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HESEINT, main conclusions PDF Print E-mail
Written by Administrator   
Tuesday, 14 August 2012

Our study has four main conclusions:

  1. The situation of indoor air quality in schools hasn't changed in the last 6 years, from the time of the HESE study. In particular, poor ventilation remain a major problem in school buildings which have been built or refurbished without attention to this issue.
  2. A CO2 alarm can be used as an inexpensive and simple tool to improve ventilation. However, it has several limitations both in usability and in effectiveness. Its effect is particularly limited when the external conditions are non-optimal (winter, outdoor pollution and/or noise) and the ventilation in the classroom is poorest: in these conditions, a conflict creates between the need of keeping the windows open for longer times (to correct ventilation), and the entrance of cold weather and pollutants. However, when the outdoor conditions are fair and the teacher is compliant, it can contribute to maintain a better air quality, to improve awareness of children on the issues of environment and health, and to improve wellbeing and satisfaction.
  3. Schools are a complex arena with many different agents. Any intervention to improve the indoor environment in schools requires the active involvement of these different communities and cannot be simply implemented with a purely technical interventions. Luckily, in all these communities there is great interest for the issues of a healthy school environment, so  their involvement should be possible. Guidelines could be a useful tool for empowering schools to develop local interventions, provided that they are deveoled specifically with the school arena in mind. The AGREE instrument can be used in the process of improving guidelines to increase their applicability.
  4. Interactions among multi-pollutants and  different exposures at school, home and outdoor can be modeled and investigated.

An additional important lesson learned concerns the design of studies for the assessment of methods to improve ventilation. In social studies, researchers should be wary of the risk of contamination, that is that the intervention unintentionally also affects the controls. We demonstrated that for a limited intervention, such as the CO2 alarm, contamination is unlikely to occur and a strategy comparing control and intervention classrooms in the same schools is preferable to one comparing intervention and control in different buildings.

All these experiences and information, included the developments in the modeling of interaction of exposures have been continued after EC co-funding, and will be useful to sustain future research

Last Updated ( Wednesday, 15 August 2012 )
 
HESEINT, the results PDF Print E-mail
Written by Administrator   
Tuesday, 14 August 2012

Persistence of problems:

The HESE study, conducted in 2004, showed that IAQ problems, especially poor ventilation, were widespread in European schools. Our preliminary aim was to investigate whether the situation could have been changed 6 year later. To this aim, we repeated the same investigations in 8 schools from two centers which had been investigated in the HESE study.

a) Questionnaire data

The percentages of pupils reporting symptoms was non significantly different from those observed in the HESE study in these two centers. Worth of note is that the percentage of asthmatic children reporting asthmatic attacks at school is double compared to the report of their parents (p=0.018, Fisher exact test), suggesting that many asthma attacks at school are unknown to parents. Also notable in the fact that more than 50% of the children still report to be exposed to second hand tobacco smoke in the last 7 days, although only 5 (from 3 different schools) reported to have been exposed at school.
Overall, these limited data are in agreement with the results of larger studies (Such as the SIDRIA/ISAAC study, Galassi et al Pediatrics. 2006;117:34-42 ) indicating that the prevalence respiratory diseases in schoolchildred has remained relatively stable in the last decades.

b) Environmental measures

CO2 was monitored in each class during schooltime for an average time of 1.5 hours at the beginning of the study. Results are shown in the following graph:

co2
Figure 1. CO2 levels in classrooms


As it can be seen, all the classes except one in Udine presented mean levels of CO2 above the recommended limit of 1000 ppm.
Temperature was recorded indoor and outside for the same period:

Temp
Figure 2: Baseline levels of indoor and outdoor temperature


Thus, temperature indoor was within comfortable levels.
Similar data were obtained for relative humidity:

Rh
Figure 3: Baseline levels of indoor and outdoor humidity

 Thus, the level of humidity was slightly higher in Siena, reflecting the outdoor level.
The levels of PM10 were recorded for a similar period:


pm10
Figure 4: Baseline levels of PM10 indoor and outdoor t

PM10 levels indoor were similar or higher that outdoor. The levels were higher than the recommended limit of 50 µg/m3 in 10 of 16 classrooms.
 
Ultrafine particle were also measured at the beginning of the study:

UFP
Figure 5: Baseline levels of Ultrafine Particles indoor and outdoor


Ultrafine particles where overall within reasonable level, except that high values were found in a school in Udine.
Finally, illumination was measured in each classroom during schooltime, averaging 20-30 measurements taken in different parts of the room (both close and far from window):
Lux
Figure 6: Classroom illumination

