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O R I G I N A L R E S E A R C H A R T I C L E Open Access
Mortality, hospital days and expenditures
attributable to ambient air pollution from
particulate matter in Israel
Gary M. Ginsberg
*
, Ehud Kaliner and Itamar Grotto
Abstract
Background: Worldwide, ambient air pollution accounts for around 3.7 million deaths annually. Measuring the
burden of disease is important not just for advocacy but also is a first step towards carrying out a full cost-utility
analysis in order to prioritise technological interventions that are available to reduce air pollution (and subsequent
morbidity and mortality) from industrial, power generating and vehicular sources.
Methods: We calculated the average national exposure to particulate matter particles less than 2.5 μm (PM2.5) in
diameter by weighting readings from 52 (non-roadside) monitoring stations by the population of the catchment
area around the station. The PM2.5 exposure level was then multiplied by the gender and cause specific (Acute
Lower Respiratory Infections, Asthma, Circulatory Diseases, Coronary Heart Failure, Chronic Obstructive Pulmonary
Disease, Diabetes, Ischemic Heart Disease, Lung Cancer, Low Birth Weight, Respiratory Diseases and Stroke) relative
risks and the national age, cause and gender specific mortality (and hospital utilisation which included neuro-
degenerative disorders) rates to arrive at the estimated mortality and hospital days attributable to ambient PM2.5
pollution in Israel in 2015. We utilised a WHO spread-sheet model, which was expanded to include relative risks
(based on more recent meta-analyses) of sub-sets of other diagnoses in two additional models.
Results: Mortality estimates from the three models were 1609, 1908 and 2253 respectively in addition to 184,000,
348,000 and 542,000 days hospitalisation in general hospitals. Total costs from PM2.5 pollution (including premature burial
costs) amounted to $544 million, $1030 million and $1749 million respectively (or 0.18 %, 0.35 % and 0.59 % of GNP).
Conclusions: Subject to the caveat that our estimates were based on a limited number of non-randomly sited stations
exposure data. The mortality, morbidity and monetary burden of disease attributable to air pollution from particulate
matter in Israel is of sufficient magnitude to warrant the consideration of and prioritisation of technological
interventions that are available to reduce air pollution from industrial, power generating and vehicular sources.
The accuracy of our burden estimates would be improved if more precise estimates of population exposure were
to become available in the future.
Keywords: Attributable mortality, Hospitalisations, Air pollution, Particulate matter
* Correspondence: gary.ginsberg@moh.health.gov.il
Israel Ministry of Health, Public Health Services, Yirmiahu Street 39, Jerusalem
9446724, Israel
© 2016 The Author(s). Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0
International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and
reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to
the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver
(http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
Ginsberg et al. Israel Journal of Health Policy Research (2016) 5:51
DOI 10.1186/s13584-016-0110-7
Background
According to the WHO, air pollution accounted in 2012
for around 7,000,000 deaths worldwide [1], of which
3,700,000 deaths were attributable to ambient air pollution
(AAP) as opposed to household air pollution [2]. The
major contributor to AAP is ambient particulate matter
pollution (APMP), with ambient ozone pollution being
a minor contributor [2]. In 2005 and 2010, it was esti-
mated that there were around 565,000 and 500,000
deaths respectively in the WHO European region attrib-
utable to APMP, of which 2552 and 2452 deaths respect-
ively occurred in Israel [1].
The WHO mortality calculations were primarily made
by multiplying average pollution levels by cause specific
relative risks (RR) based on the literature [3–6]. An unpub-
lished study commissioned by the Israeli Ministry of En-
vironment protection [7], based on aggregation of spatial
emission rates from all pollutants, estimated the monetary
costs of air pollution from transport, industrial and electri-
city generation sources, but did not estimate mortality.
