Recognizing the need for good quality cost data in setting reimbursement rates for services covered by PM-JAY and as an essential ingredient for conducting Health Technology Assessment (HTA) in the country, the government invested in the production of cost data and supported the concurrent development of a national database of healthcare costs: the National Health System Cost Database (NHSCD).  

In this blog, we briefly describe India’s cost repository and how it caters to the needs of policymakers and researchers. We share examples of health system applications of the cost database and their policy implications, demonstrating the value brought about by this initiative. Finally, we conclude by highlighting the key areas that need to be addressed in order to improve the quality and sustain the relevance of the information the database offers, and promote the use of such initiatives for evidence-informed decision-making.

What does India’s healthcare cost repository offer?

A cost database is a public good to inform evidence-based decisions and economic evaluation research by providing access to a transparent set of country-specific reference costs. India’s cost repository is being established to offer access to national cost data on primary care provisioning through community health centres, primary health care and sub-health centres, and hospital-based secondary and tertiary care from both public and private providers. The development of NHSCD, a collaborative effort by the Postgraduate Institute of Medical Education and Research, Chandigarh, India; the Department of Health Research, India; and the Centre for Global Development, has facilitated the process of collating all these data into a single dataset and promoting their use and application. This initiative will make the average health facility cost data collected from multiple states freely available for researchers and policymakers.

The National Health System Cost Database

The cost data within this database provides annual and average healthcare facility costs at different levels of healthcare delivery (i.e., the value of all input resources used to produce a service), input-wise as well as broken down by different services. Another feature available on this web-based platform is the “unit cost estimator” which based on a set of key variables known to influence the unit cost, generates an average or unit cost in the form of cost per outpatient visit or inpatient admission for different states in India. The platform also hosts a costing manual and training videos on cost analysis. The data collection tools and the methodology deployed to estimate these costs have also been made available on the website to ensure transparency and for use and application by other practitioners. Moreover, the website also provides links to useful publications and resources in the context of costing and economic evaluations.  

The utility of cost data systems for healthcare: applications and implications

The cost repository and the data held have been used in a number of ways that demonstrate its value (Figure 2). Cost data are valuable inputs to the conduct of HTA as well as budget impact assessment. More importantly, such data also aid the setting of reimbursement rates for various healthcare services. Three examples are described below.

• What is the optimal reimbursement rate for healthcare services?

As India established PM-JAY, a system for calculating reimbursement rates that adequately reflected the cost-of-service provision was needed. In view of this, HTAIn (the HTA Unit of the Government of India) at the Department of Health Research commissioned the Costing of Health Services in India (CHSI) study to estimate the costs of PM-JAY health benefits packages. The cost evidence generated from this study was used in revising the initial set of reimbursement prices. The CHSI study data were used to analyse the difference between existing reimbursement prices and costs so that the two could be aligned. Also, the data were used to identify variance in cost based on types of providers and their geographical locations, and inform a price weight scheme that compensates providers according to these factors.

• The cost implications of strengthening primary health care

Another pivotal component of the Ayushman Bharat scheme has been the setting up of Health and Wellness Centres (HWCs) for strengthening primary care in the country, the operationalization of which has been planned in a phased manner. For the scaling up of HWCs, it is fundamental to understand the resource requirements to assess the budgetary implication for the government. The cost data from the costing repository have been used to estimate the financial implications of this strategy over the next five years.

• Making HTA evidence more robust

The data from the cost repository has facilitated the growth of HTA in India by providing healthcare unit costs. For example, this data was used in the assessment of the cost-effectiveness of the typhoid conjugate vaccine (TCV) in children over six months of age, and a cost-effectiveness analysis of population-based screening for diabetes and hypertension in India. Both these studies demonstrate wider level policy implications of costing and HTA, where the former was conducted to aid policy-level decisions undertaken by India’s National Technical Advisory Group on Immunisation (NTAGI) as well as the Ministry of Health at the Central and State levels, and the latter was aligned with the government’s aims to expand primary care for diabetes and hypertension through the HWCs.

The challenges ahead

While a central repository reduces the transaction costs of obtaining cost information for a range of activities in healthcare decision making, the data embedded within the NHSCD must be expanded and updated regularly given continuous healthcare reforms as well as rising healthcare costs. Further, to model costs, it is pivotal to understand what is meant by costs and how it differs from prices. More importantly, the level of granularity of availability of records due to lack of electronic patient records extends the effort to determine costs by many folds, which is a deterrent to good quality cost data. Therefore, the government of India needs to focus on building sustainable mechanisms for setting up systems for generating accurate cost data rather than relying on resource intensive studies for cost data collection.

