m2 = 41), considering a type 1 error probability of 0.05, a 95% confidence interval, 80% power, and accounting for a 10% non-response rate, the sample size was 537. In consideration of the study being a cluster randomized control trial, the design effect was considered with a determined value of 0.95 based on the recommended intraclass correlation coefficient (ICC) of less than 0.052 for studies with more than 10 clusters. Then, by multiplying the original sample size by the design effect, the final sample size was 537*0.95 = 510(255 for each group).

Data collection

Structured interviewing questionnaires were used to collect data. The data were collected using interviewing questionnaires that contained sociodemographic, clinical, and behavioral variables, the onset of symptoms, health-seeking behavior, date of diagnosis, and treatment commencement. Experienced BSc nurses and public health officers collected the data.

Statistical analysis

Data were entered into EpiData version 4.6 and analyzed using Stata software version 17.0. Descriptive statistics were utilized to analyze the characteristics of the baseline data. Principal component analysis was used to calculate the household wealth index, considering factors such as land ownership and livestock. Initially, descriptive statistics were employed to determine the frequency, percentage, mean, and chi-square for comparing baseline and end-line data. The t-test was utilized to analyze the mean, mean difference, and standardized group mean difference (effect size) of the diagnosis delay. Comparisons of diagnosis and treatment delays within and between the intervention and control groups were conducted using t-tests.

The time from the onset of illness to the diagnosis of TB was used to calculate the incidence rate per person-day. The Kaplan-Meier survival function was estimated to determine the probability of time to diagnosis delay. The log-rank test was used to compare survival curve probabilities between the intervention and control groups. Subsequently, the Cox proportional hazard model was applied for semi-parametric multivariable analysis. Additionally, a parametric approach incorporating completely parametric survival models was used to address cluster variations more effectively. Univariable analysis was conducted to assess each explanatory variable, with variables showing significance at a level of 0.25 considered for multivariable analysis(20). In multivariable analysis, variables with a p-value of less than 0.05 were considered statistically significant. The Cox proportional hazard assumption was assessed using both graphical and statistical methods. The graphical method indicated that the assumption was met, but it relies on subjective judgment. We plotted the Weibull distributions of selected variables against their respective Kaplan-Meier curves to display the Cox-Snell residual and Nelson-Aalen cumulative hazard Cox-Snell residual, demonstrating how well the estimated Weibull survival plots fit the data (see Fig. 1). The Schoenfeld residual proportional hazard test confirmed that the assumption was met (statistically insignificant, with a p-value of 0.611). A multivariable accelerated failure time shared frailty model was utilized to determine the predictors of time to failed diagnosis. While the Cox proportional hazard model assumes a constant hazard ratio between individuals over time, our study involved 23 clusters (facilities) that may exhibit variations among them. The intra-cluster correlation was taken into account to address the unexplained covariates of the clusters. To address this correlation among the clusters, a shared frailty model was employed to identify the variance within each cluster by introducing a random effect model where individuals in a cluster are assumed to share the same frailty value. The hazard function is expected to follow a certain distribution and is influenced by an unobservable random frailty effect shared by participants within a cluster. In a shared frailty model assuming a Weibull distribution, the hazard function at time "t" for the "j^th" individual, where "j = 1, 2,..., ni," in the "i^th" group, where "i = 1, 2,..., g," is expressed as: hij(t) = Ziexp(¬β′xij) ρt ^ρ-1. Here, xij represents a vector of explanatory variables for the "j^th" individual in the "i^th" group, β is the vector of regression coefficients, ρt ^ρ−1 is the baseline hazard function, ρ is a shape parameter, and the zi are frailty effects shared by all "ni" individuals within the "i^th" group.

When considering a parametric survival model characterized by its hazard function, h(t), all functions are affected by any covariates. Whether we parameterize the model as having proportional hazards (PH) regarding changes in covariate values or accelerated failure time (AFT) due to the covariates, the hazard function at time t for individual i with covariate Xi is given by: hi(t) = exp(xiβ)pt ^p−1.

