BioMed Central

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Environmental Health

Research Open Access

A 10-year time-series analysis of respiratory and cardiovascular 

morbidity in Nicosia, Cyprus: the effect of short-term changes in air 

pollution and dust storms

Nicos Middleton*1,2, Panayiotis Yiallouros2, Savvas Kleanthous3, 

Ourania Kolokotroni2, Joel Schwartz1, Douglas W Dockery1, 

Phil Demokritou1,2 and Petros Koutrakis1

Address: 1Department of Environmental Health, Exposure, Epidemiology & Risk Program, Harvard School of Public Health, 401 Park Drive, 

Boston, MA 02215, USA, 2Cyprus International Institute for the Environment and Public Health in association with Harvard School Public Health, 

5 Iroon Str, Nicosia 1105, Cyprus and 3Air Quality Section, Department of Labour Inspection, Nicosia 1493, Cyprus

Email: Nicos Middleton* - nmitlett@hsph.harvard.edu; Panayiotis Yiallouros - pyiallouros@cyprusinstitute.org; 

Savvas Kleanthous - skleanthous@dli.mlsi.gov.cy; Ourania Kolokotroni - okolokotroni@cyprusinstitute.org; 

Joel Schwartz - jschwrtz@hsph.harvard.edu; Douglas W Dockery - ddockery@hsph.harvard.edu; 

Phil Demokritou - pdemokri@hsph.harvard.edu; Petros Koutrakis - petros@hsph.harvard.edu

* Corresponding author 

Abstract

Background -: To date, a substantial body of research has shown adverse health effects of short-term changes in levels

of air pollution. Such associations have not been investigated in smaller size cities in the Eastern Mediterranean. A

particular feature in the region is dust blown from the Sahara a few times a year resulting in extreme PM10 concentrations.

It is not entirely clear whether such natural phenomena pose the same risks.

Methods -: The effect of changes in daily levels of particulate matter (PM10) and ozone (O3) on hospitalization for all,

cardiovascular and respiratory causes in the two hospitals in Nicosia during 1 January 1995 and 30 December 2004 was

investigated using generalized additive Poisson models after controlling for long- and short-term patterns as well as for

the effect of weather. Meteorological records were reviewed to identify dust-storm days and analyses were repeated to

quantify their effect on cardio-respiratory morbidity.

Results -: For every 10 μg/m3 increase in daily average PM10 concentrations, there was a 0.9% (95%CI: 0.6%, 1.2%)

increase in all-cause and 1.2% (95%CI: -0.0%, 2.4%) increase in cardiovascular admissions. With respect to respiratory

causes, an effect was observed only in the warm months. No lagged effects with levels of PM10 were observed. In contrast,

positive associations with levels of ozone were only observed the two days prior to admission. These appeared stronger

for cardiovascular causes and independent of the effect of PM. All-cause and cardiovascular admissions were 4.8%

(95%CI: 0.7%, 9.0%) and 10.4% (95%CI: -4.7%, 27.9%) higher on dust storm days respectively. In both cases the magnitude

of effect was comparable to that seen on the quartile of non-storm days with the highest levels of PM10.

Conclusion -: We observed an increased risk of hospitalization at elevated levels of particulate matter and ozone

generally consistent with the magnitude seen across several European cities. We also observed an increased risk of

hospitalization on dust storm days, particularly for cardiovascular causes. While inference from these associations is

limited due to the small number of dust storm days in the study period, it would appear imperative to issue health

warnings for these natural events, particularly directed towards vulnerable population groups.

Published: 22 July 2008

Environmental Health 2008, 7:39 doi:10.1186/1476-069X-7-39

Received: 25 January 2008

Accepted: 22 July 2008

This article is available from: http://www.ehjournal.net/content/7/1/39

© 2008 Middleton et al; licensee BioMed Central Ltd. 

This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), 

which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Environmental Health 2008, 7:39 http://www.ehjournal.net/content/7/1/39

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Background

In the last 20 years, evidence on adverse health effects –

both increased hospitalization and mortality – of elevated

ambient levels of air pollutants has been accumulating

[1,2], more so recently with the use of meta-analyses of

single-city time-series analyses [3,4] or multi-city studies

[5,6]. With the major exception of the APHEA project, a

multi-centre European study with a common protocol [7]

in as many as 15 and 29 European cites in its phases I and

II respectively [8-10], the majority of evidence comes from

US cities e.g. the National Morbidity, Mortality and Air

Pollution Study (NMMAPS) [11,12]. While large cities in

the Eastern Mediterranean, such as Athens, Tel Aviv and

Istanbul were considered in APHEA, associations have not

been investigated in smaller size cities where socio-economic factors (such as driving patterns, time spent outdoors and access to health care) as well as climatic

conditions might vary considerably. A particular feature of

the Eastern Mediterranean is episodes of re-suspended

wind blown dust from desert regions, raising particle concentrations a few times a year considerably above European guidelines [13]. It is not entirely clear whether high

levels of particulate matter from such natural phenomena

pose the same risks on cardiovascular and respiratory

health as particles from anthropogenic sources. With

PM10 concentrations comparable to the high levels seen in

other much larger Southern European cities as well as frequently affected by dust storms, the capital of Cyprus,

