No abstract
Investigating the impact of climate change on human health requires the development of efficient tools that link patient symptoms with changing environmental variables. We developed an internet-based hay fever diary linked to simultaneously recorded pollen load and weather variables in Canberra, Australia over spring 2010. We recruited 42 hay fever sufferers to complete a simple online pollen diary daily over a period of 60 days. In conjunction, daily airborne pollen load was counted and meteorological data collected simultaneously. We focused on the relationships between temperature, rainfall, pollen count and rhinoconjunctivitis symptoms. Pollen load increased after a peak rainfall event until the end of the study. Compliance was high, averaging 79% of days per person. Nasal rhinoconjunctivitis symptoms increased in concert with increasing pollen load, and then remained high. Mucosal itching increased more gradually and strongly coincided with increased daily maximum temperature. Our study successfully demonstrated the feasibility of linking pollen load and climate variables to symptoms of rhinoconjunctivitis in the Australian community. However, a larger study would better explore the nature of associations between these variables. Similar online methods could be used to monitor a range of health responses to our changing environment.
Background: While we expect that patients who adjust their insulin delivery algorithms between clinic visits to have better glucose control compared to those who do not, this effect has not been quantified. Method: This is a single-center retrospective cohort study including pediatric and adult patients with type 1 diabetes evaluating insulin pump self-management behaviors. Basal insulin dose information was obtained from the Glooko-Diasend database, and used to quantify the frequency and magnitude of basal insulin daily dose adjustments within the 90-day window preceding HbA1c measurement. We use a linear mixed-effects model to analyze associations between frequency/magnitude of daily basal insulin changes and HbA1c. Results: We present data on 114 adult (44 ± 17 years, 60% female) and 212 pediatric (12 ± 4 years, 50% female) patients. Individuals changed their basal insulin dose on 72%-94% (interquartile range [IQR]) of observed days relative to the previous day. These changes varied 0.6%-2.4% IQR from the previous day’s value. In pediatric patients, lower HbA1c was associated with more frequent daily profile adjustments, while controlling for rate of hypoglycemia (z = -3.2, P = .001). In adults, there was no relationship between HbA1c and magnitude or frequency of basal profile adjustments. Conclusions: Pediatric patients who frequently modify their basal insulin exhibit somewhat better clinical outcomes, although the magnitude by which their basal amount is changed does not contribute to this effect.
Motifs are a powerful tool for analyzing physiological waveform data. Standard motif methods, however, ignore important contextual information (e.g., what the patient was doing at the time the data were collected). We hypothesize that these additional contextual data could increase the utility of motifs. us, we propose an extension to motifs, contextual motifs, that incorporates context. Recognizing that, o entimes, context may be unobserved or unavailable, we focus on methods to jointly infer motifs and context. Applied to both simulated and real physiological data, our proposed approach improves upon existing motif methods in terms of the discriminative utility of the discovered motifs. In particular, we discovered contextual motifs in continuous glucose monitor (CGM) data collected from patients with type 1 diabetes. Compared to their contextless counterparts, these contextual motifs led to be er predictions of hypo-and hyperglycemic events. Our results suggest that even when inferred, context is useful in both a long-and short-term prediction horizon when processing and interpreting physiological waveform data.
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