What would it cost to scale-up private sector engagement efforts for tuberculosis care? Evidence from three pilot programs in India

Policy gaps in addressing market failures and intervention misalignments in tuberculosis control: prospects for improvement in China, India, and Indonesia

India, Indonesia, and China are the top three countries with the highest tuberculosis (TB) burden. To achieve the end TB target, we analyzed policy gaps in addressing market failures as well as misalignments between National TB Programs (NTP) and health insurance policies in TB control in three countries. In India and Indonesia, we found insufficient incentives to engage private practitioners or to motivate them to improve service quality. In addition, ineffective supervision of practice and limited coverage of drugs or diagnostics was present in all three countries. The major policy misalignment identified in all three countries is that while treatment guidelines encourage outpatient treatment for drug-sensitive patients, the national health insurance scheme covers primarily inpatient services. We therefore advocate for better alignment of TB control programs and broader universal health coverage (UHC) programs to leverage additional resources from national health insurance programs to improve the effective coverage of TB care.

Operational priorities for engaging with India's private healthcare sector for the control of tuberculosis: a modelling study

OBJECTIVES

To estimate the potential impact of expanding services offered by the Joint Effort for Elimination of Tuberculosis (JEET), the largest private sector engagement initiative for tuberculosis (TB) in India.

DESIGN

We developed a mathematical model of TB transmission dynamics, coupled with a cost model.

SETTING

Ahmedabad and New Delhi, two cities with contrasting levels of JEET coverage.

PARTICIPANTS

Estimated patients with TB in Ahmedabad and New Delhi.

INTERVENTIONS

We investigated the epidemiological impact of expanding three different public-private support agency (PPSA) services: provider recruitment, uptake of cartridge-based nucleic acid amplification tests and uptake of adherence support mechanisms (specifically government supplied fixed-dose combination drugs), all compared with a continuation of current TB services.

RESULTS

Our results suggest that in Delhi, increasing the use of adherence support mechanisms among private providers should be prioritised, having the lowest incremental cost-per-case-averted between 2020 and 2035 of US$170 000 (US$110 000-US$310 000). Likewise in Ahmedabad, increasing provider recruitment should be prioritised, having the lowest incremental cost-per-case averted of US$18 000 (US$12 000-US$29 000).

CONCLUSION

Results illustrate how intervention priorities may vary in different settings across India, depending on local conditions, and the existing degree of uptake of PPSA services. Modelling can be a useful tool for identifying these priorities for any given setting.

Estimating health care costs at scale in low- and middle-income countries: Mathematical notations and frameworks for the application of cost functions

Appropriate costing and economic modeling are major factors for the successful scale-up of health interventions. Various cost functions are currently being used to estimate costs of health interventions at scale in low- and middle-income countries (LMICs) potentially resulting in disparate cost projections. The aim of this study is to gain understanding of current methods used and provide guidance to inform the use of cost functions that is fit for purpose. We reviewed seven databases covering the economic and global health literature to identify studies reporting a quantitative analysis of costs informing the projected scale-up of a health intervention in LMICs between 2003 and 2019. Of the 8725 articles identified, 40 met the inclusion criteria. We classified studies according to the type of cost functions applied-accounting or econometric-and described the intended use of cost projections. Based on these findings, we developed new mathematical notations and cost function frameworks for the analysis of healthcare costs at scale in LMICs setting. These notations estimate variable returns to scale in cost projection methods, which is currently ignored in most studies. The frameworks help to balance simplicity versus accuracy and increase the overall transparency in reporting of methods.

Engaging the Private Health Service Delivery Sector for TB Care in India-Miles to Go!