Nicosia (population approximately 270,000) offers an

opportunity to address this issue. Using a time-series

approach, this study investigates associations between

daily levels of (a) particulate matter with aerodynamic

diameter < 10 μm (PM10) on non-storm and dust storm

days separately as well as (b) ozone (O3) on counts of

hospital admissions for all, respiratory and cardiovascular

causes during the 10-year period 1995–2004.

Methods

Data and data sources

Based on ICD code of diagnosis for inpatient admission,

all cardiovascular (ICD10 codes I00–I52) and respiratory

(ICD10 codes J00–J99) admissions between 1 January

1995 to 30 December 2004 (i.e. a total of 3,652 days)

were obtained from the two public hospitals in Nicosia

(i.e. Nicosia General and Makarios Hospital) with information on gender, age, date of admission and discharge as

well as whether inpatient was resident in the district of

Nicosia. The daily volume of all-cause admissions in the

same period was obtained from the Cyprus Statistical

Services, aggregated in 8 age- and sex-strata – males and

females, aged 4 or less, 5–14, 15–64 and 65 or older.

Hourly measurements of PM10 and O3 were available

from two ambient air quality monitoring stations: (a) a

local traffic-representative station located centrally at the

Nicosia General Hospital and (b) a station reflecting background levels in the rural location of Ayia Marina, approximately 40 km from Nicosia. Continuous monitoring of

coarse particles was performed with TEOM (Tapered Element Oscilling Micro-balance) instruments. Data from

the rural station were available only from 1997 for ozone

and 1999 for PM10, reducing the study period to 8 and 6

years respectively. Thus, the main analyses presented here

were based on a single exposure series as recorded at the

traffic-representative station. Completeness of the main

data series of air pollutants was generally satisfactory and

concurred with previous practice, including the protocol

used in APHEA. Commonly, missing values are replaced

using a weighted average of values from other monitoring

stations on that day. With only one other station, located

rurally and thus not necessarily representative of exposure

in the capital, imputation of missing values was not considered not least because availability of data did not cover

the full period of investigation and would replace only a

small fraction of the missing values. The background station was mainly used to aid in the identification of duststorm days, since due to its rural location it is not affected

by local traffic pollution. Nevertheless, the effect on the

observed estimates of using the background station to

define exposure was considered in sensitivity analyses.

Daily averages of air and dew point temperature, relative

humidity, wind speed, precipitation and barometric pressure based on hourly measurements of Thermohygrographs (i.e. instantaneous values) were obtained from the

Cyprus Meteorological Services.

Identification of dust-storm days

Dust storm events affect Cyprus at least a few days a year

resulting in extreme PM concentrations, which sometimes

may persist for a few consecutive days. To construct a calendar of such events, an iterative approach was used

whereby starting with a pool of days (N = 773) with at

least 1 hourly measurement of PM10 higher than 150 μg/

m3 recorded at Nicosia Central or higher than 100 μg/m3

at the rural station (in both cases, 2 standard deviations

(SD) away from the mean of hourly values on the logarithmic scale), paper-form meteorological records from

the main International Airport at Larnaca (50 km from

Nicosia) were reviewed to identify those days when a

meteorological observer (as part of their hour-by-hour

coding of weather and visibility conditions) noted poor

visibility due to "dust in suspension" at any part of a given

day, which did not refer to hazy conditions or dust from

local sources i.e. a result of re-suspension. Candidate days

were then cross-checked against a number of data-based

criteria to assess the extent to which levels of PM10 on

those days where indeed uncharacteristic, including (i)

daily average levels of PM10 higher than 2 SDs away from

the mean as recorded at the rural station, not as prone to

traffic pollution and/or (ii) levels of PM10 higher than 2

SDs away from the mean in the centre of Nicosia after
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excluding hours of pick traffic and/or (iii) days identified

as outliers (2 SDs away from the predicted value) using a

predictive model of levels of PM10 based on levels the previous day and adjusting for weather variables including

temperature, barometric pressure, precipitation and wind

speed. The data-based criteria were not used to identify

dust-storm days per se but to correct the original calendar

which was based only on meteorological observations

(i.e. either confirm, invalidate or propose additional days)