Measuring the burden of disease from air pollution is
important not just for advocacy but also is a first step
towards carrying out a full cost-utility analysis in order
to prioritise technological interventions that are avail-
able to reduce air pollution (and subsequent morbidity
and mortality) from industrial, electricity generating
and vehicular sources.
This paper aims to estimate mortality, serious mor-
bidity (proxied by hospitalization days) and associated
expenditures from APMP in Israel.
Methods
Population-weighted PM2.5 exposure
Annual average ambient PM2.5 and/or PM10 exposure
data was calculated based on published monthly data for
2015 from 52 non-roadside monitoring stations [8]. Read-
ings from stations that only recorded PM10 were con-
verted to PM2.5 by a monthly specific PM2.5/PM10 ratio
based on stations where both measurements were made
in the same region or on national data in the event
no regional data existed.
Mid-2015 population data by towns, cities and regions
(by urban and rural status) were multiplied by the rele-
vant local monitoring stations annual average PM2.5
level and divided by the national exposed population
figure of 8,608,500 (which included 236,000 temporary
migrants) in order to arrive at the national population
weighted average PM2.5 exposure level [9, 10].
Where more than one monitoring station existed in a
city, an average PM2.5 value was calculated and applied
to that city’s population. Separately weighted urban and
rural regional average readings for each geographic region
were calculated and applied to other urban and rural pop-
ulations which were not covered by a monitoring station.
Relative risks
Age group (in five year increments) specific RR, based
on the WHO burden of disease calculations from AAP
[11], were obtained for ischemic heart disease (IHD) and
cerebrovascular disease (stroke) mortality from PM2.5
in adults aged over 25 years. Non-age specific RR were
obtained for chronic obstructive pulmonary disease
(COPD), lung cancer (LC) as well as for acute lower re-
spiratory infection (ALRI) in children under 5 years of
age. We utilised a test version of a spread-sheet for esti-
mating the burden of disease from ambient air pollu-
tion that we obtained from the WHO (based on the
methods described in http://www.who.int/phe/health_topics/
outdoorair/databases/AAP_BoD_methods_March2014.
pdf?ua=1 and http://www.who.int/phe/health_topics/out-
doorair/databases/en/). Values reported in terms of PM10
were converted to PM2.5 equivalents by multiplying by
0.73 [12].
Sensitivity analyses (Table 1)
The WHO supplied RR values were only based on lit-
erature that was available up to mid-2013. We updated
these RR by including recent papers and meta-analyses
of incidence, utilization and mortality data and expanded
the categories in the test tool model to include type 2
Diabetes in Adults [13] and Asthma [14, 15] and Low
Table 1 Diagnostic composition of different models
(ages 25+ unless otherwise stated)
ICD10 code WHO MAXI
a
WIDE
a
Ling Cancer C33-C34 X X X
Diabetes Type II E11 X X
Dementia F01–F07 X
b
Parkinson’s G20 X
b
Alzheimer’s G30 X
b
Circulatory I10–I99 X
Cardiovascular I20–I28, I30–I52
I60–I79
IHD I20–I25 X X
CHF I30–I52 X
Stroke I60–I61, I63, I64 X X
I69.0–I69.1
Respiratory J00–J99 X
ALRI J10–J22 X
c
X
d
COPD J44 X X
Asthma J45 X
Low Birth Weight P07.0–P07.1 X
e
X
e
a
Based on literature up to and including 2015
b
Only for hospitalization days not for mortality
c
Under 1 year old only
d
All ages
e
Under five years old only
Ginsberg et al. Israel Journal of Health Policy Research (2016) 5:51 Page 2 of 7
Birth Weight [LBW] in the under-fives [16] in what we
call our MAXI (category) model.
A recent study of 9.8 million subjects in the USA [17] re-
ported that PM2.5 levels were positively related to elevated
hospitalization risks for Alzheimer’s disease, Parkinson’s
disease and dementia. The results indicated that long-term
changes in PM2.5 accelerated neuro-degeneration, poten-
tially after the disease onset, hence we included the attribut-
able hospitalization days into our MAXI model. However,
we did not include estimates of attributable mortality, since
the study was unable to assess whether PM2.5 levels caused
the onset of neuro-degeneration, for which age is a pre-
dominant risk factor [18].
