prediction of onset timing of seasonal influenza epidemic ... · pdf fileprediction of onset...

1. Previous Host Immunity Affected Clustering of Influenza in Sado island, Japan

2. Prediction of Onset Timing of Seasonal Influenza Epidemic, Japan

Yugo Shobugawa, MD, PhD yugo@ med.niigata-u.ac.jp

Post Doctoral Fellow, School of Public Health, Loma Linda University, CA

Assistant Professor, Department of Public Health, Niigata University, Japan

Background

During seasonal influenza epidemics, 5-15% of the population are affected with infections.

Worldwide epidemics result in three to five million cases of severe illness and even death in each year.

Reducing morbidity and mortality with influenza is major issue in every country from the public health perspective.

Case 1.

General information, Sado island, Japan

Total population: 63,313 in May, 2011 • 0-14: 11.8% •15-64: 53.0% •65<: 35.3%

Total area: 855 square kilometers. Rural and mountain forest area covers a large part of the island. One hour by high-speed boat, 150 minutes by ferry from the main land.

Rural and mountain forest area covers a large part of the island.

Landscape in Sado island

Coast area

Sight seeing spot especially in summer “Tarai-bune” (tub-turned boat) which is a traditional

Japanese fishing boat used for catching Abalone and other mollusks.

Study flow

Step 1: Basic statistics of Flu outbreaks

Step 2: Cluster detection

Step 3: Exploring cause of clustering

Step 1: Basic statistics of Flu outbreaks Data: individual patients data (age, sex, date

of onset, zip code of resident area, and diagnosis: Flu A or B by commercial rapid test kit) were collected by physicians and pediatricians in hospitals or clinics in winter seasons, 2005-2009.

More than 80% of all the hospitals/clinics in the island cooperated with the study.

Outbreak description

Outbreaks of Flu A, no. of cases (median age, range) 2005/2006 656 (28, 0-95 y/o)

2006/2007 1,884 (16, 0-97 y/o)

2007/2008 140 (16, 0-62 y/o)

2008/2009 2,070 (14, 0-95 y/o)

Outbreaks of Flu B , no. of cases (median age, range) 2007/2008 299 (7, 0-92 y/o)

2008/2009 796 (7, 0-77 y/o)

Flu epidemic, 2005-2009 (weekly no. of cases)

2006 2007 2008 2009

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Step 2: cluster detection

Geocoding: by zip code of patients’ resident (Sado island was divided into 218 zip code areas).

Pure space clusters in each winter were detected by Poisson Model on SaTScan software. Population and age adjusted by six age subsets:

Age 0-4, 5-14, 15-19, 20-39, 40-64, >65 Maximum cluster size was set at 10% of the

population.

Flu cluster areas in each season

Locations of clusters varied by season and type.

Why?

What factor(s) affect for clustering of Flu?

>> next step

Step 3: Exploring cause(s) of clustering Binary logistic regression analysis

Dependent variable: Cluster or not? Explanatory variables:

○ Population density ○ No. of family members in each household ○ Flu vaccination rate in school aged children ○ Incident rate of Flu in former 2 seasons in school aged children

Flu vaccination rate and incident rate in school aged children were calculated by using another data set collected from all the elementary school in the island.

Calculation has been done by using SPSS software.

Significant factors for Flu clustering 2005-2006 season, Flu A

Cluster (N=46)

Non cluster (N=172)

Coefficient of regression

P value Odds ratio

95% CI

Population density,

person/km2

327.29 + 863.59

479.3 + 1165.2

0.000 0.780 1.000 1.000-1.000

No. of members/hou

sehold 3.0 + 0.6 2.8 + 0.6 0.665 0.024 1.944

1.091-3.464

Vaccination rate, % 45.1 + 10.2 44.4 + 11.7 0.008 0.605 1.008

0.978-1.038

Incident rate last season, % 9.5 + 7.7 9.3 + 9.6 -0.012 0.576 0.988

0.948-1.030

Incident rate year before

last season, % 29.4 + 11.9 31.7 + 17.2 -0.011 0.387 0.989

0.965-1.014


Cluster (N=31)

