The impact of temperature on mortality in Ludhiana City, India: A time series analysis
The widely used generalized additive model (GAM) is a flexible and effective technique for conducting non-linear regression analysis in time-series studies. There is strong evidence that episodes of extremely hot or cold temperatures are associated with increased mortality in many parts of the world. Time series designs are extremely useful to examine association between daily apparent temperature and daily mortality counts. Hence, the effect of temperature on mortality was studied in Ludhiana city of Punjab in Northern India. As a part of the Health Effects Institute (HEI), Boston USA, project, Meteorological and mortality data was obtained for the years 2002–2004. Sahnewal Airport in Ludhiana City records temperature, dew point, wind speed and relative humidity at 8.30 AM, 11.30 AM, and 5.30 PM. Daily death records were obtained from the civil registration system in Ludhiana. The association between temperature and mortality was established using the generalized additive model (GAM) with penalized spline smoothers at 8,4,4 degrees of freedom (df) in R software with mortality count (excluding accidents) as a dependent variable. Smoothers for day of the week, relative humidity and wind speed were included in the model. With rapid industrialization and extreme temperature variations in Ludhiana city, the temperature was significantly associated with mortality. Sensitivity analysis shows that elderly (>65 years) population was much affected with temperature variations, particularly, during winter season. The study shows there is need to improve the overall registration system of deaths. Cause specific analysis was not possible as cause of death was not clearly mentioned in most of the deaths.
Generalized additive model, relative humidity, windspeed, mortality, sensitivity analysis, time series analysis.