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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.

Two cross-sectional, population-based studies were conducted to assess the prevalence, awareness, treatment and control of hypertension, among people aged 20-59 years and those over 60 years in Delhi. Study 1 (20-59 years): in total,1213 subjects from 120 clusters spread across Delhi were studied. The prevalence of hypertension was 27.5%. Of the hypertensives, 53.3% were aware of their diagnosis; 42.8% were taking treatment and only 10.5% had controlled blood pressure. About 9.0% of the hypertensives had coexisting diabetes mellitus and 8.4% were suffering from coronary disease. The prevalence of hypertension was significantly higher in urban areas, but there was no significant difference in levels of awareness, treatment and control between urban and slum areas. The prevalence of hypertension was comparable in both sexes. Women, however, were more likely to be aware of their condition. Study 2 (> or =60 years): in total,1105 subjects from 110 clusters were studied. Prevalence of hypertension was 63.8%. Isolated systolic hypertension (ISH) was found in 15.3% of the subjects. About 54% of the hypertensives were aware of their diagnosis; 43.4% were taking treatment and only 8.5% had controlled blood pressure. Prevalence of hypertension and ISH were comparable among sexes. Women were more aware and better treated. About 21.3% hypertensives had coexisting diabetes mellitus, and 14.3% were suffering from coronary disease. There was no significant difference between sexes. Urban and slum areas were also found to be comparable. Over 3% of the elderly were controlling their raised blood pressure by non-pharmacological measures. They belonged to the 'aware' category yet could not be labelled as 'hypertensives', highlighting an operational fault in the Joint National Committee definition.

BackgroundThough previous work has examined some aspects of the dermatology workforce shortage and access to dermatologic care, little research has addressed the effect of rising interest in cosmetic procedures on access to medical dermatologic care. Our objective was to determine the wait times for Urgent and Non-Urgent medical dermatologic care and Cosmetic dermatology services at a population level and to examine whether wait times for medical care are affected by offering cosmetic services.MethodsA population-wide survey of dermatology practices using simulated calls asking for the earliest appointment for a Non-Urgent, Urgent and Cosmetic service.ResultsResponse rates were greater than 89% for all types of care. Wait times across all types of care were significantly different from each other (all P < 0.05). Cosmetic care was associated with the shortest wait times (3.0 weeks; Interquartile Range (IQR) = 0.4–3.4), followed by Urgent care (9.0 weeks; IQR = 2.1–12.9), then Non-Urgent Care (12.7 weeks; IQR = 4.4–16.4). Wait times for practices offering only Urgent care were not different from practices offering both Urgent and Cosmetic care (10.3 vs. 7.0 weeks).InterpretationLonger wait times and greater variation for Urgent and Non-Urgent dermatologic care and shorter wait times and less variation for Cosmetic care. Wait times were significantly longer in regions with lower dermatologist density. Provision of Cosmetic services did not increase wait times for Urgent care. These findings suggest an overall dermatology workforce shortage and a need for a more streamlined referral system for dermatologic care.