We applied age-specific relative risks for IHD and stroke
in proportion to the overall ratio of the RR calculated from
the meta-analyses to the overall RR from the WHO model.
We noticed that different meta-analyses of the long-
term effect (short-term effects were excluded) of pollut-
ants on a specific disease, did not always include identical
studies. Due to time constraints, in our calculation of
updated relative risks, we included every individual
study that had been included in meta-analyses, plus any
published data since the latest meta-analysis. However
we took care not to include multiple studies based on
the same temporal populations and preserving a hier-
archy of inclusion based primarily on mortality, then
hospitalisations, emergency room visits and incidence
risks (which we assumed will reflect proportionality of
pollution related risks).
However, we excluded studies based in the Far East
(China, South Korea, Japan etc.) as their risks (which
were usually higher) were generally based on higher
levels of air pollution than that of Israel, North America
and Europe [19].
In addition we included a WIDE category model, that
included the broad areas of all circulatory and all re-
spiratory diseases in addition to lung cancer, diabetes
and LBW.
Combined RR were calculated by applying weights in-
versely proportional to the square of the reported stand-
ard errors of the estimates of the diseases in the WIDE
and MAXI categories.
Population Attributable Fraction (PAF)
Age, gender and cause specific PAFs for APMP were
calculated according to the standard formula
PAF ¼RR−1
RR−1ðÞþ1
Attributable mortality and hospital days
Age and cause specific mortality and days of hospital
utilisation by primary cause of death and hospitalisation
for 2009–2013 were obtained from the Ministry of
Health’s national mortality and hospitalisation data bases.
These raw data were adjusted upwards by 6.8 % [9] to take
into account population growth until mid-2015. Finally
we calculated mortality and hospital days attributable to
PM2.5 by multiplying the age, gender and cause specific
mortality and hospitalization data by the relevant PAF.
Potential years of Life Lost (PYLL)
Extrapolations of age and gender specific life expectancies
to 2015 [10, 11] were multiplied by age-gender and cause
specific mortality data in order to calculate the cause spe-
cific PYLL attributable to PM2.5.
Disability adjusted life years (DALYs) lost
Age- and gender-specific disability weights, used by the
Ministry of Health, were applied to the life expectancies
in order to calculate each individual’s additional Healthy
Adjusted Life Expectancy (HALE), using a 3 % per annum
discount rate. These HALEs were subsequently multiplied
by age-gender and cause specific mortality data in order to
calculate the cause specific DALYs lost due to mortality.
Attributable direct costs of ambient PM2.5 pollution
In 2015, Israel spent around $18.5 billion on health ser-
vices [9, 10]. Around 57 % of this was spent on capital
costs, medicines, equipment and ambulatory, emergency
room and out-patient visits [9, 10]. This figure was in
turn multiplied by the percentage of hospital days from
APMP for each of our models. The general hospitalisa-
tion costs (accounting for a further 19.6 %) were then
added, taking into account that the per diem hospital
costs were higher in departments [$916 vs $869] that
cared for persons with diagnoses affected by PM2.5 than
the average hospital cost [20].
We included premature burial costs (based on discount-
ing the $5263 average burial costs over the life years lost)
as the only monetary cost (in contrast to “human costs”
reflected in lost DALYs) attributable to mortality. In
addition, we calculated a statistical value of life loss
based on valuing each member of society [regardless of
age and gender] according to the national average gross
national product (GNP) per capita of $35,222 multi-
plied by their life expectancy, using a 3 % per annum
discount rate.
The hospital, health service and premature burial costs
were also expressed in terms of their percentages of GNP.
