Optimizing Village-Level Targeting of Active Case Detection to Support Visceral Leishmaniasis Elimination in India.

Background: India has made major progress in improving control of visceral leishmaniasis (VL) in recent years, in part through shortening the time infectious patients remain untreated. Active case detection decreases the time from VL onset to diagnosis and treatment, but requires substantial human resources. Targeting approaches are therefore essential to feasibility. Methods: We analyzed data from the Kala-azar Management Information System (KAMIS), using village-level VL cases over specific time intervals to predict risk in subsequent years. We also graphed the time between cases in villages and examined how these patterns track with village-level risk of additional cases across the range of cumulative village case-loads. Finally, we assessed the trade-off between ACD effort and yield. Results: In 2013, only 9.3% of all villages reported VL cases; this proportion shrank to 3.9% in 2019. Newly affected villages as a percentage of all affected villages decreased from 54.3% in 2014 to 23.5% in 2019, as more surveillance data accumulated and overall VL incidence declined. The risk of additional cases in a village increased with increasing cumulative incidence, reaching approximately 90% in villages with 12 cases and 100% in villages with 45 cases, but the vast majority of villages had small cumulative case numbers. The time-to-next-case decreased with increasing case-load. Using a 3-year window (2016-2018), a threshold of seven VL cases at the village level selects 329 villages and yields 23% of cases reported in 2019, while a threshold of three cases selects 1,241 villages and yields 46% of cases reported in 2019. Using a 6-year window increases both effort and yield. Conclusion: Decisions on targeting must consider the trade-off between number of villages targeted and yield and will depend upon the operational efficiencies of existing programs and the feasibility of specific ACD approaches. The maintenance of a sensitive, comprehensive VL surveillance system will be crucial to preventing future VL resurgence.

Development and Evaluation of Active Case Detection Methods to Support Visceral Leishmaniasis Elimination in India.

As India moves toward the elimination of visceral leishmaniasis (VL) as a public health problem, comprehensive timely case detection has become increasingly important, in order to reduce the period of infectivity and control outbreaks. During the 2000s, localized research studies suggested that a large percentage of VL cases were never reported in government data. However, assessments conducted from 2013 to 2015 indicated that 85% or more of confirmed cases were eventually captured and reported in surveillance data, albeit with significant delays before diagnosis. Based on methods developed during these assessments, the CARE India team evolved new strategies for active case detection (ACD), applicable at large scale while being sufficiently effective in reducing time to diagnosis. Active case searches are triggered by the report of a confirmed VL case, and comprise two major search mechanisms: 1) case identification based on the index case's knowledge of other known VL cases and searches in nearby houses (snowballing); and 2) sustained contact over time with a range of private providers, both formal and informal. Simultaneously, house-to-house searches were conducted in 142 villages of 47 blocks during this period. We analyzed data from 5030 VL patients reported in Bihar from January 2018 through July 2019. Of these 3033 were detected passively and 1997 via ACD (15 (0.8%) via house-to-house and 1982 (99.2%) by light touch ACD methods). We constructed multinomial logistic regression models comparing time intervals to diagnosis (30-59, 60-89 and ≥90 days with <30 days as the referent). ACD and younger age were associated with shorter time to diagnosis, while male sex and HIV infection were associated with longer illness durations. The advantage of ACD over PCD was more marked for longer illness durations: the adjusted odds ratios for having illness durations of 30-59, 60-89 and >=90 days compared to the referent of <30 days for ACD vs PCD were 0.88, 0.56 and 0.42 respectively. These ACD strategies not only reduce time to diagnosis, and thus risk of transmission, but also ensure that there is a double check on the proportion of cases actually getting captured. Such a process can supplement passive case detection efforts that must go on, possibly perpetually, even after elimination as a public health problem is achieved.

Towards a Sustainable Vector-Control Strategy in the Post Kala-Azar Elimination Era.

