Associations of food- and waterborne gastrointestinal illnesses with climate variability, Florida, 1995–2012
Climate is one of many factors-including state or national outbreaks, changes in case definitions, laboratory practices, and reporting practices-which may impact food- and waterborne disease rates. However, these illnesses have a distinct seasonal distribution, and infection rates and outbreaks have been previously linked to climate-related variables, including temperature and precipitation. Climate prediction models suggest greater future climate variability, but the extent to which climate variability will impact human cases of these and other diseases is uncertain. As part of a larger initiative to understand the health effects associated with climate hazards in Florida, we sought to examine the relationship of climate with rates of these specific food- and waterborne diseases.
Methods: We used notifiable disease surveillance reports and annual population estimates to calculate rates of infection in Florida from 1995 through 2012. Rates were calculated by month and year, as well as by region, for Campylobacter, Cryptosporidium, Giardia, and Salmonella infections. Only cases acquired in Florida were included.
Monthly climate data were obtained from the National Climatic Data Center (NCDC) for the period of study. Seven climate divisions are specified in NCDC which were used for regional comparisons. Data included monthly average temperatures (degrees Fahrenheit), precipitation (inches), and several drought indices for Florida divisions. To assess short-term drought, we chose the one- and two-month standardized precipitation index (SPI1 and SPI2), which are the probabilities of observing a given amount of precipitation over one and two months. SPI values are categorized as follows: extremely wet (2.00 and above); very wet (1.50 to 1.99); moderately wet (1.00 to 1.49); near normal (-0.99 to 0.99); moderately dry (-1.00 to -1.49); severely dry (-1.50 to -1.99); extremely dry (-2.00 and below). Categorical SPI1 and SPI2 data were approximately normally distributed, but slightly skewed toward wetter conditions in Florida. We also included same month and one- and two-month lag data for precipitation and temperature.
All climate variables were considered as independent risk factors for increased rate of infection in Poisson regression models, adjusting for the month, year, and region of study.
Results: During the study period, there were 18,641 cases of Campylobacter reported in Florida, with infection rates increasing by 31.1% from 1995 to 2012. Same month and 2-month lag precipitation and temperature data were not associated with rates of Campylobacter infection. However, one-month lag data were significantly associated with infection rates. As both precipitation (rate ratio (RR): 1.01, p-value = 0.0027) and temperature (RR: 1.01, p-value = 0.0110) increased, rates of infection increased, adjusting for month, year, and region. In other words, for every one unit increase in either precipitation or temperature, Campylobacter rates increased by 1%. SPI2 was also associated with Campylobacter rates (p-value = 0.0074), with drought conditions being associated with reduced infection rates.
Fewer Cryptosporidium cases were reported (N = 5,550), with only a modest rate increase (1.9%) for the period. As precipitation increased, rates of infection decreased for same month (RR: 0.98, p-value = 0.0067) and two-month lag precipitation (RR: 0.99, p-value = 0.0318). Only same month temperature was associated with infection rates, with rates increasing by 1% for every one degree Fahrenheit increase in temperature (p-value = 0.0233). SPI1 and SPI2 were associated with Cryptosporidium infection rates, though effects varied. More extreme drought conditions were associated with a rate decrease (extremely dry RR: 0.79, p-value = 0.0181; very dry RR: 0.84, p-value = 0.0444), while moderate drought was associated with a rate increase for SPI1 (RR: 1.18, p-value = 0.0003). Very dry (p-value < 0.0001) was associated with a 64% increase in Cryptosporidium rates for SPI2.
For Giardia, 20,899 cases were reported with rates decreasing by 69.9% over time. Again, precipitation was associated with a decrease and temperature associated with an increase in rates. One- and two-month lag in precipitation data were associated with a 2% (p-value < 0.0001) and 1% (p-value = 0.0010) decrease in rates, respectively. All measures of temperature were associated with a 1% increase in rates, adjusting for year, month, and region. More wet and more dry conditions were associated with increased rates of Giardia, while moderately dry conditions were protective for SPI1 (p-value = 0.0002). Moderately wet conditions were protective and very wet conditions were a risk factor for SPI2 (p-value < 0.0001). Finally, 75,792 cases of Salmonella occurred during the study period, with rates increasing by 46.6% over time. Similar associations with precipitation and temperature were seen with Salmonella rates, as with Giardia. A 1% decline in rate was seen for a one inch increase in all precipitation variables (all p-values < 0.001). Temperature had a greater effect, with rates increasing by 3-5% for each one degree increase in temperature (all p-values < 0.0001). More extreme drought conditions were associated with decreased rates of Salmonella for SPI1 and SPI2, while extreme wet conditions were associated with an increased rate for both variables (both p-values < 0.001). Conclusions Incidence rates of several gastrointestinal illnesses have significantly increased over time in the US and in Florida, making them an important focus for further study and prevention and control efforts. Except with Campylobacter, increased precipitation was associated with decreased rates, and increased temperature was associated with increased rates of illness. Inconsistent associations with SPI1 and SPI2 were seen for all diseases except Salmonella, where drought conditions coincide with lower rates and extreme wet conditions coincide with higher rates of Salmonella infection. Further investigation is needed to better understand the relationships between climate and gastrointestinal illnesses, and to characterize the relationship of these climate factors on sporadic vs. outbreak-associated cases and regional variability.