Influence of Average Income on Epidemics of Seasonal Influenza

Jpn. J. Infect. Dis., 69, 457–463, 2016

Original Article Influence of Average Income on Epidemics of Seasonal Influenza

Issei Seike1,2, Norihiro Saito2,3,4, Satoshi Saito1,2, Masamichi Itoga3,4, and Hiroyuki Kayaba2,3,4* 1Hirosaki University School of Medicine, Aomori; 2Department of Clinical Laboratory Medicine, Hirosaki University Graduate School of Medicine, Aomori; 3Clinical Laboratory, Hirosaki University Hospital, Aomorii; and 4Infection Control Center, Hirosaki University Hospital, Aomori, Japan

SUMMARY: Understanding the local factors influencing the transmission of communicable diseases is important to minimize social damage. The aim of this study was to investigate local factors influencing seasonal influenza epidemics in Aomori prefecture consisting of 6 regions, i.e., Seihoku, Chunan, and Tosei on the west side, and Sanpachi, Kamikita, and Shimokita on the east side. Four indices (epidemic onset, duration, scale, and steepness of epidemic curves) were defined, and their correlations with regional characteristics and meteorological factors were investigated. Data for influenza seasons from 2006–2007 to 2014–2015 were collected. The 2009–2010 season was excluded because of the pandemic of A (H1N1)pdm09. Average income was strongly correlated with epidemic onset, duration, and scale. The ratio of children aged 5 years to the total population was strongly correlated with epidemic duration and scale. Low temperature in January showed moderate correlation with epidemic duration and scale. Cluster analysis showed that 2 isolated regions, Seihoku and Chunan, belonged to the same cluster in the 4 indices of epidemic curves, and other 2 relatively urbanized regions formed another cluster in 3 of the 4 indices. This study highlights important local factors that influence seasonal influenza epidemics and may help in implementation of preventive measures.

fectious Diseases has been accumulating data from ap- INTRODUCTION proximately 5,000 nationwide sentinel medical facilities Seasonal influenza is a common infectious disease on a weekly basis. The epidemic threshold for influenza that peaks in winter in most parts of Japan. A sentinel is defined as 1.0 case per sentinel per week based on the surveillance system for seasonal influenza, consisting of accumulated data. Epidemiological data on seasonal 3,000 pediatric and 2,000 internal medicine sites, is cur- rently operating in Japan (1). The Aomori prefecture has approximately 1.3 million people and an area of 9,644.55 square meters. It is located in the northern- most tip of the main island of Japan (Fig. 1). The Aomori prefectural government divided the Tsugaru area into the Tosei, Chunan, and Seihoku regions, and the Nambu area was divided into the Sanpachi, Kamikita, and Shimokita regions. The geographical, socio-cultural and meteorological conditions differ within Aomori prefecture. It is important to under- stand the characteristics of seasonal influenza epidemic patterns in local areas in order to implement better preventive measures. Therefore, in this study, we aimed to investigate the factors influencing the differences in the patterns of seasonal influenza epidemics among the various regions of Aomori prefecture. Fig. 1. Schematic drawing of the 6 regions in Aomori prefecture.DuringtheEdoperiod(1603A.D.to1868A.D.), MATERIALS AND METHODS Aomori was occupied by 2 domains (Tsugaru and Nambu) Data source and extraction: In Japan, influenza-like having different cultures and customs. In the chaos of the Meiji Restoration, people from the Aizu clan moved into illness has been monitored under the national surveil- Aomori and constructed towns. More than 150 years after the lance system since 1987. The National Institute of In- Meiji Restoration, Aomori still has different regional cultures as well as geographical and meteorological differences. During Received October 9, 2015. Accepted January 18, 2016. the winter season, Nambu on the eastern side of Aomori has many sunny days and not much snow; while Tsugaru on the J-STAGE Advance Publication February 19, 2016. western side, has much snow and long dark hours under snow DOI: 10.7883/yoken.JJID.2015.500 clouds. The Tosei and Sanpachi regions are located within *Corresponding author: Mailing address: Department of 25 min by bullet train (Shinkansen) connected to Tokyo. The Clinical Laboratory Medicine, Hirosaki University Gradu- Chunan region includes Hirosaki City, the former capital of the Tsugaru clan during the Edo period. The Chunan, ate School of Medicine, 5 Zaifu-cho, Hirosaki, Aomori Seihoku, and Shimokita regions are relatively isolated area. It 036-8562, Japan. Tel: ＋81-172-39-5123, Fax: ＋81-172- takes 2 h from Aomori city to Mutsu city, the main city of 39-5306, E-mail: kayaba＠hirosaki-u.ac.jp Shimokita region, and 40 min to Hirosaki by local trains.

