Implications for cost and access of site-of-service referrals for ancillary medical services in a US Medicaid population: analysis of claims data from Maryland, USA.

OBJECTIVES: Through analysis of claims and payment data, we quantified several implications of shifting ancillary healthcare services from regulated, more expensive to unregulated, less expensive sites. We also quantified the implications of this shift on access to services, with a focus on differences in access between rural and urban patients for a Medicaid (disadvantaged) population in Maryland, USA. DESIGN: Using a dataset of all Medicaid claims records for 1 year, we identified and extracted all bundles of regulated and unregulated ancillary services. Geospatial computing was used to approximate transportation costs required to access services. Including transportation enabled us to estimate net savings of any added transportation costs. We used location-allocation optimisation models to find the optimal sites to minimise net costs. SETTING: Coverage area included Medicaid patients throughout the state of Maryland. PARTICIPANTS: All rural and urban members of this Medicaid cohort. PRIMARY AND SECONDARY OUTCOME MEASURES: Change in payer costs and member travel times on shifting ancillary bundles from regulated to unregulated sites. RESULTS: Procedure cost and travel time differentials between regulated and unregulated sites strongly correlated with the percentage of procedures referred to regulated sites. Shifting regulated bundles to unregulated sites, while imposing the constraint of no increase in travel time, reduced expenditures by 15.9%. This figure exceeded 30% if no limit was placed on travel-time increases. CONCLUSION: With reasonable constraints on allowable travel time increases, shifting ancillary service bundles from regulated to unregulated sites can benefit both patients and payers in terms of cost and access.

Integrated genomic view of SARS-CoV-2 in India.

Background: India first detected SARS-CoV-2, causal agent of COVID-19 in late January 2020, imported from Wuhan, China. From March 2020 onwards, the importation of cases from countries in the rest of the world followed by seeding of local transmission triggered further outbreaks in India. Methods: We used ARTIC protocol-based tiling amplicon sequencing of SARS-CoV-2 (n=104) from different states of India using a combination of MinION and MinIT sequencing from Oxford Nanopore Technology to understand how introduction and local transmission occurred. Results: The analyses revealed multiple introductions of SARS-CoV-2 genomes, including the A2a cluster from Europe and the USA, A3 cluster from Middle East and A4 cluster (haplotype redefined) from Southeast Asia (Indonesia, Thailand and Malaysia) and Central Asia (Kyrgyzstan). The local transmission and persistence of genomes A4, A2a and A3 was also observed in the studied locations. The most prevalent genomes with patterns of variance (confined in a cluster) remain unclassified, and are here proposed as A4-clade based on its divergence within the A cluster. Conclusions: The viral haplotypes may link their persistence to geo-climatic conditions and host response. Multipronged strategies including molecular surveillance based on real-time viral genomic data is of paramount importance for a timely management of the pandemic.

Predicting the impact of patient and private provider behavior on diagnostic delay for pulmonary tuberculosis patients in India: A simulation modeling study.

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.

Modelling the impact of effective private provider engagement on tuberculosis control in urban India.

In India, the country with the world's largest burden of tuberculosis (TB), most patients first seek care in the private healthcare sector, which is fragmented and unregulated. Ongoing initiatives are demonstrating effective approaches for engaging with this sector, and form a central part of India's recent National Strategic Plan: here we aimed to address their potential impact on TB transmission in urban settings, when taken to scale. We developed a mathematical model of TB transmission dynamics, calibrated to urban populations in Mumbai and Patna, two major cities in India where pilot interventions are currently ongoing. We found that, when taken to sufficient scale to capture 75% of patient-provider interactions, the intervention could reduce incidence by upto 21.3% (95% Bayesian credible interval (CrI) 13.0-32.5%) and 15.8% (95% CrI 7.8-28.2%) in Mumbai and Patna respectively, between 2018 and 2025. There is a stronger impact on TB mortality, with a reduction of up to 38.1% (95% CrI 20.0-55.1%) in the example of Mumbai. The incidence impact of this intervention alone may be limited by the amount of transmission that has already occurred by the time a patient first presents for care: model estimates suggest an initial patient delay of 4-5 months before first seeking care, followed by a diagnostic delay of 1-2 months before ultimately initiating TB treatment. Our results suggest that the transmission impact of such interventions could be maximised by additional measures to encourage early uptake of TB services.