Non cluster (N=187)


P value Odds ratio

95% CI

Population density,

person/km2

560.5 + 1092.5

428.5 + 1112.7

0.000 0.297 1.000 1.000-1.001

No. of members/hou

sehold 2.8 + 0.4 2.9 + 0.6 -0.013 0.972 0.987

0.479-2.033


0.979-1.062


0.922-0.986


last season, % 2.4 + 2.1 3.3 + 4.0 -0.052 0.471 0.949

0.824-1.094


Cluster (N=28)

Non cluster (N=190)


P value Odds ratio

95% CI

Population density,

person/km2

509.6 + 1229.4

438.1 + 1092.6

0.000 0.289 1.000 1.000-1.001

No. of members/hou

sehold 2.8 + 0.5 2.9 + 0.6 0.398 0.359 1.489

0.636-3.487

Vaccination rate, % 53.9 + 10.7 54.4 + 10.2 -0.001 0.975 0.999

0.957-1.044


0.792-1.079


last season, % 6.2 + 5.1 12.8 + 6.7 -0.162 0.000 0.851

0.792-0.914


Cluster (N=44)

Non cluster (N=174)


P value Odds ratio

95% CI

Population density,

person/km2

959.4 + 1358.6

317.7 + 999.2 0.000 0.006 1.000 1.000-1.001

No. of members/hou

sehold 2.7 + 0.5 2.9 + 0.6 -0.079 0.823 0.924

0.464-1.840


0.984-1.050


0.940-1.043


last season, % 1.0 + 1.6 4.3 + 9.2 -0.166 0.035 0.847

0.726-0.989

Significant factors for Flu clustering 2007-2008 season, Flu B

Cluster (N=22)

Non cluster (N=196)


P value Odds ratio

95% CI

Population density,

person/km2

1231.8 + 2224.8

359.2 + 869.9 0.000 0.006 1.000 1.000-1.001

No. of members/hou

sehold 2.7 + 0.2 2.9 + 0.6 -0.144 0.770 0.966

0.330-2.271


0.954-1.055


0.575-0.974


last season, % 11.9 + 5.9 11.9 + 7.0 -0.008 0.823 0.992

0.924-1.065

Significant factors for Flu clustering 2008-2009 season, Flu B

Cluster (N=32)

Non cluster (N=186)


P value Odds ratio

95% CI

Population density,

person/km2

604.0 + 1149.1

420.3 + 1102.1

0.000 0.552 1.000 1.000-1.000

No. of members/hou

sehold 2.7 + 0.4 2.9 + 0.6 -0.254 0.525 0.775

0.354-1.699


0.994-1.068

Incident rate last season, % 12.8 + 4.1 11.8 + 7.3 0.027 0.370 1.027

0.968-1.090


last season, % 1.0 + 0.8 4.1 + 8.9 -0.149 0.072 0.861

0.732-1.014

Discussions High population density and greater number of

people in a household can get more frequent chance to be infected because influenza virus can be transmitted from human to human by droplet.

Previous infection with influenza may reduce chance to be infected with influenza which reason is possibly explained by pre-existing host immunity.

For areas where Flu outbreak did not occur largely former years, preventive activity should be enforced from the public health perspective (cough etiquette, hand hygiene, monitoring and so on).

Limitations

Only data of school aged children in vaccination and incident rate is available.

No laboratory confirmation has been done, but Flu was diagnosed by commercial rapid test kit (which has still high sensitivity and high specificity).

Conclusions

Locations of Flu cluster varied by season and type of Flu.

High density of population and more family member/household promoted Flu clustering.

High incident rate of Flu in school aged children in former seasons suppressed Flu clustering.

From these results, we can plan any effective interventions for outbreak prevention before epidemic.