457 influenza epidemics were collected for the 2006–2007 to During the study period (from 2006 to 2014, excluding the 2014–2015 influenza seasons using data from the 2009), data on the total population and population sentinel surveillance system in Aomori prefecture. The density were available until 2014 (8 fiscal years); on the local governmental organizations in Aomori, as a divi- number of doctors, until 2013 (7 fiscal years); on the sion of national surveillance system, in each of the 6 average income, until 2012 (6 fiscal years); and on the regions in Aomori compile cases of influenza from the household population, until 2010 (4 fiscal years). The 65 local sentinel sites. All data since 2007 are available average values of these regional characteristics during to the public on the Aomori prefectural government the available period were calculated for each of the 6 website. Seasonal influenza epidemics started in late regions as well as the 4 indices. Subsequently, the corre- January in the 2006–2007 season; therefore, data col- lations between the average value of the 4 indices and lected from the 2006–2007 to the 2014–2015 seasons the regional characteristics, both representing the 6 were obtained. Data of the 2009–2010 season, in which regions, were investigated. The correlation was consid- the A (H1N1)pdm09 strain prevailed, were excluded ered as weak, moderate, strong, and very strong for from this study. Four indices were defined: epidemic absolute CC values of 0.20–0.39, 0.40–0.59, 0.60–0.79 onset, duration, scale, and the steepness of the epidem- and 0.80–1.00, respectively. ic curve. The onset of an influenza epidemic was de- CC of meteorological factors: Seasonal influenza finedastheweekwhenthenumberofcasespersentinel endemic patterns vary every year. We investigated cor- site exceeded 1.0, and the end of the epidemic was de- relations of the above mentioned 4 indices of influenza fined as the week when that number dropped below epidemic curves every year and monthly meteorological 1.0. For the comparison of the onset and the end of epi- data from November to March by calculating the Pear- demics, the week including January 4 was defined as son product-moment correlation coefficient. Meteoro- the ``0'' point. If the epidemic began 1 week before (or logical data were obtained from the Japan Meteorologi- after) the ``0'' point, the time of onset was counted as cal Agency website and included the seasonal average －1 (or 1). The epidemic scale is the sum of the reported temperature, maximum temperature, minimum temper- cases from the sentinels during the epidemic (patients/ ature, and difference between the maximum and mini- season). The steepness of the epidemic curve was cal- mum temperatures, daily maximum temperature, daily culated by dividing the largest number of cases in the minimum temperature, daily temperature difference, season by the epidemic duration (Fig. 2). Differences in snow accumulation, and absolute and relative humidi- the patterns of seasonal influenza epidemics in Aomori ty. The data on both seasonal influenza epidemics and were analyzed using the following 3 steps. the meteorological records on temperature and snow Cluster analyses: The epidemic patterns were classi- accumulation were available in every region during the fied by cluster analysis of the 4 indices. Data of each in- study. Each of the 6 regions has 8 data sets; hence, the dex were collected from the 8 seasons (the 2006–2007 to total number of data sets was 48 in the investigation for the 2014–2015 seasons excluding the 2009–2010 sea- the correlations between the 2 meteorological factors son). Hence, each region had 8 data sets. Common (atmospheric temperature and snow accumulation) and characteristics of the regions with similar epidemic pat- the 4 indices. The data on humidity were available in 3 terns were examined. regions for 8 seasons, and the total number of data sets Correlation coefficient (CC) of the regional was 24. characteristics: We investigated correlations among Statistical analysis: The statistical analysis was car- the 4 indices and regional characteristics by calculating ried out using EZR (2) and Mulcel (OMS Publishing, the Pearson product-moment correlation coefficient. Saitama, Japan), an add-in software for Excel Data were obtained from the Aomori prefectural (Microsoft, Redmond, WA, USA). Significance was set government website. Regional characteristics included at p ＜ 0.05. total population, the number of doctors, population Ethics statement: The ethics committee of Hirosaki density, the number of households and average income. University Graduate School of Medicine deemed this study as an analysis of de-identified and publicly available data, which did not need to be reviewed.

