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The adjusted models are based on a sample size of n = 339 because of missing temperature data for 15 hikers.
For instance, scientific studies with mammalian cells that are very sensitive to temperature fluctuations were difficult to carry out because of a missing temperature control unit.
We used these series as a basis for imputing data for days with missing temperature at the narrower level of conurbation.
A total of 19 homes were excluded from the analysis 15 because of missing temperature data and four because of missing income data leaving 332 homes for analysis.
We excluded 1,292 patients (missing diagnosis in 71; missing APACHE III score in 203, missing temperature in 19 and missing outcome in 972) and thus studied a total of 15,818 patients.
Thus, if a subject's highest temperature was 38.5°C and had at least 1 missing temperature measurement, then this subject would be included in the analysis of fever ≥ 38°C and excluded from the analysis of fever >39°C.
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Three days of missing temperatures were imputed with the prior and subsequent days.
To obtain complete weather data for all 100 counties, the missing mean temperature and mean dew point temperature data were imputed by taking the average of the mean temperature and/or mean dew point temperature of all counties that bordered the county that was missing data.
When observations were missing for temperature, sedimentation, POC, PN, PP, chl-a, DOC and SS, mean values of data before and after the missing data point were used to fill data gaps to be able to run the PLS regression.
The variables with missing values (temperature, age and number of illness days) were included in an imputation model with all other symptom variables, influenza epidemic and RSV epidemic.
Cases that occurred on days with missing ambient temperature (N = 2,973) did not differ from the total case group in the distribution of measured covariates.
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