However since the statistical value of life computation is
based on “virtual”as opposed to real resource costs, this
was not expressed in terms of percentage of GNP.
Results
The population weighted average PM2.5 exposure in
Israel in 2015 was 21.6 μg/m
3
. The calculated diagnostic
Ginsberg et al. Israel Journal of Health Policy Research (2016) 5:51 Page 3 of 7
specific RR due to 10 μg/m
3
changes in PM2.5 that we
used for the non-WHO models are listed along with
their diagnoses in Additional file 1: Appendix I. Risks
for ALRI (RR = 1.10, 95 % CI 1.06–1.12), Alzheimers
(3.00, 2.40–3.70), Asthma (1.02, 1.01–1.03), Dementia
(1.16, 1.10–1.22), Diabetes (1.05, 1.01–1,08), IHD (1.11,
1.08–1.15), Lung Cancer (1.11, 1.05–1.16), Parkinson’s
(1.88, 1.44–2.40) and Respiratory Diagnoses (1.04, 1.001–
1.08) were all significant. COPD (1.03, 0.997–1.07) and
LBW (1.06, 0.989–1.12) were marginally not-significant,
whilst there was a non-significant elevated risk for Strokes
[1.08, 0.93–1.24].
According to the WHO model, 1609 (95 % CI 863–
2361) deaths (or 3.6 % of all fatalities) were attributable
to ambient PM2.5. Around half were due to IHD and a
quarter attributable to strokes (Table 2).
The Wide list (containing wide circulatory and respira-
tory categorisations) estimated 15 % more deaths (1908,
95 % CI 1121–2804 being 4.3 % of all deaths) than the
WHO model, Circulatory disorders accounted for 64 %
of attributable mortality, with lung cancer and respira-
tory disorders each accounting for 18 % and 14 % re-
spectively (Table 3).
The maxi list (containing many more, but narrower
disease categories, than the wide list) produced an esti-
mate, 40 % higher than the WHO model, of 2253 (95 %
CI 632–2904) deaths, being 5.1 % of all deaths. IHD, CHF
lung cancer and stroke accounting for 41 %, 18 %, 16 %
and 14 % of all attributable deaths respectively (Table 4).
Table 5 shows that PM2.5 pollution accounted for
between 183,000–591,000 days in general hospitals,
costing between $168 million–$592 million, 3.5–11.4 % of
all general hospital costs. Total health costs from PM2.5
pollution were between $541 million - $1028 million
accounting for between 2.4–4.6 % of health expendi-
tures in Israel. Total costs from PM2.5 pollution (in-
cluding premature burial costs) amounted to between
$544 million–$1749 million or 0.18 %–0.59 % of GNP.
Using a statistical value of life based on GNP per capita
methodology would add between $584 million–$797
million to the morbidity costs of PM2.5 pollution.
Discussion
In contrast to deaths which are clearly attributable to a
given causality (such as automobile accidents, suicides,
drowning), deaths due to air pollution and to personal
behaviour, such as smoking, nutritional habits and phys-
ical exercise are harder to identify. Despite this difficulty,
ambient particulate matter pollution has been implicated
as a factor in many causes of death [8].
The range of mortality from our three estimates of
between 1609–2253 deaths from PM2.5 alone is be-
tween four and five times that of road accident fatalities
(although road fatalities have a higher PYLL due to the
younger age of deceased persons) and between 10–16
times that of homicides in Israel [10]. Mortality attrib-
utable to PM2.5 is however lower than deaths from
smoking [21], obesity [22] and sedentariness [23].
Our estimated deaths from PM2.5 are lower than the
2452 estimated by the WHO European region in 2010
[1] partly due to our model taking into account the fact
that the southern desert region of the country has higher
particulate levels but a far lower population density.