A frailty model in the univariate case introduces an unobservable multiplicative effect, denoted by α, on the hazard. This means that conditional on the frailty, h(t|α) = αh(t), where α is a random positive quantity assumed to have a mean of one for the purpose of model identifiability and a variance of θ. Individuals with α > 1 are considered more frail, leading to an increased risk of failure, though the reasons for this frailty are not explained by the covariates. On the other hand, individuals with α < 1 are less frail and tend to survive longer, all else being equal (i.e., given a certain covariate pattern). Since α is a multiplicative effect that accounts for the cumulative impact of one or more omitted covariates, the relationship between the hazard and survival functions can be shown as the individual survival function conditional on frailty, S(t|α) = {S(t)}α, where S(t) is the survival function from a standard survival model that may include ancillary parameters and covariate effects.

Shared frailty distribution and parametrization: A parametric survival model follows a known distribution. We fitted the Weibull, exponential, log-logistic, lognormal, and generalized gamma distributions by considering both the gamma and inverse-Gaussian frailty distributions. The Weibull AFT inverse-Gaussian shared frailty model, which had the smallest AIC and BIC values, was selected to analyze the data. Finally, the variance of the random effect (θ), Kendall's Tau (τ), the regression coefficients, and the acceleration factor (δ) with a 95% confidence interval were estimated. The estimated variability (heterogeneity) in the population of clusters (facilities) was determined using the Weibull inverse-Gaussian shared model. In addition, the goodness of fit was checked using the Cox-Snell residuals plot. The model closely followed the 45-degree straight line, with a slight deviation on the left tail. This indicated that the model was well-fitted to the time-to-diagnosis delay (Fig. 1).

A total of 510 participants diagnosed with TB were included in the study, with 255 in the control group and 255 in the intervention group. The mean (SD) age of participants was 36.2 (± 16.4) years in the intervention group and 41.4 (± 15.6) years in the control group. Among participants, 142 patients had pulmonary-positive TB, with 49 in the intervention group and 93 in the control group. Additionally, 244 participants had EPTB, with 153 in the intervention group and 91 in the control group. The mean distance from home to the health facility was 4.7 (± 3.6) kilometers in the intervention group and 4.2 (± 2.8) kilometers in the control group (Table 1).

Table 1

Baseline characteristics of the study participants in the intervention and control groups in northwest Ethiopia
Variables	Control group (n = 255)	Intervention group (n = 255)	Total (N = 510)	P value
Socio-demographic characteristics
Sex Male Female	149 106	130 125	279 231	0.091
Residence Urban Rural	127 128	121 134	248 262	0.595
Age in year	41.4(± 15.6)	36.2(± 16.4)		0.657
Distance from home to health facility in kilo meter	4.2(± 2.8)	4.7(± 3.6)		0.120

Behavioral and clinical characteristics

At baseline, there was no statistical difference in alcohol consumption between the intervention and control groups (p = 0.337). Similarly, there was no statistically significant difference in cigarette smoking between the two groups (p = 0.154). Additionally, this study has not shown a significant difference in drug use between the intervention and control groups (p = 0.411) (Table 2).

Table 2

Behavioural and clinical characteristics of study participants in intervention and control arms in the South Gondar zone, northwest Ethiopia
Variables	Control group (n = 255)	Intervention group (n = 255)	Total (N = 510)	P-value
Behavioural characteristics
Alcohol drinking Yes No	7 248	11 244	18 492	0.337
Substance use (Smoking) Yes No	6 249	2 253	8 502	0.154
Drug user Yes No	4 251	2 253	6 504	0.411
Clinical characteristics
Types of TB PTB+ PTB- EPTB	93 71 91	49 53 153	142 124 244	0.01
TB category New Relapse	248 7	246 9	494 16	0.611
HIV status Positive Negative	26 229	20 235	46 364	0.354
Lung disease other than TB Yes No	7 248	6 249	13 497	0.779
Co-morbidity Yes No	17 238	16 239	33 477	0.857
Notes: PTB+: =pulmonary positive tuberculosis, PTB-: pulmonary negative tuberculosis

The patient, diagnosis, and treatment delays

Patient delay in the intervention group significantly decreased after the implementation of the intervention, with a mean difference of 16 days (P = 0.001). However, the health system delay did not show a significant decrease following the integration of traditional care with modern care (t = 0.792, P = 0.215). The diagnosis delay was significantly reduced after the implementation of the intervention, with a mean difference of 23.678 days (P = 0.008).

