Population exposure to CO: some Italian experiences€¦ · COHb in Emergency Room. n°ER-ABS=...

Population exposure to CO:some Italian experiences

Zauli Sajani S.1, Marchesi S. 1, Ottone M. 1, Veronesi A. 2, Percoraro V. 2, Trenti T. 2, Lauriola P. 1

1 Centre for Environment and Health, Arpae Emilia-Romagna (Italy)2 Local Health Authority of Modena (Italy)

Toronto, 9-10 November 2016

Ambient CO

The EXPOLIS project

Indoor /outdoor measurements in school

Air pollutants

• Nitrogen dioxide• Carbon monoxide• PM10• Benzene • Formaldehyde

Children exposure to CO: the SEARCH project


Indoor Outdoor Station 1 Station 2

Emilia-Romagna Region

Province of Modena

A database of COHb levels in general population

Population: 700,000 inhabitants Total area: 2,688 km2

• Provincial coverage (some 700.000 inhabitants)• About 10 millions bio-chemical assays per year• Availability of name, surname, date of birth, address of the patient for each exam• Availability of data from ABL90FLEX providing data of 17 parameters in real time (see below)

Data from ABL90FLEX

Electrolytes

Blood gas

pH

Metabolites

CO-oximetry


The database includes, for each subject, the :• age• sex• nationality• residence address• domicile address• clinical wards of the patients• fiscal code

Database offers the possibility of record linkage with other databases such as mortality data, geographical localization and the possibility of including only the first available test result for each patient in order to form a database of test results “on admission”.


The centralized laboratory executes 10 millions bio-chemical assays per year on average

- Data of both arterial blood gas (ABS) and venous blood gas (VBS)- COHb data available from October 2014- Some data come from the 9 emergency rooms (ERs) in the Province of Modena

2015 dataTotal COHb testsn°ABS= 50000, n°VBS= 40500, Total BGA n°= 90500COHb in Emergency Roomn°ER-ABS= 13200, n°ER-VBS=6600, ER-Total BGA n°=19800


Differences of COHb mean concentrations between areas and seasons

** Highly significant - Wilcoxson test

Mountain vs main city Mountain vs rural area

Winter vs summer

1.70 vs 1.43**

1.80 vs 0.87 **

1.70 vs 1.43 **

1.58 vs 1.52**

Winter vs summer (mountain area)

First results


Strengths

• Easy access to laboratory data retrieved by standardized methods and techniques

• Possibility of record linkage with mortality data, hospital admissions, emergency department visits, hyperbaric oxygen chamber data

• Possibility of comparison among high levels of COHb individuals and individuals who underwent HOBT treatment

• Possibility of geographical investigations

• Potential of prevention actions by alerting individuals with high levels of COHb but without CO poisoning diagnosis


Issues to be scrutinized• How much COHb is indicative of CO exposures?

• Is it possible to disentangle the various type of exposures causing high COHb levels?

• Is it possible to increase the value of the database by cross analyses of the various parameters? How?

• Would it be possible to identify smokers based on cross analyses?

• Would it be possible to identify endogenous and exogenous reasons for high levels of COHb?

• What’s the relative weight of indoor and outdoor sources of CO exposure?


We are finalizing a systematic review of the literature to evaluate the current evidence about the usefulness of COHb as biomarker of unintentional CO exposure

Search strategy, eligibility criteria and study selectionWe performed a systematic research using the following terms: ”carboxyhemoglobin”, “carbon monoxide” “poisoning”, “biological marker”, “blood oxygenation”, “blood oxygen saturation”, “blood gas analysis”, “population surveillance”, “public health surveillance”, “indoor air pollution”, and it was adapted to each database.The literature search identified 1183 bibliographic records

19 studies were included

Review on CO exposure and COHb levels in blood

Seven studies reported information about the air-CO concentration and COHb

Study air-CO (ppm) COHb (%) CorrelationSmokers/

total

Pollard 2014

RGa median=0.4UGb median=5.8

(p<0.01)

RG median=0.96UG median=1.56

p=0.04NA 1/104

Eppler 2013

Post-SIc mean=0.8 Pre-SIc mean=3.6

p<0.0001

Post-SI mean=1.18 CI 95% (1.12-1.25)Pre-SI mean=1.76 CI 95% (1.62-1.91)

p<0.001NA 0/35

Bono 2007

mean=2.98ppmmean=3.41mg/m3 (SD=1.38)

range=[1.65-5.70]ppm

Non-smoker group mean=0.6 (SD=0.3)Smoker group mean=2.4 (SD=1.8)

Only in non-smoker group significant relationship (p<0.05) between

Ln(air-CO mg/m3) and COHb%52/260

Keyes 2001

BCd median=2ACe median=19

p=0.005

BC median=0.3AC median=1.2

p<0.001NA 0/22

Amitai 1998

range=[17-100]mean=61 (SD=24)

range= [1-11]mean=4 (SD=3)

Pearson correlation testr= 0.39, p=0.01 14/92

Chung 1994

range= [0.6-11.5] (24h CO exposure)

mean=5.4 (SD=2.5)range=[1.2-5.0]

mean=2.2 (SD=0.8) COHb=1.34+0.18CO, R=0.55 0/30

Creswell 2015 range=[45-165]

HGf mean= 13.0 (SD=3.6)SGg mean =9.7 (SD=3.0)

p<0.001NA 1/91

a) RG= rural group, b) UG= urban group, c) SI= Stove installation, d) AC= before cooking, e) BC=after cooking, f) HG=hockey-player group, g) SG=spectator group

Review on CO exposure and COHb levels in blood

The data from Modena’s centralized laboratory for blood samplesseems promising in order to an easy and systematic access toCOHb data within a CO poisoning surveillance framework.

