Seasonal variation in hospital admission for community-acquired pneumonia: A 5-year study Academic Article uri icon

abstract

  • We conducted a retrospective analysis of computerized hospitalization and regional meteorological and geophysical data in a university hospital in southern Israel. The aim of the study was to determine and depict the seasonal variation in hospitalization for community-acquired pneumonia (CAP-H) and the factors affecting it, for all age groups combined and by age group, over a 5-year period. All cases of CAP-H over the period from January 1, 1990 to December 31, 1994 were studied by season of the year and age group. The rates of CAP-H for the four seasons were compared by t-tests. Mathematical models based on quasi-Fourier generalized linear models were developed and used to evaluate potential variables and their relative contributions to CAP-H. A total of 4101 CAP-H were analysed in the study. Throughout the study period the prevalence of CAP-H was significantly higher in the winter and spring than in the summer and fall for all age groups combined and within each age group (P<0.00001). When CAP-H was compared between the winter and the spring, we found that in the 0-16 age group CAP-H was higher in the winter (P<.0.00001), in the 17-64 age group it was higher in the spring (P<0.002), and in the 65+ age group as well as for all age groups combined there were no significant differences between these two seasons. The most important factor explaining the variance in CAP-H in the 0-16 age group were direct and indirect effects of minimum daily temperature (31%), in the 17-64 age group direct and indirect effects of the difference between minimum and maximum daily temperatures (19%), and in the 65+ age group it was geophysical factors (13%). There is a significant seasonal variation in CAP-H with higher rates for all age groups in the winter and spring. The extent to which the prevalence of CAP-H is dominant in the winter and spring seasons differs among the age groups, as does the principal variable explaining these differences. The most important factor is the direct and indirect effects of meteorological variables in the 0-16 and 17-64 age groups, and a geophysical one among the more elderly patients.

publication date

  • January 1, 1999