Particulate matter data in Israel are strongly impacted
by synoptic phenomena such as the occurrence of “dust
Table 2 Mortality attributable to ambient air pollution from
PM2.5 (Israel 2015) (WHO model)
Deaths 95 % Lcl 95 % Ucl Discounted
PYLL HALE
losses
HALE
losses
Lung cancer 232 55 380 3952 3056 2249
COPD 110 44 189 1178 866 695
IHD 850 586 1112 10,460 7958 6075
Stroke 417 175 680 4937 3678 2842
ALRI 1 0 1 42 36 15
TOTAL 1609 861 2361 20,569 15,595 11,877
Table 3 Mortality attributable to ambient air pollution by
pollutant (Israel 2015) (WIDE list)
PM2.5 Deaths 95 % Lcl 95 % Ucl Discounted
PYLL HALE
losses
HALE
losses
Diabetes 78 24 122 820 600 481
LBW 0 −0 0 17 15 6
Lung cancer 349 199 478 5945 4598 3384
Circulatory 1217 890 1585 12,445 9181 7274
Respiratory 265 8 618 2776 2047 1601
TOTAL 1908 1121 2804 22,003 16,441 12,745
Table 4 Mortality attributable to ambient air pollution from
PM2.5 (Israel 2015) (MAXI list–Single Pollutant Models)
PM2.5 Deaths 95 % Lcl 95 % Ucl Discounted
PYLL HALE
losses
HALE
losses
Diabetes 78 24 122 820 600 481
LBW 0 0 0 17156
Lung cancer 349 199 478 5945 4598 3384
COPD 78 −8 157 834 614 492
IHD 914 696 1142 11,118 8447 6457
CHF 417 180 618 4313 3194 2474
Stroke 278 −362 703 3659 2759 2078
ALRI 116 68 172 1155 853 657
Asthma 22 15 30 305 227 166
TOTAL 2253 811 3424 28,167 21,307 16,195
Ginsberg et al. Israel Journal of Health Policy Research (2016) 5:51 Page 4 of 7
storms”from surrounding deserts. Our estimates were
limited to pollution data from only 2015, when there was
a below average incidence of such storms. Hence our
overall estimates of mortality, hospitalizations and costs
are more likely to be downwardly biased than if they were
to have been based on multi-year pollution data.
Our estimates were based on the 52 non-roadside
monitoring stations, which fall far short of the current
infeasible goal of having monitoring stations in every
neighbourhood or street. These stations are not distrib-
uted randomly in the urban space, but are located after
careful thought, often in places of special interest (e.g.
potential hot spots, town halls etc.). Thus, averaging PM
concentrations over monitoring stations (for either a city
or a region) does not necessarily give a very good esti-
mate of the true population exposure. In addition, there
might also be data quality issues that need to be assessed
and corrected by air pollution experts. Nevertheless, we
consider our estimates to be an acceptable pragmatic
compromise for the purpose of an initial estimation of
the mortality effects from particulates. We consider our
estimation method to be preferable to estimates based
on industrial and transport emission volumes, where wind
direction and natural pollutant sources such as sand act
as confounders.
We consider the methodology for exposure assessment
used in this paper to be a valid and generally acceptable
for the purpose of making a national estimate of mortal-
ity. However, future localized estimates could be based
on improved methodologies utilizing spatial models of
particulate matter based on integrating data from moni-
toring stations, meteorology, traffic and other inputs.
A major limitation of our estimates is that due to the
lack of such studies in Israel, we employed, as an accept-
able compromise, relative risk estimates from studies in
countries where the PM2.5 is at a different exposure
level. In the event of non-linearity between risk and ex-
posure this would cause biased estimates. However, these
biases were lessened by our exclusion of Asian based stud-
ies, which tended to have higher PM2.5 levels.
A further source of potential bias is that the sources and
hence composition of PM2.5 and subsequent composition-
specific relative risks [24, 25] in international studies are
different from that in Israel. While relying on meta analyses
of risks might reduce any difference with Israel, an overall
bias cannot be ruled out.
