70 research outputs found

    Effect of heavy precipitation on GI calls.

    No full text
    <p>Confident intervals (95%) of associations along 0–21 lag days between heavy precipitation events and nurse advice calls relating to gastrointestinal symptoms.</p

    Effect of consecutive days with dry or wet weather.

    No full text
    <p>Relative effects (with 95% CIs) of consecutive wet or dry weather (middle station) on A: turbidity, B: E. Coli, C: coliforms, D: Clostridium perfringens. The reference is set to ten consecutive dry days using a continuous predictor (shaded), and at least ten dry days using a categorical predictor (bars). Seven wet days (bar) represent seven wet days or more.</p

    Descriptive statistics AGI visits and weather.

    No full text
    <p>Descriptive statistics, (percentiles, mean and standard deviation (SD)), of daily visits to primary health care centers diagnosed ICD A00-A09, and precipitation observations, in the period 2007–2012.</p><p>Descriptive statistics AGI visits and weather.</p

    Association between consecutive weather and AGI visits.

    No full text
    <p>Effect of consecutive days of wet or dry weather on AGI visits. Bars represent 95% CIs, and <i>n</i> is the number of observation days in each factor level. One or two consecutive wet or dry days are set as reference level.</p

    Effect of heavy precipitation across lags.

    No full text
    <p>Estimated relative effects of precipitation events (>15 mm/24-h) on river water quality parameters along 0 to 15 lag days: A: turbidity, B: E. Coli, C: coliforms, D: Clostridium perfringens. 95% confidence intervals are illustrated with bars (unconstrained distributed lag model) and with shaded area (DLNM model).</p

    Short-term effect AGI calls on AGI visits.

    No full text
    <p>Relative risks of AGI visits across 0–7 lag days of an IQR increase of AGI calls. Bars represent 95% confidence intervals.</p

    AGI visits relative AGI calls.

    No full text
    <p>Comparison of AGI visits relative AGI calls using a binomial regression model including all data (City of Gothenburg). A. Long term trend—estimated odds ratios by year. B. Seasonal difference—estimated odds ratios for month of year. C. Week day effect—estimated odds ratios for weekdays. D. Estimated odds ratio of precipitation lag 0. Bars and filled area represent 95% confidence intervals.</p

    Effect modifications lag 2 across season.

    No full text
    <p>Relative effects of precipitation (>15 mm/24-h) 2 days later across seasons on A: turbidity, B: E. Coli, C: coliforms, D: Clostridium perfringens. Each estimate covers a range of 90 days: 45 days prior and 45 days following. Vertical bars represent 95% confidence intervals.</p

    Data.

    No full text
    <p>Time series data for 2004 through 2010 for (from top) A: turbidity, B: E.coli, C: coliforms, D: Clostridium (river water intake Alelyckan), E: precipitation (Alvhem), and F: daily mean temperature (Gothenburg). A moving average is projected with a spline using 10 df/year for turbidity and 7 df/year for indicator bacteria and temperature. Data transformed by the natural logarithm (A–D).</p
    • …
    corecore