Secondly, to promote the use of standardized cost evidence as inputs for HTA, an issue of paramount importance is that the existence of the cost repository reaches, is accepted, and used by the research community and policymakers. Finally, it is worth acknowledging that there still exists an aperture between the researchers and policymakers, especially in terms of understanding cost evidence. Hence, it is all the more important that the existing and forthcoming data systems ensure relevance, validity, smooth usability, and practicability.

Conclusion

As the Indian health system embarked upon adopting an evidence-informed and inclusive health policy, it identified the lack of cost evidence as one of the key gaps requiring attention. The development of the cost repository is one of the crucial steps in providing access to transparent, country-specific reference costs, and has proved to be an invaluable resource for priority-setting and decision-making. 

Yesterday, Ghana launched its first Strategy for Health Technology Assessment (HTA).

This strategy sends out a clear message of Ghana’s ambitious commitment to evidence informed priority setting. It recognizes that building an HTA function demands action on several areas, including capacity development, topic selection, and bespoke methods guidelines, all linked by a strong governance framework.

In Sub-Saharan Africa, the use of HTA is still very limited, and the Ghanaian progress in this area represents a regional achievement that others can learn from. The strategy builds on iDSI supported work on hypertension, which involved the creation of a Technical Working Group (TWG), a concept that is now part of the architecture of HTA.

Mr Agyeman-Manu, Ghana’s Health Minister, said that so far work in HTA had begun to demonstrate value addition in areas including assessing value in changing from amoxicillin suspension to dispersible tablets, assessment of the cost components of Ghana’s COVID-19 Vaccination Plan as well as the cost-effectiveness of treatment for newly diagnosed hypertension cases.

The launch and new strategy is also key opportunity for regional and continental leadership and shared learning – there has been interest in Ghana’s journey from officials in Tanzania, and participants at the launch event included representative from the Ethiopian Public Health Institute and RSSB Rwanda.

iDSI has been building strong partnerships in Ghana for over 10 years. As the first Sub-Saharan African nation to introduce a tax-funded National Health Insurance Scheme (NHIS) in 2003, Ghana is committed to achieving universal health coverage (UHC) by 2030. To enable this vision, iDSI has continued collaborations with governmental, clinical and academic partners in-country to strengthen evidence-based decision making in healthcare for Ghana’s population of 30 million.

Progress on HTA has been led by a committed team in Ghana that includes Martha Gyansa-Lutterodt, (MoH Ghana), Brian Asare (HTA secretariat, MoH Ghana) and Justice Nonvignon (University of Ghana & co-Chair of the TWG). Martha Gyansa-Lutterodt in particular had a key role in driving HTA development through her leadership as Director of Pharmaceutical Services at the MoH, and now as Director of Technical Coordination within that ministry.

CGD and our iDSI partners (LSHTM, NIPH and the University of Ghana) will continue to support the strengthening of the nascent HTA structures, with an emphasis on institutional coordination and “end to end” HTA thinking – from topic selection to strategies for implementing HTA findings and assessing impact. 

(53) iDSI WEBINAR – Dr Hugo Turner – YouTube

Since January 2018, iDSI has been working closely with the Global Fund to fight against AIDS, TB and Malaria (henceforth the Global Fund), the Kenyan Ministry of Health (National AIDS and STI Control Program -NASCOP- and the National AIDS Control Council -NACC) to assess the impact of implementation of Quality Improvement (QI) in support to differentiated care (DC) models on the quality of HIV care in Kenya. This was a unique project: the topic was scoped through lengthy consultations and was entirely led by local partners, the Global Fund and iDSI providing support on research methods, analysis and write-up. This modality of collaboration ensured that the study focus was aligned with future programming plans and strategic policy interests, and that the overall research being conducted with local ownership throughout.

Why is this important?

The study looks at the use of Quality Improvement to support the roll out of one of the most important changes in HIV/AIDs clinical guidelines in the country of the recent year. In 2016, the Ministry of Health issued comprehensive guidelines on Antiretroviral Therapy (ART), which introduced differentiated care (DC) pathways also known as “the DC ART model”. The DC ART model separated patients into different clinical groups demanding on their clinical needs, with specific differentiated patient pathways for each group so as to easily address their clinical needs. Prior to the introduction of DC, all patients were cared under one clinical pathway, e.g. whether they were stable or unstable (e.g. not virally suppressed or with a history of poor adherence). With the introduction of DC, stable patients (which represent > 75% of all patients) have fewer clinical visits and can pick up ART refills directly from pharmacies; freeing up resources (e.g., clinical staff time) to more intensive care for unstable patients with poor health outcomes.

