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

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1 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 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

2 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.

3 Case 1.

General information, Sado island, Total population: 63,313 in May, 2011 0-14: 11.8% 15-64: 53.0% 65<: 35.3% Total area: 855 square kilometers.

4 General information, Sado island, Total population: 63,313 in May, : 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. Japan

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

6 Landscape in Sado island Coast area

7 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.

8 Study flow Step 1: Basic statistics of Flu outbreaks Step 2: Cluster detection Step 3: Exploring cause of clustering

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

10 Outbreak description Outbreaks of Flu A, no. of cases (median age, range) 2005/ (28, 0-95 y/o) 2006/2007 1,884 (16, 0-97 y/o) 2007/ (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/ (7, 0-92 y/o) 2008/ (7, 0-77 y/o)

11 Flu epidemic, (weekly no. of cases) Influenza A cases 300 Influenza B cases Weekly no. of Flu cases w0552 w0626 w0652 w0726 w0752 w0826 w0852 w0926

12 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.

14 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

15 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.

16 Significant factors for Flu clustering season, Flu A Cluster (N=46) Population density, person/km No. of members/hou sehold Vaccination rate, % Incident rate last season, % Non cluster (N=172) Coefficient of regression P value Odds ratio % CI Incident rate year before last season, %

17 Significant factors for Flu clustering season, Flu A Cluster (N=46) Population density, person/km No. of members/hou sehold Vaccination rate, % Incident rate last season, % Non cluster (N=172) Coefficient of regression P value Odds ratio % CI Incident rate year before last season, %

18 Significant factors for Flu clustering season, Flu A Cluster (N=31) Population density, person/km No. of members/hou sehold Vaccination rate, % Incident rate last season, % Non cluster (N=187) Coefficient of regression P value Odds ratio % CI Incident rate year before last season, %

19 Significant factors for Flu clustering season, Flu A Cluster (N=31) Population density, person/km No. of members/hou sehold Vaccination rate, % Incident rate last season, % Non cluster (N=187) Coefficient of regression P value Odds ratio % CI Incident rate year before last season, %

20 Significant factors for Flu clustering season, Flu A Cluster (N=28) Population density, person/km No. of members/hou sehold Vaccination rate, % Incident rate last season, % Non cluster (N=190) Coefficient of regression P value Odds ratio % CI Incident rate year before last season, %

21 Significant factors for Flu clustering season, Flu A Cluster (N=28) Population density, person/km No. of members/hou sehold Vaccination rate, % Incident rate last season, % Non cluster (N=190) Coefficient of regression P value Odds ratio % CI Incident rate year before last season, %

22 Significant factors for Flu clustering season, Flu A Cluster (N=44) Population density, person/km No. of members/hou sehold Vaccination rate, % Incident rate last season, % Non cluster (N=174) Coefficient of regression P value Odds ratio % CI Incident rate year before last season, %

23 Significant factors for Flu clustering season, Flu A Cluster (N=44) Population density, person/km No. of members/hou sehold Vaccination rate, % Incident rate last season, % Non cluster (N=174) Coefficient of regression P value Odds ratio % CI Incident rate year before last season, %

24 Significant factors for Flu clustering season, Flu B Cluster (N=22) Population density, person/km No. of members/hou sehold Vaccination rate, % Incident rate last season, % Non cluster (N=196) Coefficient of regression P value Odds ratio % CI Incident rate year before last season, %

25 Significant factors for Flu clustering season, Flu B Cluster (N=22) Population density, person/km No. of members/hou sehold Vaccination rate, % Incident rate last season, % Non cluster (N=196) Coefficient of regression P value Odds ratio % CI Incident rate year before last season, %

26 Significant factors for Flu clustering season, Flu B Cluster (N=32) Population density, person/km No. of members/hou sehold Vaccination rate, % Incident rate last season, % Non cluster (N=186) Coefficient of regression P value Odds ratio % CI Incident rate year before last season, %

27 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).

28 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).

29 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.

30 Case 2.

31 Data collected from whole Japan by law Influenza-like illness (ILI) surveillance data: Infectious Disease Weekly Report Infectious Agents Surveillance Report ( 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.

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

33 How to count ILI cases Sum of these ILI cases from the all sentinels divided by no of sentinels was reported weekly. 6 v 5 v 12 v 15 v 9 v 1 v 21 v 7 v 3 v 11 v 0 v 5 v 8 v In this prefecture, Sum of the ILI = 153 No. of sentinels = 13 Av. no. of ILI cases = 153/13 = 11.8 H Sentinel hospital/clinic

34 Japan ILI >> movie

It takes the squared difference in coordinate values between each point and the weighted mean center and multiplies

35 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.

36 WSD: Compactness of distributions Clustered Dispersed 2 nd week, th week, 2002

37 Japan ILI with WSD >> movie

39 Trend of WSD and ILI cases, Weighted standard distance, km Apr-99 Jul-99 Oct-99 Jan-00 Apr-00 Jul-00 Oct-00 Jan-01 Apr-01 Jul-01 Oct-01 Jan-02 Apr-02 Jul-02 Oct-02 Jan-03 Apr-03 Jul-03 Oct-03 Jan-04 Apr-04 Jul-04 Oct-04 Jan-05 Apr-05 Jul-05 Oct-05 Jan-06 Apr-06 Jul-06 Oct-06 Jan-07 Apr-07 Jul-07 Oct-07 Jan-08 Apr-08 Jul-08 Oct-08 Jan-09 Apr-09 Weekly reported ILI cases Standard distance Average weekly reported ILI cases per sentinel

warning is stated by IDSC (Infectious Diseases Surveillance

40 Gap between minimum standard distance week and initiation week for influenza outbreak WSD trend ILI trend Influenza outbreak warning is stated by IDSC (Infectious Diseases Surveillance Center) on the week, ILI cases reached 10.0 or greater in Japan.

41 Relationship between gap duration and the prevalence of influenza virus type/subtype Table 1. Relationship between gap duration and the prevelance of influenza virus type/subtype Seasons Lowest WSD week ILI>10.0 week* Gap duration (weeks) Prevalence of type or subtype of influenza virus A/H3N2 A/H1N1 B 1999/ st, nd, % 62.2% 0.2% 2000/ th, th, % 38.3% 45.6% 2001/2002 1st, th, % 39.8% 22.3% 2002/ nd, st, % 0.0% 33.6% 2003/ nd, rd, % 0.1% 5.8% 2004/ nd, th, % 3.0% 55.4% 2005/ rd, st, % 25.4% 9.8% 2006/ th, th, % 11.9% 41.0% 2007/ th, rd, % 81.8% 6.9% 2008/ th, nd, % 46.8% 27.1% * The week when ILI cases reached 10.0 or greater for the first time in each season through all Japan.

42 Proportion of circulating influenza type/subtype 100% 80% 60% 40% 20% R = A/H3N2 A/H1N1 B Linear (A/H3N2) Linear (A/H1N1) Linear (B) 0% Gap duration, week

43 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.

44 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.

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

46 Thank you!

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