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Waiting time for non-emergency medical care in developing countries is rarely of immediate concern to policy makers that prioritize provision of basic health services. However, waiting time as a measure of health system responsiveness is important because longer waiting times worsen health outcomes and affect utilization of services. Studies that assess socio-economic inequalities in waiting time provide evidence from developed countries such as England and the United States; evidence from developing countries is lacking. In this paper, we assess the relationship between social class i.e. caste of an individual and waiting time at health facilities—a client orientation dimension of responsiveness. We use household level data from two rounds of the Indian Human Development Survey with a sample size of 27,251 households in each wave (2005 and 2012) and find that lower social class is associated with higher waiting time. This relationship is significant for individuals that visited a male provider but not so for those that visited a female provider. Further, caste is positively related to higher waiting time only if visiting a private facility; for individuals visiting a government facility the relationship between waiting time and caste is not significant. In general, caste related inequality in waiting time has worsened over time. The results are robust to different specifications and the inclusion of several confounders. [ABSTRACT FROM AUTHOR]

Background: Even though cholera has existed for centuries and many parts of the country have sporadic, endemic and epidemic cholera, it is still an under-recognized health problem in India. A Cholera Expert Group in the country was established to gather evidence and to prepare a road map for control of cholera in India. This paper identifies cholera burden hotspots and factors associated with an increased risk of the disease. Methodology/Principle findings: We acquired district level data on cholera case reports of 2010–2015 from the Integrated Disease Surveillance Program. Socioeconomic characteristics and coverage of water and sanitation was obtained from the 2011 census. Spatial analysis was performed to identify cholera hotspots, and a zero-inflated Poisson regression was employed to identify the factors associated with cholera and predicted case count in the district. 27,615 cholera cases were reported during the 6-year period. Twenty-four of 36 states of India reported cholera during these years, and 13 states were classified as endemic. Of 641 districts, 78 districts in 15 states were identified as “hotspots” based on the reported cases. On the other hand, 111 districts in nine states were identified as “hotspots” from model-based predicted number of cases. The risk for cholera in a district was negatively associated with the coverage of literate persons, households using treated water source and owning mobile telephone, and positively associated with the coverage of poor sanitation and drainage conditions and urbanization level in the district. Conclusions/Significance: The study reaffirms that cholera continues to occur throughout a large part of India and identifies the burden hotspots and risk factors. Policymakers may use the findings of the article to develop a roadmap for prevention and control of cholera in India. [ABSTRACT FROM AUTHOR]

Background: The monitoring and evaluation of lymphatic filariasis (LF) has largely relied on the detection of antigenemia and antibodies in human populations. Molecular xenomonitoring (MX), the detection of parasite DNA/RNA in mosquitoes, may be an effective complementary method, particularly for detecting signals in low-level prevalence areas where Culex is the primary mosquito vector. This paper investigated the application of a household-based sampling method for MX in Tamil Nadu, India. Methods: MX surveys were conducted in 2010 in two evaluation units (EUs): 1) a hotspot area, defined as sites with community microfilaria prevalence ≥1%, and 2) a larger area that also encompassed the hotspots. Households were systematically selected using a sampling interval proportional to the number of households in the EU. Mosquito pools were collected and analyzed by real-time polymerase chain reaction (qPCR). Two independent samples were taken in each EU to assess reproducibility of results. Follow-up surveys were conducted in 2012. Results: In 2010, the proportion of positive pools in the hotspot EU was 49.3% compared to 23.4% in the overall EU. In 2012, pool positivity was significantly reduced to 24.3% and 6.5%, respectively (p<0.0001). Pool positivity based on independent samples taken from each EU in 2010 and 2012 were not significantly different except for the hotspot EU in 2012 (p = 0.009). The estimated prevalence of infection in mosquitoes, measured by PoolScreen, declined from 2.2–2.7% in 2010 to 0.6–1.2% in 2012 in the hotspot area and from 0.9–1.1% to 0.2–0.3% in the larger area. Conclusions: The household-based sampling strategy for MX led to mostly reproducible results and supported the observed LF infection trends found in humans. MX has the potential to be a cost-effective, non-invasive monitoring and evaluation tool with sensitive detection of infection signals in low prevalence settings. Further investigation and application of this sampling strategy for MX are recommended to support its adoption as a standardized method for global LF elimination programs. [ABSTRACT FROM AUTHOR]

Background: As Bangladesh, India and Nepal progress towards visceral leishmaniasis (VL) elimination, it is important to understand the role of asymptomatic Leishmania infection (ALI), VL treatment relapse and post kala-azar dermal leishmaniasis (PKDL) in transmission. Methodology/ Principal Finding: We reviewed evidence systematically on ALI, relapse and PKDL. We searched multiple databases to include studies on burden, risk factors, biomarkers, natural history, and infectiveness of ALI, PKDL and relapse. After screening 292 papers, 98 were included covering the years 1942 through 2016. ALI, PKDL and relapse studies lacked a reference standard and appropriate biomarker. The prevalence of ALI was 4–17-fold that of VL. The risk of ALI was higher in VL case contacts. Most infections remained asymptomatic or resolved spontaneously. The proportion of ALI that progressed to VL disease within a year was 1.5–23%, and was higher amongst those with high antibody titres. The natural history of PKDL showed variability; 3.8–28.6% had no past history of VL treatment. The infectiveness of PKDL was 32–53%. The risk of VL relapse was higher with HIV co-infection. Modelling studies predicted a range of scenarios. One model predicted VL elimination was unlikely in the long term with early diagnosis. Another model estimated that ALI contributed to 82% of the overall transmission, VL to 10% and PKDL to 8%. Another model predicted that VL cases were the main driver for transmission. Different models predicted VL elimination if the sandfly density was reduced by 67% by killing the sandfly or by 79% by reducing their breeding sites, or with 4–6y of optimal IRS or 10y of sub-optimal IRS and only in low endemic setting. Conclusion/ Significance: There is a need for xenodiagnostic and longitudinal studies to understand the potential of ALI and PKDL as reservoirs of infection. [ABSTRACT FROM AUTHOR]