Modified BG Prasad socioeconomic scale is widely used to determine the socioeconomic status of study subjects in health studies in India. It is an income-based scale and, therefore, has to be constantly updated to take inflation and depreciation of rupee into account. The Consumer Price Index (CPI) for industrial workers (IW) is used to calculate updated income categories for January 2014. Details of the calculations involved will enable young researchers to calculate specific income categories for their research work. State-specific CPI values are also available on the Department of Labour website and should be used to determine more accurate income categories for the study area.

Objective:To study the prevalence of health disorders among computer professionals and its association with working environment conditions.Study design:Cross sectional.Materials and Methods:A sample size of 200 computer professionals, from Delhi and NCR which included software developers, call centre workers, and data entry workers.Result:The prevalence of visual problems in the study group was 76% (152/200), and musculoskeletal problems were reported by 76.5% (153/200). It was found that there was a gradual increase in visual complaints as the number of hours spent for working on computers daily increased and the same relation was found to be true for musculoskeletal problems as well. Visual problems were less in persons using antiglare screen, and those with adequate lighting in the room. Musculoskeletal problems were found to be significantly lesser among those using cushioned chairs and soft keypad.Conclusion:A significant proportion of the computer professionals were found to be having health problems and this denotes that the occupational health of the people working in the computer field needs to be emphasized as a field of concern in occupational health.

Background. Tobacco use is one of the major preventable causes of premature death and disease in the world. Many psychosocial factors were found to influence tobacco use. Therefore the present study was designed to determine the role of psychosocial factors associated with tobacco use among school going adolescents in Delhi, India. Methods. Cross-sectional study was conducted from February 2013 to September 2013 in four government schools in South district of Delhi, India. The questionnaire contains questions adapted from GYTS (Global Youth Tobacco Survey) to find the prevalence and pattern of tobacco use among adolescents. Data were analyzed using SPSS version 21. Results. The prevalence of ever and current tobacco use was found in 16.4% and 13.1%. Current smoking and current tobacco chewing were found in 10.2% and 9.4% students, respectively. The risk of current tobacco use was found to be higher among males (P value = 0.000) and in those who got higher pocket money (P value = 0.000). Psychosocial factors like lower general self-efficacy and maladjustments with peers, teachers, and schools were also found to be significant predictors of current tobacco use. Conclusion. The study has revealed higher prevalence of ever and current tobacco use among adolescent students in Delhi, India.

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Background. Malaria still remains a public health problem in developing countries and changing environmental and climatic factors pose the biggest challenge in fighting against the scourge of malaria. Therefore, the study was designed to forecast malaria cases using climatic factors as predictors in Delhi, India. Methods. The total number of monthly cases of malaria slide positives occurring from January 2006 to December 2013 was taken from the register maintained at the malaria clinic at Rural Health Training Centre (RHTC), Najafgarh, Delhi. Climatic data of monthly mean rainfall, relative humidity, and mean maximum temperature were taken from Regional Meteorological Centre, Delhi. Expert modeler of SPSS ver. 21 was used for analyzing the time series data. Results. Autoregressive integrated moving average, ARIMA (0,1,1) (0,1,0)12, was the best fit model and it could explain 72.5% variability in the time series data. Rainfall (P value = 0.004) and relative humidity (P value = 0.001) were found to be significant predictors for malaria transmission in the study area. Seasonal adjusted factor (SAF) for malaria cases shows peak during the months of August and September. Conclusion. ARIMA models of time series analysis is a simple and reliable tool for producing reliable forecasts for malaria in Delhi, India.