There is a lot of interest and enthusiasm about DC globally, and it is seen as a means to achieve global HIV targets, especially in resource-constrained high prevalence settings[1]. However, there are anecdotal reports of barriers and challenges in implementation of DC pathways[2], including in Kenya where our study took place. As a result, the Kenya Ministry of Health and the Global Fund piloted early on a QI Program in support of DC implementation (with a focus on facilities where performance along HIV targets was not satisfactory). The intervention was implemented from December 2017 to May 2019, with 70 sites participating across 7 counties in the country. It followed a ‘Plan-Do-Act-Study’ process, whereby facilities diagnosed implementation barriers and developed with the support of NASCOP and other facilities local tailored solutions, testing change in real time. In addition, additional training and coaching on the new ART guidelines (including DC pathways) was provided in intervention facilities. There was some training to control sites, but it was far less intensive than in facilities receiving QI.

This is to our knowledge, the first comprehensive evaluation of a practical support program to DC pathway implementation. The study yields significant learnings for other countries looking to implement DC in their countries.

How was the study conducted?

The study followed a simple yet robust evaluation method relying on a single end-point evaluation between intervention and control facilities, identified through propensity score matching (PSM). PSM utilized data pre-intervention (June 2016) to ensure that intervention and control facilities were comparable prior to the implementation of QI. 70 intervention sites were matched with 193 sites based on facility level, volume of patients on ART, county epidemiological characteristics (e.g., HIV prevalence, population density) and proportion of patients with a viral load test in the last year (as an indicator of ‘performance’). Only the best 15 matches out of the 70 sites were selected for the study, for a final sample of 30 facilities. In all facilities, three survey instruments were used: (i) patient survey (coupled with chart abstraction for viral load and information on ARV regimens, weight, height etc.), (ii) facility costing tools (including time and motion survey administered to a sub-sample of patients) and (iii) provider survey (to measure satisfaction and knowledge of guidelines). Ethical clearance was granted by the Red Cross Ethical Review Committee.

Descriptive statistics were produced (means comparison between the two groups), and significance of the difference in means was tested using t-tests and Chi2 tests.

What have we learned?

Processes of care varied widely between facilities in both control (facilities implementing DC alone) and intervention (facilities implementing both QI and DC) groups. For instance, some facilities applied changes that improved service delivery, e.g. changes in opening times (e.g. ARV pick up organized from 6am) or appointment days (e.g., pediatric ART versus stable patient days) to implement DC pathways. Those innovations helped staff manage more complex care pathways and keep track of patient needs, although it is not clear whether they supported more effective implementation (not within the scope of this study). Another common theme across all sites was long waiting times for clinical appointments, especially in high volume facilities. Patients spent on average 92.8 minutes in facilities, and only 8.1 minutes were spent actively seeking services (e.g. clinical consultation, nutritional support, pharmacy time). Those results also support the overall rationale for DC: patients who were present for ARV pick up spent less than 25mins in total in the facility. This is in line with the global literature showing that DC models utilise health resources more efficiently, but may also align better with patient preferences given the otherwise long waiting times. It should be highlighted that waiting times in intervention facilities was lower than in control facilities.

Patient satisfaction with HIV services overall was high across all facilities. Patients were most satisfied with drug availability, and the least satisfied with waiting times. Intervention sites scored higher than control sites along the following dimensions: waiting times, convenience of the appointment, time spent with the clinician and observation of privacy. It is worth noting that while the intervention involved coaching on DC guidelines, we found no statistical difference in provider knowledge of DC guidelines between control and intervention groups, and patient satisfaction with provider technical skills. However, 82% of providers in intervention sites reported that the intervention helped them improve their knowledge of DC pathways very much. Patients scored also higher in ‘knowledge questions’ in intervention sites compared to controls: for instance, to the question “what do you do if you have forgotten your medication?”, 70.6% of respondents in intervention sites answered correctly compared to 58.7% in control sites.

There were notable differences in patient outcomes between control and intervention sites. Patients cared for in intervention sites reported a lower incidence of opportunistic infections (9% compared to 12% in control), higher rate of viral suppression (92.7% compared to 89.4%) and higher quality of life as measured by EQ5D 5L instrument. All those differences were significant at the 5% level. This points to the fact that DC+QI did improve processes of care at the facility level, which resulted in greater patient health.

All in all, the costs of implementing DC+QI versus DC alone amounted to 516 KES  (or less than 5 dollars) per patient per year (a health systems perspective was adopted for costing). Overall costs varied significantly across facilities (e.g. based on facility size) and across different patient groups (e.g. between stable and unstable patients). Almost half of the DC+QI implementation costs (43%) were spent on organizing and learning sessions. Another 39% was attributed to administration and overheads.