All the classes except one had levels of illumination above 300 lux, the minimum lever requested for listening to the teacher. In more than half, however (9/16), the level of illumination was lower than 500 lux, the minimum recommended level for reading and writing at the bench or at the blackboard.
In conclusion,  poor ventilation is widespread, levels of PM10 are often high, illumination is suboptimal. The heating systems appear to compensate well external changes in temperature, but not humidity. The levels of ultrafine particles are usually low, but high values are occasionally encountered.
These were exactly the same findings that we found in the HESE study in 2004, so we conclude that the environmental conditions aren't changed during this time, and the same problems persist.
Slightly better results were observed about school policies: two of the schools reported to have a policy for the administration of drugs to asthmatic children by the school personnel  (in one case also before exercise) on request of the family and directions by the family physician, and two a written policy about cleaning activities. All the schools declared to be smoke-free inside, although smoking was allowed outdoor. Still, none had a school nurse available at any time, had a registry of asthmatic children, or provided education on asthma to children, teachers and schoolworkers including schoolbus drivers. Most of the schools had problems with water leaks, although not in the classrooms (mostly in bathrooms or corridors), and some had visible moulds or smell. Thus, none of the schools still would qualify as «Asthma friendly».


 Use of a CO2 alarm to improve ventilation

The main objective of the study was the evaluation of a CO2 alarm as a tool to improve ventilation in the classrooms. Since it was unclear whether it was better to compare one class with the alarm with one without in the same school (assuring more similar baseline conditions, but with the risk of contamination, i.e. that also the control class would improve the awareness and start opening the windows more often) or to use different schools as either intervention or control, we used in the two centers two different study design. In Siena, after been assured that the did not share any teacher, we selected in each school one class for the intervention and the other as control.  In Udine, we randomized two schools for the intervention (giving the CO2 alarm to both classes) and two as controls. We will see the results separately.

Results of the effect of the CO2 alarm in Siena

The results on the levels of CO2 in the class during schooltime (from 8.30 to 12.30) in the 4 school in Siena are shown in Fig 7.


co2si
Figure 7: Levels of CO2 over 2 weeks in 4 schools in Siena, in classrooms with (Y) or without (N) alarm.

It is apparent how the median levels in the class with the alarm (Y) are always lower than the correspondent control class (N) of the same school. Indeed the difference is statistically significant, with a mean difference of 646 ±264 ppm (p=0.014 with a robust correction clustering by school). Nightime levels (midnight to 7AM) where similar in classrooms with or without alarm (not shown), indicating that this difference was not due to technical artefacts.
Recordings of temperature and humidity in the same schools are shown in fig 8 and 9: no differences are apparent between the classrooms with or without alarm.


t_si
Figure 8: Temperature during schooltime over 2 weeks in Siena in classrooms with (Y) and without (N) the CO2 alarm



Classroom humidity during schooltime over 2 weeks in 4 school in Siena.

rh_si
Figure 9: Relative humidity over 2 weeks in Siena, in classroom with (Y) or without (N) alarm

Thus, in Siena we documented that the CO2 alarm was able to significantly improve classroom ventilation, without significant effects on temperture and humidity.

 

Results of the effect of the CO2 alarm in Udine

Two weeks CO2 recordings during schooltime in the four school in Udine are shown in the figure 10. Surprisingly, the two schools with the alarm have higher levels of CO2 than the ones without.

co2ud
Figure 10: Levels of CO2 in Udine over 2 weeks, in schools with (Y) or without (N) CO2 alarm


Also in Udine, classroom temperature temperature does not change between schools with or without alarm. Visible in the next graph are several outlier points of lower temperature in the schools with the alarm, possibly indicating a fall in temperature consequent to the opening of the windows. Inspection of the tracings of CO2 recordings confirmed the presence of  drips in CO2 concentration, indicating opening of the windows.

t_ud
Figure 11: Clasroom temperature in Udine, in schools with (y) or without (N) alarm


Our explanation is that the two schools randomly selected for the intervention had poorer baseline ventilation compared to the controls. Nevertheless, even in this case it is evident that the CO2 alarm is not sufficient to lower the CO2 concentration even at the baseline level of the untreated ones.


To explain these results, we can have a closer look to some of the results obtained in Siena. The next graph shows the continuous tracings of CO2 during one morning in one classroom with the alarm (right) and in the paired classroom of the same school.

co2day
Figure 12: Continuous tracing of CO2 levels on the same day in two classroom of the same school in Siena, with (right) and without (left) CO2 Alarm


The levels of CO2 raise steadily in the control classroom (reaching very high values), while in the other they go down several times corresponding to the opening of the windows. Also clear is that the effect of opening the windows is only temporary, as the levels start to raise again as they are closed. It is also interesting to look at the temperature tracings in the same day, shown in the next graph.
t_day
Figure 13: Continuous tracing of temperature in the same day and classrooms of figure 12

Indeed, the temperature in the classroom decreases with the opening of the windows. If this was acceptable in this case, with external temperatures above 20°, it might be a problem with lower external temperatures.
Next graph shows the levels of CO2 at different hours in a school in Siena during one week  in which the alarm was initially cautiously set to 2000 ppm.
alarm2000
Figure 14: Levels of CO2 in two classrooms in Siena, one without (blue) and one with (pink) the CO2 alarm, set for a treshold of 2000 ppm

After discussing with the teacher, she accepted to lower the alarm to 1500 ppm in the second week, shown in the next graph.
alarm1500
Figure 15: Levels of CO2 in the following week in the same classrooms of figure 14, with the alarm set to a treshold of 1500 ppm

As it can be seen, the results improved, but this required a cautious, gradual approach.