with the main aim of categorizing days into confirmed

events, where the criteria were in agreement with the

meteorological observation and suspected events, where all

criteria suggest an event that might have been overlooked

or coded otherwise by the observer. Possible markers of

dust storm events used in the past, such as low carbon

monoxide levels [14], reduced visibility range [15], or

based on investigation of aerosol optical depth [16,17],

were not electronically available. Finally, in accordance to

previous practice [16,18-20], backwards wind trajectories

for up to 4 days ending in Cyprus (35°N, 33°E) on the

day and about the time the meteorological observation

was made were used to track the possible source of each

identified event in the Sahara or Arabian peninsula using

the National Oceanic and Atmospheric Administration

(NOAA) HYSPLIT model.

Statistical analyses

Only days with at least 12 hourly measurements were

used to calculate daily averages of air pollutants (PM10

and O3) as well as daily 8-hour maximum moving average

for O3. The effect of changes in levels of air pollutants on

the number of daily admissions was investigated in Poisson regression models (a) as linear terms, expressed as

percentage increase in mean number of daily admissions

per 10 μg/m3 or 10 ppb increase in levels of PM10 and O3

respectively, (b) across quartiles of increasing levels of

PM10 (after including and excluding dust storm days) and

O3 to assess non-linearity of effects and (c) to include a

categorical variable for dust storm days and restrict the

estimation of a linear effect to non-storm days only (i.e.

Dust Storm Day (= 0 or 1) + PM10 daily average × [1-Dust

Storm Day]). The magnitude of effect on these days was

then compared to the effects seen across quartiles of all

non-storm days with increasing levels of PM10. In accordance to previous practice, to ensure that extreme values of

PM10 (in this case, most likely to be a result of air blown

dust) would not influence the estimation of linear effects,

the main analyses excluded days with average PM10 concentrations greater than 150 μg/m3 (25 days). Furthermore, to assess the extent to which associations persist at

lower ambient levels of air pollution, analyses were

repeated restricted to days with daily averages of PM10 less

than 100 μg/m3 or 75 μg/m3 (the European standard at

the time). Finally, to assess and correct for overdispersion,

or extra-Poisson variation in the data, models were

repeated using negative Binomial models (i.e. an overdispersed Poisson distribution). Model fit was assessed by

inspection of the overdispersion parameter, the model

deviance as well as patterns and magnitude of the residuals.

Generalized Additive Models (GAM) with natural splines

were used to remove long-term seasonality (starting with

the practical choice of 40 degrees of freedom to capture 4

seasons over the 10 year period) and penalized cubic

splines to control for possible non-linear effects of the

meteorological variables on the outcome (with a maximum of 5 degrees of freedom). In order to control for

short-term patterns of admissions, day of the week was

included in the models as a categorical variable. Before

including the air pollution variables in the model, a base

model was constructed to remove seasonality i.e. longterm trend and weekly patterns. Minimising the absolute

value of the sum of the partial autocorrelation function

was used to assess the appropriateness of the degree of

smoothing. To avoid oversmoothing and, thus, eliminating patterns due to the exposure under study, windows

below 2 months were generally not considered [21]. The

weather variables as well as the appropriate lags for these

to include in the models were then chosen by minimizing

the Unbiased Risk Estimator (UBRE) and/or the Akaike's

Information Criterion (AIC). All models were checked for

remaining autocorrelation by examining plots of the partial autocorrelation function and, if necessary, sensitivity

of the inferences was assessed in autoregressive Poisson

models. The final model controlled for long- and shortterm trend, temperature on the same day as well as the

two previous days (lags 1 and 2) and relative humidity on

the same day. Wind speed, precipitation and barometric

pressure were not considered as confounders of the association between air pollutants and hospitalization. The

effect of ozone was assessed before and after controlling

for the effect of PM10.

Same-day and lagged exposure (up to 2 previous days)

were considered. Due to lack of statistical power, distributed lag models were not considered [22]. Similarly, due

to the small number of daily cause-specific admissions,

analyses were only stratified by gender (all ages combined) or age (i.e. younger/older than 15 years of age).

Models were repeated to include interaction terms

between levels of pollutants and season to investigate evidence for effect modification either (a) during cold and

warm months indexed by monthly average temperature

levels or (b) on days when average daily temperature was

higher or lower than 20 or 30°. Finally, where possible

(i.e. cause-specific investigation), all analyses were

repeated to exclude non-Nicosia residents since these may

represent transfers from other hospitals and, as such, can

dilute any effect since day of admission may not accu-
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rately reflect day of exposure. Data manipulation was performed in STATA SE 9.0. Analyses were performed using

the MGCV package in the R software (R 2.2.0).