RESULTS Average of the indices: The average for each index was calculated (Table 1). In the Kamikita region, which lies east of the Hakkoda Mountains, seasonal influenza epidemics affect a large number of people and have an early onset. In the Chunan and Seihoku regions, in the western part of the prefecture, epidemics were smaller in scale. Classification of epidemic curves in the 6 regions of Aomori: Cluster analyses showed similarities in the epidemics (Fig. 3). The Tosei and Sanpachi regions, which Fig. 2. Schematic drawing of a seasonal influenza epidemic are the urbanized regions of Aomori prefecture, were in curve and the 4 indices studied. The onset, duration, steep- the same cluster with respect to epidemic onset, scale, ness, and size of the epidemic were calculated and analyzed in the present study. Dotted area represents the size of the epi- and steepness of the epidemic curve. In addition, the demic. Chunan and Seihoku regions, which lie in the west of

458 Income and Seasonal Influenza Epidemics

Fig. 3. Results of cluster analyses of the 4 indices. Dendrogram of the 4 indices of seasonal influenza epidemics for the 6 evaluated regions is presented. The Chunan and Seihoku regions grouped into the same cluster for epidemic onset (panel A). The Chunan and Tosei regions grouped into the same cluster for the epidemic duration (panel B), and the Seihoku region was also close to these 2 regions. The Chunan and Seihoku regions grouped into the same cluster for scale (panel C) and steepness of epidemic curve (panel D).

Table 1. Averages of 4 indices in each of the 6 regions of very strong negative correlation with epidemic onset Aomori for the 2006–2007 to 2014–2015 seasons (excluding the and a very strong positive correlation with epidemic 2009–2010 season) duration and scale. The percentage of children aged 0–5 Epidemic Epidemic Epidemic Steepness of years, and in particular, the rate of children aged 3–5 onset duration scale epidemic curves years who started attending preschool, showed a very strong positive correlation with epidemic duration and Tosei －0.25 19.88 263.39 2.13 scale. The percentage of primary school children also Chunan －0.50 19.00 171.35 1.65 showed a very strong correlation with epidemic dura- Sanpachi －0.88 20.38 228.82 1.80 tion. Seihoku 0.13 17.38 176.82 1.66 Correlationwithmeteorological factors: We investi- Kamikita －1.38 23.25 370.95 2.71 gated correlations of the 4 indices every year with Shimokita －0.38 20.25 293.86 2.70 meteorological factors. Factors for temperature included monthly average temperature, maximum temperature, minimum temperature, difference between Aomori prefecture, were in a cluster with respect to epi- maximum and minimum temperature, daily maximum demic onset, scale, and steepness of the epidemic curve. temperature, daily minimum temperature, and daily Correlation with regional characteristics: We investi- temperature difference for November to March. Fac- gated correlations of each of the 4 indices with regional tors that were correlated with 4 indices were extracted characteristics (Table 2). Average income showed a andarepresentedinTable3.

