A Community Level KAP Study on Mosquito Control in Jamnagar District

Background: The vectors borne diseases poses an immense public health concern and are major impediments in the path of socio-economic development. Objective: To assess domestic environment as well as community level KAP on mosquito control measures in Jamnagar district. Methods & statistics: It was a cross-sectional survey of 450 household by a pre-tested proforma analyzed by Microsoft excel office 2007. It was carried out in urban, urban slum and rural areas of Jamnagar district. Results: Rural domestic environment was favorable for mosquito breeding. Most of the respondents were unaware about the places where mosquito bred. The knowledge regarding vector, routes and symptoms of malaria was good, while majority were unaware about types of malaria and other mosquito borne diseases. Active malaria surveillance activity was totally lacking in urban area (94%), while it was very poor in rural and slum area. The preferred treatment providers in the community neither screened malaria nor imparted health education about mosquito control. 56% of the respondents were practicing at least one personal protective and larvae control measure, but less efficient one. Conclusion: Community participation in term of KAP regarding vector control is deficient at places & needs to be addressed for effective mosquito control

Feasibility of decentralised deployment of Xpert MTB/RIF test at lower level of health system in India.

BackgroundXpert MTB/RIF is an automated cartridge-based nucleic acid amplification test that has demonstrated its potential to detect tuberculosis and rifampicin resistance with high accuracy. To assist scale-up decisions in India, a feasibility assessment of Xpert MTB/RIF implementation was conducted within microscopy centres of 18 RNTCP TB units.MethodsAs part of programme-based demonstration of Xpert MTB/RIF implementation, we recorded and analysed association between key implementation factors and the ability of test to produce valid results. Factors contributing to test failures were analysed from GeneXpert software data which provides 'failure codes' and causes for test failures.ResultsFrom March'12 to January'13, total 40,035 suspects were tested by Xpert MTB/RIF, and 39,680 (99.1%) received valid results (Cumulative: 37157 (92.8%) on first attempt, 39410 (98.4%) on second attempt, 39637 (99.0%) on third attempt and 39680 (99.1%) on more attempts). Overall initial test failure was 2,878 (7.2% (4%-17%)); of these, 2,594 (90.1%) were re-tested and produced valid results. Most frequent reason of test failure was inadequate sample processing or equipment malfunction (3.9%). Other reasons included power failure (1.1%), cartridge integrity/component failure (0.8%), device-computer communication error (0.5%), and temperature-related errors (0.08%). Significant variation was observed in failure rates both across instruments and over time; furthermore, substantial variation was observed in failure rate in two cartridges lots.ConclusionInstallation required minimal infrastructure modifications and concerns about adequacy of human resources under public sector facilities and temperature extremes proved unfounded. Under routine conditions, Xpert MTB/RIF provided 99.1% valid results in TB suspects with low overall failure rates (7.2% initial failure, 0.9% final failure); devices provided valuable real-time feedback on reasons for test failure, which were used for rapid corrective action. High modular replacement (32%) and inter-lot cartridge performance variation remain sources of concern, and warrant close monitoring of failure rates as a key quality indicator

Quality of active case-finding for tuberculosis in India: a national level secondary data analysis

Background India has been implementing active case-finding (ACF) for TB among marginalised and vulnerable (high-risk) populations since 2017. The effectiveness of ACF cycle(s) is dependent on the use of appropriate screening and diagnostic tools and meeting quality indicators. Objectives To determine the number of ACF cycles implemented in 2021 at national, state (n = 36) and district (n = 768) level and quality indicators for the first ACF cycle. Methods In this descriptive study, aggregate TB program data for each ACF activity that was extracted was further aggregated against each ACF cycle at the district level in 2021. One ACF cycle was the period identified to cover all the high-risk populations in the district. Three TB ACF quality indicators were calculated: percentage population screened (≥10%), percentage tested among screened (≥4.8%) and percentage diagnosed among tested (≥5%). We also calculated the number needed to screen (NNS) for diagnosing one person with TB (≤1538). Results Of 768 TB districts, ACF data for 111 were not available. Of the remaining 657 districts, 642 (98%) implemented one, and 15 implemented two to three ACF cycles. None of the districts or states met all three TB ACF quality indicators’ cut-offs. At the national level, for the first ACF cycle, 9.3% of the population were screened, 1% of the screened were tested and 3.7% of the tested were diagnosed. The NNS was 2824: acceptable (≤1538) in institutional facilities and poor for population-based groups. Data were not consistently available to calculate the percentage of i) high-risk population covered, ii) presumptive TB among screened and iii) tested among presumptive. Conclusion In 2021, India implemented one ACF cycle with sub-optimal ACF quality indicators. Reducing the losses between screening and testing, improving data quality and sensitising stakeholders regarding the importance of meeting all ACF quality indicators are recommended

Use of Xpert MTB/RIF in Decentralized Public Health Settings and Its Effect on Pulmonary TB and DR-TB Case Finding in India

Xpert MTB/RIF, the first automated molecular test for tuberculosis, is transforming the diagnostic landscape in high-burden settings. This study assessed the impact of up-front Xpert MTB/RIF testing on detection of pulmonary tuberculosis (PTB) and rifampicin-resistant PTB (DR-TB) cases in India. This demonstration study was implemented in 18 sub-district level TB programme units (TUs) in India in diverse geographic and demographic settings covering a population of 8.8 million. A baseline phase in 14 TUs captured programmatic baseline data, and an intervention phase in 18 TUs had Xpert MTB/RIF offered to all presumptive TB patients. We estimated changes in detection of TB and DR-TB, the former using binomial regression models to adjust for clustering and covariates. In the 14 study TUs, which participated in both phases, 10,675 and 70,556 presumptive TB patients were enrolled in the baseline and intervention phase, respectively, and 1,532 (14.4%) and 14,299 (20.3%) bacteriologically confirmed PTB cases were detected. The implementation of Xpert MTB/RIF was associated with increases in both notification rates of bacteriologically confirmed TB cases (adjusted incidence rate ratio [aIRR] 1.39; CI 1.18-1.64), and proportion of bacteriological confirmed TB cases among presumptive TB cases (adjusted risk ratio (aRR) 1.33; CI 1.6-1.52). Compared with the baseline strategy of selective drug-susceptibility testing only for PTB cases at high risk of drug-resistant TB, Xpert MTB/RIF implementation increased rifampicin resistant TB case detection by over fivefold. Among, 2765 rifampicin resistance cases detected, 1055 were retested with conventional drug susceptibility testing (DST). Positive predictive value (PPV) of rifampicin resistance detected by Xpert MTB/RIF was 94.7% (CI 91.3-98.1), in comparison to conventional DST. Introduction of Xpert MTB/RIF as initial diagnostic test for TB in public health facilities significantly increased case-notification rates of all bacteriologically confirmed TB by 39% and rifampicin-resistant TB case notification by fivefol

Initial test results and final test results on Xpert MTB/RIF (N = 40,035 patients tested).

<p>* <i>Break up of test failure results - Error, Invalid, No results</i></p

Trend in error rates of modules.

<p>Trend in error rates of modules.</p

Reasons for test failure.

<p>Reasons for test failure.</p

Site Locations: Geographical locations of the 18 project sites across India.

<p>Site Locations: Geographical locations of the 18 project sites across India.</p

Diagnostic Algorithm.

<p>Diagnostic Algorithm.</p