Results

Admissions in Nicosia hospitals nearly doubled in the 10-

year period. There has been a 3-fold increase in cardiovascular admissions rising from an average of 1 in the early

years to 4 towards the end, averaging at 3 daily. For respiratory causes, admissions averaged approximately 4 daily

in much of the 10 years. Table 1 shows summary statistics

of the daily number of all-cause and cause-specific admissions. Numbers are also shown before and after restricting

to Nicosia residents. Combining all age/sex groups

ensured that there would be at least 1 cause-specific

admission in at least 85% of days. The low number of

daily events meant that it was not uncommon for as many

as 75% of days with no admissions if age- and sex-groups

would be considered separately. As about 25% and 14%

of those admitted for cardiovascular and respiratory cause

respectively were non-Nicosia residents, the figures reduce

to an average of 2.2 and 3.5 admissions daily.

Table 1 also shows the distribution of daily 24-hour average concentrations of PM10 and daily maximum 8-hour

moving average of O3 as measured at either station as well

as daily averages of the meteorological factors considered

in the models. In addition to 354 (9.7%) and 387

(10.6%) days for which no PM10 and O3 were recorded at

the central station respectively, an additional 81 (2.2%)

and 57 (1.6%) days were excluded from the analyses as

only fewer than 12 hourly measurements were available.

Similarly, for the background station, around 15% of days

were excluded. Daily mean levels of PM10 in Nicosia Central ranged from 5.0 to 1370.6 μg/m3 (interquartile range:

40.0–64.1) with, as expected, slightly higher concentrations during the colder months. In the rural station, levels

of PM10 ranged from 6.3 to 952.4 μg/m3 (interquartile

range: 17.0–36.3) and, in contrast to the pattern observed

in the central station, appeared lower during the cold seaTable 1: Summary statistics for daily number of admissions, levels of air pollutants and meteorological factors in the 10-year period 1 

Jan 1995–30 Dec 2004 (n = 3652 days).

A. Daily number of hospital admissions for all, cardiovascular and respiratory causes

Total number (% Nicosia residents)1 Mean SD Min 25% Median 75% Max

All causes 178 091 48.8 20.1 4 31 50 63 111

Cardiovascular2 10 896 (75%) 3.0 (2.2) 2.4 (1.9) 0 (0) 1 (1) 3 (2) 4 (3) 22 (11)

Respiratory2 14 827 (86%) 4.1 (3.5) 3.7 (3.1) 0 (0) 1 (1) 3 (2) 6 (5) 20 (18)

B. Levels of air pollutants, shown separately for cold and warm months3

Number of Days (% of total days)4 Mean SD Min 5% Median 95% Max

Nicosia Central

PM10 24-hour average (μg/m3) Cold 1553 (85.7%) 57.6 52.5 5.0 20.0 50.8 103.0 1370.6

Warm 1664 (90.4%) 53.4 30.7 18.4 32.0 50.5 77.6 933.5

O3 8-hour MA max (ppb) Cold 1514 (83.6%) 28.7 12.6 3.7 9.9 27.5 50.2 63.6

Warm 1692 (92.0%) 44.4 10.3 7.8 24.4 46.1 58.8 71.1

Ayia Marina5

PM10 24-hour average (μg/m3) Cold 918 (84.5%) 25.9 28.0 6.3 9.3 19.0 62.3 553.2

Warm 903 (81.8%) 35.7 40.5 8.1 16.0 30.9 58.9 952.4

O3 8-hour MA max (ppb) Cold 1155 (80.1%) 45.7 6.8 30.2 35.1 44.6 58.4 71.0

Warm 1247 (84.7%) 54.9 8.2 28.9 40.6 55.2 68.1 78.7

C. Meteorological factors

Temperature Cold 1812 (100%) 12.9 3.6 1.9 7.4 12.5 19.1 27.2

Warm 1840 (100%) 25.8 3.9 11.8 19.0 26.3 31.4 35.5

Rel. Humidity 3591 (98.3%) 65.0 14.0 16.6 38.5 66.2 85.8 96.5

Dew Point 3591 (98.3%) 11.3 5.5 -7.6 2.4 10.8 20.4 24.2

Notes: 1 In the case of cause-specific admissions, information on residence was available allowing the analyses to be restricted to those resident in 

the Nicosia district; the same was not possible for all-cause admissions as only aggregated data were available. In parentheses, the proportion of 

Nicosia residents among total number of admissions. 2 Summary statistics when restricting numbers to Nicosia residents are shown in parentheses. 3 Cold months include Jan, Feb, Mar Apr, Nov and Dec, based on monthly average temperatures, while warm months include all the rest. 4 About 