On the other hand, the intervention did not significantly decrease patient, diagnosis, and treatment delays in the control group. The intervention had a moderate effect on patient delay, diagnosis delay, and total delay, with Cohen’s d values of 0.281, 0.211, and 0.213, respectively (Table 3).

Table 3

Comparisons of patient, diagnosis, and treatment delay before and after the intervention between the intervention and control groups in northwest Ethiopia
Variables	Control group (n = 255)			Intervention group (n = 255)
Variables	Before	After	t-test (p-value)	Before	After	t-test (p-value)	Mean difference (95% CI)	Effect size Cohen’s d (95% CI)
Patient delay Mean (SD)	35.300 (± 56.877)	33.623 (± 61.128)	0.316 (0.376)	49.222 (± 67.463)	33.204 (± 44.308)	3.169 (0.001)	16.019 (6.089, 25.949)	0.281 (0.106, 0.455)
Health system delay Mean (SD)	65.707 (± 137.101)	65.556 (± 81.517)	0.015 (0.494)	82.886 (± 101.336)	76.616 (± 75.041)	0.792 (0.215)	6.251 (-9.263, 21.774)	0.070 (-0.103, 0.244)
Diagnosis delay Mean (SD)	101.008 (± 153.047)	98.474 (± 105.921)	0.216 (0.414)	132.090 (± 131.319)	108.411 (± 88.553)	2.387 (0.008)	23.678 (4.192, 43.165	0.211 (0.037, 0.385)
Treatment delay Mean (SD)	2.753 (± 13.496)	6.419 (± 38.771)	-1.395 (0.918)	2.039 (± 2.404)	1.863 (± 2.885)	0.740 (0.226)	0.176 ( -0.285, 0.638)	0.066( (-0.107, 0.240)
Total delay Mean (SD)	103.761 (± 153.323)	104.894 (± 114.884)	-0.093 (0.537)	134.129 (± 131.365)	110.274 (± 88.161)	2.407 (0.008)	23.855 (4.391,43.319	0.213 (0.039, 0.387)

Time to failure to TB diagnosis among the study population

The total delay in TB diagnosis was 4.578 per 1000 person-days. The delay the intervention group was 4.185 per 1000 person-days (95% CI: 5.031 per 1000 person-days). The median time to failure was 135 days (95% CI: 102, 223). The total follow-up period was 55, 026 person-days of observation, with an average follow-up time of 107.894 days. Figure 2 showed that there was no significant difference in diagnosis delay between the intervention and control groups before the intervention (baseline).

Figure 3 shows that the risk of diagnosis delay in the intervention group decreased after the intervention was implemented compared to before the intervention. The comparison between the baseline and end-line data in the control group did not significantly decrease the time to diagnosis (Fig. 4). The cumulative hazard estimates decreased following the intervention compared to the baseline (Fig. 5).

The time to diagnosis delay significantly decreased by 22.7% (δ = 0.773; 95% CI: 0.617, 0.967) among patients with higher education compared to illiterate patients. Diagnosis delay increased by 1.026 units as distance increased by one kilometer (δ = 1.026; 95% CI: 1.007, 1.046) compared to their counterparts. Time to diagnosis significantly decreased by 30.4% (δ = 0.696; 95%CI: 0.558, 0.867) among knowledgeable participants compared to participants with poor knowledge about TB. In this study, the variance (random effect) within the cluster was statistically significant (θ = 3.426, p-value = 0.0001) (Table 4).

