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From diagnosis to treatment: Streamlining TB care pathways in the hilly terrains of Shimla district
For correspondence: Dr Vivek Chauhan, Department of Medicine, Indira Gandhi Medical College, Shimla 171 001, Himachal Pradesh, India e-mail: drvivekshimla@gmail.com
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Received: ,
Accepted: ,
Abstract
Background & objectives
Tuberculosis (TB) remains a significant public health challenge in resource-limited settings like Shimla District, Himachal Pradesh, where delays in anti-tubercular treatment (ATT) initiation persist due to geographic and systemic barriers. This study aimed to evaluate the effectiveness of quality improvement interventions in reducing treatment delays using plan-do-study-act (PDSA) cycles.
Methods
The study implemented three interventions targeting different operational barriers: decentralising treatment initiation by routing diagnostic results to local TB units, enabling treatment initiation during holidays through accredited social health activists (ASHAs), and enhancing communication between molecular testing laboratories and peripheral units via multimedia groups. Data on delays (≥3 days) were collected from the Nikshay portal and analysed pre- and post-intervention.
Results
The interventions significantly reduced treatment delays, with the proportion of delayed cases dropping from 31 to 15 per cent after the first intervention. While the second intervention sustained a delay rate of 16 per cent, the third intervention further improved timeliness in rural hospitals, highlighting the critical role of efficient communication systems. However, resistance from healthcare providers in district hospitals limited the overall impact of the interventions. Operational challenges such as overburdened molecular laboratories and delays in updating results on the Nikshay portal were also identified.
Interpretation & conclusions
Quality improvement strategies, including decentralisation, holiday coverage, and enhanced communication, effectively reduced ATT initiation delays, particularly in rural settings. Addressing systemic inefficiencies and engaging stakeholders are critical for sustained improvements. These findings offer scalable solutions for TB programmes in similar resource-constrained contexts, contributing to global TB control goals.
Keywords
Communication systems
decentralisation
PDSA cycle-quality improvement
treatment delay
tuberculosis
Tuberculosis (TB) care is hampered because of three types of delay that are (i) first care seeking delay (≥15 days from symptom onset to contact with health care providers), (ii) diagnostic delay (≥15 days from contact with health care providers to diagnosis) and (iii)Treatment delay (treatment initiation ≥ 7 days after diagnosis of TB)1.
A study involving over 5000 TB patients from seven Mediterranean countries divided the delays into patient delays (symptom onset to first medical contact- FMC) and system delays (FMC to treatment initiation)2. In this study, the system delay ranged from five days in Iraq to 90.7 days in Pakistan2.
Treatment delay was studied in slums of Mumbai, India by Mistry et al1, who reported a 17 per cent treatment delay among pulmonary TB (PTB) patients. Using a similar definition, Paul et al3 in their cohort of 990 TB patients from Jharkhand and Gujarat in India, have reported a treatment delay in 41 per cent of PTB and 70.2 per cent of extrapulmonary TB (EPTB) patients. Similarly, in Ethiopia, treatment delay in a systematic review was found to be present in 50.4 per cent (43.2-57.6) patients4. They found that female patients, the absence of chest pain or any other significant disability with the TB infection, alcoholics, and unmarried individuals had higher chances of treatment delay4.
TB is endemic in Shimla District, Himachal Pradesh, where challenging hilly terrains hinder timely healthcare delivery. Molecular diagnostic facilities for TB are available at the medical college, the district hospital, and six other rural hospital sites. However, these facilities are located far from sputum collection centres, leading to a delay of 3-4 days in sample transportation and processing.
An analysis of Nikshay data for 2,263 TB patients from previous years revealed that only 39 per cent of patients diagnosed via molecular methods began treatment on the same day of diagnosis. This figure was comparatively higher (49%) for patients diagnosed using microscopy, as microscopy centres are closer to villages and enable prompt treatment initiation. These findings highlight that delays in treatment initiation are more prevalent when molecular diagnostic laboratories are involved, primarily due to logistical and systemic inefficiencies.