A research collaboration led by local actors

This research was entirely driven by NASCOP and NACC, and the results of the report were used to discuss funding and modalities for scaling up the intervention in other counties. This is an example of a collaboration with academic partners, donors, led by country partners to ensure direct application into decision-making, as well as building awareness and capacity around project evaluation and costing methods.

Read the full report here

[1] El-Sadr WM, Rabkin M, DeCock KM. Population health and individualized care in the global AIDS response: synergy or conflict? AIDS. 2016;30(14):2145–8.

[2] https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6738628/

Academics at the University of Strathclyde have produced a simulation tool for assessing the return on investment in Health Technology Assessment Agencies (HTAs) as part of a country’s healthcare system.

The spreadsheet-based tool was produced as part of the Evaluating the Value of a Real-world HTA Agency (EVoRA) project, a collaboration with Thailand’s Health Intervention and Technology Assessment Program (HITAP).

HTAs are bodies that assess and evaluate health technologies. They work with healthcare stakeholders to produce reports and recommendations on which technologies should be adopted as well as producing guidelines for clinical practice.

The simulation-based approach allows policymakers to directly compare health expenditures and outcomes in a scenario where an HTA agency exists and where no such agency exists.

The spreadsheet-based tool was produced as part of the Evaluating the Value of a Real-world HTA Agency (EVoRA) project, a collaboration with Thailand’s Health Intervention and Technology Assessment Program (HITAP).

HTAs are bodies that assess and evaluate health technologies. They work with healthcare stakeholders to produce reports and recommendations on which technologies should be adopted as well as producing guidelines for clinical practice.

The simulation-based approach allows policymakers to directly compare health expenditures and outcomes in a scenario where an HTA agency exists and where no such agency exists.

Practical demonstration

Professor Alec Morton, Head of the Department of Management Science, who created the tool along with Knowledge Exchange Fellow Dr Euan Barlow, said: “As countries develop their health systems on the path to Universal Health Coverage, many are experimenting with the development of a health technology assessment agency in order to set priorities for health benefit packages, make go/no go decisions about innovative new health interventions or to support pricing negotiations.

“Our simulation model is designed to provide users with a practical demonstration of the expected value which could be returned from investment into a health technology assessment (HTA) agency.

The simulation model provides a quantitative comparison of alternative approaches to making decisions on which potential health technology (HT) projects should be allocated funding.

“Comparisons are given in terms of the overall financial impact and anticipated health benefits which could be expected from pursuing the alternative funding approaches.

“Our simulated HTA agency makes decisions on the basis of a cost/quality-adjusted life years rule with a threshold set so that it will accept roughly half of available health technologies.

“We then compare it with a situation where the country accepts technologies on a first-come-first-served basis, without considering cost-effectiveness. Our simulation generates 1,000 random scenarios.

“The threshold rule leads on average to more interventions being funded, higher health benefits for the population and lower costs for payers.

“Inspection of the scenarios shows that in no scenario are costs higher and health benefits lower with the threshold, than with the first-come-first-served approach demonstrating the power of cost-effectiveness analysis.

“Furthermore, the spreadsheet model demonstrates the approach to HT funding decisions which could be expected under an HTA agency, where decisions are transparent and justifiable, and can be tailored to target the priority measures of impact as appropriate to the localised funding situation.”

The EVoRA project generated three separate outputs, each of which is intended to provide an alternative view towards the evaluation of an HTA agency. The outputs along with a policy brief which draws on their findings and a video explaining the project can be found on the HITAP website.

The Health Intervention and Technology Assessment Program (HITAP) is funded by the Thailand Research Fund under a Senior Research Scholar grant.

HITAP’s International Unit is supported by the International Decision Support Initiative (iDSI) to provide technical assistance on health intervention and technology assessment to governments in low- and middle-income countries.

iDSI is funded by the Bill & Melinda Gates Foundation, the UK’s Department for International Development, and the Rockefeller Foundation. Overseas Development Institute Fellows at HITAP support its work in the region.

Recently the ABPMJAY, which covers 10 million vulnerable families, reached a milestone of providing 100 million treatments. However, given the size of the scheme, ensuring cost-effectiveness within such a large scheme is highly dependent upon having detailed and robust information on economic costs within the health system. Here we discuss, the role of costing in priority setting, price negotiations and the measures that India is taking in this area, as part of its efforts to ensure equity and cost-effectiveness within its healthcare system.

Poor cost data can lead to the misallocation of resources 

Priority setting is the process of making decisions about how best to allocate limited resources to improve population health. Priority setting within healthcare can be facilitated through health technology assessments (HTA) which includes quantifying whether investments in healthcare are both clinically effective and cost-effective and through exploring the key factors within the healthcare system that drive costs.

