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Low density of formal care providers in rural India results in restricted and delayed access to standardized management of hypertension. Task-sharing with pharmacies, typically the first point of contact for rural populations, can bridge the gap in access to formal care and improve health outcomes. In this study, we implemented a hypertension care program involving task-sharing with twenty private pharmacies between November 2020 and April 2021 in two blocks of Bihar, India. Pharmacists conducted free hypertension screening, and a trained physician offered free consultations at the pharmacy. We calculated the number of subjects screened, initiated on treatment (enrolled) and the change in blood pressure using the data collected through the program application. Of the 3403 subjects screened at pharmacies, 1415 either reported having a history of hypertension or had elevated blood pressure during screening. Of these, 371 (26.22%) were enrolled in the program. Of these, 129 (34.8%) made at least one follow-up visit. For these subjects, the adjusted average difference in systolic and diastolic blood pressure between the screening and follow-up visits was −11.53 (−16.95 to −6.11, 95% CI) and −4.68 (−8.53 to −0.82, 95% CI) mmHg, respectively. The adjusted odds of blood pressure being under control in this group during follow-up visits compared to screening visit was 7.07 (1.29 to 12.85, 95% CI). Task-sharing with private pharmacies can lead to early detection and improved control of blood pressure in a resource-constrained setting. Additional strategies to increase patient screening and retention rates are needed to ensure sustained health benefits.

This study is motivated by the challenges faced by clinics in sub-Saharan Africa in allocating scarce and unreliable supply of antiretroviral drugs (ARVs) among a large pool of eligible patients. Existing discussion of ARV allocation is focused on qualitative rules for prioritizing certain socioeconomic and demographic patient segments over others at the national level. However, such prioritization rules are of limited utility in providing quantitative guidance on scaling up of treatment programs at individual clinics. In this study, we take the perspective of a clinic administrator whose objective is to maximize the quality-adjusted survival of the entire patient population in its service area by allocating scarce and unreliable supply of drugs among two activities: initiating treatment for untreated patients and continuing treatment for previously treated patients. The key trade-off underlying this allocation decision is between the marginal health benefit obtained by initiating an untreated patient on treatment and that obtained by avoiding treatment interruption of a treated patient. This trade-off has not been explicitly studied in the clinical literature, which focuses either on the incremental value obtained from initiating treatment (over no treatment) or on the value of providing continuous treatment (over interrupted treatment) but not on the difference of the two. We cast the clinic's problem as a stochastic dynamic program and provide a partial characterization of the optimal policy, which consists of dynamic prioritization of patient segments and is characterized by state-dependent thresholds. We use this structure of the optimal policy to design a simpler Two-Period heuristic and show that it substantially outperforms the Safety-Stock heuristic, which is commonly used in practice. In our numerical experiments based on realistic parameter values, the performance of the Two-Period heuristic is within 4% of the optimal policy whereas that of the Safety-Stock heuristic can be as much as 20% lower than that of the optimal policy. Our model can serve as a basis for developing a decision support tool for clinics to design their ARV treatment program scale-up plans.

Only an estimated 62% of Indian children under the age of 2 years are fully immunized. We examined the association between India's Mission Indradhanush (MI)-a periodic intensification of the routine immunization program-which was implemented in phases across districts between March 2015 and July 2017, and routine vaccination coverage and timeliness among children. We used data from a 2015 to 2016 national survey of children (n = 29,532) and employed difference-in-difference regressions to examine binary indicators of receipt of 11 vaccines and whether vaccines were received at recommended ages. The full immunization rate was 27% higher among children under 2 years old residing in MI phase 1 and 2 districts (intervention group) as compared with those residing elsewhere (control group). The rate of receiving all vaccines at recommended ages was 8% higher in the intervention group. Receiving doses of oral polio vaccine (OPV) birth dose, OPV dose 1 (OPV1), OPV2, OPV3, bacillus Calmette-Guérin, and hepatitis B birth dose vaccines were 9%, 9%, 11%, 16%, 5%, and 19% higher in the intervention group than the control group, respectively. More research is required on the cost-effectiveness of investing in MI-type programs as compared with routine immunization.