Effects of the CO2 alarm on other pollutants

Ozone was measured with a passive sampler over the whole period of two weeks. Results are shown in the next graph.

o3
Figure 16: Indoor and outdoor levels of ozone over two weeks in the two study centers

The levels of ozone in the classrooms where consistently lower than outdoors both in Siena and Udine, without differences between the two centers or the use of the alarm.
Levels of NO2 were recorded with the same approach and are shown in the next graph.
no2
Figure 17: Indoor and outdoor levels of NO2 over two weeks in the two study centers

Again, no differences were seen between the two centers or according the use of the alarm.
Formaldehyde was measured with a passive sampler exposed during the first week.
Form
Figure 18: Indoor and outdoor levels of ozone over one week in the two study centers

Indoor levels of Formaldehyde were higher in Siena than in Udine, but always well below the recommended threshold of 64 µg/m3. Again, no differences were seen with the use of the alarm.
Total bacteria were sampled over a period of 4 hrs:
totbact
Figure 19: Indoor and outdoor levels of airborne bacteria in the two study centers

Despite some trend, no significant differences were observed. Viable bacteria were sampled with the same method:
vbact
Figure 20: Indoor and outdoor levels of airborne viable bacteria in the two study centers

Again, no differences were observed with the use of the alarm.
Similar results were observed for total moulds, sampled with the same method:
totm
Figure 21: Indoor and outdoor levels of airborne moulds in the two study centers

Finally, the levels of viable moulds in the air were higher in some schools in Siena than in Udine, again without differences with the CO2 alarm:
vmould
Figure 22: Indoor and outdoor levels of airborne viable moulds in the two study centers


 While all these measurement were performed during the use of the alarm, respirable particles were measured using a particle counter for a period of 1.5 hours before the study and in the last day of use of the alarm. Results of PM10 (Dust-trak II, TSI) are shown:
pm10ii
Figure 23: Mean levels of PM10 before (blue) and at the end (pink) of the period of intervention, in classrooms with or without CO2 alarm

Outdoor values of PM10 were higher in the final assessment (pink), well above the recommended limit of 50 µg/m3. Nevertheless, indoor levels were similar in the two groups.

Ultrafine particles (p-Trak, TSI) are shown in the next graph.
UFPii
Figure 24: Mean levels of Ultrafine particles before and at the end of the period of intervention, in classrooms with or without CO2 alarm

Again, high levels were found in a single school in Udine, but overall there were no significant changes between the two periods.

Effects on health and perceptions

The follow-up questionnaire for the pupils contained a number of questions regarding the occurrence of a variety of clinical manifestations during the two weeks of the study. It also contained questions about the perceptions about the quality of the classroom environment and (limited to the classes where the alarm was used) about its use. It also had a space for free comments. Since we could not observe any difference in these questions between the two centers, we report all together for clarity.

The percentage of children reporting to have had a number of  health problems during the two weeks is reported in the table.

 Symptoms or diseaseControl
Alarm
Skin rash hands or forearm
 3.1 ±2.1% 4.5 ±2.6%
 Skin rash face or throat 1.5 ±1.5% 1.5 ±1.5%
 Eczema 3.1 ±2.1% 4.5 ±2.6%
 Itchy face or throat 9.2 ±3.6% 12.1 ±4.0%
 Itchy hands or forearm 10.8 ±3.8% 6.1 ±2.9%
 Burning eyes 18.5 ±4.8% 18.2 ±4.7%
 Itchy eyes 18.5 ±4.8% 13.6 ±4.2%
 Dry eyes 9.2 ±3.6% 13.6 ±4.2%
 Sandy eyes 10.8 ±3.8% 12.1 ±4.0%
 Red eyes 15.4 ±4.5% 16.6 ±4.5%
 Swollen eyes 7.7 ±3.3% 6.1 ±2.9%
 Itchy nose 30.8 ±5.7% 37.9 ±6.0%
 Runny nose 27.8 ±5.6% 21.2 ±5.0%
 Sneezes 60.0 ±6.1%
 68.2 ±5.7%
 Stuffy nose 29.2 ±5.6% 34.8 ±5.9%
 Dry throat 27.7 ±5.5% 18.2 ±4.7%
 Sore throat 38.5 ±6.% 24.2 ±5.3%
 Dry cough 13.8 ±4.2%
 19.7 ±4.9%
 Difficult breathing 18.5 ±4.8%

 4.5 ±2.6% (P=0.039)

 Feeling a cold arriving 29.2 ±5.6% 31.8 ±5.7%
 Headache 30.7 ±5.7% 39.4 ±6.0%
 Feeling sick 21.5 ±5.1% 16.7 ±4.6%
 Fatigue 26.2 ±5.4% 27.3 ±5.5%
 Got a cold 38.4 ±6.0% 36.4 ±5.9%
 Got flu or fever 18.4 ±4.8% 13.6 ±4.2%
 Wheezing 4.6 ±2.6% 3.0 ±2.1%
 Wheezing dyspnea 4.6 ±2.6% 9.1 ±3.5%












































There was no statistical difference in the occurrence of any of these symptoms between the children of the classrooms where the CO2 alarm was used or no, except for «difficult breathing». Also this difference, however, disappeared after applying a robust correction clustering by school. Furthermore, it seems inconsistent with other responses (such as the ones on wheezing). It seems therefore quite possible that it is a chance finding (possibly related to the "healthy air" effect induced by the CO2 alarm, see below), and it seems safe to conclude that the use of the CO2 alarm doesn't have major immediate effects on the health of the majority of children.

