Weather conditions and spine patients

Abstract

Purpose of study: To evaluate the effects of various weather conditions on reported health status in a large group of patients seen by spine care practitioners.Methods used: Initial visit patient data from 23 centers across the United States that participate in the National Spine Network were obtained. These data included various patient demographic information, including age and gender, and Short Form (SF)-36–based health status. Weather conditions when and where patients were seen were obtained from the National Climatic Data Center and US Naval Observatory. SF-36 outcomes were predicted using multiple regression techniques from these weather parameters, which included high and low temperature for the day, average dew point and barometric pressure, total precipitation and phase of the moon. In addition, the ability of patient demographics to enhance prediction was evaluated.of findings: In total 26,877 patients from an initial sample of 54,062 patients were identified that had complete weather, personal demographics and SF-36 scores. The initial high attrition was largely the result of patients whose initial visits fell before 1996 when the weather data became available and because of missing values on the SF-36 scales. Patient age at initial visit averaged 48.6±15.2 years and ranged from 18 to 101 years. There were more women than men (53.2% vs. 46.8%, p<.0001), and the women were on average 1.3 years older than the men (49.2 vs. 47.9 years, respectively, p<.0001). With a sample of 26,877, statistical significance becomes secondary to practical implications. For example, with this sample size, correlations as low as .013 are statistically significant at p<.05, and correlations of .016 or more are significant at p<.01. Thus, the magnitude of the R2 value in the multiple regression results will guide interpretation rather than statistical significance. Two regression models were compared: Model 1, which included the set of seven weather predictors, as summaries in Table 1Table 1SF-36 outcomesModel 1 R2Model 2 R2GH.0053.0084PF.0047.0442BP.0034.0055RP.0025.0072PCS.0050.0367MH.0021.0115EF.0034.0149SF.0046.0064RE.0016.0022MCS.0020.0115Model 1: moon phase, high temperature, low temperature, dew point, wet bulb, rain, barometric pressureModel 2: Model 1 plus age, genderMCS = mental composite score; PCS = physical composite score; PF = physical function., and Model 2, which added patient age and gender to the predictor set. For each of the 10 SF-36 scales, the eight subscales and the physical and mental composite scores, both Model 1 and Model 2 were statistically significant, although only Model 2 for the physical function scale produced an R2 value greater than 1%. Barometric pressure was the only weather predictor that was consistently significant. In all cases, the coefficient was negative, and indicated increased barometric pressure was associated with worse outcomes. Although age and gender were significant additions to the prediction equation, overall the practical contribution was minimal and these two additional predictors did not weaken the significance of the barometric pressure predictor.Relationship between findings and existing knowledge: Previous reports have had mixed opinions as regarding the effect of climate on pain. This is the largest series of patients that we know of to be analyzed.Overall significance of findings: Although there is a statistically significant effect of climate on spinal patients, the magnitude of this effect is small, and its clinical applicability remains questionable.Disclosures: No disclosures.Conflict of interest: No conflict.