A mathematical, classical stratification modeling approach to disentangling the impact of weather on infectious diseases: A case study using spatio-temporally disaggregated Campylobacter surveillance data for England and Wales.

Disentangling the impact of the weather on transmission of infectious diseases is crucial for health protection, preparedness and prevention. Because weather factors are co-incidental and partly correlated, we have used geography to separate out the impact of individual weather parameters on other seasonal variables using campylobacteriosis as a case study. Campylobacter infections are found worldwide and are the most common bacterial food-borne disease in developed countries, where they exhibit consistent but country specific seasonality. We developed a novel conditional incidence method, based on classical stratification, exploiting the long term, high-resolution, linkage of approximately one-million campylobacteriosis cases over 20 years in England and Wales with local meteorological datasets from diagnostic laboratory locations. The predicted incidence of campylobacteriosis increased by 1 case per million people for every 5° (Celsius) increase in temperature within the range of 8°-15°. Limited association was observed outside that range. There were strong associations with day-length. Cases tended to increase with relative humidity in the region of 75-80%, while the associations with rainfall and wind-speed were weaker. The approach is able to examine multiple factors and model how complex trends arise, e.g. the consistent steep increase in campylobacteriosis in England and Wales in May-June and its spatial variability. This transparent and straightforward approach leads to accurate predictions without relying on regression models and/or postulating specific parameterisations. A key output of the analysis is a thoroughly phenomenological description of the incidence of the disease conditional on specific local weather factors. The study can be crucially important to infer the elusive mechanism of transmission of campylobacteriosis; for instance, by simulating the conditional incidence for a postulated mechanism and compare it with the phenomenological patterns as benchmark. The findings challenge the assumption, commonly made in statistical models, that the transformed mean rate of infection for diseases like campylobacteriosis is a mere additive and combination of the environmental variables.

Ambient Temperature and Emergency Hospital Admissions in People Experiencing Homelessness: London, United Kingdom, 2011-2019.

Objectives. To assess the impacts of ambient temperature on hospitalizations of people experiencing homelessness. Methods. We used daily time-series regression analysis employing distributed lag nonlinear models of 148 177 emergency inpatient admissions with "no fixed abode" and 20 804 admissions with a diagnosis of homelessness in London, United Kingdom, in 2011 through 2019. Results. There was a significantly increased risk of hospitalization associated with high temperature; at 25°C versus the minimum morbidity temperature (MMT), relative risks were 1.359 (95% confidence interval [CI] = 1.216, 1.580) and 1.351 (95% CI = 1.039, 1.757) for admissions with "no fixed abode" and admissions with a homelessness diagnosis, respectively. Between 14.5% and 18.9% of admissions were attributable to temperatures above the MMT. No significant associations were observed with cold. Conclusions. There is an elevated risk of hospitalization associated with even moderately high temperatures in individuals experiencing homelessness. Risks are larger than those reported in the general population. Public Health Implications. Greater emphasis should be placed on addressing homeless vulnerabilities during hot weather rather than cold. Activation thresholds for interventions such as the Severe Weather Emergency Protocol (SWEP) could be better aligned with health risks. Given elevated risks at even moderate temperatures, our findings support prioritization of prevention-oriented measures, rather than crisis response, to address homelessness. (Am J Public Health. 2023;113(9):981-984. https://doi.org/10.2105/AJPH.2023.307351).