13 research outputs found
An empirical wildfire risk analysis: the probability of a fire spreading to the urban interface in Sydney, Australia
Development of Risk Prediction Equations for Incident Chronic Kidney Disease
IMPORTANCE ‐ Early identification of individuals at elevated risk of developing chronic kidney disease
could improve clinical care through enhanced surveillance and better management of underlying health
conditions.
OBJECTIVE – To develop assessment tools to identify individuals at increased risk of chronic kidney
disease, defined by reduced estimated glomerular filtration rate (eGFR).
DESIGN, SETTING, AND PARTICIPANTS – Individual level data analysis of 34 multinational cohorts from
the CKD Prognosis Consortium including 5,222,711 individuals from 28 countries. Data were collected from April, 1970 through January, 2017. A two‐stage analysis was performed, with each study first
analyzed individually and summarized overall using a weighted average. Since clinical variables were often differentially available by diabetes status, models were developed separately within participants
with diabetes and without diabetes. Discrimination and calibration were also tested in 9 external
cohorts (N=2,253,540).
EXPOSURE Demographic and clinical factors.
MAIN OUTCOMES AND MEASURES – Incident eGFR <60 ml/min/1.73 m2.
RESULTS – In 4,441,084 participants without diabetes (mean age, 54 years, 38% female), there were
660,856 incident cases of reduced eGFR during a mean follow‐up of 4.2 years. In 781,627 participants
with diabetes (mean age, 62 years, 13% female), there were 313,646 incident cases during a mean
follow‐up of 3.9 years. Equations for the 5‐year risk of reduced eGFR included age, sex, ethnicity, eGFR,
history of cardiovascular disease, ever smoker, hypertension, BMI, and albuminuria. For participants
with diabetes, the models also included diabetes medications, hemoglobin A1c, and the interaction
between the two. The risk equations had a median C statistic for the 5‐year predicted probability of
0.845 (25th – 75th percentile, 0.789‐0.890) in the cohorts without diabetes and 0.801 (25th – 75th
percentile, 0.750‐0.819) in the cohorts with diabetes. Calibration analysis showed that 9 out of 13 (69%)
study populations had a slope of observed to predicted risk between 0.80 and 1.25. Discrimination was
similar in 18 study populations in 9 external validation cohorts; calibration showed that 16 out of 18
(89%) had a slope of observed to predicted risk between 0.80 and 1.25.
CONCLUSIONS AND RELEVANCE – Equations for predicting risk of incident chronic kidney disease
developed in over 5 million people from 34 multinational cohorts demonstrated high discrimination and
variable calibration in diverse populations
An empirical wildfire risk analysis: the probability of a fire spreading to the urban interface in Sydney, Australia
We present a method and case study to predict and map the likelihood of wildfires spreading to the urban interface through statistical analysis of past fire patterns using 15 000 lines from 677 fires with known ignition points and date and random potential end points on the urban interface of Sydney, Australia. A binomial regression approach was used to model whether the fire burnt to the end point of the lines as a function of measures of distance, fuel, weather and barriers to spread. Fire weather had the strongest influence on burning likelihood followed by the percentage of the line that was forested, distance and time since last fire. Fuel treatments would substantially reduce risk from fires starting 1-4 km away from the interface. The model captured 90% of variation in burning with 98% predictive accuracy on test data and was not affected by spatial autocorrelation. We apply the method to map fire risk in Sydney and discuss how the method could be expanded to estimate total risk (from ignition to impact on assets). The method has considerable promise for predicting risk, especially as a complement to simulation methods