Table 4

Multivariable analysis of time to diagnosis delay using frailty shared survival model in northwest Ethiopia.
Variables	Category	Β	S.E.	p-value	95%CI	δ	95% CI for δ
Intercept		5.008	0.52	0.0001	(4.906, 5.110)	149.605	135.156, 165.697
Sex	Male	Ref.
	Female	0.041	0.062	0.505	(-0.079, 0.162)	1.042	(0.923, 1.175)
Age		-0.002	0.002	0.237	(-0.006, 0.002)	0.997	(0.994, 1.002)
Education	Illiterate	Ref.
	Primary education	0.109	0.082	0.181	(-0.051, 0.269)	1.116	(0.950, 1.309)
	High school	0.169	0.134	0.205	(-0.093, 0.433)	1.185	(0.911, 1.542)
	Higher education	-0.257	0.114	0.025	(-0.482,0.033)	0.773	(0.617, 0.967)
Occupation	Employee	Ref.
	Housewife	0.043	0.131	0.740	(-0.213, 0.301)	1.044	(0.807,1.350)
	Student	-0.123	0.126	0.330	(-0.369, 0.124)	0.884	(0.691, 1.132)
	Farmer	0.056	0.106	0.597	(-0.151, 0.263)	1.057	(0.859,1.301)
	Merchant	0.151	0.171	0.376	(-0.184, 0.486)	1.163	(0.832, 1.626)
	Daily worker	0.047	0.133	0.725	(-0.214, 0.307)	1.048	(0.807, 1.360)
Types of TB	PTB+	Ref.
	PTB-	-0.067	0.081	0.401	(-0.225, 0.090)	0.934	(0.798, 1.094)
	EPTB	0.031	0.073	0.664	(-0.111, 0.174)	1.032	(0.895,1.189)
Distance in km		0.026	0.009	0.007	(0.007, 0.045)	1.026	(1.007,1.046)
Knowledge of participants	Poor knowledge	Ref.
Knowledge of participants	Knowledgeable	-0.363	0.112	0.001	(-0.583, -0.142)	0.696	0.558, 0.867
Wealth index	Poor	Ref.
	Middle	0.033	0.081	0.679	(-0.126, 0.193)	1.034	(0.882,1.213)
	Rich	0.109	0.071	0.122	(-0.029, 0.249)	1.116	(0.971, 1.283)
Random effect	ln (ρ) = 1.313 (S. E = 0.073), p-value = 0.001 ρ = 3.717 (S. E = 0.269) 1/ ρ = 0.269 (S. E = 0.019) θ = 3.426 (S. E = 1.472) AIC = 388.201 BIC = 468.617 -2LM = 350.201

The duration from the onset of TB to diagnosis and treatment takes a longer time in countries with limited resources(21). Previous studies have revealed that diagnosis and treatment delays are associated with various factors(5, 22, 23). This suggests that there are other unaddressed factors contributing to the delay. From our experience, people in Ethiopia often go to traditional healers and holy water when they feel sick in order to receive diagnosis and treatment services. This practice has been in place for many years and continues in the community to this day (7, 8). This implies that focusing solely on the biomedical approach is not the most effective way to reduce diagnosis and treatment initiation delays. Instead, considering locally and culturally practiced traditional medicine alongside the modern healthcare system is crucial to decreasing diagnosis and treatment delays and improving the health outcomes of the victims.