10% of the 3652 days in the 10-year study period were excluded as no data were available as well as an additional 2% of days for which only less 

than 12 hourly measurements were recorded. 5 PM10 and O3 data from the station of Ayia Marina were available only from 1999 and 1997 onwards 

reducing the study period to 6 and 8 years respectively.
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son since winter levels are mainly influenced by local

sources. With the exception of extreme values thought to

be the result of dust storms, levels of air pollutants in

Nicosia were comparable to those seen in many southern

European cities and exceeded the European standard of 75

μg/m3 (at the time) between 11–57 days a year. The maximum 8-hour moving average for ozone ranged between

3.7 and 71.1 ppb (interquartile range: 26.0–48.0) in Nicosia and 28.9 and 78.7 ppb (interquartile range: 43.2–

57.0) in Ayia Marina.

Table 2 presents the percentage increase in all, cardiovascular and respiratory admissions per 10 μg/m3 increase in

PM10 and 10 ppb increase in O3 concentrations in Nicosia

Central as estimated in single-pollutant models. For a 10

μg/m3 increase in same-day levels of PM10, there was a

0.85% (95%CI: 0.55%, 1.15%) increase in all-cause

admissions. At 1.18% (95%CI: -0.01%, 2.37%) increase

for every 10 μg/m3 in PM10, a stronger association, and

only just short of statistical significance, was observed

with admissions for cardiovascular causes. As expected,

wider confidence intervals were observed after adjusting

for overdispersion. However, at 0.83% (95%CI: 0.38%,

1.28%) increase in all-cause admissions and 1.19%

(95%CI: -0.10%, 2.49%) in cardiovascular admissions

per 10 μg/m3 increase in PM10, the magnitude of effects

and, thus, inferences remain largely unaffected. Surprisingly, no overall association between levels of PM10 and

respiratory admissions was observed. Furthermore,

observed associations did not appear to be simply driven

by days with the highest levels of PM10; similar, if not

stronger, associations were observed when analyses were

restricted to days with PM10 concentrations less than 100

μg/m3(N = 3133) and 75 μg/m3 (N = 2801) – not shown

in detail. Generally, no positive associations were

observed with levels of PM10 the 2 previous days. In contrast, only effects of lagged exposure to ozone were

observed. Effects appeared much stronger for cardiovascular causes, with a 2.91% (95%CI: 0.12%, 5.77%) increase

for a 10 ppb increase in levels of ozone the previous day

(lag 1). With the exception of a possible association in

women, no overall association was observed with respiratory admissions. While it does not necessarily describe the

exposure experience of the population in Nicosia, some

similar associations and patterns were observed in sensitivity analyses where exposure was defined in terms of the

Table 2: Percentage increase (and 95% CI) in admissions after adjusting for long- and short-term patterns as well as the effect of 

weather per 10 μg/m3 increase in PM10 and 10 ppb increase in O3 concentrations in Nicosia Central.

A. Per 10 μg/m3 increase in daily 24-hour average PM10 4

All admissions2 Cardiovascular3 Respiratory Cardiovascular + Respiratory

Lag 05 Lag 05 Lag 05 Lag 05

All age/sex groups 0.85 (0.55,1.15) 1.18 (-0.01,2.37) 0.10 (-0.91,1.11) 0.56 (-0.21,1.34)

Nicosia residents1 0.73 (-0.62,2.09) 0.25 (-0.84,1.36) 0.38 (-0.47,1.23)

Males 0.96 (0.54,1.39) 1.27 (-0.15,2.72) -0.06 (-1.37,1.26) 0.63 (-0.34,1.62)

Females 0.74 (0.31,1.18) 0.99 (-1.11,3.14) 0.39 (-1.21,2.02) 0.59 (-0.68,1.87)

Aged <15 0.47 (-0.13,1.08) -0.35 (-1.77,1.08)

Aged >15 0.98 (0.63,1.33) 0.59 (-0.87,2.07)

B. Per 10 ppb increase in daily maximum 8-hour moving average O3

All admissions2 Cardiovascular3 Respiratory Cardiovascular + Respiratory

Lag 25 Lag 15 Lag 25 Lag 25

All age/sex groups 0.51 (-0.16,1.18) 2.91 (0.12,5.77) 0.44 (-1.85,2.78) 0.70 (-1.05,2.49)

Nicosia residents1 2.48 (-0.72,5.78) 0.73 (-1.75,3.27) 0.81 (-1.13,2.80)

Males 0.58 (-0.35,1.52) 2.70 (-0.63,6.13) -1.76 (-4.63,1.19) -0.00 (-2.17,2.22)