We hypothesised that post-diagnosis process improvements could significantly reduce delays in initiating anti-tubercular treatment (ATT) for patients diagnosed through molecular methods. Recognising this as a healthcare quality issue, we implemented and tested various quality improvement initiatives using plan-do-study-act (PDSA) cycles. This study aimed to identify and establish effective practices to minimise delays in ATT initiation in Shimla District.
Materials & Methods
This study was undertaken by the department of medicine, Indira Gandhi Medical College (IGMC), Shimla, Himachal Pradesh, India after obtaining the ethical clearance prior to the study initiation from September 1, 2022 to September 30, 2023.
Study details
Study design
Operational research using Quality Improvement methods through PDSA cycles.
Sample size
All consecutive patients diagnosed using molecular methods in Shimla District, Himachal Pradesh were included.
Inclusion and exclusion criteria
We included patients diagnosed with TB via molecular identification methods (e.g., CBNAAT, TRUNAT) within Shimla District, Himachal Pradesh, India. Patients already on ATT at the time of molecular diagnosis were excluded from this study.
Methodology
Baseline data collection (phase 1)
Baseline data on treatment delays post-molecular diagnosis were collected for two months. Data sources included online entries of TB patients from Shimla District, cross-verified telephonically by project staff with accredited social health activists (ASHA) workers. Key variables recorded: dates for sample collection, receipt in the laboratories, molecular testing, reporting, and ATT initiation. Discrepancies in the online data were corrected during the confirmation process.
Three intervention phases
Each intervention phase lasted two months, with timelines and outcomes analysed at the end of each phase. Brainstorming sessions were held with stakeholders (doctors, TB health workers, laboratory personnel, and ASHA workers) to identify feasible interventions to reduce delays. Common discussion points during brainstorming included current patient load and benefits of prompt ATT initiation, obstacles to initiating treatment immediately post-diagnosis, facility requirements, and potential solutions for early treatment initiation.
Interventions tested
A list of interventions was developed based on stakeholder feedback and implemented during the PDSA cycles. The intervention of the first phase was not withdrawn during successive phases, and it continued throughout the project. Similarly, the intervention of the second phase continued in the third phase, and finally, in the third phase, there were three interventions together.
Two-monthly reviews
After every two months, data were analysed and reviewed with stakeholders to refine and adapt interventions.
Repetition of PDSA Cycles
Three PDSA cycles were conducted, each aimed at further reducing the delay in ATT initiation after molecular diagnosis.
Data collection and analysis
The key variable of the study was time (in days) between diagnosis via molecular methods and ATT initiation.
Data collection process
The date of diagnosis was retrieved from molecular diagnosis laboratory records. The actual date of initiation of ATT was confirmed through ASHA workers.
Analysis plan
Data were expressed as the mean and standard deviation. Each intervention’s impact was assessed by comparing the reduction in time delay against the baseline data. P value of <0.05 calculated using the means and standard deviations of two groups was taken as significant. It was calculated using online software provided by www.graphpad.com .
Results
Patient data overview
We analysed data from 492 TB-positive patients diagnosed using molecular methods in Shimla District between October 1, 2022, and July 31, 2023. The data were sourced from the Nikshay portal and cross-verified with ASHA workers. These patients were distributed across four study phases that included all consecutively enrolled patients with TB on the Nikshay Platform from Shimla District. Timelines of various project activities is shown in table I.
| Phases (2 months each) | Number of patients |
|---|---|
| Baseline (pre-intervention) | 122 |
| First intervention | 108 |
| Second intervention | 115 |
| Third intervention | 147 |
Descriptive analysis of patient population
There were 201 females (41%) and 291 males (59%), with a mean age was 40.7±20 yr (43.9 yr for males, 35.5 yr for females). Molecular methods used were CBNAAT (399 patients, 81%) and TRUNAT (93 patients, 19%).
