Anemia and VAD are important public health problems among tribal population in spite of the rich biodiversity. A robust disaggregated understanding of the determinants of tuberculosis (TB) in each local setting is essential for effective health system and policy action to control TB. The objective of the study was to identify population attributable risk (PAR) for TB disease based on the locally available evidences for Kerala, India. Systematic review was done for risk factors of TB in the state. The second set of searches was done to understand the prevalence of the identified risk factors in general population in Kerala. With all available studies and reports, an expert group consensus was made to finalize state-specific prevalence of risk factors. Population attributable fractions were calculated for identified risk factors. PAR for TB disease in Kerala obtained was 24% for undernutrition, 15% for diabetes, 15% for tobacco use, and 1% for HIV. Kerala state's PAR for TB was comparatively lower for HIV but higher for diabetes mellitus. Similar exercises for summarizing population risk factors need to happen at all states for making plans to effectively combat TB. Kerala state's PAR for TB was comparatively lower for HIV but higher for diabetes mellitus. Similar exercises for summarizing population risk factors need to happen at all states for making plans to effectively combat TB. The study investigates the cost incurred by leptospirosis patients as either out-of-pocket expenditure (OOPE) or opportunity cost (OC) and recommends accordingly for the national program on leptospirosis in India. The objective of this study is to determine leptospirosis-related OOPE and OC at a government tertiary care hospital and to disaggregate the total OOPE into contributing cost domains. The OOPE data were collected by the personal interview of confirmed leptospirosis cases who took complete treatment at the hospital in year 2009 using a prestructured questionnaire. The patients were interviewed daily until discharge to know daily OOPE. The mean OOPE per patient was Rs. 2157/-, Median Rs. 1880/-, 25 -75 percentile Rs. 1446 - Rs 2587.5). The lowest quintile for OOPE was Rs. 1330/- and the highest quintile was Rs. 2874/-. Loss of daily wages was 68% (Rs. 1458.9/-) of the total OOPE. Other major expenditure included cost of drugs Rs. 308.8/- (14%), expenditure on food Rs. 173/- (8%), and travelling expenses Rs. 204.4/- (9%). Rs. 2157/- is significant OOPE, and hence, important factor in understanding health-seeking behavior and compliance of leptospirosis patients. The OC (loss of daily wages) amounts to 68% of total OOPE which has to addressed by the government to realize universal health coverage. Rs. 2157/- is significant OOPE, and hence, important factor in understanding health-seeking behavior and compliance of leptospirosis patients. The OC (loss of daily wages) amounts to 68% of total OOPE which has to addressed by the government to realize universal health coverage. India has >135 million obese individuals at present. Body mass index (BMI) has been used to assess obesity until recent times. Later, studies have shown that central body fat (BF) measurements as a reliable predictor of metabolic diseases. Hence, normal-weight obesity (NWO) is defined. Those with a normal range of BMI but increased fat percentage are found to be having metabolic syndromes at a very early life. https://www.selleckchem.com/products/ly2606368.html The young adult group is specifically focused on the study with diet and physical activity as potential determinants; as an intervention at the right time can prevent the development of many noncommunicable diseases. The aim of this study is to estimate the prevalence of obesity and its determinants with special reference to NWO. A cross-sectional study was conducted based on diet, physical activity, and other lifestyle factors on a sample of 269 young adults. Using Harpenden skinfold calipers, BF percentage was calculated based on Jackson and Pollock and Siri's equation. Binary logistic regression was also applied appropriately. The proportion of obesity was 42.01%, and that of NWO was 16.1%. Sex, high protein diet, number of restaurant visits, less homemade tiffin intake, heavy physical activity, alcohol intake were found to be significantly associated with obesity. Intake of fish, physical activity, protein diet, day-time sleep were found to be significantly associated with NWO. The study emphasizes the need for including BF percentage in addition to BMI in regular clinical practice. It may help in preventive and promotive efforts. The study emphasizes the need for including BF percentage in addition to BMI in regular clinical practice. It may help in preventive and promotive efforts. Coronary artery disease (CAD) is the blockage of coronary arteries, usually consequent to atherosclerosis. CAD is a lifestyle disease with an increasing disease burden in society. Evaluation of risk factors for CAD is crucial for its prevention. Lifestyle components like calorie consumption chronology, saturated fatty acid (SAFA) intake, reclining time, nocturnal eating and intermittent fasting were considered. To correlate calorie distribution, SAFA intake, reclining time, nocturnal eating and intermittent fasting with occurrence of CAD. A case-control study consisting of 235 cases and 185 controls. Questionnaire was self-designed according to NIN guidelines. Study was ICMR funded and data analysis was done using Microsoft Excel and IBM SPSS. Across case and control groups, total calorie consumption difference was insignificant ( = 0.42). Calories consumed in breakfast slot ( = 0.001) and dinner slot ( = 0.003) were significantly different possibly due to discrepancy among circadian variation in insulin sensitivity and calorie consumption distribution. Reclining time <1 h in afternoon (odds ratio [OR] = 2.24, 95%, 1.481-3.356) and night (OR = 2.05, 95% confidence limit [CL], 1.233-3.410), SAFA consumption (OR = 2.006, 95% CL, 1.214-3.316), intermittent fasting (OR = 1.748, 95% CL, 0.997-3.067) and nocturnal eating (OR = 1.291, 95% CL, 0.779--2.141) are potential risk factors. Calorie consumption chronology, SAFA intake, Reclining time, Nocturnal eating and intermittent fasting emerged as significant risk factors. Calorie consumption chronology, SAFA intake, Reclining time, Nocturnal eating and intermittent fasting emerged as significant risk factors.