Perceived effects of the CO2 alarm

To assess the perception of indoor air quality we used two different approaches.
One question asked how the child evaluated air quality in the classroom during the study, using a discrete ordinal scale. The answers are reported below.

How was air quality in the classroom in the last 2 weeks?
                     Control       Alarm
                
Very poor    1.6%            0.0%
                
Fairly poor    4.7%           4.6%
                
Fairly good    67.2%        63.6%
                
Very good    21.9%         30.3%

As it can be seen, there was no difference between the two groups, nor there was any statistical relation between these answers and the level of CO2 or any other environmental variable measured in the classroom.

The second approach, was to use Visual Analog Scales to evaluate the perception of air quality, humidity, and the presence of dust in the classroom. In this case the child was invited to put a mark between the two extremes of a 10 cm line, on a continuous scale. The results are depicted below:


vas
Figure 25: Children perception of IAQ, humidity and dust in the classoom during the two weeks of the intervention, measured on a 10 cm VAS. Note the wide scatter in the scores.


The answers were very scattered, with many children polarizing to the extremes values, and little coherence even among children of the same classroom. The final result is that there are no statistical differences between the two groups, but our main conclusion is that the perceptions of children are not consistent in the evaluation of these factors (this may apply to adults as well).

The situation changes, however, when considering two further questions, asking about the perception of changes in air quality and wellbeing during the two weeks of the experiment.






                          Iaq change          Wellbeing
                          Control  Alarm    Control    Alarm
    Much worst    0.0%    0.0%    4.7%        0.0%    
            
    A little worst    7.8%   3.0%     3.1%        6.1%
                
    No change       46.9%  24.2%  56.3%    28.8%
                
    A little better     28.1%  39.4%  21.9%    33.3%
                
    Much improved  17.2%  33.3% 14.1%    31.8%    

In this case, children who had used the CO2 alarm reported a significantly greater improvement than the controls (p= 0.0016 and 0.0014, respectively, Wilkoxon rank-sum test). It must be remembered that our experiment had no control for a placebo effect, thus the children could be influenced by the experience of having a new gadget rather than by its real effects on the environment. Nevertheless these data strongly support the acceptability of the CO2 alarm by the classroom.

This is confirmed by the specific questions about the experience with the alarm (asked only to the classroom who had used it).

How useful is the CO2 alarm?    

Completely useless    1.5%

Quite useless             3.0%

Neutral                       3.0%

Somewhat useful     34.9%

Very useful              57.6%


How much disturbance causes the CO2 alarm?

Not at all       60.6%

A little           28.8%

Very much   10.6%

 

How often did the alarm ring?

Seldom                21.2%

Rarely enough    21.2%

Quite often          51.5%

Very often            6.1%

 

Thus, more than 90% thought that it was useful, and over 60% were not disturbed at all., despite the fact that the alarm ringed often.

The free comments where equally divided between enthusiastic comments on the alarm and the study personnel who had given it to them, and a similar number of children complaining about the cold when opening the windows (also in the Schools of Siena).  


Similar data were provided by the teachers.
 While all the teachers of the control classrooms reported air quality and wellbeing as unchanged during the two weeks, most of the teachers of the intervention classroom reported that thy had increased, with the majority reporting them as «much improved». Unlike the children, all of the teachers reported that it caused a little disturbance, and also reported a higher frequency of ring alarm, with most teachers reporting that it rang at least three times per day, and in one case more than once per hour. Finally, some teachers reported that it was impossible to open the window, because of weather or noise, up to 50%  of the times when the alarm rang.