459 Table 2. Correlations between averages of 4 indices and regional characteristics

Index

Steepness of Factor Epidemic onset Epidemic duration Epidemic scale epidemic curves

CC p CC p CC p CC p

0–14 yr (z) －0.247 0.636 －0.206 0.695 －0.262 0.616 0.655 0.158 0–5 yr (z) －0.786 0.064 0.907 0.0125 0.851 0.032 0.780 0.066 3–5 yr (z) －0.764 0.077 0.901 0.014 0.871 0.024 0.796 0.058 Primary school children (z) －0.786 0.064 0.829 0.041 0.751 0.086 0.685 0.133 15–64 yr (z) －0.402 0.429 0.495 0.318 0.355 0.490 0.261 0.617  65 yr (z) 0.595 0.213 －0.685 0.133 －0.527 0.282 －0.411 0.418 Number of doctors (z) 0.037 0.944 －0.202 0.701 －0.477 0.339 －0.470 0.350 Population density －0.099 0.852 －0.068 0.898 －0.349 0.498 －0.541 0.268 Household number －0.180 0.733 －0.241 0.646 －0.259 0.620 －0.749 0.088 Average income －0.880 0.021 0.987 0.0002 0.898 0.015 0.747 0.088

CC, correlation coefficient.

Epidemic onset had a weak positive correlation with climate in November delays the onset of influenza epi- average temperature in November, daily minimum demics, and warm and humid climate in March short- temperature in November, and minimum temperature ens the duration of influenza epidemics. A large tem- in December, and a weak negative correlation with temperature difference in January and low daily minimum perature difference in December. The higher the tem- temperature in February tended to increase the epidem- perature in late autumn, the later the epidemic tended ic scale. The relative humidity in March affected the to begin. Furthermore, the larger the difference be- scale and steepness index of influenza epidemics (Table tween the minimum and the maximum temperature in 5). December, the earlier the epidemic tended to begin. Epidemic duration had a weak-to-moderate negative DISCUSSION correlation with the minimum temperatures from December to March, the average temperature from A variety of factors have been proposed to explain January to March, and the daily minimum temperature the seasonality of influenza, including low temperature, from January to March. The temperature difference in low humidity, solar radiation, daylight hours, crowd- the month and the daily temperature difference among ing, population movement, age distribution, melatonin months during the epidemic season had weak-to-mod- level, vitamin D level, and selenium level (1,3–5). Most erate positive correlations with the epidemic duration. countries have 1 annual influenza epidemic. In con- In addition, epidemic scale had a weak-to-moderate trast, about half of the tropical countries have 2 or 3 negative correlation with the average value of daily influenza endemics per year (6). The epidemic pattern minimum temperatures and the minimum temperature of seasonal influenza was thought to differ from place of the month in December, January, and February. to place in Aomori prefecture; however, to our knowl- The daily temperature difference showed a weak-to- edge, no epidemiological study has been performed pre- moderate positive correlation with the epidemic scale. viously regarding this issue. In this study, several local The temperature difference in January had a strong meteorological, social, and demographic factors were positive correlation with the scale. Steepness of epi- found to be correlated with the epidemic patterns of demic curves had a weak positive and moderate-nega- seasonal influenza. tive correlation with the daily temperature difference in The rate of children aged 0–5 years had a strong cor- November and the maximum temperature in Decem- relation with epidemic duration, scale, and steepness of ber, respectively. There was no correlation between the epidemic curves. Children develop anti-influenza virus amount of snow in the season and the 4 indices. CD8 T-cell memory comparable with that of adults by Epidemic onset showed a moderate correlation with the age of 15 years (3). In this study, we found an in- the absolute humidity in November. High absolute hu- creased risk of influenza transmission through pre- midity in November tended to delay the epidemic onset. school contact as compared with school and adult con- Further, the absolute humidity in March had a moder- tact. Children are vulnerable to influenza infection (7); ate negative correlation with epidemic onset and dura- in particular, children aged 3–5 years who start pre- tion. Relative humidity in March showed a moderate school do not have adequate immunity, and are thus positive correlation with the epidemic scale and steep- more likely to become infected. Therefore, they are ness of the epidemic curve (Table 4). thought to play an important role in seasonal influenza Multiple linear regression analysis revealed that, epidemics. In a study including households with pri- among the social factors investigated in this study, the mary or junior high school-aged children, influenza average income was a dominant factor influencing the transmission was most commonly observed between onset duration and scale of epidemic curve. Regression primary school children and parents, followed by trans- equations derived by multiple regression analysis for mission from primary school children to siblings (8). meteorological factors suggested that warm and humid For the control of seasonal influenza, vaccination of