More than half of the people with TB in India seek care from the private sector, where suboptimal quality of care is a concern. Significant progress has been made over the last five years to expand the coverage and to involve more private sector providers in TB care under the National TB Elimination Program (NTEP) in India. The objective of this review is to describe the major efforts and the progress made with regard to the engagement of the 'for-profit' private health service delivery sector for TB care in India, to critically discuss this, and to suggest the way forward. We described the recent efforts by the NTEP for private sector engagement based on the literature, including strategy documents, guidelines, annual reports, evaluation studies, and critically looked at the strategies against the vision of partnership. The NTEP has taken a variety of approaches, including education, regulation, provision of cost-free TB services, incentives, and partnership schemes to engage the private sector. As a result of all these interventions, private sector contribution has increased substantially, including TB notification, follow-up, and treatment success. However, these still fall short of achieving the set targets. Strategies were focused more towards the purchase of services rather than creating sustainable partnerships. There are no major strategies to engage the diverse set of providers, including informal health care providers and chemists, who are the first point of contact for a significant number of people with TB. India needs an integrated private sector engagement policy focusing on ensuring standards of TB care for every citizen. The NTEP should adopt an approach specifically tailored to the various categories of providers. For meaningful inclusion of the private sector, it is also essential to build understanding and generate data intelligence for better decision making, strengthen the platforms for engagement, and expand the social insurance coverage.

Integrating private health facilities in government-led health systems: a case study of the public-private mix approach in Ethiopia

BACKGROUND

Private health care facilities working in partnership with the public health sector is one option to create sustainable health systems and ensure health and well-being for all in low-income countries. As the second-most populous country in Africa with a rapidly growing economy, demand for health services in Ethiopia is increasing and one-quarter of its health facilities are privately owned. The Private Health Sector Program (PHSP), funded by the United States Agency for International Development, implemented a series of public-private partnership in health projects from 2004 to 2020 to address several public health priorities, including tuberculosis, malaria, HIV/AIDS, and family planning. We assessed PHSP's performance in leadership and governance, access to medicines, health management information systems, human resources, service provision, and finance.

METHODS

The World Health Organization's health systems strengthening framework, which is organized around six health system building blocks, guided the assessment. We conducted 50 key informant interviews and a health facility assessment at 106 private health facilities supported by the PHSP to evaluate its performance.

RESULTS

All six building blocks were addressed by the program and key informants shared that several policy and strategic changes were conducive to supporting the functioning of private health facilities. The provision of free medicines from the public pharmaceutical logistics system, relaxation of strict regulatory policies that restricted service provision through the private sector, training of private providers, and public-private mix guidelines developed for tuberculosis, malaria, and reproductive, maternal, newborn, child, and adolescent health helped increase the use of services at health facilities.

CONCLUSIONS

Some challenges and threats to sustainability remain, including fragile partnerships between public and private bodies, resource constraints, mistrust between the public and private sectors, limited incentives for the private sector, and oversight of the quality of services. To continue with gains in the policy environment, service accessibility, and other aspects of the health system, the government and international communities must work collaboratively to address public-private partnerships in health areas that can be strengthened. Future efforts should emphasize a mechanism to ensure that the private sector is capable, incentivized, and supervised to deliver continuous, high-quality and equitable services.

The impact of the COVID-19 pandemic and associated suppression measures on the burden of tuberculosis in India

Background

Tuberculosis (TB) is a major cause of death globally. India carries the highest share of the global TB burden. The COVID-19 pandemic has severely impacted diagnosis of TB in India, yet there is limited data on how TB case reporting has changed since the pandemic began and which factors determine differences in case notification.

Methods

We utilized publicly available data on TB case reporting through the Indian Central TB Division from January 2017 through April of 2021 (prior to the first COVID-19 related lockdown). Using a Poisson model, we estimated seasonal and yearly patterns in TB case notification in India from January 2017 through February 2020 and extended this estimate as the counterfactual expected TB cases notified from March 2020 through April 2021. We characterized the differences in case notification observed and those expected in the absence of the pandemic by State and Territory. We then performed a linear regression to examine the relationship between the logit ratio of reported TB to counterfactual cases and mask use, mobility, daily hospitalizations/100,000 population, and public/total TB case reporting.

