Does weather trigger urologic chronic pelvic pain syndrome flares? A case‐crossover analysis in the multidisciplinary approach to the study of the chronic pelvic pain research network

• Published in: Neurourology and urodynamics 2020-06, Vol.39 (5), p.1494-1504
• Main Authors: Li, Jieni, Yu, Tiange, Javed, Irum, Siddagunta, Chaitanya, Pakpahan, Ratna, Langston, Marvin E., Dennis, Leslie K., Kingfield, Darrel M., Moore, David J., Andriole, Gerald L., Lai, H. Henry, Colditz, Graham A., Sutcliffe, Siobhan
• Format: Article
• Language: English
• Subjects: Adult; Aged; Aged, 80 and over; bladder pain syndrome; Chronic Disease; Chronic Pain; chronic pelvic pain syndrome; chronic prostatitis; Cross-Over Studies; Cystitis, Interstitial - diagnosis; Cystitis, Interstitial - etiology; Female; flare; Humans; Humidity; interstitial cystitis; Longitudinal Studies; Male; Meteorological Concepts; Middle Aged; Pain; Pelvic Pain - diagnosis; Pelvic Pain - etiology; Population studies; Prostatitis - diagnosis; Prostatitis - etiology; Relative humidity; Statistical analysis; Studies; Surveys and Questionnaires; Symptom Flare Up; Syndrome; trigger; Weather; Young Adult
• ISSN: 0733-2467; 1520-6777
• DOI: 10.1002/nau.24381

Background To investigate whether meteorological factors (temperature, barometric pressure, relative humidity, ultraviolet index [UVI], and seasons) trigger flares in male and female urologic chronic pelvic pain patients. Methods We assessed flare status every 2 weeks in our case‐crossover study of flare triggers in the Multidisciplinary Approach to the Study of Chronic Pelvic Pain 1‐year longitudinal study. Flare symptoms, flare start date, and exposures in the 3 days preceding a flare or the date of questionnaire completion were assessed for the first three flares and at three randomly selected nonflare times. We linked these data to daily temperature, barometric pressure, relative humidity, and UVI values by participants’ first 3 zip code digits. Values in the 3 days before and the day of a flare, as well as changes in these values, were compared to nonflare values by conditional logistic regression. Differences in flare rates by astronomical and growing seasons were investigated by Poisson regression in the full study population. Results A total of 574 flare and 792 nonflare assessments (290 participants) were included in the case‐crossover analysis, and 966 flare and 5389 nonflare (409 participants) were included in the full study analysis. Overall, no statistically significant associations were observed for daily weather, no patterns of associations were observed for weather changes, and no differences in flare rates were observed by season. Conclusions We found minimal evidence to suggest that weather triggers flares, although we cannot rule out the possibility that a small subset of patients is susceptible.