460 Income and Seasonal Influenza Epidemics

Table 3. Correlation between 4 indices and temperature Table 4. Correlation between 4 indices and humidity

Index Month Temperature CC p Index Factor CC p

Epidemic November average temperature 0.359 0.012 Epidemic onset November absolute humidity 0.574 0.003 onset daily minimum tem- 0.359 0.012 March absolute humidity －0.506 0.012 perature December minimum temperature 0.309 0.033 Epidemic duration March absolute humidity －0.451 0.027 temperature difference －0.397 0.005 Epidemic scale March relative humidity 0.425 0.038 Epidemic December minimum temperature －0.508 0.0002 March minimum relative 0.485 0.017 humidity duration temperature difference 0.523 0.0001 daily temperature 0.526 0.0001 Steepness of epidemic March relative humidity 0.508 0.011 difference curves January average temperature －0.348 0.015 minimum temperature －0.479 0.0006 temperature difference 0.435 0.002 Table 5. Multiple linear regression analysis for the impact of so- daily minimum tem- －0.504 0.0003 cial and meteorological factors on epidemic curves perature A. Social factors daily temperature 0.566 0.00003 difference Epidemic curve Regression equation p value February average temperature －0.294 0.043 index minimum temperature －0.407 0.004 Onset (wk) － 0.00137 (avg. income) ＋ 2.67 0.021 temperature difference 0.437 0.002 Duration (wk) 0.0057 (avg. income) ＋ 6.77 0.00024 daily minimum tem- －0.391 0.006 Scale (patients) 0.20 (avg. income) － 222.6 0.015 perature Steepness index 2.21 (3–5-year-old children [z]) 0.060 daily temperature 0.389 0.006 － 2.97 difference March average temperature －0.405 0.004 B. Temperature minimum temperature －0.495 0.0003 Epidemic curve temperature difference 0.500 0.0003 index Regression equation p value daily minimum tem- －0.488 0.00043 perature Onset (wk) 1.33 (Nov. avg. temp.) 0.0026 daily temperature 0.412 0.004 － 0.34 (Dec. temp. diff.) － 3.25 difference Duration (wk) － 0.82 (Mar. daily min. temp.) 0.00000173 ＋ 0.42 (Mar. temp. diff.) Epidemic December minimum temperature －0.329 0.023 ＋ 0.4 (Dec. temp. diff.) scale daily minimum tem- －0.326 0.024 ＋ 0.38 (Jan. daily temp. diff.) perature － 0.85 daily temperature 0.389 0.006 Scale (patients) 13.3 (Jan. temp. diff.) 0.0000118 difference － 11.4 (Feb. daily min. temp.) － 28.9 January minimum temperature －0.526 0.0001 Steepness index 0.34 (Nov. daily temp. diff.) 0.000174 temperature difference 0.606 0.000005 － 0.22 (Dec. max. temp.) ＋ 1.80 daily minimum tem- －0.348 0.015 perature C. Humidity daily temperature 0.443 0.002 difference Epidemic curve Regression equation p value February average temperature －0.308 0.033 index maximum temperature －0.331 0.022 Onset (wk) 5.0 (Nov. absolute humid.) － 2.97 0.0011 minimum temperature －0.447 0.001 (Mar. absolute humid.) － 17.6 daily minimum tem- －0.445 0.002 Duration (wk) － 4.1 (Mar. absolute humid.) ＋ 37.1 0.027 perature Scale 8.2 (Mar. relative humid.) 0.019 daily temperature 0.375 0.009 (patients × 103) ＋ 7.8 (Mar. min. relative humid.) difference － 521.2 Steepness index 0.14 (Mar. relative humid.) － 7.5 0.022 Steepness November daily temperature 0.327 0.023 of epidemic difference curves December maximum temperature －0.437 0.002 ment of people is more closely correlated with regional spread of influenza than geographical distance between 80z of the children has been reported to be as effective regions (11,12). Regions with high economic activity as vaccination of 80z of the entire population (9). are thought to comprise more people who travel; there- However, it was reported that vaccination did not fore, seasonal influenza epidemics in these areas may reduce the frequency of infection in children aged ＜13 start early and expand over a larger area, as was ob- years (10). As such, comprehensive prevention of infec- served in Seihoku and Sanpachi regions. The Seihoku tion of preschool children is important. and Sanpachi regions include Aomori city with a popu- Unexpectedly, among the various factors investigated lation of 300,000 and Hachinohe city with a population in this study, the average income showed a very strong of 240,000, respectively, and are relatively urbanized correlation with 4 indices. The average income includes regions in Aomori prefecture. Furthermore, Aomori employee compensation, property income, and entre- city and Hachinohe city are directly connected with preneurial income, reflecting economic activity. Move- Shinkansen by a 25-min route and are linked to Tokyo.