 

In conclusion, our data show that the CO2 alarm is acceptable (and in many cases enthusiastically accepted) in the classroom as a tool to improve ventilation. We also provide evidence that it has severe limitations, due to limited effectiveness, its effect being transient, particularly in poorly ventilated rooms, and to outside conditions, such as pollution, noise and weather. Still, it could be an inexpensive and effective palliative tool in less polluted areas during the fair seasons.
However, we did not demonstrate any positive (or negative) effects on symptoms, possibly because the relatively small sample and duration of the study. Indeed, the main expected effects of improving ventilation would be a reduction of asthmatic attacks and of contagious viral infections, due to a reduction in the concentration of dust allergens and virus in indoor air, two outcomes that would require a much longer observation to be evaluated. We also failed to observe any effect of indoor air quality, except for a reduction of CO2 (only in one center) and the improvement of the subjective perception by children and teachers, which could be due to a placebo effect. Indeed, one would expect that the concentration of individual pollutants would became more similar to outdoor air in the classrooms, but this didn't occur in our experiments. This actually occurred for measurement of bacteria and moulds, whose level showed a (nonsignificant) trend to equilibrate with the outdoor air in the classrooms using the alarm. However, while for bacteria this means a reduction in the concentration in the classroom, for moulds, whose level was higher outside, means an increase, that could not be desirable. Given the higher pathogenic potential of bacteria (and of virus, that we haven't measured but could expected to behave similarly), the balance could still considered positive. Indeed, although the presence of moulds in buildings is consistently associated with respiratory symptoms, this is relative to moulds originating from within the building, because of excessive humidity, and not from outside (except possibly in the alternaria season, for the occasional allergic subject). More troublesome could be entrance of pollens, that could limit the use of the device since the end of winter (when in Italy clinically relevant pollens appear: parietaria in the South, cypress in the center, birch in the North) and mostly from spring to the end of the school, as it is highly likely that at least one child allergic to grass pollen will be present in every classroom.
For all the other pollutants (ozone, NO2, formaldehyde), no trend could be observed to equilibrate their concentrations with outdoor air (which for the first two, which are higher outside, could be a positive finding). It must be noted, however, that the passive samplers were exposed continuously for two weeks, including night and holidays,  while the alarm was used only for a few hours during schooldays. Therefore, the reading of the passive samplers mostly represent times in which there was no ventilation and it was unlikely that was influenced by the short time in which the alarm was used. This is important for future studies, suggesting that strategies should be sought to limit the sampling during school hours, by opening the samplers only during schooltime (that would be feasible but somewhat cumbersome) or using different types of detectors providing time point exposure data.
Finally, we provide evidence that a study design randomizing intervention and control classrooms in the same school is more efficient than one randomizing separate schools. The main reason for using the latter strategy is the fear of contamination, that is that the presence of the CO2 alarm in one class would influence the control class, making them to increase ventilation as well. The effect of contamination, however, would be to reduce the differences between intervention and control. The fact that we could detect an effect in the center using a same school approach (where contamination could occur) but not in the one using the parallel design, demonstrated that the risk of contamination, if any, is lower than the risk of comparing schools with different baseline conditions.
We consider all these findings as significant improvements for future research.

Promotion of further interventions to improve school regulations about indoor air quality.

When interviewed about possible interventions to promote and support improvement of air quality, most school principals have been very clear that it would have been impossible to implement a standardized protocol for intervention applicable to all the schools. Schools are very busy with a variety of projects, and their interest and ability to participate to such a program would vary even within the same area. Furthermore, responsibility for key activities relevant to air quality, such as cleaning, cooking and building maintenance are heterogeneously distributed over a variety of different levels beyond the schools. Cooking is done in house in some cases, and centralized or externalized in others. Cleaning can also be externalized with contracts on which the school has little control. School buildings are often the propriety of local administrations, not of the schools, that therefore have little control on maintenance and gardening. Organization of specific programs for asthmatic children would require collaboration with local healthcare system and family doctors, whose availability varies. Thus, any intervention should be specifically tailored for the local conditions, availability and resources, requiring interaction with a variety of agents. If we found such a variability in only one country, this can only increase at the European level.
We speculated that in these conditions, our attempt should have been to provide the schools with tools that they can use to build their own intervention.  In clinical medicine, this is normally the role of clinical practice guidelines, «systematically developed statements to assist practitioner and patient decisions about appropriate health care for specific clinical circumstances.» (Field MJ, Lohr KN. Institute of Medicine. Committee on Clinical Practice Guidelines. Division of Health Care Services. Guidelines for clinical practice: from development to use. Washington: National Academy Press, 1992). Indeed, guidelines on indoor air quality in schools are available in several European countries, including Italy ("Linee di indirizzo per la prevenzione nelle scuole dei fattori di rischio indoor per allergie ed asma", endorsed by the Italian National and Regional governments). However, while the role of guidelines is reasonably established in clinical medicine, they are relatively new in Public Health. For such guidelines to work in Public Health like in clinical medicine, then, prospective users should be given the same tools that physician use to evaluate and be prepared to apply them. The major tool used by physician worldwide to assess clinical practice guideline in view of their application is the AGREE instrument,  
This is a simple, valid and reliable checklist that comprises 23 items organized into 6 quality domains: i) scope and purpose; ii) stakeholder involvement; iii) rigour of development; iv) clarity of presentation; v) applicability; and vi) editorial independence.   Each of the 23 items targets various aspects of practice guideline quality.  The AGREE II also includes 2 final overall assessment items that requires the appraiser to make overall judgments of the practice guideline and considering how they rated the 23 items. Each item is rated on a  likert scale, ranging from 1 (strongly disagree) to 7 (strongly agree). The score of each domain is computed as the average of the items belonging to it. The instrument can be used by individual users or, more commonly, by a panel of prospective users of the guideline, making their evaluation available to other prospective users and to the developers of the guideline, to recursively promote an improvement in their quality..
The Italian guidelines were evaluated by 5 school principals, who were previously briefed on the use of the AGREE instrument according to the User' Manual, which is part of the complete AGREE II document or «package» and includes specific information and guidance for each of the 23 items.
All the principals reported the exercise as useful and informative. They also felt confident in answering all the questions except the ones about «rigour of development», that contained technical jargon and concepts from clinical medicine largely unknown by educators (such as systematic searches and critical appraisal of evidence). Concordance within the group of scorers was acceptable, with a Kendall tau of 0.58 (P<0.00001), and no systematic differences were detected among individual scores.
The results for the 6 domains are shown in the following figure:

agree
Figure 26: Scores of the 6 domains of the AGREE instrument of the Italian national guidelines on IAQ in schools, rated by school principals


As it can be seen, scores were high for the domains of aims and scope, clarity and editorial independence, they were very scattered for rigour of development, reflecting the poor understanding of the terms involved, and were low for involvement of stakeholders and applicability. The low scores for involvement are explained by the fact that the guideline does not provide information to answer two key question of this domain, «The guideline development group includes individuals from all relevant professional groups» and «The views and preferences of the target population have been sought».  Indeed, the composition of the group is not reported, and no mention is present about the views of teachers, students or families, so these items were given low scores. The domain of applicability contains four items: «The guideline describes facilitators and barriers to its application», «The guideline provides advice and/or tools on how the recommendations can be put into practice», «The potential resource implications of applying the recommendations have been considered», and «The guideline presents monitoring and/or auditing criteria». The scores are reported individually in the following graph:
Dim5
 

Thus, scores were low for all the items of this domain, particularly so for the one regarding the resources that are expected to be used to implement the guideline.
Despite this fact, all the scorers concluded that they would recommend the guideline for use, one as it is and four only with modifications.
In conclusion, we provide evidence that guidelines can be accepted by the school community and could used as tools to promote interventions in the complex arena of the schools. More importantly, we provide evidence that the AGREE instrument can be used also outside the clinical field to evaluate and to assist in the improvement of guidelines, so that they could be better accepted and implemented. Our results also indicate the need to identify better strategies for risk communication, to to make it more understandable by the school operators and ease the evaluation of questions in the domain of rigour of development. This could be done exploiting the experiences of decision-aid tools currently developed in the field of shared decision-making to improve patient communication and understanding in clinical medicine. This is the first time, to our knowledge, that the AGREE instrument was used outside the field of clinical medicine and Health Technology Assessment, and could consist in an important contribution to the development of translational research and technology transfer from scientific research to the complex arena of the schools.

Interactions between outdoors/domestic and school exposure

The aim of this part of the study consisted in the investigation of the contribution of outdoor air pollution to indoor air pollution, the considered indoor settings including the house and the school. For reaching this scope, the concentrations of outdoor air pollutants had to be estimated.
During the life of the project, three steps could be accomplished:

  • Modeling of outdoor air pollution: finalisation of the protocol and of the methodology
  • Investigation of the variability of indoor and outdoor air pollutants
  • Taking into account of multi-pollution

 

Modeling of outdoor air pollution


In WP7, it had been planned to estimate the concentrations of outdoor air pollutants through a model able to estimate air pollution at proximity of an exact address in order to dispose of the concentrations of the air pollutants in the outdoor environments where children aged 10 years in mean spent most of their time, namely the house and the school. This in order to better take into account children' exposure to air pollution.

Although initially it had been planned to use the STREET model, we decided to use the ADMS model that has become more largely employed worldwide in particular by the Air Quality Monitoring Stations of many towns in Europe. This choice would have allowed international comparisons. To this extent, we proceeded to:

  1. list the schools recruited in the HESE Study and recorded their addresses;
  2. list the Air Quality Monitoring Stations in the towns participating in HESEINT and checked whether they would have been available to provide the HESEINT Study with the air pollution data at exact addresses. Provided outdoor pollutants should have included major urban air pollutants like particulate matter, NOx, NO2;
  3. GIS-locate the schools. The GIS-localisation of the schools was useful also to take aerial photographs of the schools and to determine their topographical characteristics; 
  4. recuperate the meteorological data for the year of the survey and the towns of interest;
  5. the protocol of the epidemiological and statistical analyses was decided. The analysis was intended to explore air pollution per se and in relation to child's health.
  6. Outdoor air pollution vs. indoor air pollution. Exposure to outdoor air pollution as expressed by major air pollutants' concentrations had to be considered as a continuous variable when studying the contribution of outdoor air to indoor air. Indoor/outdoor ratios would have been estimated for the air pollutants assessed indoors and outdoors. Successively, the cumulative exposure of the child to each pollutant would have been estimated as the sum of the concentrations of this pollutant in each settings time the number of hours spent in each settings
  7. Air pollution vs. health. Exposure to outdoor air pollution as expressed by major air pollutants' concentrations had to be considered as a continuous variable when investigating the relationship between exposure to outdoor air pollution and health. Considered outdoor pollutants included particulate matter, NOx, NO2. All the models relating outdoor air pollution to health would have to be adjusted on potential confounders
  8. Through the aerial photographs, we would have been able to identify potential air pollution at proximity of schools. We implemented a list of such hazards.

