Chronical Illness-related Limitations of the Ability to Cope With Rising Temperatures: an Observational Study

The CLIMATE Observational Study aims to analyse to what extent chronically ill patients experience adverse health effects because of heat and whether the patients' specific efforts, self-efficacy, or other factors are associated with these effects. This cohort study is based on an online survey of patients with chronic illness who are recruited in GP practices across all regions of Germany. After the baseline assessment, participants fill out symptom diaries on one specific day of observation per week over a maximum period of 12 weeks. The specific days of observation within the time frame between 29 July and 8 September are selected based on the maximum temperature that can be expected within the respective weeks. The weather forecast will be checked every Friday. If, in the upcoming 4 days, the maximum temperature is expected to exceed 30°C, the warmest day in this time frame will be chosen. Otherwise, the weather forecast will be checked again on Tuesday to choose the warmest of the remaining days of the week. The specific days of observation within the time frame between 9 September and 27 October are randomly selected. On each day of observation, patients are notified by email at 6 pm. Baseline assessment includes socio-demographic data, health-related quality of life, efforts against adverse effects of heat, somatosensory amplification, self-efficacy, health literacy and commitment to the GP. Local data on temperatures and humidity will be assessed by the measurement stations of Germany's National Meteorological Service. Degree of urbanisation of administration districts is provided by the German Federal Institute for Research on Building, Urban Affairs and Spatial Development. Additionally, small scale data on population density, number of one-person households, unemployment rates and other neighbourhood characteristics are provided by the German Statistical Offices. Data will be analyzed by multivariable, multilevel regression analyses adjusted for possible confounders and random effects on the administration district and practice within administration district level.