In February 2017, India capped the retail price of coronary stents and restricted the channel margin to bring Percutaneous Transluminal Coronary Angioplasty (PTCA) procedure, which uses coronary stents, within reach of millions of patients who previously could not afford it. Prior research shows that care providers respond to such regulations in a way that compensates for their loss in profits because of price control. Therefore, price control policies often introduce unintended consequences, such as distortions in clinical decision making. We investigate such distortions through empirical analysis of claims data from a representative public insurance program in the Indian state of Karnataka. Our data comprises 25,769 insurance claims from 69 private and seven public hospitals from February 2016 to February 2018. The public insurance context is ideal for investigating distortions in clinical decisions as the price paid by patients, and thereby access to the treatment, does not change after price control. We find that the change in the average volume of PTCA procedures per hospital per month after price control disproportionately increased when compared to the change in the clinical alternative – Coronary Artery Bypass Graft (CABG) procedures. This increase corresponds to 6% of the average number of PTCA procedures and 28% of the average number of CABG procedures before the price control. In addition, disproportionate increase in PTCA procedures occurred only among private hospitals, indicating the possibility of profit-maximization intentions driving the clinical choices. Such clinical distortions can have negative implications for patient health outcomes in the long run. We discuss alternative policies to improve access and affordability to healthcare products and services which are likely to not suffer from similar distortions.

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. 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.

Impact of novel high-quality tuberculosis (TB) tests such as Xpert MTB/RIF has been limited due to low uptake among private providers in high-burden countries including India. Our objective was to assess the impact of a demand generation intervention comprising field sales force on the uptake of high-quality TB tests by providers and its financial sustainability for private labs in the long run. We implemented a demand generation intervention across five Indian cities between October 2014 and June 2016 and compared the change in the quantity of Xpert cartridges ordered by labs in these cities from before (February 2013–September 2014) to after intervention (October 2014–December 2015) to corresponding change in labs in comparable non-intervention cities. We embedded this difference-in-differences estimate within a financial model to calculate the internal rate of return (IRR) if the labs were to invest in an Xpert machine with or without the demand generation intervention.

India’s lockdown and subsequent restrictions against SARS-CoV-2, if lifted without any other mitigations in place, could risk a second wave of infection. A test-and-isolate strategy, using PCR diagnostic tests, could help to minimise the impact of this second wave. Meanwhile, population-level serological surveillance can provide valuable insights into the level of immunity in the population. Using a mathematical model, consistent with an Indian megacity, we examined how seroprevalence data could guide a test-and-isolate strategy, for fully lifting restrictions. For example, if seroprevalence is 20% of the population, we show that a testing strategy needs to identify symptomatic cases within 5–8 days of symptom onset, in order to prevent a resurgent wave from overwhelming hospital capacity in the city. This estimate is robust to uncertainty in the effectiveness of the lockdown, as well as in immune protection against reinfection. To set these results in their economic context, we estimate that the weekly cost of such a PCR-based testing programme would be less than 2.1% of the weekly economic loss due to the lockdown. Our results illustrate how PCR-based testing and serological surveillance can be combined to design evidence-based policies, for lifting lockdowns in Indian cities and elsewhere.

The impending scale up of noncommunicable disease screening programs in low- and middle-income countries coupled with limited health resources require that such programs be as accurate as possible at identifying patients at high risk. The aim of this study was to develop machine learning–based risk stratification algorithms for diabetes and hypertension that are tailored for the at-risk population served by community-based screening programs in low-resource settings. We trained and tested our models by using data from 2278 patients collected by community health workers through door-to-door and camp-based screenings in the urban slums of Hyderabad, India between July 14, 2015 and April 21, 2018. We determined the best models for predicting short-term (2-month) risk of diabetes and hypertension (a model for diabetes and a model for hypertension) and compared these models to previously developed risk scores from the United States and the United Kingdom by using prediction accuracy as characterized by the area under the receiver operating characteristic curve (AUC) and the number of false negatives.

Xpert MTB/RIF (Xpert) has been recommended by WHO as the initial diagnostic test for TB and rifampicin-resistance detection. Existing evidence regarding its uptake is limited to public health systems and corresponding resource and infrastructure challenges. It cannot be readily extended to private providers, who treat more than half of India’s TB cases and demonstrate complex diagnostic behavior. We used routine program data collected from November 2014 to April 2017 from large-scale private sector engagement pilots in Mumbai and Patna. It included diagnostic vouchers issued to approximately 150,000 patients by about 1400 providers, aggregated to 18,890 provider-month observations. We constructed three metrics to capture provider behavior with regards to adoption of Xpert and studied their longitudinal variation: (i) Uptake (ordering of test), (ii) Utilization for TB diagnosis, and (iii) Non-adherence to negative results. We estimated multivariate linear regression models to assess heterogeneity in provider behavior based on providers’ prior experience and Xpert testing volumes.

Although widespread vaccinations can help address the COVID-19 pandemic in India, multiple barriers may limit vaccine uptake. Strategies from the studies of behavioral economics can help overcome the barriers. The strategies can address the intention-action gap, thus increasing not only the initial willingness to take the vaccines but also the subsequent action in actually doing so. The paper highlights multiple recommendations for policy. The authors review studies from behavioral economics and apply the insights to COVID vaccination needs in India.