 

Investigation of the variability of indoor and outdoor air pollutants


We were interested in the variability of air pollutants. So far, there is a lack of sufficient literature reporting the variability of air pollutants in the case of different sources indoors and outdoors. The HESEINT study would have been perfect to investigate it because of the availability of indoor and outdoor assessments of air pollution. We then developed the methodology.

We have computed the between and within school variability of indoor air pollutants' concentrations objectively measured within the classrooms of each school as well as in the school proximity. From these estimates, the intra-class correlation (ICC) coefficients, which measure the similarity of the measured air pollutants' concentrations within the same school (and city) were computed to investigate similarities among pollutants with respect to the site of measurement.
We applied a linear mixed-effects model including number of classrooms, number of schools,number of cities, the measured pollution concentration of compound Y in classroom i within school j within city k, the intercept or overall mean of compound Y, the deviation from  due to the effect of city k, the deviation from city k mean due to the effect of school j,  the deviation from school j due to the effect of classroom i, either as  fixed- or  random-effects.
The variance components are the level-1 or between-classes (error) variance,  the level-2 or between-schools variance,  the level-3 or between-cities variance, and the total variance is equal to the sum of the three variances,  The ICC coefficients are computed as:
For classrooms (in schools within cities): measures similarity of classrooms within the same school (and city).
For classrooms (in cities): measures similarity of classrooms within the same city.
For schools (in cities): measures similarity of schools with the same city.
Statistical analysis was performed by SAS version 9.2 or higher (SAS Institute, NC, Cary, USA) using the 'MIXED' procedure.  The 'between' and 'within' variances for each pollutant were estimated by fitting models with pollutant concentration as the outcome, and (i) cities and (ii) schools nested within cities, as two separate random-effects, in the same model.  Compound symmetry covariance matrix was adopted for the outcome variable under the assumption of exchangeability of outcomes within the same cluster.  Restricted maximum likelihood method was used to estimate the variance components with Kenward and Roger method to estimate the denominator degrees of freedom.  The statistical significance of the random-effects variances was carried out based on a mixture of  distributions ( + ) on each occasion; i.e., by halving the P values corresponding to the Likelihood Ratio Test statistic difference between the 3-stage multilevel (full) model and the 2-stage multilevel (reduced) model without 'cities' random-effect (Singer, 1998; Snijders and Boskers, 1999; Verbeke and Molenberghs, 2000), and in a similar way tested for models without 'schools' random-effect versus the previous reduced model.
Lastly, assessments in the proximity of the schools were used to estimate the indoor/outdoor (I/O) ratios and correlations (rI/O) of air pollutants concentrations in order to better interpret the obtained results.
In the absence of the HESEINT data, this method was applied to other data very similar to HESEINT data. We have analysed the variability in the measurement of five indoor air pollutants: fine particulate matter of size < 2.5 µm (PM2.5), nitrogen dioxide (NO2), and three Volatile Organic Compounds (VOC), namely formaldehyde, acetaldehyde and acrolein, objectively measured over five days of a week at representative points in 401 classrooms of 109 schools and courtyards in six French cities spread out over all seasons of the year.  To this extent, separate 3-stage multilevel models were fitted to partition of the different variance components (i.e., classroom, school and city levels), and then intra-class correlation (ICC) coefficients were computed to bring out the similarities of pollutants' concentrations among these units.  The indoor PM2.5 and NO2 concentrations showed a high degree of similarity (ICC coefficients equal to 76% and 81%, respectively) between the classrooms of a school (and city), whereas the VOC (formaldehyde, acetaldehyde and acrolein) concentrations showed low to moderate degree of similarity (ICC coefficients equal to 25%, 36% and 57%, respectively) between the classrooms.  We conclude that to study the impact of indoor air pollutants and to minimise the effect of exposure misclassification, a multilevel approach taking into account the full design of the study would be the most appropriate. This work has been submitted to a journal (Banerjee as first author).

C) Taking into account of multi-pollution
Since air pollutants are highly correlated each other, it was planned to take into account the phenomenon of exposure to multi-pollution. The EPAR Department has an expertise in this field (cf Billionnet et al. Ann of Epidemiol 2012).

Thus, we provided significant theoretical progress it this field, which could be exploited in future research.

Last Updated ( Wednesday, 15 August 2012 )
 
HESEINT Work-packages PDF Print E-mail
Written by Administrator   
Monday, 29 August 2011

HESEINT is organized in 7 work-packages:

WP1. Coordination, Leader Piersante Sestini (UNISI)

This WP is responsible for the coordination of the study, together with the Steering Committee composed of the scientific coordinators of all the participating centers.