It should be borne in mind that our estimates only relate
to one pollutant, particulate matter. A companion article
will estimate the mortality attributable to two other air
pollutants (Ozone and Nitrogen Dioxide). Due large nega-
tive and smaller positive correlations with particulate
matter levels respectively, a simple addition of all three
individual pollutant models will overestimate the total
deaths attributable to ambient air pollution. Therefore
adjustments will be made to the estimated total deaths
by means of combining data from three studies [26–28]
that have reported results of multi-pollution models
(i.e.: that adjusted for the other two pollutants).
The WHO estimates, have a great advantage in that they
allow for uniform comparisons with other countries, and
that their relative risk information for IHD and Stroke was
age-specific. However their disadvantage is that their RR
were based on information that was available three years
ago in 2013.
Our WIDE and MAXI lists incorporated data from
studies on Diabetes, which had a significant RR. How-
ever, it could be considered contentious that we included
categories whose RR were marginally significant (COPD,
LBW) or not significant (Strokes), although Strokes
were considered significant in the WHO model. The
Table 5 Deaths, hospital utilization and costs from PM2.5 (Israel 2015)
Total WHO WIDE MAXI
Deaths 44,354 1609 1908 2253
3.6 % 4.3 % 5.1 %
Hospital days 5,172,000 183,276 348,039 591,014
3.5 % 6.7 % 11.4 %
General Hospital Costs $4,495,153,288 $167,941,477 $318,918,656 $541,563,409
As % of Gen Hosp costs 3.7 % 7.1 % 12.0 %
All Health Costs $22,432,880,000 $541,213,353 $1,027,757,036 $1,745,258,843
As % of all health costs 2.4 % 4.6 % 7.8 %
Mortality Costs
Friction costs $69,686,591 $2,461,150 $2,680,943 $3,366,447
Total Costs $22,502,566,591 $543,674,503 $1,030,437,979 $1,748,625,290
% of GNP 7.6 % 0.18 % 0.35 % 0.59 %
Value of Statistical Life
(GNP per capita based)
$16,346,340,983 $583,629,920 $631,308,623 $796,589,039
Ginsberg et al. Israel Journal of Health Policy Research (2016) 5:51 Page 5 of 7
inclusion of LBW did not affect the WIDE estimates
magnitude, since LBW contributed close to zero attribut-
able deaths. However the inclusion of COPD and Strokes
(in addition to LBW) in the MAXI list added 356 [95 %
CI, −370, +860] deaths.
The mortality, morbidity (between 3.5 %–11.4 % of
general hospital days) and monetary burden (between
$544–$1748 million annually) of diseases attributable
to air pollution in Israel is of sufficient magnitude to war-
rant the consideration and prioritisation of technological
interventions that are available to reduce air pollution
from industrial and vehicular sources.
While some interventions will be on a national scale
(eg: limits on vehicle emissions), others might be aimed
at local hot spots of high industry or vehicular pollution
where a significantly large population is being exposed.
Thus further analysis of our data (at pollution station
level) will be required to identify and prioritise high risk
localities and search for possible supplementary inter-
ventions (to national level interventions).
The data in this study provides a basis of mortality,
DALY and health costs that can form the basis of any
future cost-utility analyses of interventions (with proven
efficacy) to reduce the burden of disease from man-
made sources of particulate matter pollution. Interven-
tions will have the potential not only to reduce mortality
(and morbidity) but also to generate reductions in attribut-
able health service costs that account for between 2.4 %–
7.8 % of all health expenditures in Israel.
In the UK in 2005 [1, 29], road transport accounted
for around 40 % of premature deaths from APMP, other
transport (20 %), power generation (20 %) and other
sectors(20%).Aslongastwentyyearsago,aconsider-
able number of deaths from particulate matter in Tel-
Aviv, Israel were shown to be attributable to diesel fuels
[30]. Ways have been suggested to almost eradicate re-
duce these emissions and hence their related mortality
and morbidity [31] by increasing the use of catalytic
converters and moving over to hybrid, electrical and
LPG powered vehicles–especially trucks and buses.