Spread manner of Flu has still not been elucidated from this study.

Case 2.

Data collected from whole Japan by law

Influenza-like illness (ILI) surveillance data: Infectious Disease Weekly Report Infectious Agents Surveillance Report

(http://idsc.nih.go.jp/index.html) ILI cases were reported by clinician in sentinel

hospital/clinic. Average ILI cases in each prefecture were used for analysis.

ILI is defined by sudden onset of fever >38°C, respiratory symptoms, and myalgia.

Total of 11.1 million ILI cases from 1999 to 2009 for 46 prefectures were enrolled.

How to count ILI cases e.g. in certain prefecture

How to count ILI cases

Sum of these ILI cases from the all sentinels divided by no of sentinels was reported weekly.

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In this prefecture, Sum of the ILI = 153 No. of sentinels = 13 Av. no. of ILI cases = 153/13 = 11.8

Japan ILI

>> movie

Spatial indicator for representing of compactness of distribution

Weighted Standard Distance (WSD)

(X, Y ) indicates the mean center for all the features in the study area. It takes the squared difference in coordinate values between each point and the weighted mean center and multiplies it by the weight, sums the weighted differences, then divides the summed values by the sum of the weights. X and Y are the x- and y- coordinates of the weighted mean center.

WSD: Compactness of distributions

2nd week, 2002 19th week, 2002

Clustered Dispersed

Japan ILI with WSD

>> movie

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Trend of WSD and ILI cases, 1999-2009 Weekly reported ILI cases Standard distance

Gap between minimum standard distance week and initiation week for influenza outbreak

Influenza outbreak warning is stated by IDSC (Infectious Diseases Surveillance Center) on the week, ILI cases reached 10.0 or greater in Japan.

WSD trend

ILI trend

Table 1. Relationship between gap duration and the prevelance of influenza virus type/subtype

A/H3N2 A/H1N1 B

1999/2000 51st, 1999 2nd, 2000 3 37.7% 62.2% 0.2%

2000/2001 48th, 2000 10th, 2001 14 16.1% 38.3% 45.6%

2001/2002 1st, 2002 5th, 2002 4 37.9% 39.8% 22.3%

2002/2003 52nd, 2002 1st, 2003 1 66.4% 0.0% 33.6%

2003/2004 52nd, 2003 3rd, 2004 3 94.1% 0.1% 5.8%

2004/2005 42nd, 2004 4th, 2005 15 41.6% 3.0% 55.4%

2005/2006 43rd, 2005 1st, 2006 10 64.8% 25.4% 9.8%

2006/2007 49th, 2006 7th, 2007 10 47.2% 11.9% 41.0%

2007/2008 39th, 2007 3rd, 2008 16 11.3% 81.8% 6.9%

2008/2009 44th, 2008 2nd, 2009 10 26.1% 46.8% 27.1%

* The week when ILI cases reached 10.0 or greater for the first time in each season through all Japan.

Prevalence of type or subtype of influenza virusSeasons Lowest WSD week ILI>10.0 week* Gap duration

(weeks)

Relationship between gap duration and the prevalence of influenza virus type/subtype

0%

20%

40%

60%

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100%

0 5 10 15

A/H3N2

A/H1N1

B

Linear (A/H3N2)

Linear (A/H1N1)

Linear (B)

R = -0.69

Gap duration, week

Prop

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influ

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type

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Result summary

Standard distance weighted by average no. of ILI cases from sentinels decreased to a minimal value before the each peak.

Duration gap between the lowest WSD week and week ILI reached 10.0 or greater varied by season. However, the gap showed significant negative correlation with proportion of prevalence of A/H3N2 virus.

Discussion and Conclusion

Weighted standard distance will be a possible indicator to predict timing of onset of influenza epidemic.

A/H3N2 virus showed faster spreading pattern rather than in A/H1N1 and B.

Limitations

Only Japanese data has been analyzed. Application this method to continental countries is needed.

Thank you!

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