461 Although the Chunan and Seihoku regions were once gin and the earlier it may end. The result that warm and the main regions of the Tsugaru domain, they are now humid climate in November delays the onset of epidem- relatively isolated regions in Aomori prefecture. The ics and warm and humid climate in March shortens the average income has a very strong positive correlation duration was plausible. However, it was unclear why with the average percentage of children aged 0–5 years the absolute humidity in March had a negative correla- (CC ＝ 0.934, p ＝ 0.0063), as more children may be tion with the epidemic onset, and relative humidity in born in regions with high economic activity. However, March affected the epidemic scale and steepness index. no correlation was observed between snow accumula- Taken together, these results related to the regional tion and the 4 indices analyzed in this study. Heavy characteristics may explain the results of cluster analy- snowfall during winter in the Aomori prefecture poses sis. Seihoku and Chunan regions are both relatively difficulty. Snow accumulation may prevent people isolated regions that share the same cultural and geo- from going outdoors and therefore could contain the graphical background, formed a cluster in the 4 indices spread of seasonal influenza. However, regardless of for epidemic curves, and Tosei and Sanpachi regions, the snow, children go to their nursery, preschool, or which are relatively urbanized area of Aomori prefec- elementary school just as adults go to their workplace. ture connected with Shinkansen, formed another In this study, the minimum temperature and temper- cluster in 3 of the 4 indices. ature difference correlated with epidemic onset, dura- This study has some study limitations. This study tion, and scale. Specifically, minimum temperature and was a local, not global, study conducted using local temperature difference in December were correlated meteorological and demographic data of Aomori pre- with epidemic onset (CC, 0.309 and －0.397, respec- fecture, Japan. And this study did not include analysis tively), which may be because the seasonal influenza of several important factors (e.g., vaccination rate, epidemics start in December in many cases. Epidemic educational activities for infection control, consump- duration was correlated with the daily minimum tem- tion rate of hand disinfectant products, and traffic perature (CC ＝－0.504) and temperature difference density) that may affect the epidemic curve of seasonal (CC ＝ 0.435) in January, which is the coldest month of influenza, because we could not obtain sufficient data theyear.Theepidemicscalewascorrelatedwiththe representing these factors foreachregionofAomori minimum temperature (CC ＝－0.526) and tempera- prefecture. ture difference (CC ＝ 0.606) in January. Low tempera- In conclusion, the results of this study indicate the ture causes cold stress, which depresses immune importance of local and socialfactorssuchasclimate, responses (13,14). Furthermore, cold stress due to tem- population composition, income, and urbanization, for peratures ＜09C causes death from respiratory disease seasonal influenza epidemics. It would be beneficial to in humans (15,16). The atmospheric temperature dif- determine the regional differences of these factors in ference also affects humans. Medium-term (7–10 days) order to develop effective preventive countermeasures and long-term ( 3 weeks) atmospheric temperature for seasonal influenza epidemics and other communica- changes were associated with highly significant changes ble diseases. in rates of death from pneumonia (17,18). Constantly low temperature and a large temperature difference Acknowledgments The authors thank Ms. Kanako Aoyagi and may lead to a longer duration of epidemics. The lowest Ms. Tomoko Hamaya for their suggestions regarding the figures in temperature and largest temperature difference in a this report. month may cause epidemics on a large scale. Addition- ally, a high maximum temperature in December could Conflicts of interest None to declare. cause a gentle epidemic curve slope. 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