Results

We found 1,320,203 expected cases of TB (95% uncertainty interval (UI) 1,309,612 to 1,330,693) were not reported during the period from March 2020 through April 2021. This represents a 63.3% difference (95% UI 62.8 to 63.8) in reporting. We found that mobility data and average hospital admissions per month per population were correlated with differences in TB case notification, compared to the counterfactual in the absence of the pandemic (p > 0.001).

Conclusion

There was a large difference between reported TB cases in India and those expected in the absence of the pandemic. This information can help inform the Indian TB program as they consider interventions to accelerate case finding and notification once the pandemic related TB service disruptions improve. Mobility data and hospital admissions are surrogate measures that correlate with a greater difference in reported/expected TB cases and may correlate with a disruption in TB diagnostic services. However, further research is needed to clarify this association and identify other key contributors to gaps in TB case notifications in India.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12879-022-07078-y.

STEPS - a patient centric and low-cost solution to ensure standards of TB care to patients reaching private sector in India

BACKGROUND

More than half of the TB patients in India seek care from the private sector. Two decades of attempts by the National TB Program to improve collaboration between the public and private sectors have not worked except in a few innovative pilots. The System for TB Elimination in Private Sector (STEPS) evolved in 2019 as a solution to ensure standards of TB care to every patient reaching the private sector. We formally evaluated the STEPS to judge the success of the model in achieving its outcomes and to inform decisions about scaling up of the model to other parts of the country.

METHODS

An evaluation team was constituted involving all relevant stakeholders. A logic framework for the STEPS model was developed. The evaluation focused on (i) processes - whether the activities are taking place as intended and (ii) proximal outcomes - improvements in quality of care and strengthening of TB surveillance system. We (i) visited 30 randomly selected STEPS centres for assessing infrastructure and process using a checklist, (ii) validated the patient data with management information system of National TB Elimination Program (NTEP) by telephonic interview of 57 TB patients (iii) analysed the quality of patient care indicators over 3 years from the management information system (iv) conducted in-depth interviews (IDI) with 33 beneficiaries and stakeholders to understand their satisfaction and perceived benefits of STEPS and (v) performed cost analysis for the intervention from the perspective of NTEP, private hospital and patients.

RESULTS

Evaluation revealed that STEPS is an acceptable model to all stakeholders. IDIs revealed that all patients were satisfied about the services received. Data in management information system of NTEP were consistent with the hospital records and with the information provided by the patient. Quality of TB care indicators for patients diagnosed in private hospitals showed improvements over years as proportion of TB patients notified from private sector with a microbiological confirmation of diagnosis improved from 25% in 2018 to 38% in 2020 and the documented treatment success rate increased from 33% (2018 cohort) to 88% (2019 cohort). Total additional programmatic cost (deducting cost for patient entitlements) per additional patient with successful treatment outcome was estimated to be 67 USD. Total additional expense/business loss for implementing STEPS for the hospital diagnosing 100 TB patients in a year was estimated to be 573 USD while additional minimum returns for the hospital was estimated to be 1145 USD.

CONCLUSION

Evaluation confirmed that STEPS is a low cost and patient-centric strategy. STEPS successfully addressed the gaps in the quality of care for patients seeking care in the private sector and ensured that services are aligned with the standards of TB care. STEPS could be scaled up to similar settings.

Engaging Informal Private Health Care Providers for TB Case Detection: Experiences from RIPEND Project in India

Background

Informal (unqualified) health care providers are an important source of medical care for persons with presumptive TB (PPTB) in India. A project (titled RIPEND) was implemented to engage informal providers for the identification of PPTBs and TB patients in 4 districts of Telangana State, India, during October 2018-December 2019 project period. Engagement involved sensitizing the informal providers about TB, providing them financial incentives to identify PPTBs, and linking these PPTBs to diagnostic and treatment services provided by the Government of India's National TB Elimination Programme.

Objectives

To describe (a) the characteristics of the informal providers, along with their self-reported practices on TB diagnosis, treatment, and challenges encountered by the RIPEND project staff in engaging them in the project and (b) the outputs and outcomes of this engagement.