 

 WP2. Dissemination,  Leader Piersante Sestini (UNISI)

This WP was responsible for dissemination activities. Giovanni Viegi also participate in this WP with the valuable support of the Press Office of the Italian National Research Council (CNR)

 

WP3. Evaluation, Leader Piersante Sestini (UNISI)

This WP was responsible for monitoring the quality of processes, output  and outcomes of the study.

 

WP4. Survey, Leader Piersante Sestini (UNISI)

This WP was responsible for the organization of the local surveys and collection of questionnaire and clinical data.

 

WP5. Environmental Measures, leader Dan Norback (UU)

This WP was responsible for the collection and analysis of all the environmental data. It also provide technical personnel participating to the surveys for the collection of samples. It was the WP that contributed most to the project.

 

WP6. Biomarkers, leader Torben Sigsgaard (UA)

This WP was responsible for the measurement of biomarkers in samples collected during the surveys. Due to the insufficient sample size reached in the surveys, unfortunately had to remain mostly  idle.

 

WP7. Modeling, leader Isabella Annesi-Maesano (UPMC).

This WP was responsible for developing and testing a model to study the interaction of school, home, and outdoor exposure. Due to insufficient data from the study, the model developed in this WP was successfully tested on data from a different study performed in France.

 

 WP8. Database and analysis, leader Giovanni Viegi (CNR-IFC)

 This WP was responsible for the assembly of the database and statistical analyses.

 

 
HESEINT, the works PDF Print E-mail
Written by Administrator   
Sunday, 19 December 2010
The objectives of the study have been only partially reached. In particular, administrative problems and difficulties in obtaining ethical approval in two centers has delayed the survey in two countries beyond the time allowed for the study by the funding agency. A motivated request for extension was submitted, but was rejected for formal reasons as it was considered to be too late.
The study then closed having performed only 50% of the intended surveys. As a result, the sample (8 schools in tho centers, both in Italy) resulted underpowered for all the studies regarding clinical parameters and biomarkers (that were performed only in a random subsample of children in each class) and for a comparison among schools in change of practice. Therefore, these parts of the study were dropped and the samples that were collected in the two surveys performed were not analyzed. Also, the sample was too limited for the study of environmental interactions. In this case, however, the opportunity of the study was used for the theoretical development of the model, so, although we could not perform the actual analysis, these objectives have been partially achieved.
With these limitations, and with the limitations of a sample limited to two very different areas of a single country, we achieved most of the objectives that we had planned, concerning the persistence of problems, the effectiveness of a CO2 alarm as tool to improve ventilation, and the evaluation of interventions to improve school policies about indoor air quality.
 
HESEINT, The methods PDF Print E-mail
Written by Administrator   
Tuesday, 16 December 2008
We carried a field survey on air quality and symptoms in 8 primary schools in two different centers. Questionnaires were obtained both by the pupils and their parents. Environmental evaluation included a building inspection and measurement of levels of CO2, temperature, humidity, ozone, formaldehyde, NO2, total and viable molds and bacteria, PM10, PM0.1 (ultrafine particles), illumination. A questionnaire about school policies about IAQ and management of children with asthma was also obtained by each school.

Half of the classes were then given a CO2 monitor/alarm (TIM10 , CO2meter.com, Ormond Beach, FL).  This is a small alarm clock that, in addition to the data and time, also shows temperature, humidity and CO2 level. In addition it has a user-settable alarm that rings if the level of CO2 in the air trespasses a pre-determined threshold.  All the alarms were checked with a reference instrument (Q-trak, TSI) before use.
TIM10
The CO2 Alarm

On the first day, the class was briefed about the importance of a healthy environment, respiration, CO2, ventilation, and how to use the alarm. They were also shown how to open the windows to reduce the level of CO2 when the alarm ringed, opening also the door if needed to increase the air flow.
 While we initially planned to set the alarm to 1000 ppm (The limit recommended by ASHRAE and OSHA for optimal comfort), we quickly found that this was impossible in the majority of classrooms, causing the alarm to ring too often, disrupting school class activities, and forcing window opening for more time than acceptable. We therefore set the alarm to 1500 or 2000 ppm, depending on the local situation. Levels of CO2, temperature and humidity were continuously monitored for two weeks.
At the end of the period, the survey was repeated and the effects and acceptability of the intervention evaluated.
In addition, we interviewed school teachers and principals about the level of compliance of the school with current recommendations about air quality management and asthma friendliness, and about the opportunities, needed resources and possible barriers to their full implementation.
With the exception of two short questionnaires to evaluate the efficacy and acceptability of the  intervention with the CO2 alarm, all questionnaires and methods were the same as previously developed and used in the HESE study (DG-SANCO contract 2002/391), and reported in the technical report of that study. In addition, we used the AGREE-II instrument (www.agreetrust.org), an instrument developed and widely used by the healthcare community for the evaluation of clinical practice guideline, for the assessment of current National guidelines on indoor air quality in schools by school principals.
Last Updated ( Tuesday, 16 December 2008 )
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