Comparison of observational studies evaluating the effectiveness of seasonal influenza vaccination to prevent 2009 pA/H1N1 morbidity in civilian populations.

<p>All studies were conducted during the first wave of the 2009 pandemic (April–July 2009) and most include a majority of young adults.</p>a<p>Negative estimates of vaccine effectiveness indicate that vaccination may be a risk factor for 2009 pandemic illness, while positive estimates suggest a protective effect.</p>b<p>Three other study designs are considered in this publication but the Sentinel system is the most well-established.</p>c<p>Adjusted for age, comorbidities, province, interval between symptoms onset and sample collection.</p>d<p>Adjusted for age and comorbidities.</p>e<p>Adjusted for age.</p><p>ILI, influenza like illness.</p

Time Trends in Influenza Vaccine Coverage and Influenza-Related Mortality in People 65 Years and Older in the US, Based on Two Death Categories

<p>(A) All-cause mortality. (B) Pneumonia and influenza mortality. The black curve illustrates observed monthly mortality rates, the purple curve represents a monthly model baseline above which mortality is attributed to influenza [<a href="http://www.plosmedicine.org/article/info:doi/10.1371/journal.pmed.0050216#pmed-0050216-b001" target="_blank">1</a>], and the green curve represents trends in seasonal vaccine coverage in people 65 years and older. Red shaded areas represent seasonal estimates of excess mortality attributed to influenza (observed over baseline), while blue areas represent non-influenza mortality. Red stars indicate epidemic seasons dominated by the more severe A/H3N2 influenza viruses [<a href="http://www.plosmedicine.org/article/info:doi/10.1371/journal.pmed.0050216#pmed-0050216-b010" target="_blank">10</a>]. Grey arrows indicate the two periods used in Kwong et al.'s comparative study to evaluate the benefits of universal immunization in Ontario, Canada (1997–2000 and 2000–2004) [<a href="http://www.plosmedicine.org/article/info:doi/10.1371/journal.pmed.0050216#pmed-0050216-b009" target="_blank">9</a>]. Note the less frequent circulation of severe A/H3N2 viruses in the second part of Kwong et al.'s study period, 2000–2004. Trends in influenza burden estimates for these periods are provided for the US and Ontario in <a href="http://www.plosmedicine.org/article/info:doi/10.1371/journal.pmed.0050216#pmed-0050216-t001" target="_blank">Table 1</a>.</p

Correlation between pneumonia death rate before pandemic influenza and pneumonia death rate during pandemic influenza in 66 large US cities.

<p>The correlation between the mean baseline pneumonia death rate during 1910–1917 and the pneumonia death rate in 1918, 1919 and 1920 across the 66 US cities.</p

Spearman correlation coefficient (and corresponding P value) between pneumonia and influenza mortality rates in baseline years (1910–17) and pandemic years (1918–20) in 66 US cities.

<p>Spearman correlation coefficient (and corresponding P value) between pneumonia and influenza mortality rates in baseline years (1910–17) and pandemic years (1918–20) in 66 US cities.</p

Comparison of Influenza Seasonal Patterns in Temperate and Tropical Countries in the Americas (From Lat +39°N to -35°S) The latitude of the capital city is indicated for each country in the legend

<div><p>(A) Weekly distribution of influenza laboratory isolates (from week 1 to week 52; weekly frequency ( <i>y</i>-axis) is calculated as the weekly number of isolates divided by the annual number of isolates). </p> <p>(B) Weekly numbers of isolates were aggregated over four-week periods to show a (nearly) monthly distribution of influenza circulation. Each color represents a different month (color bar on the right). Note the transition in seasonal patterns from north to south, ranging from marked seasonal winter activity centered around January in the US, to uniform circulation throughout the year in Columbia and again, strong winter epidemics center around July in Argentina.</p> <p>Influenza viral surveillance data compiled from WHO Flunet.</p></div

Latitude and longitude coordinates, population size, and mean baseline pneumonia and influenza death rates for 66 large US reporting cities (1910–1920) with 100, 000 or more inhabitants [10].

<p>Latitude and longitude coordinates, population size, and mean baseline pneumonia and influenza death rates for 66 large US reporting cities (1910–1920) with 100, 000 or more inhabitants <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0023467#pone.0023467-Bureau1" target="_blank">[10]</a>.</p

Correlation between influenza mortality rate before and during pandemic influenza in 66 large US cities.

<p>The correlation between the mean baseline influenza death rate during 1910–1917 and the influenza death rate in 1918, 1919 and 1920 across the 66 US cities.</p

Relationship between the number of influenza deaths and population size for 66 US cities.

<p>Relationship between the total number of influenza deaths and population size for the 66 US cities. The dashed blue line represents the best linear fit to the data in log-log scale. A solid black line representing a slope of one is shown as a reference to illustrate the expected relationship if influenza mortality rates did not vary with population size. The slope of the observed data is ‘linear’ for all years (invariant death rates across cities) except for a slope less than one for year 1918 suggesting that less populous cities were more heavily affected during the 1918 influenza pandemic.</p

Parameter estimates and corresponding confidence intervals, and the Geweke index are provided for models with and without mixing.

<p>Parameter estimates and corresponding confidence intervals, and the Geweke index are provided for models with and without mixing.</p

Specification of <i>R</i>_<i>0</i>.

<p>A diagram showing the three critical points (<i>p</i>_<i>1</i>, <i>p</i>_<i>2</i>, <i>p</i>_<i>3</i>) used to define the relationship between specific humidity and <i>R</i>_<i>0</i>. The points were allowed to vary across indicated ranges (dashed lines). The central point, <i>p</i>_<i>1</i>, was allowed to vary along the x- and y-axes; whereas <i>p</i>_<i>2</i> and <i>p</i>_<i>3</i> only varied along the y-axis as 0 and 23 g/kg were the bounds of specific humidity.</p