Our study is the first to investigate the effect of integrating traditional care providers with the modern care providers to decrease diagnosis and treatment delays. The integration of traditional care with modern care significantly reduced the patient delay following the intervention with a mean difference of 16 days in the intervention group compared to the control group. Similarly, the integration of modern and traditional care showed a considerable reduction in the time to diagnosis delay in the intervention group, with a mean difference of 24 days. Generally, diagnosis delay and TB care were significantly decreased following the integration of traditional care with the modern healthcare system. Although the intervention to the practitioner facilitates the reduction of the time to diagnosis delay, factors such as the distance of facilities from home, knowledge about TB, and educational status contribute to long diagnosis delay. People who accessed healthcare facilities after traveling a long distance experienced a diagnosis and treatment delays, as supported by similar literature (14, 24, 25). Our study showed that participants with higher educational status significantly decreased the time to diagnosis delay, supported by previous study (26). Similarly, people with adequate knowledge about TB contribute to decreased diagnosis and treatment delays compared to patients with limited knowledge, which is supported by other studies (27–29). Evidence shows that people living in rural areas with EPTB are at increased risk of delay in diagnosis and treatment(26, 30), although these factors did not show a significant association in our study. Prolonged diagnosis delay remains challenging, and the engagement of the community, such as traditional healers and religious leaders is vital in protecting against prolonged diagnosis and treatment delays. To tackle such public health issues, community engagement is important in preventing diagnosis and treatment delays (31–33), and the integration of traditional care with modern care was accepted by different stakeholders which could increase the effectiveness and sustainability of the implementation (34). Therefore, integrating traditional care with the national TB program is important to reduce patient and diagnosis delay and transmission of the disease in the community. Integrating traditional care with modern national TB programs is a strategy employed in our study resulting in significantly reduced patient and diagnosis delays. Based on our findings, we recommend that TB programmers and policymakers should implement and expand the intervention to countries with similar contexts. It is also important to include traditional healers and religious leaders in TB care to reduce the long-term delay in TB diagnosis.

The method used in this study was stringent to control selection and information bias, leading to increase in the reliability and validity. However, some participants experienced recall bias regarding on the symptoms of their sickness, as they were unable to remember the exact date when they first occurred.

This study revealed that individuals who feel sick often seek out traditional healers and holy water for diagnosis and treatment services. The study suggests that incorporating locally and culturally accepted traditional medicine into the modern healthcare system is crucial for reducing delays in diagnosis and treatment, ultimately improving health outcomes. The involvement of traditional care providers in the TB control program has led to a significant decrease in patient and diagnosis delays. Following the intervention, total delay was significantly decreased in the intervention group compared to the control group. Factors such as higher education, distance, and knowledge about TB were found to be significantly associated with diagnosis delay. These findings underscore the importance of integrating traditional care into modern healthcare systems to effectively combat TB in high-burden settings like Ethiopia.

DOT: Direct observation treatment

EPTB: Extrapulmonary tuberculosis

TB: Tuberculosis

Ethical approval and consent to participate

Ethical approval was obtained from the Bahir Dar University College of Medicine and Health Sciences institutional review board (IRB) with Ref No.353/2021. The protocol was registered at ClinicalTrials.gov with protocol ID: NCT05236452. The study protocol has been previously published(18). Study participants or their parents provided written informed consent and assent. Permission was obtained at each level of regional, zonal, woreda, and health facility administration.

Consent for publication: This manuscript does not report personal data such as individual details, pictures, or videos; therefore, consent for publication is not necessary.

Competing interests: The authors declare that they have no competing interests

Availability of data and materials: The study datasets are available from the corresponding author and can be shared upon reasonable request

Funding: Bahir Dar University, College of Medicine and Health Sciences, Amhara Regional Health Bureau, and Amhara Public Health Institute funded this project. The grant was funded for a manual standardization workshop, to provide training practitioners and data collection. However, the funder had no role in the study design, data collection, data analysis, and decision to publish the paper.

Authors' contributions

DA conceptualize the project, KAA wrote the methodology, and FA edited and approved the project. DA, KAA and FA took part in data collection and analysis. DA wrote the first draft of the manuscript and revised subsequent drafts by KAA and FA. All authors read and approved the final manuscript.

Acknowledgments

We thank the Amhara Public Health Institute, Amhara Health Bureau, and Bahir Dar University for their technical and financial assistance with this study. We also like to thank the supervisors and data collectors for their hard work in guaranteeing high-quality data. We also express our gratitude to the, the Zonal health offices and the woreda health office, and the experts who participated in the manual standardization workshop and gave comments on the training manual. We also thank traditional care providers for their efforts in screening and linking suspected patients to health centers and hospitals.