In India, as in many low- and middle-income countries (LMICs), there have been challenges in systematically incorporating explicit priority setting or HTA into healthcare decision-making in India. A key barrier has been the complex and fragmented healthcare system with several different insurance and “assurance” arrangements, at both the central and state level. Despite these challenges, the government of India has begun to take proactive steps towards institutionalising HTA. It has established its own HTA agency at the national level (HTAIn) in the Ministry of Health and Family Welfare, and HTAIn has been developing HTA standards and initiating the first health technology appraisals.

But, as HTA rolls out in India, the limited availability of cost data has been highlighted as a key concern by both government actors and the press. The availability of cost data is in turn constrained by limited  cost data collection activities, the inadequacy of information systems to meet costing needs, and the lack of political interest in costing. A typical problem is when only some of the costs relevant to delivery of a drug or diagnostic tool are assessed (e.g. excluding patient monitoring or patient incurred costs). An intervention can then appear more or less cost-effective than they actually are and fail to acknowledge the cost burden placed on patients.

This is a problem found in many LMICs but with political will, a standardised, central, and freely available source of health service cost data can be developed to address this gap (such as in Thailand or Cambodia). As a result it will lead to a fall in the duplication of efforts and the expense of data collection to improve the quality of HTA.

Good quality cost information can help governments negotiate better prices

The terms “cost” and “price” are often, mistakenly used interchangeably. However, they are extremely different things. Specifically, prices do not necessarily reflect costs. Prices are the negotiated rate for a good e.g. drugs or service such as consultations. Set too high, prices can over-stretch a budget, limiting spending in other areas and setting up barriers to care and, where individuals pay for care, lead to catastrophic health expenditure. At the same time, high reimbursement rates can result in the over-use of certain treatments such as c-sections and have even led to unwanted hysterectomies. Set too low and the prices can contribute to over-use of some therapies such as antibiotics. Good quality cost information and HTA can help regulate prices so that they reflect value for money.

Regulating prices can be easier within health systems that have a central purchaser such as the UK, France, Australia and Thailand where prices are set in accordance with costs. Within these countries, uniform reimbursement rates are set using data on the cost of health service provision collected through the mandated submission of cost data from all providers or, in the case of Thailand, comprehensive cost surveys conducted by the Health Intervention and Technology Assessment Program of the Ministry of Public Health (HITAP).

Such a system which involves a central regulator encourages transparency and can help contain growth in costs through both accountability as well as economies of scale. For example, using reliable cost information in an HTA process allowed the Thai government to negotiate an affordable price for the HPV vaccines, demonstrating how monopsony power (when there is only one buyer in a market) combined with good cost information can contain costs.

Regulating prices is trickier in fragmented healthcare systems (e.g. USA or India) which have many different types of providers and purchasers (insurers/government). In India, the fragmented system has resulted in large scale variations in prices for similar services across and between states and providers. The majority of fee rates within India’s many public health insurance schemes have been set using various processes and fee rates with different incentives for different services resulting in a process that is “non-transparent and often arbitrary and irrational.” These prices are likely to be inefficient and highly incentivise certain types of services at the expense of others, such as the use of high technology stent implants that have no evidence based benefit over cheaper models. Gathering information on  coronary stent prices revealed price mark ups of between 4-6 times the cost price, leading government price capping and up to 85 percent price reductions. Similarly, a recent Indian initiative to improve TB testing in the private sector has shown how standardisation of prices can be achieved by bringing private laboratories together under a single regulatory body, India has reduced the cost of accredited TB tests to affordable levels. The issue has been highlighted during the COVID-19 pandemic with private hospitals accused of charging exorbitant prices, making the government mandate hospitals to share COVID-19 fee details and some evidence of drops in non COVID related healthcare utilisation due to financial barriers.

As publicly funded health insurance schemes expand to cover a greater portion of the population and consume a greater portion of the healthcare budget, the need for prices to be set at efficient levels is more pressing. The demand for freely available good quality cost information to inform price-setting therefore becomes increasingly important.

India is beginning to build a cost evidence-base

Until now, costing information in India has largely been fragmented, not available across states or levels of the health system and highly disease specific. In fact, the major source of cost data has been individual cost studies which have been mixed in validity and reliability. This has been further compounded by the fact that there is a limited pool of health system experts with costing experience in India.

In recognition of the lack of costing capacity within India, the Department of Health Research (DHR) along with academic experts like PGIMER Department of Community Medicine and School of Public Health have taken a proactive approach to strengthen the costing capacity of the health system. Alongside the establishment of a technical working group on costing, there has been support for the development of training material for economic evaluation more generally and subsequently in specific topics including costing. These take the form of online modules, workshops for policy-makers and practitioners and a forthcoming costing manual which lays out principles and standards for costing health services in India.