Females 0.45 (-0.50,1.41) 3.46 (-1.53,8.70) 3.89 (0.12,7.80) 1.93 (-1.03,4.97)

Aged <15 1.58 (0.25,2.92) 2.27 (-0.95,5.60)

Aged >15 0.15 (-0.62,0.92) -1.65 (-4.89,1.70)

Notes: 1 After restricting the analyses to Nicosia residents. 2 Only aggregate numbers of all-cause admissions were available, thus it was not possible 

to restrict numbers to Nicosia residents. 3 Only includes people of adult age (15+) due to the rarity of cardiovascular events in those aged less than 

15. 4 Restricted to days with daily average < 150 μg/m3. 5 Only the lag (lag 0 = same day or lag 1, 2 = previous two days) with the strongest positive 

association is shown.
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rural station for the reduced period for which data were

available. For example, a comparable 0.81% (95%CI:

0.36%, 1.27%) increase in all-cause admissions was estimated per 10 μg/m3 increase in same-day levels of PM10 as

recorded in the background station. In contrast, only weak

non-significant associations were observed with causespecific admissions. Furthermore, while generally statistically non-significant, a similar pattern of lagged effects of

levels of ozone at the rural station was also observed.

Figure 1 presents the percentage increase in admissions

per 10 ppb increase in O3 up to two days previously before

(represented with solid squares) and after adjusting for

same-day levels of PM10(represented with empty squares).

Associations with levels of ozone appeared independent

of the effect of particles as adjustments either had no effect

or only strengthened the observed associations. While

associations with PM10 slightly attenuated in co-pollutant

models, the conclusions were generally unchanged.

Effects of ozone levels on cardiovascular admissions

picked at lag 1 while for respiratory admissions were only

apparent at lag 2 and appeared restricted to women and,

possibly, the younger age-group. While it is true that multiple testing across sub-groups and at different lags might

produce some statistically significant results, the observed

patterns of a lagged effect appear consistent (even though

non-significant) across causes and groups.

While no overall association with respiratory admissions

was observed, there appeared to be some pronounced differences in effect estimates by season. Table 3 shows the

percentage increase in respiratory admissions (along with

p-values for effect modification) across cold and warm

months. While there was no effect during the colder

months, an increase of 1.80% (95%CI: -0.22%, 3.85%)

on admissions per 10 μg/m3 increase in PM10 was

observed during the warm season. While slight short of

statistical significance, there was some suggestion of effect

modification by season (p-value < 0.10). The effect, if any,

seemed to be restricted to people of adult age (p-value for

effect modification = 0.02) and appeared much stronger

in women (p-value = 0.05). In contrast, no differential

effects by season were observed in the case of all-cause and

cardiovascular admissions. Figure 2 presents the estimated percentage increase in all and cause-specific admissions by (a) cold and warm months, as indexed by average

monthly temperature and (b) cold and warm days as

indexed by mean daily temperature lower or higher than

20 or 30°. While certainly not as pronounced (or statistically significant), the pattern of differential effects on respiratory admissions was replicated when models

considered cold and warm days as indexed by the average

daily temperature (instead of months). In this case, some

stronger (albeit not statistically significant) effects of PM10

on the warmest of days (temperature > 30°) were also

observed on cardiovascular admissions. Any inference,

however, was constrained by the wide confidence intervals around these estimates due to the small number of

days with mean daily temperature higher than 30° (N =

233). As expected, an effect of ozone on respiratory

admissions also appeared somewhat stronger during the

warm season. The opposite, however, was observed in the

case of cardiovascular admissions, with some possibly

stronger effects of ozone during the colder months.

A total of 63 candidate dust storm days were identified in

the period under investigation (Table 4). Amongst these,

a total of 45 days with uncharacteristically high levels of

PM10 at both stations were confirmed by meteorological

records. An additional 11 days identified by the Meteorological Services as dust-storm events showed relatively

usual levels of PM10. With maximum hourly levels of

PM10 in the background station only ranging between

22.63 and 66.6 across these days, these possibly describe

events of a milder intensity. Finally, 7 days were identified

that, while recorded as "Haze" by the Meteorological Services, these were nonetheless days when extreme values of

PM10 were recorded at both stations. Since these were (a)

either consecutive days (such as an event on 4–6 April

2000) or (b) they were days on either side of confirmed

dust storms (such as 7 February 1996), they most likely

depict "true" events overlooked by the meteorological

observer. Hourly PM10 concentrations at either station

during the "suspected" event of 4–6 April 2000 are shown

in Figure 3. Both levels and patterns of PM10 during the

three-day period appeared similar to those observed during the confirmed dust-storm event of 3–6 April 2003.