Large desert areas account for the fact that the Middle
East is the region with the highest percentage of PM2.5
pollutants from natural sources [32], being around 52 %
compared with 42 % Japan, 22 % Africa, 21 %, India,
17 % China, 10 % USA and 5 % Western Europe. So the
potential for decreasing the percentage of particulate
mass concentrations (used in this paper) through techno-
logical improvements is lower in the Middle East than in
other regions (both developed and developing).
The effect of surrounding deserts on air Pollutant
levels in Israel was described almost a decade ago [33].
A natural experimental study on the Day of Atonement
from 2000–2008, when nearly all industry and vehicular
travel ceases, based on four stations in three cities,
reported a reduction in particulate concentrations ran-
ging from 11.4 %–21.7 % [34]. However, a similar study
over a longer period (1998–2012) estimated a 74 % con-
tribution by natural sources to PM2.5 pollution [35].
Assuming 74 % of particulate pollution comes from
natural sources in Israel, means that for every 10 % rela-
tive decrease in man-made PM2.5 attained through the
implementation of intervention strategies [36], between
42–59 lives will be saved each year, (in addition to be-
tween $14 million and $21 million in resource costs).
Conclusions
The considerable mortality and morbidity burden attribut-
able to ambient particulate matter pollution, cries out for
the establishment of an inter-ministerial plan to identify
and implement those intervention strategies that are cost-
effective, in order to decrease the considerable burden of
mortality and morbidity, in both human and monetary
terms, from ambient air pollution in Israel.
Additional file
Additional file 1: Appendix I. Studies contained in meta-analyses of RR
due to 10 ug/m3 changes in PM2.5. (DOC 192 kb)
Abbreviations
AAP: Ambient air pollution; ALRI: Acute Lower Respiratory tract Infection;
APMP: Ambient Particular Matter Pollution; COPD: Chronic Obstructive
Pulmonary Disease; DALY: Disability adjusted life year; GNP: Gross national
product; HALE: Healthy Adjusted Life Expectancy; IHD: Ischemic Heart
Disease; LBW: Low Birth Weight; LC: Lung cancer; PAF: Population
Attributable Fraction; PM10: Particulate Matter Particles less than 10
micrometers in diameter; PM2.5: Particulate Matter Particles less than 2.5
micrometers in diameter; PYLL: Potential years of Life Lost; RR: Relative risk;
UK: United Kingdom of Great Britain and Northern Ireland; WHO: World
Health Organization
Acknowledgements
To Dr. Annette Prüss-Ustün and Pierpaulo Mudu of the Department of Public
Health and Environmental and Social Determinants, WHO, Geneva for
allowing us to use a test version of their spread-sheet for estimating the
burden of disease from ambient air pollution. To Ziona Haklai and Nehama
Goldberger of the Health Ministry’s Statistical Unit in supplying the raw
mortality and hospitalization data.
Funding
Not applicable.
Availability of data and materials
The datasets during and/or analysed during the current study available from
the corresponding author on reasonable request.
Authors’contributions
GMG designed the study, collected the data, carried out the data analysis,
wrote the initial and wrote read and approved the final manuscript. EK
contributed to the interpretation of the data, made critical revision and
wrote, read and approved the final manuscript. IG initiated the study and
wrote, read and approved the final manuscript.
Competing interests
All the authors are salaried staff of the Ministry of Health and there are no
competing interests to declare.
Ginsberg et al. Israel Journal of Health Policy Research (2016) 5:51 Page 6 of 7
Ethics approval and consent to participate
As the study is based on published literature and a built spreadsheet, no
human subjects were involved–hence there is no need for ethical approval
or consent to participate.
Received: 18 April 2016 Accepted: 10 October 2016
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