Methods

We used a combination of one-on-one interviews with informal providers, group interviews with RIPEND project staff, and secondary analysis of data available within the project's recording and reporting systems.

Results

A total of 555 informal providers were actively engaged under the project. The majority (87%) had a nonmedicine-related graduate degree and had been providing medical care for more than 10 years. Most (95%) were aware that a cough for 2 weeks or more is a symptom of pulmonary TB and that such patients should be referred for sputum-smear microscopy at a government health facility. Challenges in engaging the informal providers included motivating them to participate in the study, suboptimal mobile usage for referral services, and delays in providing financial incentives to them for referring PPTBs. During the project period (October 2018-December 2019), 8342 PPTBs were identified of which 1003 TB patients were detected and linked to TB treatment services.

Conclusion

This project showed that engaging informal providers is feasible and that a large number of PPTB and TB patients can be identified through this effort. The Government of India should consider engaging informal providers for the early diagnosis of TB to reduce the missing TB cases in the country.

TB notification rates across parliamentary constituencies in India: a step towards data-driven political engagement

OBJECTIVE

National averages obscure geographic variation in program performance. We determined Parliamentary Constituency (PC)-wise estimates of TB notification to guide political engagement.

METHODS

We extracted district-level TB notification data from the 2018 annual TB report. We derived PC-level estimates by building a 'cross-walk' between districts and PCs using boundary shapefiles. We described the spatial distribution of the PC-wise estimates of Total Notification Rate and percentage of Private Sector Notification.

RESULTS

The median PC-wise Total Notification Rate was 126.24/100 000 (IQR: 94.86/100 000, 162.22/100 000). The median PC-wise Percentage Private Sector Notification was 18.03% (IQR: 9.56%, 26.84%). Only 16 (2.94%) PCs met the target of 50% private sector notification. Most of high notification rates in PCs were driven by high notification in public sector. There was geographic - both interstate and within state inter-PC - variation in the estimates of these indicators. The study identified some geographic patterns of notification - high positive outlier PCs with adjoining PCs in lower deciles of notification rates, intra-state differences in PC performance, and similarities in notification rates of adjoining PCs in different states.

CONCLUSION

In addition to regional inequality, the study identified geospatial patterns that can aid in the formulation of suitable interventions. These include decongestion of overburdened facilities by strengthening poorly performing units. The PCs with a high percentage Private Sector Notification can act as role models for neighbouring PCs to improve private sector engagement. MPs can play a crucial role in mobilising additional resources, creating awareness, and establishing inter-PC and inter-state collaboration to improve TB program performance.

Social Enterprise Model (SEM) for private sector tuberculosis screening and care in Bangladesh

BACKGROUND

In Bangladesh, about 80% of healthcare is provided by the private sector. Although free diagnosis and care is offered in the public sector, only half of the estimated number of people with tuberculosis are diagnosed, treated, and notified to the national program. Private sector engagement strategies often have been small scale and time limited. We evaluated a Social Enterprise Model combining external funding and income generation at three tuberculosis screening centres across the Dhaka Metropolitan Area for diagnosing and treating tuberculosis.

METHODS AND FINDINGS

The model established three tuberculosis screening centres across Dhaka Metropolitan Area that carried the icddr,b brand and offered free Xpert MTB/RIF tests to patients visiting the screening centres for subsidized, digital chest radiographs from April 2014 to December 2017. A network of private and public health care providers, and community recommendation was formed for patient referral. No financial incentives were offered to physicians for referrals. Revenues from radiography were used to support screening centres' operation. Tuberculosis patients could choose to receive treatment from the private or public sector. Between 2014 and 2017, 1,032 private facilities networked with 8,466 private providers were mapped within the Dhaka Metropolitan Area. 64, 031 patients with TB symptoms were referred by the private providers, public sector and community residents to the three screening centres with 80% coming from private providers. 4,270 private providers made at least one referral. Overall, 10,288 pulmonary and extra-pulmonary tuberculosis cases were detected and 7,695 were bacteriologically positive by Xpert, corresponding to 28% of the total notifications in Dhaka Metropolitan Area.