Major strengths are the possibility to have surveillance data onCOHb levels for the general population and the possibility ofrecord linkage with other bio-chemical parameters and healthdatabases.

Some points needs to be scrutinized such as the relationshipbetween COHb levels and the various CO sources of exposures andpossibility to identify smoking individuals and those withpathologies leading to high COHb leves via cross analyses ofavailable parameters.

Conclusions and perspectives


Ministry of Health and Ministry of Justice funded a project to verifywhether indoor sources may induce bad air quality and health effects

In the period Sep 2015 – Aug 2016 monitoring campaigns were carriedout in 19 prisons located in different regions in Italy

Measurements included Temperature, Humidity, PM2.5, VOC and CO

Indoor air quality in prisons

Relevant Indoor sources of air pollution in prisons

One CO monitor in each prison

Instruments located in corridors, not in cells

Measurements campaigns were conducted with LANGAN T15z


In the coming weeks typical air quality conditions experienced by prisonerswithin cells will be reproduced defining a protocol based on questionnaires

0

2

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CO

[ppm

]


1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24

0

200

400

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Num

ero

di o

reIndoor air quality in prisons

Num

bero

fexc

edan

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of75

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cent

ile

Hyperbaric centres in Italy

34 4

31

1

61

5 1

124

4

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16

Hyperbaric Hoxygen Therapy

Number of patients treated for CO poisoning in each region

Region 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013

Lombardia 251 130 248 188 239 226 160 168 162 246

Emilia- Romagna 292 313 255 354 295 333 373 252 412 309

Veneto 173 165 201 131 185 177 168 132 173 174

Toscana 35 64 48 54 59 84 94 79 79 89

Liguria 20 61 56 42 40 28 48 51 62 55

Calabria 3 28 12 6 3 18 19 29 5 6

Lazio 35 48 29 13 28 83 50 58 31 45

Campania 22 0 7 12 11 12 11 8 9 23

Piemonte 154 174 135 167 192 211 233 114 154 131

Puglia 0 0 0 13 14 4 10 8 0 .

Trentino A.A. 45 38 38 12 45 28 44 50 29 31

Sardegna 7 10 8 1 11 15 24 12 0 25

Sicilia 12 23 7 1 5 39 31 17 19 15

Molise 0 0 16 13 4 2 7 7 2 .

Marche 9 10 14 28 12 47 0 2 57 49

Friuli 10 17 29 14 21 26 30 25 22 17



Treated patients / population (*100.000 inhab)

Regione 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013

Lombardia 2.672 1.372 2.598 1.950 2.453 2.300 1.613 1.732 1.654 2.467

Emilia- Romagna 7.034 7.475 6.038 8.279 6.800 7.576 8.415 5.805 9.412 6.950

Veneto 3.681 3.482 4.211 2.711 3.787 3.603 3.402 2.720 3.544 3.532

Toscana 0.973 1.768 1.319 1.469 1.591 2.252 2.507 2.154 2.139 2.373

Liguria 1.256 3.789 3.483 2.609 2.477 1.733 2.969 3.254 3.961 3.455

Calabria 0.149 1.397 0.601 0.299 0.149 0.896 0.945 1.481 0.255 0.303

Lazio 0.664 0.905 0.528 0.234 0.498 1.461 0.873 1.055 0.558 0.767

Campania 0.380 0.000 0.121 0.206 0.189 0.206 0.189 0.139 0.156 0.392

Piemonte 3.556 4.008 3.101 3.794 4.332 4.746 5.227 2.616 3.521 2.953

Puglia 0 0 0 0.319 0.343 0.098 0.244 0.198 0

Trentino A.A. 4.617 3.857 3.820 1.191 4.418 2.723 4.243 4.856 2.789 2.947

Sardegna 0.424 0.604 0.482 0.060 0.658 0.897 1.432 0.733 0 1.503

Sicilia 0.239 0.458 0.140 0.020 0.099 0.773 0.614 0.340 0.380 0.294

Molise 0 0 4.999 4.052 1.247 0.625 2.189 2.235 0.638

Marche 0.593 0.654 0.911 1.803 0.765 3.014 0 0.130 3.689 3.155

Friuli 0.830 1.407 2.392 1.146 1.706 2.107 2.428 2.053 1.801 1.383


Strengths

Weaknesses

• Lack of information on the severity of poisoning• Lack of informations about the cause(s) of poisoning (non-fire-

related/fire-related, unintentional/intentional)• No follow up of subjects

• Long time series of data• Good geographical distribution• Data related to residents and non-residents• Possible improvement of quality and quantity of information via

record linkage and questionnaires

Poison centers

9 Poison centers

MilanoTorino

Pavia

Firenze

Roma

Napoli

Padova

Genova

Thank you for your attention

Population exposure to CO: some Italian experiences€¦ · COHb in Emergency Room. n°ER-ABS=...

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