Delays in care pathway
The delay from the symptom onset to FMC was a mean of 4 wk and a median of 2 wk. FMC was located in government hospitals in 413 (84%) and private practitioners in 79 (16%). The duration from FMC to sample collection was a mean of 15 days and a median of 4 days. Sputum was the commonest sample tested in 379 (77%), bronchoscopy or gastric lavage in 29 (6%), fluids (pleural, ascitic, CSF or pericardial) in 34 (7%), and other samples included lymph node aspirates, pus, and tissue samples. The sample collection to molecular testing mean duration was one day.
The molecular testing laboratory for the samples was located in the medical college (IGMC, Shimla) for 237 samples, the district hospital (Deendyal Upadhyay Zonal Hospital, DDUZH) for 150 samples, and the six rural hospitals for 105 samples.
Operational problems identified
Focus group discussions revealed several issues that included: Peripheral patients traveling to Shimla for treatment initiation caused delays, delays during holidays due to unavailability of treatment services, inefficient communication between laboratories and treatment providers, national TB programme guidelines allowed up to seven days for treatment initiation, inadequate staff to upload reports to Nikshay portal in timely manner, and SMS alerts sent to patients did not contain the contact details of the treatment providers.
Interventions and impact
First intervention
Patients were started on treatment at their nearest hospital instead of traveling to Shimla by decentralisation of the treatment process and transferring the case online to the local treatment units.
Second intervention
Treatment was initiated on holidays via ASHA workers.
Third intervention
A multimedia communication group for laboratory technicians and peripheral TB supervisors expedited information relay.
Key results
At the baseline or prior to the intervention, 31 per cent of patients had a treatment delay of ≥3 days post-diagnosis, which was reduced after intervention 1 to 15 per cent, after intervention 2 to 16 per cent, and after intervention 3 to 20 per cent. The mean delay prior to the intervention was 2.68±3.53 days, which reduced to 1.64±2.12 days at the end of the study, demonstrating the effect of the interventions in reducing the treatment delays (Table II).
| Treatment initiation | Baseline (n=122), n (%) | Intervention 1, n (%)(n=108) | Intervention 2, n (%) (n=115) | Intervention 3, n (%) (n=147) |
|---|---|---|---|---|
| Day 0 | 31 (25) | 42 (39) | 43 (37) | 48 (33) |
| Day 1 | 30 (25) | 38 (35) | 40 (35) | 45 (31) |
| Day 2 | 23 (19) | 12 (11) | 14 (12) | 24 (16) |
| Day ≥3 | 38 (31) | 16 (15) | 18 (16) | 30 (20) |
| Mean delay (mean±SD; days) | 2.68±3.53 | 1.43±2.67 (P<0.01) | 1.45±2.01 (P<0.01) | 1.64±2.12 (P<0.01) |
Subgroup analysis
We assessed treatment delays across medical college, district hospital, and rural hospitals. The intervention 1 reduced delays in medical college and district hospitals but had a limited impact in rural hospitals (Figure). Intervention 2 had overall minimal additional effect due to operational challenges during holidays. Intervention 3 was more effective in rural hospitals by improving laboratory-provider communication, resulting in a sharp decline in patient delays in rural hospitals. This subgroup analysis shows that different interventions work in different settings, rural and urban, and therefore the future interventions need to be tailored to each setting.
- Subgroup analysis of delays by facility type (A) Medical college, (B) district hospital, and (C) rural hospitals.
Delays persisted in the district hospital as doctors insisted that patients revisit before treatment initiation. These were the MD medicine physicians who insisted on getting the baseline liver function tests, HIV testing, and blood sugars before initiating the patient’s treatment, and therefore, they still asked the patients to visit the hospital for which they had to travel to Shimla, resulting in the delay.