Figure 4 shows 4-day backwards trajectories ending in

Cyprus on the day/time of the four suspected dust storm

events identifying the Sahara desert or the Arabian Peninsula as a likely source. Similar southerly (mainly in the

spring) or easterly (mainly in the autumn) wind trajectories, generally consistent with previously described transport patterns [23], were observed on all other days.

Figure 5 plots the percentage increase in hospital admissions observed on (i) all 63 days as well as (ii) across the

56 days of confirmed events irrespective of levels of PM10

i.e. including storms of milder intensity and (iii) restricted

to the 45 days of confirmed events with the highest levels

of PM10. On the 63 dust-storm days, admissions were

4.8% (95%CI: 0.7%, 9.0%), 10.4% (95%CI: -4.7%,

27.9%) and 3.1% (95%CI: -10.2%, 18.3%) higher for all,

cardiovascular and respiratory admissions respectively.

While the observed effect achieved statistical significance

only in the case of all-cause admissions, this is likely to be

a result of the small number of daily events in the case of

cause-specific admissions. The overall effect of dust

storms on the risk of hospitalisation appeared similar in

men and women, 5.7% (95%CI: 0.1%, 11.6%) and 4.0%
Environmental Health 2008, 7:39 http://www.ehjournal.net/content/7/1/39

Page 7 of 16

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The effect of short-term changes in ozon Figure 1 e concentrations on hospital admissions

The effect of short-term changes in ozone concentrations on hospital admissions. Percentage increase (and 95% 

CI) in all, cardiovascular and respiratory admissions per 10 ppb increase in the daily maximum 8-hour moving average levels of 

O3 in Nicosia Central the same (lag 0) and two previous days (lags 1 and 2) before (solid squares) and after (empty squares) controlling for levels of PM10 as estimated in models adjusting for long- and short-term patterns as well as the effect of weather.

All-cause admissions

-5

-4

-3

-2

-1

0

1

2

3

4

Lag

0

Lag

1

Lag

2

Lag

0

Lag

1

Lag

2

Lag

0

Lag

1

Lag

2

Lag

0

Lag

1

Lag

2

Lag

0

Lag

1

Lag

2

All age/sex groups combined Males Females Aged <15 Aged >15

Percentage increase

Cardiovascular admissions

-10

-8

-6

-4

-2

0

2

4

6

8

10

Lag

0

Lag

1

Lag

2

Lag

0

Lag

1

Lag

2

Lag

0

Lag

1

Lag

2

Lag

0

Lag

1

Lag

2

Lag

0

Lag

1

Lag

2

All age/sex groups combined Males Females Aged <15 Aged >15

Percentage increase

Respiratory admissions

-10

-8

-6

-4

-2

0

2

4

6

8

10

Lag

0

Lag

1

Lag

2

Lag

0

Lag

1

Lag

2

Lag

0

Lag

1

Lag

2

Lag

0

Lag

1

Lag

2

Lag

0

Lag

1

Lag

2

All age/sex groups combined Males Females Aged <15 Aged >15

Percentage increase
Environmental Health 2008, 7:39 http://www.ehjournal.net/content/7/1/39

Page 8 of 16

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(95%CI: -1.8%, 10.1%) respectively, or in those younger/

older than 15 years of age, 5.6% (95%CI: -2.4%, 14.2%)

and 4.7% (95%CI: -0.0%, 9.6%) respectively. Restricting

the analyses to the 56 or 45 confirmed dust-storm days

did not affect the conclusions; if anything, the effect on

cardiovascular admissions appeared stronger.

For comparison purposes, figure 5 also presents the percentage increase in admissions across quartiles of nonstorm days with increasing levels of PM10 after adjustments for seasonality and the confounding effect of

weather. Stepwise increases were observed for all and cardiovascular causes, with 5.4% (95%CI: 3.6%, 7.2%) and

Seasonal effects of short- Figure 2 term changes in concentrations of air pollutants on hospital admissions

Seasonal effects of short-term changes in concentrations of air pollutants on hospital admissions. Percentage 

increase (and 95% CI) in all-cause, cardiovascular and respiratory admissions per 10 μg/m3increase in PM10 or 10 ppb increase 

in O3 (after adjusting for levels of PM10) by A) cold and warm months as indexed by monthly average temperature and B) cold 

and warm days as indexed by mean daily temperature lower or higher than 20 or 30° (shown only for PM).