WHO. Global tuberculosis report. Geneva, Switzerland: World Health Organization; 2019.
WHO. Global tuberculosis report 2022. Geneva: World Health Organization; 2022. Licence: CC BY-NC-SA 3.0 IGO.
EI O. Tuberculosis diagnostic and treatment delays among patients in Uganda. Health Sci Rep. 2023;6(11):1-5.
Teo AKJ SS, Prem K, Hsu LY, Siyan Y. Duration and determinants of delayed tuberculosis diagnosis and treatment in high-burden countries: a mixed-methods systematic review and meta-analysis. Respir Res 2021;22(251).
Ereso BM SM, Gradmann C, Yimer SA Total delay and associated factors among tuberculosis patients in Jimma Zone, Southwest Ethiopia. PLoS ONE 2023;18(2).
Animut Y GA, Nigatu SG, Abiy SA Delay in seeking treatment and associated factors among pulmonary tuberculosis patients attending public health facilities in the Metekel zone, Benishangul Gumuz region, Western Ethiopia. Front Public Health. 2024;12( 1356770).
Usure RE KD, Mekasha YT, Hasen G, Chura Waritu N, Dubale S et al. Traditional herbal medicine regulatory implementation in Ethiopia: a qualitative study. Front Pharmacol. 2024;15(1392330.).
Kebede Deribe Kassaye KD AA, Getachew B, Mussema Y. A historical overview of traditional medicine practices and policy in Ethiopia. EthiopJHealth Dev. 2006;20(2).
Huang Y, Huang J, Su X, Chen L, Guo J, Chen W, et al. Analysis of the economic burden of diagnosis and treatment on patients with tuberculosis in Bao’an district of Shenzhen City, China. PloS one. 2020;15(8):e0237865.
Aung ST, Thu A, Aung HL, Thu M. Measuring Catastrophic Costs Due to Tuberculosis in Myanmar. Tropical medicine and infectious disease. 2021;6(3):130.
Kirigia JM, Muthuri RDK. Productivity losses associated with tuberculosis deaths in the World Health Organization African region. Infectious diseases of poverty. 2016;5(1):1-12.
Shiferaw MB, Zegeye AM. Delay in tuberculosis diagnosis and treatment in Amhara state, Ethiopia. BMC health services research. 2019;19(1):1-8.
Yimer S BG, Alen G. Diagnosis and treatment delay among pulmonary tuberculosis patients in Ethiopia: a cross sectional study BMC infectious diseases. 2005;5:112.
Getnet F, Demissie M, Worku A, Gobena T, Seyoum B, Tschopp R, et al. Determinants of patient delay in diagnosis of pulmonary tuberculosis in Somali Pastoralist Setting of Ethiopia: a matched case-control study. International journal of environmental research and public health. 2019;16(18):3391.
Korobitsyn A, Bobokhojaev O, Mohr T, Ismoilova J, Makhmudova M, Trusov A. TB case detection in Tajikistan-analysis of existing obstacles. Central Asian journal of global health. 2013;2(2).
Tadesse T, Demissie M, Berhane Y, Kebede Y, Abebe M. Two-thirds of smear-positive tuberculosis cases in the community were undiagnosed in Northwest Ethiopia: population based cross-sectional study. PLoS One. 2011;6(12).
Basit A, Khan MA, Dost M, Ahmad M, Ullah Z, Iqbal Z, et al. Need for establishing a linkage between tertiary care hospitals and peripheral DOTS centers. Pakistan Journal of Chest Medicine. 2015;19(3).
Amare D, Ambaw F, Alene KA. Effect of integrating traditional care with modern healthcare to improve tuberculosis control programs in Ethiopia: a protocol for a cluster-randomized controlled trial. Trials. 2023;24(1):582.
Asres A, Jerene D, Deressa W. Delays to anti-tuberculosis treatment intiation among cases on directly observed treatment short course in districts of southwestern Ethiopia: a cross sectional study. BMC infectious diseases. 2019;19:1-9.
Austin PC. A tutorial on multilevel survival analysis: methods, models and applications. International Statistical Review. 2017;85(2):185-203.
Jiang Y, Luo L, Gui M, Liu L, Lin Y, Deng G, et al. Duration and Determinants of Delayed Diagnosis with Tuberculosis in Shenzhen, China: A Cross-Sectional Study. Risk Management and Healthcare Policy. 2022:1473-81.