Discussion
This study highlights the impact of quality improvement interventions using the PDSA cycles to address these delays and improve patient outcomes. It revealed a significant reduction in the proportion of patients experiencing treatment initiation delays (≥3 days) from 31 per cent at baseline to 15 per cent after the first intervention. This reduction demonstrates the effectiveness of decentralising treatment initiation by relaying diagnostic results to local TB units rather than requiring patients to travel to centralised facilities. Previous research supports the value of decentralisation in TB care5. Decentralised TB treatment significantly reduces patient costs and delays, improving adherence and outcomes.
The second intervention – initiating treatment during holidays through ASHA workers – also showed some success, maintaining the reduced delay rate at 16 per cent. However, operational challenges limited its broader impact. The lack of a continuous supply chain and personnel availability on holidays impeded efforts to ensure timely TB treatment.
The third intervention, focusing on improving communication between molecular testing laboratories and peripheral TB units via multimedia groups, was particularly effective in rural hospitals. This intervention underscores the importance of efficient communication systems in bridging gaps between diagnosis and treatment initiation, a theme echoed by Lee et al6, who highlighted the potential of digital tools to streamline TB care in rural areas. However, over time, the first intervention showed diminished effectiveness in the district hospital, likely due to non-compliance by some medicine specialists working in the District Hospital who insisted on centralised treatment initiation after doing baseline liver functions, HIV testing, and ruling out diabetes. This resistance points to systemic inertia, which has been recognised as a barrier in implementing quality improvement interventions7.
The subgroup analysis provided valuable insights into the differential effectiveness of interventions across healthcare settings. The success of interventions in rural hospitals illustrates the adaptability of quality improvement processes in decentralised contexts. However, the stagnation of delays in district hospitals highlights the need for tailored approaches that consider institutional dynamics and stakeholder resistance.
The study identified several operational barriers, including the overburdened molecular laboratories and delays in uploading results to the online portal. Streamlining laboratories workflows and automating data sharing could further reduce delays, as demonstrated by the success of similar systems in countries like South Africa8. The study also noted delays associated with holidays, which affected the second intervention’s success. Addressing this issue requires structural changes, such as implementing on-call TB treatment services during holidays.
This study’s findings have significant implications for TB control programmes in regions with challenging terrains and limited resources. The reduction in treatment delays not only improves patient outcomes but also minimises the risk of TB transmission in the community. WHO recommends prompt initiation of ATT that can substantially reduce the infectious period, contributing to community-level TB control5. The scalability and cost-effectiveness of the interventions are particularly noteworthy. Decentralised treatment initiation and improved communication systems can be replicated in similar settings with minimal additional resources. However, the study also underscores the importance of continuous stakeholder engagement to address resistance and ensure compliance with interventions.
While the study achieved notable success, it had some limitations. The reliance on Nikshay data and telephonic confirmation may have introduced reporting biases. Additionally, the study’s duration was relatively short, and long-term sustainability of the interventions was not assessed. Future studies should focus on evaluating the durability of these interventions over extended periods and across diverse settings.
This study demonstrates that quality improvement processes, such as PDSA cycles, significantly reduced the mean delays in ATT initiation after molecular diagnosis of TB. The interventions decentralising treatment initiation, ensuring holiday coverage, and enhancing communication proved effective, particularly in rural settings. By leveraging these insights and incorporating lessons from relevant literature, TB control programmes can make significant strides toward eliminating treatment delays and achieving global TB eradication goals.
Financial support & sponsorship
This study received funding support from the India TB Research Consortium of the Indian Council of Medical Research, New Delhi, under the ‘Thematic area of Implementation research’ grants (Project ID: 5/8/5/8/IR/IRTC/2022/ECD-1).
Conflicts of Interest
None.
Use of Artificial Intelligence (AI)-Assisted Technology for manuscript preparation
The authors confirm that there was no use of AI-assisted technology for assisting in the writing of the manuscript and no images were manipulated using AI.
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