Modelling the impact of climate change on health
The main objective of this thesis is to develop a robust statistical model by accounting the non-linear relationships between hospital admissions due to lower respiratory (LR) disease and factors of climate and pollution, and their delayed effects on hospital admissions. This study also evaluates whether the model fits can be improved by considering the non-linearity of the data, delayed effect of the significant factors, and thus calculate threshold levels of the significant climate and pollution factors for emergency LR hospital admissions. For the first time three unique administrative datasets were merged: Hospital Episode Statistics, Met office observational data for climate factors, and data from London Air Quality Network. The results of the final GLM, showed that daily temperature, rain, wind speed, sun hours, relative humidity, and PM10 significantly affected the LR emergency hospital admissions. Then, we developed a Distributed lag non-linear model (DLNM) model considering the significant climate and pollution factors. Time and ‘day of the week’ was incorporated as linear terms in the final model. Higher temperatures around ≥270C a quicker effect of 0-2 days lag but lower temperatures (≤00C) had delayed effects of 5-25 days lag. Humidity showed a strong immediate effect (0-3 days) of the low relative humidity at around ≤40% and a moderate effect for higher humidity (≥80%) with lag period of 0-2 days. Higher PM10 around ≥70-μg/m3 has both shorter (0-3 days) and longer lag effects (15-20 days) but the latter one is stronger comparatively. A strong effect of wind speed around ≥25 knots showed longer lag period of 8-15 days. There is a moderate effect for a shorter lag period of 0-3 days for lower wind speed (approximately 2 knots). We also notice a stronger effect of sun hours around ≥14 hours having a longer lag period of 15-20 days and moderate effect between 1-2 hours of 5-12 days lag. Similarly, higher amount of rain (≥30mm) has stronger effects, especially for the shorter lag of 0-2 days and longer lag of 7- 10 days. So far, very little research has been carried out on DLNM model in such research area and setting. This PhD research will contribute to the quantitative assessment of delayed and non-linear lag effects of climate and pollutants for the Greater London region. The methodology could easily be replicated on other disease categories and regions and not limited to LR admissions. The findings may provide useful information for the development and implementation of public health policies to reduce and prevent the impact of climate change on health problems.
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