Agricultural and Socioeconomic Factors Associated with Farmer Household Dietary Diversity in India: A Comparative Study of Visakhapatnam and Sonipat.

Using primary data from 479 farmer households, this study examined the associations between agricultural and socioeconomic factors and farmer household dietary diversity in Visakhapatnam and Sonipat. Cropping intensity was positively associated with farmers' household dietary diversity score (HDDS), suggesting that higher cropping intensity may expand the gross cropped area and improve food security among subsistence farmers. Distance to food markets was also significantly associated with farmer HDDS, which suggests that market integration with rural households can improve farmer HDDS in Visakhapatnam. In Sonipat, wealth index had a positive association with farmer HDDS, targeting the income pathway by improving farmer HDDS in this region. Considering the relative contribution of these factors, distance to food markets, cropping intensity, and crop diversity were the three most important factors affecting farmer HDDS in Visakhapatnam, whereas wealth index, cropping intensity, and distance to food markets emerged as the top three important factors contributing to farmer HDDS in Sonipat. Our study concludes that the associations between agricultural and socioeconomic factors and farmer HDDS are complex but context- and location-specific; therefore, considering the site- and context-specific circumstances, different connections to HDDS in India can be found to better support policy priorities on the ground.

Regional differences in agricultural and socioeconomic factors associated with farmer household dietary diversity in India.

This paper investigated whether there are any regional-level differences in factors associated with farmer household dietary diversity using the Food Consumption Score (FCS), in two states of India: Haryana and Gujarat. Our results suggest that the factors associated with farmer household dietary diversity were region-specific, with diverse drivers across districts. For example, in Vadodara (Gujarat), farmers who had greater crop diversity and planted more cash crops had higher dietary diversity while large landholders in Bhavnagar (Gujarat) had higher dietary diversity. In Karnal (Haryana), more educated farmer households and those who cultivated large landholdings had higher dietary diversity while farmers in Bhiwani (Haryana) who were more educated and sold more crops to market had higher dietary diversity. Thus, factors associated with FCS differed even within the same state. These results suggest that in some regions of India, higher crop diversity and better education could improve farmer household dietary diversity. On the other hand, in some other regions, dietary diversity is best improved through the income generation pathway, where households that earn increased income from selling more crops were able to purchase more diverse food from markets. Our study suggests that the drivers of household dietary diversity across rural India are complex and heterogeneous; thus, future policies and programs to improve farmer household nutrition should be tailored considering regional differences in the factors associated with household nutrition.

Impact of Advocacy, Communication, Social Mobilization and Active Case Finding on TB Notification in Jharkhand, India.

Community-level benefits of screening for active tuberculosis (TB) disease remain uncertain. Project Axshya (meaning free of TB) conducted advocacy, communication, social mobilization, and active case finding among vulnerable/marginalized populations of India. Among 15 districts of Jharkhand state, the project was initiated in 36 subdistrict level administrative units - tuberculosis units (TUs) in a staggered manner between April 2013 and September 2014, and continued till the end of 2015. Seven TUs did not implement the project. We assessed the relative change in the quarterly TB case finding indicators (n = 4) after inclusion of a TU within the project. By fitting four multilevel models (mixed-effects maximum likelihood regression using random intercept), we adjusted for secular (over previous five quarters) and seasonal trends, baseline differences within Axshya and non-Axshya TUs, and population size and clustering within districts and within TUs. After inclusion of a TU within the project, we found a significant increase [95% confidence interval (CI)] in TU-level presumptive TB sputum examination rate, new sputum-positive TB Case Notification Rate (CNR), sputum-positive TB CNR, and all forms TB CNR by 12 (5.5, 18.5), 1.1 (0.5, 1.7), 1.3 (0.6, 2.0), and 1.2 (0.1, 2.2) per 100,000 population per quarter, respectively. Overall, the project resulted in an increase (95% CI) in sputum examination and detection of new sputum-positive TB, sputum-positive TB and all forms of TB patients by 22,410 (10,203, 34,077), 2066 (923, 3210), 2380 (1162, 3616), and 2122 (203, 4059), respectively. This provides evidence for implementing project Axshya over and above the existing passive case finding.