CONCLUSION

The model established a network of private providers who referred individuals with presumptive tuberculosis without financial incentives to icddr,b's screening centres, facilitating a quarter of total tuberculosis notifications in Dhaka Metropolitan Area. Scaling up this approach may enhance national and international tuberculosis response.

Enhanced Private Sector Engagement for Tuberculosis Diagnosis and Reporting through an Intermediary Agency in Ho Chi Minh City, Viet Nam

Under-detection and -reporting in the private sector constitute a major barrier in Viet Nam’s fight to end tuberculosis (TB). Effective private-sector engagement requires innovative approaches. We established an intermediary agency that incentivized private providers in two districts of Ho Chi Minh City to refer persons with presumptive TB and share data of unreported TB treatment from July 2017 to March 2019. We subsidized chest x-ray screening and Xpert MTB/RIF testing, and supported test logistics, recording, and reporting. Among 393 participating private providers, 32.1% (126/393) referred at least one symptomatic person, and 3.6% (14/393) reported TB patients treated in their practice. In total, the study identified 1203 people with TB through private provider engagement. Of these, 7.6% (91/1203) were referred for treatment in government facilities. The referrals led to a post-intervention increase of +8.5% in All Forms TB notifications in the intervention districts. The remaining 92.4% (1112/1203) of identified people with TB elected private-sector treatment and were not notified to the NTP. Had this private TB treatment been included in official notifications, the increase in All Forms TB notifications would have been +68.3%. Our evaluation showed that an intermediary agency model can potentially engage private providers in Viet Nam to notify many people with TB who are not being captured by the current system. This could have a substantial impact on transparency into disease burden and contribute significantly to the progress towards ending TB.

Leveraging Providers' Preferences to Customize Instructional Content in Information and Communications Technology-Based Training Interventions: Retrospective Analysis of a Mobile Phone-Based Intervention in India

Background

Many public health programs and interventions across the world increasingly rely on using information and communications technology (ICT) tools to train and sensitize health professionals. However, the effects of such programs on provider knowledge, practice, and patient health outcomes have been inconsistent. One of the reasons for the varied effectiveness of these programs is the low and varying levels of provider engagement, which, in turn, could be because of the form and mode of content used. Tailoring instructional content could improve engagement, but it is expensive and logistically demanding to do so with traditional training

Objective

This study aimed to discover preferences among providers on the form (articles or videos), mode (featuring peers or experts), and length (short or long) of the instructional content; to quantify the extent to which differences in these preferences can explain variation in provider engagement with ICT-based training interventions; and to compare the power of content preferences to explain provider engagement against that of demographic variables.

Methods

We used data from a mobile phone–based intervention focused on improving tuberculosis diagnostic practices among 24,949 private providers from 5 specialties and 1734 cities over 1 year. Engagement time was used as the primary outcome to assess provider engagement. K-means clustering was used to segment providers based on the proportion of engagement time spent on content formats, modes, and lengths to discover their content preferences. The identified clusters were used to predict engagement time using a linear regression model. Subsequently, we compared the accuracy of the cluster-based prediction model with one based on demographic variables of providers (eg, specialty and geographic location).

Results

The average engagement time across all providers was 7.5 min (median 0, IQR 0-1.58). A total of 69.75% (17,401/24,949) of providers did not consume any content. The average engagement time for providers with nonzero engagement time was 24.8 min (median 4.9, IQR 2.2-10.1). We identified 4 clusters of providers with distinct preferences for form, mode, and length of content. These clusters explained a substantially higher proportion of the variation in engagement time compared with demographic variables (32.9% vs 1.0%) and yielded a more accurate prediction for the engagement time (root mean square error: 4.29 vs 5.21 and mean absolute error: 3.30 vs 4.26).

Conclusions

Providers participating in a mobile phone–based digital campaign have inherent preferences for instructional content. Targeting providers based on individual content preferences could result in higher provider engagement as compared to targeting providers based on demographic variables.