The Impact of Weather on Influenza and Pneumonia Mortality in New York City, 1975–2002: A Retrospective Study

The substantial winter influenza peak in temperate climates has lead to the hypothesis that cold and/or dry air is a causal factor in influenza variability. We examined the relationship between cold and/or dry air and daily influenza and pneumonia mortality in the cold season in the New York metropolitan area from 1975–2002. We conducted a retrospective study relating daily pneumonia and influenza mortality for New York City and surroundings from 1975–2002 to daily air temperature, dew point temperature (a measure of atmospheric humidity), and daily air mass type. We identified high mortality days and periods and employed temporal smoothers and lags to account for the latency period and the time between infection and death. Unpaired t-tests were used to compare high mortality events to non-events and nonparametric bootstrapped regression analysis was used to examine the characteristics of longer mortality episodes. We found a statistically significant (p = 0.003) association between periods of low dew point temperature and above normal pneumonia and influenza mortality 17 days later. The duration (r = −0.61) and severity (r = −0.56) of high mortality episodes was inversely correlated with morning dew point temperature prior to and during the episodes. Weeks in which moist polar air masses were common (air masses characterized by low dew point temperatures) were likewise followed by above normal mortality 17 days later (p = 0.019). This research supports the contention that cold, dry air may be related to influenza mortality and suggests that warning systems could provide enough lead time to be effective in mitigating the effects

In Defense of the Epistemic Imperative

Sample (2015) argues that scientists ought not to believe that their theories are true because they cannot fulfill the epistemic obligation to take the diachronic perspective on their theories. I reply that Sample’s argument imposes an inordinately heavy epistemic obligation on scientists, and that it spells doom not only for scientific theories but also for observational beliefs and philosophical ideas that Samples endorses. I also delineate what I take to be a reasonable epistemic obligation for scientists. In sum, philosophers ought to impose on scientists only an epistemic standard that they are willing to impose on themselves

A Comparison of Administrative and Physiologic Predictive Models in Determining Risk Adjusted Mortality Rates in Critically Ill Patients

Hospitals are increasingly compared based on clinical outcomes adjusted for severity of illness. Multiple methods exist to adjust for differences between patients. The challenge for consumers of this information, both the public and healthcare providers, is interpreting differences in risk adjustment models particularly when models differ in their use of administrative and physiologic data. We set to examine how administrative and physiologic models compare to each when applied to critically ill patients.We prospectively abstracted variables for a physiologic and administrative model of mortality from two intensive care units in the United States. Predicted mortality was compared through the Pearsons Product coefficient and Bland-Altman analysis. A subgroup of patients admitted directly from the emergency department was analyzed to remove potential confounding changes in condition prior to ICU admission.We included 556 patients from two academic medical centers in this analysis. The administrative model and physiologic models predicted mortalities for the combined cohort were 15.3% (95% CI 13.7%, 16.8%) and 24.6% (95% CI 22.7%, 26.5%) (t-test p-value<0.001). The r(2) for these models was 0.297. The Bland-Atlman plot suggests that at low predicted mortality there was good agreement; however, as mortality increased the models diverged. Similar results were found when analyzing a subgroup of patients admitted directly from the emergency department. When comparing the two hospitals, there was a statistical difference when using the administrative model but not the physiologic model. Unexplained mortality, defined as those patients who died who had a predicted mortality less than 10%, was a rare event by either model.In conclusion, while it has been shown that administrative models provide estimates of mortality that are similar to physiologic models in non-critically ill patients with pneumonia, our results suggest this finding can not be applied globally to patients admitted to intensive care units. As patients and providers increasingly use publicly reported information in making health care decisions and referrals, it is critical that the provided information be understood. Our results suggest that severity of illness may influence the mortality index in administrative models. We suggest that when interpreting "report cards" or metrics, health care providers determine how the risk adjustment was made and compares to other risk adjustment models

Identification of 12 mortality “episodes” that exceeded the z≥1 criterion.

<p>The time series is <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0034091#pone-0034091-g001" target="_blank">Figure 1b</a> smoothed with a 17-day centered moving average filter. A centered smoother is used here to more clearly present the peak times of the mortality episodes (see <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0034091#pone-0034091-t002" target="_blank">Table 2</a>).</p

Outcomes following the use of angiotensin II in patients with postoperative vasoplegic syndrome: A case series

Catecholamine-resistant postoperative vasoplegic syndrome (PVS) lacks effective treatment modalities. Synthetic angiotensin II was recently approved for the treatment of vasodilatory shock; however, its use in PVS is not well described. We report outcomes in six patients receiving angiotensin II for the treatment of isolated PVS. All patients achieved their MAP goal and the majority showed improvement in lactate and background catecholamine dose; however, variables of perfusion changed discordantly. Three of six patients survived to hospital discharge

Results of <i>t</i>-tests comparing weather variables between mortality event days to non-event days.

<p>Air mass analysis could not be run without smoothing (n/a = not applicable). Results with p≤0.05 are shown in bold. Mean values for events and non-events are air temperature (T) and dew point temperature (T_d) departures from the long-term daily mean in z-score units. Air mass values are mean frequencies based on a 7-day centered moving average filter. The z-score values for events in the 1-sample test are the same as in column 2.</p

Scatter plots of pneumonia and influenza mortality episode duration and total mortality vs. dew point temperature.

<p>a) (left) Total episode duration (days) <i>vs.</i> mean episode dew point temperature (°C) (r = −0.61). b) (right) Total episode mortality (in z-score units) <i>vs.</i> mean episode dew point temperature (°C) (r = −0.56). The regression line shown in both graphs is for the least squares linear regression of the full data set. Both of these relationships were determined to have statistically significant slopes based upon 10,000 bootstrapped samples.</p

Time series of pneumonia and influenza mortality for New York City from September, 1975–May, 2002.

<p>a) (top) Daily age-standardized pneumonia and influenza mortality time series (deaths per million; June, July and August have been deleted). The relevant periods for the International Classification of Diseases (ICD) are identified by a thick vertical line; b) (bottom) Resulting mortality time series after removing the seasonality and converting to z-scores for each ICD period. Vertical dividers identify influenza seasons (September–May) with the year assigned to the January–May period (i.e., December, 1979 is in the 1980 flu “season,” labeled as “80” on the x-axis).</p