Visceral leishmaniasis (VL) is a potentially deadly parasitic disease. In the Indian sub-continent, VL is caused by Leishmania donovani and transmitted via the bite of an infected Phlebotomus argentipes female sand fly, the only competent vector species in the region. The highest disease burden is in the northern part of the Indian sub-continent, especially in the state of Bihar. India, Bangladesh, and Nepal embarked on an initiative, coordinated by World Health Organization, to eliminate VL as a public health problem by the year 2020. The main goal is to reduce VL incidence below one case per 10,000 people through early case-detection, prompt diagnosis and treatment, and reduction of transmission using vector control measures. Indoor residual spraying, a major pillar of the elimination program, is the only vector control strategy used by the government of India. Though India is close to its VL elimination target, important aspects of vector bionomics and sand fly transmission dynamics are yet to be determined. To achieve sustained elimination and to prevent a resurgence of VL, knowledge gaps in vector biology and behavior, and the constraints they may pose to current vector control methods, need to be addressed. Herein, we discuss the successes and failures of previous and current vector-control strategies implemented to combat kala-azar in Bihar, India, and identify gaps in our understanding of vector transmission towards development of innovative tools to ensure sustained vector control in the post-elimination period.

Costs and outcomes of active and passive case detection for visceral leishmaniasis (Kala-Azar) to inform elimination strategies in Bihar, India.

BACKGROUND: Effective case identification strategies are fundamental to capturing the remaining visceral leishmaniasis (VL) cases in India. To inform government strategies to reach and sustain elimination benchmarks, this study presents costs of active- and passive- case detection (ACD and PCD) strategies used in India's most VL-endemic state, Bihar, with a focus on programme outcomes stratified by district-level incidence. METHODS: Expenditure analysis was complemented by onsite micro-costing to compare the cost of PCD in hospitals alongside index case-based ACD and a combination of blanket (house-to-house) and camp ACD from January to December 2018. From the provider's perspective, a cost analysis evaluated the overall programme cost of each activity, the cost per case detected, and the cost of scaling up ACD. RESULTS: During 2018, index case-based ACD, blanket and camp ACD, and PCD reported 1,497, 131, and 1,983 VL-positive cases at a unit cost of $522.81, $4,186.81, and $246.79, respectively. In high endemic districts, more VL cases were identified through PCD while in meso- and low-endemic districts more cases were identified through ACD. The cost of scaling up ACD to identify 3,000 additional cases ranged from $1.6-4 million, depending on the extent to which blanket and camp ACD was relied upon. CONCLUSION: Cost per VL test conducted (rather than VL-positive case identified) may be a better metric estimating unit costs to scale up ACD in Bihar. As more VL cases were identified in meso-and low-endemic districts through ACD than PCD, health authorities in India should consider bolstering ACD in these areas. Blanket and camp ACD identified fewer cases at a higher unit cost than index case-based ACD. However, the value of detecting additional VL cases early outweighs long-term costs for reaching and sustaining VL elimination benchmarks in India.

Generalized confidence interval for an agreement between raters.

Estimation and inference are two key components toward the solution of any statistical problem; however, the inferential issues of statistical assessment of agreement among two or more raters have not been well developed as compared to the development of estimation procedures in this area. The fundamental reason for this gap is the complex expression of the concordance correlation coefficient (CCC) that is frequently used in assessing agreement among raters. Large sample-based statistical tests for CCC often fail to produce desired results for small samples. Hence, inferential procedures for small samples are urgently needed to evaluate agreement between raters. We argue that hypothesis testing of CCC has little value in practice due to the absence of a gold standard of agreement. In this article, we construct the generalized confidence interval (GCI) for CCC utilizing a bivariate normal distribution of measurements, and also develop a large sample-based confidence interval (LSCI). We establish satisfactory performance of GCI by providing the desired coverage probability (CP) via simulation. Results of GCI and LSCI are illustrated and compared with a data set of a recent study performed at U.S. Department of Veterans Affairs, Hines.

A Mixed-Effects Model for Detecting Disrupted Connectivities in Heterogeneous Data.

The human brain is an amazingly complex network. Aberrant activities in this network can lead to various neurological disorders such as multiple sclerosis, Parkinson's disease, Alzheimer's disease, and autism. functional magnetic resonance imaging has emerged as an important tool to delineate the neural networks affected by such diseases, particularly autism. In this paper, we propose a special type of mixed-effects model together with an appropriate procedure for controlling false discoveries to detect disrupted connectivities for developing a neural network in whole brain studies. Results are illustrated with a large data set known as autism brain imaging data exchange which includes 361 subjects from eight medical centers.