In services where teams come together for short collaborations, managers are often advised to strive for high team familiarity so as to improve coordination and consequently, performance. However, inducing high team familiarity by keeping team membership intact can limit workers’ opportunities to acquire useful knowledge and alternative practices from exposure to a broader set of partners. We introduce an empirical measure for prior partner exposure and estimate its impact (along with that of team familiarity) on operational performance using data from the London Ambulance Service. Our analysis focuses on ambulance transports involving new paramedic recruits, where exogenous changes in team membership enable identification of the performance effect. Specifically, we investigate the impact of prior partner exposure on time spent during patient pickup at the scene and patient handover at the hospital. We find that the effect varies with the process characteristics. For the patient pickup process, which is less standardized, greater partner exposure directly improves performance. For the more standardized patient handover process, this beneficial effect is triggered beyond a threshold of sufficient individual experience. In addition, we find some evidence that this beneficial performance impact of prior partner exposure is amplified during periods of high workload, particularly for the patient handover process. Finally, a counterfactual analysis based on our estimates shows that a team formation strategy emphasizing partner exposure outperforms one that emphasizes team familiarity by about 9.2% in our empirical context., Deloitte Institute of Innovation and Entrepreneurship

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.

India has experienced a substantial increase in the coverage of routine childhood vaccines in recent years. However, a large fraction of these vaccines is not delivered in a timely manner, i.e., at the recommended age. Further, substantial disparities exist in both coverage and timeliness across states. We aim to quantify the changes in coverage and timeliness of routine childhood vaccination in India over time, their variation across states, and changes in these variations over time.We used data from two rounds of India's National Family Health Surveys, NFHS-3 (2005-06) and NFHS-4 (2015-16) on bacille Calmette-Guerin vaccine (BCG), three doses of diphtheria, pertussis, and tetanus vaccine (DPT1, DPT2, DPT3), and measles-containing vaccine (MCV). We used the Turnbull estimator to estimate the cumulative distribution function (CDF) of administering each vaccine by a certain age while accounting for two-sided censoring in the survey data. We then used these estimated CDFs to calculate coverage and timeliness at the national and state levels.At the national level, both vaccination coverage and timeliness estimates increased from NFHS-3 to NFHS-4 for all vaccines. The increase in timeliness ranging from 27.3% for DPT3 to 74.0% for MCV continued to be lower than coverage, ranging from 75.3% (95% CI 57.7-87.2) for DPT3 to 74.0% (95% CI 42.2-33.0) for MCV, for all vaccines. Cross-state variation in timeliness was greater than the variation in coverage. Variation in both timeliness and coverage reduced from NFHS-3 to NFHS-4. However, this reduction was greater for timeliness than for coverage.A large fraction of the children in India receive vaccines later than the recommended age thereby keeping them exposed to vaccine-preventable diseases. Interventions that specifically focus on improving the timely delivery of vaccines are needed to improve the overall effectiveness of the routine immunization program.

This paper studies pricing and rationing strategies of luxury retailers and identifies exclusivity-seeking consumer behavior as another rational behind price markdowns in seasonal conspicuous goods.

Background: Despite the availability of effective and affordable treatments, only 14% of hypertensive Indians have controlled blood pressure. Increased hypertension treatment coverage (the proportion of individuals initiated on treatment) and adherence (proportion of patients taking medicines as recommended) promise population health gains. However, governments and other payers will not invest in a large-scale hypertension control program unless it is both affordable and effective. Objective: To investigate if a national hypertension control intervention implemented across the private and public sector facilities in India could save overall costs of CVD prevention and treatment. Methods: We developed a discrete-time microsimulation model to assess the cost-effectiveness of population-level hypertension control intervention in India for combinations of treatment coverage and adherence targets. Input clinical parameters specific to India were obtained from large-scale surveys such as the Global Burden of Disease as well as local clinical trials. Input hypertensive medication cost parameters were based on government contracts. The model projected antihypertensive treatment costs, avoided CVD care costs, changes in disability-adjusted life year (DALYs) and incremental cost per DALY averted (represented as incremental cost-effectiveness ratio or ICER) over 20 years. Results: Over 20 years, at 70% coverage and adherence, the hypertension control intervention would avert 1.68% DALYs and be cost-saving overall. Increasing adherence (while keeping coverage constant) resulted in greater improvement in cost savings compared to increasing coverage (while keeping adherence constant). Results were most sensitive to the cost of antihypertensive medication, but the intervention remained highly cost-effective under all one-way sensitivity analyses. Conclusion: A national hypertension control intervention in India would most likely be budget neutral or cost-saving if the intervention can achieve and maintain high levels of both treatment coverage and adherence.

