2019 | Feb
The pandemic flu A(H1N1) pdm09 of 2009, which was initially detected in Mexico and rapidly spread throughout the globe, has been classified among pandemics as the quickest in modern human history. On the 10th of August 2010, the World Health Organization (WHO) announced that the virus H1N1 had completed its circle and the pandemic was finally over (WHO, 2009). In Greece, the first case of the flu A(H1Ν1) was reported on 18th May 2009. It involved a patient, aged 19, who had just returned from a trip to New York two days earlier (Panagiotopoulos et al. 2009).
Glezen (1996) compares the pandemics H1N1 of the years 1892, 1918, 1936,1957 per age and concludes from reviewing the special infection rate per age, that children of school age typically have the highest rates of infection during different pandemics (and interpandemic) periods. What is more, children play a significant role in the transmission of the virus in the general public due to their gathering at school and the fact that they fail to wash hands with soap and dry them with clean paper or air. In the current paper, for the first time in Greece, a mapping of school absences is formed in Primary Education for Attiki per Kapodistrian Municipality as we hold the notion that school absences are directly connected with the spread of the epidemic, the flu H1N1. The analysis was based on weekly data from 13/10 to 17/12 of the year 2009. An analysis of spatial autocorrelation was conducted by calculating the total and spatial rate Moran’s I for the ten week study. The spatial rates were classified and presented in the form of spatial standard maps, on which the areas where an outbreak of the phenomenon is noticed, are featured. These maps show the existence of spatial autocorrelation for the three most crucial weeks of analysis of the phenomenon as we will see further down. Furthermore, the chart of the average rates of pupil absences directly visualizes the development of the phenomenon as it also does with the counterpart check of statistical significant difference among the average rates. The location of municipalities with a high rate of absences can abet the head of health prevention policy in making more notified decisions (Ministry of Health, HCDCP, primary education). Even though the analysis concerns data of 2009, which was compiled for this purpose, the cases of the virus A, continue every year marking this analysis as up-to-date.As so, the total handling of incidents and deaths due to the virus H1N1 per region (Δούκισσας κ.α., 2016α) in combination with the school absences may be used as a future tool of clearer management and coordination of a potential new pandemic.
In the current assignment, data from HCDCP has been used (weekly reports of epidemiological surveillance of the flu during the period 2009 – 2010) __which can be found at the electronic address www.Keelpno.gr. Furthermore, the population data resulted from the census in 2011 which was conducted by the Greek Statistic authority (www. statistics.gr). The data concerning the absences of students in Primary and Secondary Education was gathered from the National School of Public Health. This data, was gathered in close collaboration with the Institute of Technology, Computer, and Education) and is supervised by the Ministry of Education, Research and Religion. More specifically, the data concern the total of absences which were made by enrolled students, the time period between 25/9/2009 and 17/11/2009 on a daily basis. The recording of absences occurred in every school in Greece and included all levels of education and all types of schools. The data is organized in tables. Every entry on the table concerns the total absences for (the absences due to the virus are not documented individually in the system) and it includes consisted a0 the code of each school, b) the date of documentation of absences, c) the number of students absent per class , and e)the total of active students in each class. Initially a check was made as to the thoroughness and validity of the recordings of each entry. There was a very small number of entries which did not correspond to a school via its code and for this reason were excluded. We created cross questions and after its execution it was noticed that in some entries the municipality in which the school belongs to, was not that which resulted from its code. Therefore, the necessary corrections were made and then moved on to the editing of the data. By creating sequential queries we were able to create the final inquiry, By leaving out Monday and Friday due to increase deviation (in order to avoid false observation) the final variable was calculated which is the average rate of absences during the days Tuesday -Wednesday and Thursday, leaving out the days that schools are closed (public holiday like 28/10 or 17/11). The geographical detail of the variable is local authority Kapodistria. The analysis in the current article concerns Attica only.
The major difference from the pandemic between the years 1999 to 2009 and the pandemic of 2009 was the actual age group infected in morbidity and deaths (ECDC, 2010). The pandemic of the virus A(H1N1) affected the younger age group and mostly school students (Health Protection in Midlands, 2009) not to mention, households (Nishiura et al, 2009). In Greece, it is calculated that the clinical rate of infection was higher for children between the ages 5 -19 years old but was much smaller for those who were over 64 years of age. It is generally estimated that approximately 19.7% percent of the Greek population was infected (HCDC, 2010). By calculating the morbidity rate (Tsimpos, 2004) concerning the virus A(H1N1) per 100.000 residents we noticed a rapid increase of incidents up to the age of 15. To be more specific, up to the age of 5, 200 cases per 100.000 residents were documented. The numbers showed a rise (300 cases) with a minor change among the age group 5 – 10. Furthermore, the increase among the age group 10-15 is quite impressive and holds the highest rate (500 cases/100.00). This data proves that children, mostly in primary schools, constitute the initial cluster of the virus A(H1N1) (Doukissas et al., 2016). The pandemic reached its peak between the 48th and 49th week of 2009 (Maltezou et al., 2011).
The thematic mapping deals with the creation and studying of thematic maps. In the thematic map, what actually interests us is the spatial distribution of the data whereas the positions and distances of the spatial units are relative and symbolic. The most common thematic map is the choropleth map. One way of exploring the spatial dependence is by using techniques which examine the rates of different spatial units. Goodchild (1987) mentions that in general, spatial autocorrelation is interested in finding out to what extent a variable recorded in one spot is similar to the recorded variables of the same variable which is geographically close to it. In other words, spatial autocorrelation is an evaluation of the spatial structure of a variable concerning the spatial position of its rate. The establishment of spatial standards holds the greatest interest from the perspective of coming to a conclusion concerning the spatial distribution of the variable.
Results of spatial analysis
In figure 1 (Maps 1-6) as well as in figure 2 (Maps 7-12)the mapping is shown per week (from the 42nd to the 51st week of the year 2009)of the absence percentage at primary education in Attiki.. The numbering begins from top left to bottom right. The phenomenon begins to heighten in the Western part of Attiki, especially in the municipality of Ano Liosia and the community of Krioneri. During the 44th week, we notice a peak in absences mostly in the municipalities of Kruoneri, Kamatero, Peristeri, Filothei, Kropiou and Markopoulou and municipality of Kapandriti. Of course, it is important to mention that the 44th week was the week of a national holiday celebrated on the 28th October, therefore a high number of absences may have been due to the National holiday. The last map from Figure 1 which documents data of the 46th week shows rather high rates in the Western part of Attiki Zefiriou, Mandras, Erithron, Geraka and the communities of Krioneri and Afidnon. Lastly, we can notice a high percentage of absences in the Southern part of Attiki as well, in the area of Ag. Constandinou.
Figure 1: Mapping of the absentee rates in primary schools during 13/10 to 12/11/2009 in Attiki (Maps 1- 6)
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