To improve the availability of data, a National Health System Cost Database website is being built as a public good, by PGIMER Chandigarh, with the support of the International Decision Support Initiative (iDSI). This database currently includes data on the unit costs of health services from 167 public health facilities (district and below) located in 6 different states across India, collected in collaboration with PGIMER’s partners IIT Madras, PHFI Delhi, TISS Mumbai.

In addition to the development of the database and website, the HTAIn has launched a national cost study-Costing of Health Systems (CHSI)-to collect further cost information from public and private healthcare tertiary and district level providers located across 11 different Indian states. The data will be used for HTA and has been used to estimate the unit costs of the AB-PMJAY health benefit packages (HBP). The National Health System Cost Database website continues to be updated with new data (such as the CHSI results) as these become available, as well as the latest methodological standards and guides.

The database website also hosts a user friendly and unique unit cost predictor (based on a statistical cost function). The predictor allows users to generate state specific average outpatient visit and inpatient admission costs for use in their own analyses. For example, a researcher wanting to do an HTA specific to the state of Andhra Pradesh would be able to extract a mean cost for their locality rather than use a national level average.

These first incremental steps towards generating nationally representative health service cost data for India are already proving their value. Since the launch of these two initiatives, the CHSI study costs results have been used to inform reimbursement rates for AB-PMJAY as well as for as well as for the costing of PMJAY COVID-19 HBPs.

What next?

India has initiated a welcome and multi-faceted approach for increasing costing capacity, improving cost data and generating a robust evidence base for HTA. These initiatives are already facilitating priority setting and a more transparent price setting process. But there is still work to be done. The role of costing in decision-making needs to be higher up in the healthcare policy makers’ agenda and become an integral part of the evidence base. Healthcare providers and academic centres can facilitate this by adapting information systems to meet cost data collection needs. More critical, is the need for greater transparency around fees and charges. In the future, Ministry of Health; State Departments of Health; National and State public health insurance agencies can make publication and/or submission of provider healthcare costs or fees a mandatory requirement for all providers and in particular publicly funded healthcare. These incremental but exceedingly important steps will help create more transparent healthcare decision-making in the country.

Authors: Lorna Guinness, Hiral Anil Shah, Abha Mehndiratta and Shankar Prinja

Thank you to Kalipso Chalkidou for valuable oversight.

With a plethora of evidence generated since the outbreak, maintaining a good standard of reporting and ensuring accountability of the three stakeholders (Funders/Development Partners, Modelers/Technical Experts, and Decision-Makers/Policy Makers) becomes challenging. Therefore, we propose a “Reporting Standard” that the stakeholders must agree on before collaborating to ensure (i) quality and (ii) accountability.

The intention of this survey is to gather your perspective on the concept, criteria, timelines, foreseeable challenges, and prior experiences that the we can learn from and further adapt this proposed reporting standard to make it feasible and acceptable to all stakeholders.

The survey may take 15-20 minutes to complete.

Click here to access the survey or alternatively, use the link below

https://docs.google.com/forms/d/e/1FAIpQLSd4aUhQgisol6kUytt8BQBw4TGoETvrpQIsd26_uc4d2Gtq9A/viewform

Should you have any questions, please address queries to sarin.k@hitap.net

To find out more about CMCC: https://decidehealth.world/CMCC

In this blog, Samantha Hollingworth, Francis Ruiz and Kalipso Chalkidou from the International Decision Support Initiative (iDSI), discuss the findings of their recently published Brief Report, which aims to rapidly assess current health system priorities and policy areas of demand for health technology assessment (HTA) in Sub-Saharan Africa, and identify key gaps in data and skills to inform targeted capacity building.

Most countries have committed to universal health coverage (UHC) in the context of the sustainable development goals for affordable access to essential medicines and other health technologies. Many countries in sub-Saharan Africa (Africa) have established national health insurance systems (or are planning to) but they require governments to set health priorities within a limited health budget.

Supporting countries to make informed decisions

Health Technology Assessment (HTA) provides a structured way to bring together evidence of clinical and cost effectiveness to inform priority-setting activities. In establishing HTA systems, you need to involve many stakeholders including (government) decision makers, clinicians, academics, consumers, development partners, and HTA knowledge brokers. The International Decision Support Initiative (iDSI) is a global network of health, policy and economic expertise, which seeks to support countries to make better decisions about efficient spending on healthcare. iDSI has been working in Africa since 2013 to develop local capacity and support implementation of robust HTA processes.