First National Healthcare (FNH) runs a large network of hospitals and has worked to systematically reduce waiting times in its emergency departments. One of FNH's regional networks has run a successful marketing campaign promoting its low ED waiting times that other regions want to emulate. The corporate quality manager must now determine whether to allow these campaigns to be rolled out and, if so, which waiting time estimates to use. Are the numbers currently being reported accurate? Is there a more accurate way of estimating patient waiting time that can be easily understood by consumers?

Many service environments such as outpatient departments comprise repeated episodes of finite duration wherein customers arriving during an episode must be served before the end of that episode. Scheduling resources and customer arrivals in such settings involves trading off customer wait against resources’ time and effort. We hypothesize that this tradeoff leads to a “slow first, fast later” pattern of system speed, which allows build-up of inventory earlier for more efficient utilization of faster system speed later. As a natural corollary, we also hypothesize that greater anticipated workload, which causes faster inventory build-up, leads to a larger increase in system speed earlier in the service episode than later. We empirically validate these hypotheses using operational data from a high volume tertiary care outpatient department. Our estimation results suggest that due to hypothesized system speed pattern, patients arriving around the middle (later) part of the episode experience 20% (63%) shorter length of stay compared to those arriving at the beginning. Further, additional anticipated workload of 1 patient per hour leads to 14%, 4%, and negligible reduction in the average length of stay during the earlier, middle, and later part of the service episode, respectively. Counterfactual simulations based on our model estimates show that “slow first, fast later” system speed pattern yields faster completion of work as compared to an equivalent constant system speed pattern. This article is protected by copyright. All rights reserved.

Background 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. Methods 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. Results Uptake of Xpert increased considerably in both Mumbai (from 36 to 60.4%) and Patna (from 12.2 to 45.1%). However, utilization of Xpert for TB diagnosis and non-adherence to negative Xpert results did not show systematic trends over time. In regression models, cumulative number of Xpert tests ordered was significantly associated with Xpert uptake in Patna and utilization for diagnosis in Mumbai (p-value Conclusions Private sector engagement led to substantial increase in uptake of Xpert, especially among high-volume providers, but did not show strong evidence of Xpert results being integrated with TB diagnosis. Increasing availability and affordability of a technically superior diagnostic tool may not be sufficient to fundamentally change diagnosis and treatment of TB in the private sector. Behavioral interventions, specifically aimed at, integrating Xpert results into clinical decision making of private providers may be required to impact patient-level outcomes.

