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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.

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.

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As the COVID-19 pandemic marches exponentially, epidemiological data is of high importance to analyse the current situation and guide intervention strategies. This study analyses the epidemiological data of COVID-19 from 17 countries, representing 85 per cent of the total cases within first 90 days of lockdown in Wuhan, China. It follows a population-level observational study design and includes countries with 20,000 cases (or higher) as of 21 April 2020. We sourced the data for these 17 countries from worldometers. info, a digital platform being used by several media and reputed academic institutions worldwide. We calculated the prevalence, incidence, case fatality rate and trends in the epidemiology of COVID-19, and its correlation with population density, urbanisation and elderly population. The analysis represents 85 per cent ( N = 2,183,661) of all cases within the first 90 days of the pandemic. Across the analysed period, the burden of the pandemic primarily focused on high- and middle-income countries of Asia, Europe and North America. While the total number of cases and deaths are highest in USA, the prevalence, incidence and case fatality rates are higher in the European countries. The prevalence and incidence vary widely among countries included in the analysis, and the number of cases per million and the case fatality rate are correlated with the proportion of the elderly population and to a lesser extent with the proportion of the urban population.