There is growing interest across the continent but little is known about the current HTA landscape. We rapidly assessed current health system priorities and policy areas of demand for HTA in Sub-Saharan Africa, to identify key gaps in data and skills to inform targeted capacity building.  We revised an existing survey, delivered it to 357 participants, then analysed responses and explored key themes. There were 51 respondents (14% response rate) across 14 countries.

We found that HTA is an important and valuable priority-setting tool with a key role in designing health benefits packages, developing clinical guidelines, and improving services.  Medicines were a technology type that would especially benefit from using HTA. Participants highlighted that HTA can be used to address safety issues (e.g. low quality medicines) and explore value for money. Local data to support HTA – including its availability and accessibility – was widely considered inadequate and limited. Participants outlined the need for training support in research methodology and data gathering.

A growing interest in HTA

This is, as far as we know, the first survey of the HTA landscape in Africa. Despite the low response rate, we received in-depth responses from many respondents. Their views were as individuals so may not reflect those of particular agencies or governments.  Our results are similar to the results from the African region in the WHO Global Survey on Health Technology Assessment by National Authorities. They also found that the main barriers were a lack of qualified human resources, funding, and information.

Priority setting is inevitable: the question is not whether, but rather how, to set them. Although HTA is not widely used in Africa, there is growing political commitment and policy interest. The interest in using HTA to support priority-setting decisions for medicines, devices, and vaccines is welcome as these are high cost items in a country’s health budget. The use of HTA is even more important in the context of two current challenges: donor funding and COVID-19.  As many countries in Africa grow, they will need to reduce their reliance on donor funds, especially in health. COVID-19 has exposed some weakness in health systems and the need to consider not only maintaining the essential package of health services such as for malaria, childhood immunisation, and TB, but to also consider the costs – and cost-effectiveness – of testing and treating patients with COVID-19.

There are growing numbers of economic evaluations in Africa but only one in four of the first authors of these evaluations were affiliated with African institutions. The main challenge is being able to produce high quality evidence and share knowledge among institutions. Perhaps these challenges could be helped if countries pooled their resources and harmonised policies in health, as they have done for medicines regulations with the African Union. Our survey is an important first step in raising awareness of HTA as a tool for priority setting in Africa. We will develop a more refined survey to support increased engagement and building capacity.

This article is included in the International Decision Support Initiative gateway on F1000Research.

The analysis of Interventions in Development Aid (AIDA) study aimed to explore and propose a principled framework for thinking about and discussing donor co-financing decisions. With support from iDSI, researchers from the University of Strathclyde and the University of Ghana explored the issue in the context of Gavi, the Vaccine Alliance (Gavi) co-financing countries as they transition towards graduation and fully financing their vaccination programmes, focusing on Ghana and a set of comparator countries at different points on the transition pathway.

We summarise here the methods and the recommendations offered by the authors of the study. A full report and related publications are forthcoming.

Report authors: Itamar Megiddo, Justice Nonvignon, Richmond Owusu, and Alec Morton

Methods

The study was a mixed-methods approach that included both a quantitative analysis based on a donor-country (DC) model and qualitative research, largely based on a stakeholder forum to explore the DC model approach and the wider political economy issues. The DC model suggests that to achieve the greatest health gains countries should fund cost-effective interventions (compared to a cost-effectiveness threshold) and donors such as Gavi should prioritise co-financing interventions that are “just cost-ineffective” by providing funds to make them cost-effective from the country perspective.

To perform the quantitative analysis, we explored the DC model in the context of Gavi co-financing vaccines and we compared its suggested outcomes and Gavi co-financing policy. We explored the model using four types of data: a review of gross domestic product (GDP)-based cost-effectiveness thresholds, data on countries GDP, a review of vaccine cost-effectiveness studies in the study settings, and data on Gavi co-financing from Gavi country progress reports and decision letters. The stakeholder forum was conducted online, due to challenges posed by the Coronavirus disease pandemic. It included participants with significant experiences in vaccination programmes, including from the different parts of the Ghana government, the WHO, and Path.