BACKGROUND Diagnostics in many low- and middle-income countries are conducted through centralized laboratory networks. Samples are collected from patients at remote point-of-care health facilities, and diagnostic tests are performed at centralized laboratories. Sample transportation systems that deliver diagnostic samples and test results are crucial for timely diagnosis and treatment in such diagnostic networks. However, they often lack the timely and accurate data (eg, the quantity and location of samples prepared for collection) required for efficient operation. OBJECTIVE This study aims to demonstrate the feasibility, adoption, and accuracy of a distributed data collection system that leverages basic mobile phone technology to gather reports on the quantity and location of patient samples and test results prepared for delivery in the diagnostic network of Malawi. METHODS We designed a system that leverages unstructured supplementary service data (USSD) technology to enable health workers to submit daily reports describing the quantity of transportation-ready diagnostic samples and test results at specific health care facilities, free of charge with any mobile phone, and aggregate these data for sample transportation administrators. We then conducted a year-long field trial of this system in 51 health facilities serving 3 districts in Malawi. Between July 2019 and July 2020, the participants submitted daily reports containing the number of patient samples or test results designated for viral load, early infant diagnosis, and tuberculosis testing at each facility. We monitored daily participation and compared the submitted USSD reports with program data to assess system feasibility, adoption, and accuracy. RESULTS The participating facilities submitted 37,771 reports over the duration of the field trial. Daily facility participation increased from an average of 50% (26/51) in the first 2 weeks of the trial to approximately 80% (41/51) by the midpoint of the trial and remained at or above 80% (41/51) until the conclusion of the trial. On average, more than 80% of the reports submitted by a facility for a specific type of sample matched the actual number of patient samples collected from that facility by a courier. CONCLUSIONS Our findings suggest that a USSD-based system is a feasible, adoptable, and accurate solution to the challenges of untimely, inaccurate, or incomplete data in diagnostic networks. Certain design characteristics of our system, such as the use of USSD, and implementation characteristics, such as the supportive role of the field team, were necessary to ensure high participation and accuracy rates without any explicit financial incentives.

At the time of writing of this Editorial in April 2021, Covid-19 continues to ravage our planet, with an official global death toll now exceeding three million, and a horrendous legacy of economic and human damage. The roll-out of vaccination has given hope that we will soon reach the end of this chapter of history. However, it will take years for the world to overcome this calamity and many individuals whose health or livelihoods have been destroyed will never fully recover. This failure of the world to effectively respond to the challenge of Covid-19 is all the more bitter because the outbreak of a novel pathogen was entirely predictable; the spread, preventable; and the suffering, avoidable. The experience of different countries around the world shows that the ability to plan, and to execute plans in a disciplined fashion, can make all the difference between relative security and catastrophe. The challenge for Management Scientists is to show that our discipline can have a role – a critical role – as a part of this planning. Epidemiological models of disease dynamics have been prominent through this crisis but do not fully capture the constraints in the health system and cannot directly support many of the management decisions which have to be made as part of the response. As Management Scientists, our perspective and our modelling tools have the potential to address those shortcomings; but if our profession cannot demonstrate our ability to add value, others will do so in our place.

India's lockdown 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 lifting a lockdown. 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.

BACKGROUND 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. OBJECTIVE 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. METHODS 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. RESULTS We found that models based on random forest had the highest prediction accuracy for both diseases and were able to outperform the US and UK risk scores in terms of AUC by 35.5% for diabetes (improvement of 0.239 from 0.671 to 0.910) and 13.5% for hypertension (improvement of 0.094 from 0.698 to 0.792). For a fixed screening specificity of 0.9, the random forest model was able to reduce the expected number of false negatives by 620 patients per 1000 screenings for diabetes and 220 patients per 1000 screenings for hypertension. This improvement reduces the cost of incorrect risk stratification by US $1.99 (or 35%) per screening for diabetes and US $1.60 (or 21%) per screening for hypertension. CONCLUSIONS In the next decade, health systems in many countries are planning to spend significant resources on noncommunicable disease screening programs and our study demonstrates that machine learning models can be leveraged by these programs to effectively utilize limited resources by improving risk stratification.