Yating Zhang Y ZB, Hao X, Wang W, Zhang X, Chunfu Fang C et al. Factors associated with diagnostic delay of pulmonary tuberculosis among children and adolescents in Quzhou, China: results from the surveillance data 2011–2021. BMC Infectious Diseases 2023;23(541).
Roberts DJ MT, Verlander NQ, Anderson C. Factors associated with delay in treatment initiation for pulmonary tuberculosis. ERJ Open Res 2019;6:00161-2019
Fluegge K, Malone LL, Nsereko M, Okware B, Wejse C, Kisingo H, et al. Impact of geographic distance on appraisal delay for active TB treatment seeking in Uganda: a network analysis of the Kawempe Community Health Cohort Study. BMC public health. 2018;18:1-14.
Alene M, Assemie MA, Yismaw L, Gedif G, Ketema DB, Gietaneh W, et al. Patient delay in the diagnosis of tuberculosis in Ethiopia: a systematic review and meta-analysis. BMC infectious diseases. 2020;20:1-9.
Fetensa G, Wirtu D, Etana B, Tolossa T, Wakuma B. Magnitude and determinants of delay in diagnosis of tuberculosis patients in Ethiopia: a systematic review and meta-analysis: 2020. Archives of Public Health. 2022;80(1):78.
Arja A, Godana W, Hassen H, Bogale B. Patient delay and associated factors among tuberculosis patients in Gamo zone public health facilities, Southern Ethiopia: An institution-based cross-sectional study. PloS one. 2021;16(7):e0255327.
Eltayeb D, Pietersen E, Engel M, Abdullahi L. Factors associated with tuberculosis diagnosis and treatment delays in Middle East and North Africa: a systematic review. EMHJ. 2020;26(4-2020).
Ereso BM, Sagbakken M, Gradmann C, Yimer SA. Total delay and associated factors among tuberculosis patients in Jimma Zone, Southwest Ethiopia. PLoS One. 2023;18(2):e0281546.
Shiferaw MB, Zegeye AM. Delay in tuberculosis diagnosis and treatment in Amhara state, Ethiopia. BMC health services research. 2019;19:1-8.
Datiko DG, Jerene D, Suarez P. Patient and health system delay among TB patients in Ethiopia: Nationwide mixed method cross-sectional study. BMC public health. 2020;20(1):1-10.
Arja A, Bogale B, Gebremedhin M. Health system delay and its associated factors among tuberculosis patients in Gamo Zone public health facilities, Southern Ethiopia: An institution-based cross-sectional study. Journal of Clinical Tuberculosis and Other Mycobacterial Diseases. 2022;28:100325.
Asres M, Gedefaw M, Kahsay A, Weldu Y. Patients' delay in seeking health care for tuberculosis diagnosis in East Gojjam zone, Northwest Ethiopia. The American journal of tropical medicine and hygiene. 2017;96(5):1071.
Amare D, Alene KA, Ambaw F. Acceptability of integrating traditional tuberculosis care with modern healthcare services in the Amhara Regional State of Northwest Ethiopia: A qualitative study. Preventive Medicine Reports. 2023;34:102231.

Supplementaryfilesdpm.docx

Download PDF

Editorial decision: Major revision
28 Jul, 2024
Reviewers agreed at journal
15 Jul, 2024
Reviewers invited by journal
15 Jul, 2024
Editor assigned by journal
08 Jul, 2024
First submitted to journal
08 Jul, 2024

You are reading this latest preprint version

Effects of integrating traditional and modern healthcare system on tuberculosis diagnosis delay in Ethiopia: a clustered randomized controlled study

Status:

Version 1

Abstract

Figures

Introduction

Materials and Methods

Study setting

Study design

Intervention group

Control group

The outcome of interest and its definition

Participant recruitment and randomization

Data safety and adverse effects monitoring

Sample size determination

Data collection

Statistical analysis

Results

Behavioral and clinical characteristics

The patient, diagnosis, and treatment delays

Time to failure to TB diagnosis among the study population

Discussion

Conclusion

Abbreviations

Declarations

References

Supplementary Files

Status:

Version 1