Recommendations

1. The inconsistency of cost components across cost-effectiveness analyses (CEAs) of vaccines poses a challenge for developing a cost-effectiveness based donor co-financing framework. Different studies give different results but cannot be directly compared because of differences in the costing assumptions. This is currently a major stumbling block in taking an evidence-based approach to vaccination policy for both countries and donors, though variability in reporting is understandable due to the differing study purposes and audiences.
2. Reporting of inputs and outputs of CEA studies should be transparent and complete to allow reproducibility from different perspectives. The data which is gathered within a CEA is typically of considerable value for a range of planning tasks. The current standard of reporting in the literature does not permit that data to be extracted and checked. As a result, the scientific community is punching below its weight.
3. Gavi should use an explicit normative model to underpin its co-financing policy. Currently, Gavi co-financing appears ad hoc and hard to understand. In the interests of both equity between partner countries and accountability to its funders, Gavi should make more explicit use of normative economic models in determining its contributions.
4. More research is needed on how donor co-financing can encourage sustainability.  By co-financing vaccination programmes and by setting explicit expectations about transition, Gavi does support countries to think about financial sustainability for vaccination. However, sustainability remains a challenge for many graduating countries. More work is needed to understand how sustainable financing for vaccination can be facilitated and incentivised.
5. The burden of disease should remain an important factor when making decisions on which vaccines to co-finance.  A clear message from stakeholders was that while cost-effectiveness is useful, disease burden (and thus population health and budget impact) is key for in-country decision-makers. Donor policy and theoretic analysis should explicitly account for this dimension alongside information about cost-effectiveness. 

Acknowledgements

We would also like to thank the stakeholders that participated in the stakeholder forum: Kingsley Addai Frimpong (World Health Organization Ghana Country Office), Brian Asare (Health Technology Assessment Secretariat, the Ghana Ministry of Health), George Armah (Noguchi Memorial Institute, University of Ghana and Ghana National Immunisation Technical Advisory Group [NITAG]), Angela Ackon (Health Technology Assessment Secretariat, the Ghana Ministry of Health), KwadwoOdei Antwi-Agyei (Center for Vaccine Access and Innovation, PATH Ghana), John Bawa (Center for Vaccine Access and Innovation, PATH Ghana ), and Genevieve Cecilia Aryeetey (School of Public Health, University of Ghana).

The survey is a unique collaborative effort coordinated by the Health Economics Unit, University of Cape Town and supported by the International Decision Support Initiative (iDSI), the African Health Economics and Policy Association (AfHEA), and the Teaching Helath
Economics Special Interest Group of the International Health Economics Association (iHEA). It aims to generate a clear picture of teaching health economics in sub-Saharan Africa, providing a foundation for collaboration and planning and enabling a collective and locally driven vision for the future of health economics on the continent. The survey is available in English and French and is aimed at post graduate degree coordinators at institutions in sub-Saharan Africa that are teaching health economics, the survey link is available below.

https://www.surveymonkey.com/r/Health_econ_ENG
Completion date for the survey is Wednesday, 17th June 2020

If you are not a degree coordinator at an institution in sub-Saharan Africa but are interested in being involved in this initiative, please contact Tommy Wilkinson tommy.wilkinson@uct.ac.za for more information.

Appel à tous ceux qui enseignent l’économie de la santé en Afrique subsaharienne –
une invitation à participer à une enquête visant à identifier les établissements d’enseignement

Nous sommes dans une période critique pour l’enseignement de l’économie de la santé en Afrique subsaharienne. De nombreux pays s’acheminent vers la couverture sanitaire universelle tout en se détachant du financement des donateurs. La pandémie COVID-19 a démontré la convergence essentielle entre la santé et les comportements, le financement de la santé, les systèmes de santé, et la macroéconomie dans la prise de décision et la politique de santé. Le besoin d’économistes de la santé locaux en Afrique n’a jamais été aussi pressant, mais nous ne disposons que de très peu de connaissances coordonnées sur les lieux d’enseignement de l’économie de la santé, sur ce qui est enseigné, et sur le nombre de diplômés. Cette enquête vise à fournir ces réponses et bien plus encore.

L’enquête est le fruit d’un effort de collaboration unique coordonné par le Health Economics Unit de l’Université du Cap (UCT), et soutenu par l’initiative Internationale d’aide à la décision (iDSI), l’Association Africaine d’Économie et de Politique de la Santé (AfHEA) et le groupe d’intérêt spécial “Teaching Health Economics” de l’Association internationale d’économie de la santé (iHEA). Elle vise à produire une image actualisée de l’enseignement de l’économie de la santé en Afrique subsaharienne, en créant une fondation pour la collaboration et la planification et en permettant une vision collective et locale pour l’avenir de l’économie de la santé sur le continent. L’enquête est disponible en anglais et en français et s’adresse aux coordinateurs des diplômes de troisième cycle des institutions d’Afrique
subsaharienne qui enseignent l’économie de la santé. Le lien de l’enquête est disponible ci-dessous.

https://www.surveymonkey.com/r/Health_econ_FR
La date d’achèvement de l’enquête est Mercredi 17 Juin 2020

Si vous n’êtes pas coordinateur de diplômes dans un établissement d’Afrique subsaharienne mais que vous souhaitez participer à cette initiative, veuillez contacter Tommy Wilkinson tommy.wilkinson@uct.ac.za pour plus d’informations.