Background Tuberculosis (TB) incidence in India continues to be high due, in large part, to long delays experienced by patients before successful diagnosis and treatment initiation, especially in the private sector. This diagnostic delay is driven by patients’ inclination to switch between different types of providers and providers’ inclination to delay ordering of accurate diagnostic tests relevant to TB. Our objective is to quantify the impact of changes in these behavioral characteristics of providers and patients on diagnostic delay experienced by pulmonary TB patients. Methods and findings We developed a discrete event simulation model of patients’ diagnostic pathways that captures key behavioral characteristics of providers (time to order a test) and patients (time to switch to another provider). We used an expectation-maximization algorithm to estimate the parameters underlying these behavioral characteristics, with quantitative data encoded from detailed interviews of 76 and 64 pulmonary TB patients in the 2 Indian cities of Mumbai and Patna, respectively, which were conducted between April and August 2014. We employed the estimated model to simulate different counterfactual scenarios of diagnostic pathways under altered behavioral characteristics of providers and patients to predict their potential impact on the diagnostic delay. Private healthcare providers including chemists were the first point of contact for the majority of TB patients in Mumbai (70%) and Patna (94%). In Mumbai, 45% of TB patients first approached less-than-fully-qualified providers (LTFQs), who take 28.71 days on average for diagnosis. About 61% of these patients switched to other providers without a diagnosis. Our model estimates that immediate testing for TB by LTFQs at the first visit (at the current level of diagnostic accuracy) could reduce the average diagnostic delay from 35.53 days (95% CI: 34.60, 36.46) to 18.72 days (95% CI: 18.01, 19.43). In Patna, 61% of TB patients first approached fully qualified providers (FQs), who take 9.74 days on average for diagnosis. Similarly, immediate testing by FQs at the first visit (at the current level of diagnostic accuracy) could reduce the average diagnostic delay from 23.39 days (95% CI: 22.77, 24.02) to 11.16 days (95% CI: 10.52, 11.81). Improving the diagnostic accuracy of providers per se, without reducing the time to testing, was not predicted to lead to any reduction in diagnostic delay. Our study was limited because of its restricted geographic scope, small sample size, and possible recall bias, which are typically associated with studies of patient pathways using patient interviews. Conclusions In this study, we found that encouraging private providers to order definitive TB diagnostic tests earlier during patient consultation may have substantial impact on reducing diagnostic delay in these urban Indian settings. These results should be combined with disease transmission models to predict the impact of changes in provider behavior on TB incidence., Sarang Deo and co-workers study providers' approaches to tuberculosis diagnosis in India., Author summary Why was this study done? India contributes more than a quarter of the 10 million global tuberculosis (TB) cases every year. Several studies capture long, circuitous care pathways followed by TB patients until their diagnosis. However, these studies do not quantify the link between diagnostic delay and underlying patient and provider behavior characteristics. What did the researchers do and find? We developed a quantitative simulation model to estimate the impact of behavioral characteristics of patients and providers on diagnostic delay and estimated the parameters of this model using data from detailed interviews of 76 patients from Mumbai and 64 patients from Patna. We found that earlier test ordering by providers would yield a much larger reduction in diagnostic delay than increasing their diagnostic accuracy. What do these findings mean? Policy-makers and implementing agencies should encourage early test ordering behavior by providers to reduce diagnostic delay, and, consequently, to reduce disease transmission.

Problem definition: Healthcare systems in resource-limited settings rely on diagnostic networks in which medical samples (e.g., blood, sputum) and results need to be transported between geographically dispersed healthcare facilities and centralized laboratories. Academic/practical relevance: Existing sample transportation (ST) systems typically operate fixed schedules, which do not account for demand variability and lead to unnecessary transportation visits as well as delays. Methodology: We design an optimized sample transportation (OST) system that comprises two components: (i) a new approach for timely collection of information on transportation demand (samples and results) using low-cost technology based on feature phones, and (ii) an optimization-based solution approach to the problem of routing and scheduling courier trips in a multistage transportation system. Results: Our solution approach performs well in a range of numerical experiments. Furthermore, we implement OST in collaboration with Riders For Health, who operate the national ST system in Malawi. Based on analysis of field data describing over 20,000 samples and results transported during July–October 2019, we show that the implementation of OST routes reduced average ST delays in three districts of Malawi by approximately 25%. In addition, the proportion of unnecessary trips by ST couriers decreased by 55%. Managerial implications: Our approach for improving ST operations is feasible and effective in Malawi and can be applied to other resource-limited settings, particularly in sub-Saharan Africa. Funding: This work was supported by Bill and Melinda Gates Foundation [Grant OPP1182217] and by the National Institute of Biomedical Imaging and Bioengineering of the National Institutes of Health [Grant U54EB027049]. The content is solely the responsibility of the authors and does not necessarily represent the official views of the funding organizations. Supplemental Material: The e-companion is available at https://doi.org/10.1287/msom.2022.1182 .