A) B)

Differential effects of PM10 by cold and warm months 

-2

-1

0

1

2

3

4

Cold

Overall effect

Warm

Cold

Overall effect

Warm

Cold

Overall effect

Warm

All-cause - Lag 0 (p-value=0.54) Cardiovascular - Lag 0 (p-value=0.63) Respiratory - Lag 0 (p-value=0.10)

Percentage increase

Differential effects of O3 by cold and warm months 

-5

-3

-1

1

3

5

7

Cold

Overall effect

Warm

Cold

Overall effect

Warm

Cold

Overall effect

Warm

All-cause - Lag 2 (p-value=0.08) Cardiovascular - Lag 1 (p-value=0.13) Respiratory - Lag 1 (p-value=0.10)

Percentage increase

Differential effects of PM10 by days when mean daily temperature lower or higher 30o

-4

-2

0

2

4

6

8

10

Days < 30

Overall effect

Days > 30Days < 30

Overall effect

Days > 30Days < 30

Overall effect

Days > 30

All-cause - Lag 0 (p-value=0.07) Cardiovascular - Lag 0 (p-value=0.58) Respiratory - Lag 0 (p-value=0.30)

Percentage increase

Differential effects of PM10 by days when mean daily temperature lower or higher 20o

-2

-1

0

1

2

3

4

Days < 20

Overall effect

Days > 20Days < 20

Overall effect

Days > 20Days < 20

Overall effect

Days > 20

All-cause - Lag 0 (p-value=0.47) Cardiovascular - Lag 0 (p-value=0.76) Respiratory - Lag 0 (p-value=0.44)

Percentage increase

Table 3: Differential effects of a 10 μg/m3 increase in PM10 on respiratory admissions during the cold and warm season after adjusting 

for long- and short-term patterns as well as the effect of weather.

Percentage increase (and 95% CI) per 10 μg/m3 increase in PM10 1

Cold months2 Warm months3 P-value for effect modification

All age/sex groups -0.33 (-1.47,0.82) 1.42 (-0.42,3.31) 0.102

Nicosia Residents4 -0.22 (-1.45,1.02) 1.80 (-0.22,3.85) 0.083

Males -0.16 (-1.76,1.46) 1.10 (-1.47,3.74) 0.397

Females -0.26 (-2.18,1.70) 3.27 (-0.00,6.65) 0.059

Aged <15 -0.31 (-2.02,1.42) -0.59 (-3.53,2.45) 0.872

Aged 15+ 0.02 (-1.76,1.83) 3.89 (1.05,6.80) 0.018

Notes: 1 Restricted to days where daily mean PM10 < 150 μg/m3.

2 3 Cold months include Jan, Feb, Mar Apr, Nov and Dec based on monthly average 

temperature; warm months include all the rest. 4 After restricting the analyses to Nicosia residents.
Environmental Health 2008, 7:39 http://www.ehjournal.net/content/7/1/39

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7.1% (95%CI 0.1%, 14.6%) increased admissions respectively in the quartile of days with the highest levels of

PM10 (>64 μg/m3) compared to the quartile of days with

the lowest levels (PM10 < 40 μg/m3); in both cases, p-value

for linear trend < 0.01. This was not the case with respiratory admissions, where the risk of admissions rose by

2.4% (95%CI: -3.0%, 8.2%), 4.2% (95%CI: -1.5%,

10.2%) and 2.8% (95%CI: -2.8%, 8.8%) across quartiles,

at least partly explaining the lack of an overall linear association. In all cases, however, the magnitude of effects on

dust-storm days appeared at least comparable to the

effects seen on non-storm days with the highest PM levels.

Discussion

Main findings

Short-term changes in PM10 increase the risk of same-day

hospitalization for all-cause and cardiovascular causes in

Nicosia hospitals. An effect on respiratory health was

Typical levels of PM10 Figure 3 concentrations during a dust storm

Typical levels of PM10 concentrations during a dust storm. Hourly levels of PM10 concentrations as recorded at Nicosia 

Central and Ayia Marina stations during A) a suspected dust storm, not confirmed by Meteorological Services records, between 

4–6 April 2000, B) a dust storm confirmed by Meteorological Services records between 3–6 April 2003 and C) a confirmed 

dust storm with the highest recorded levels of PM10 (log-scale) between 29–30 May 2003.

A) 

0

50

100

150

200

250

300

350

400

450

0 3 6 9 12 15 18 21 0 3 6 9 12 15 18 21 0 3 6 9 12 15 18 21 0 3 6 9 12 15 18 21 0 3 6 9 12 15 18 21

34567

April

Levels of PM10

Nicosia

Ayia Marina

B) 

0

50

100

150

200

250

300

350

400

450

0 3 6 9 12 15 18 21 0 3 6 9 12 15 18 21 0 3 6 9 12 15 18 21 0 3 6 9 12 15 18 21 0 3 6 9 12 15 18 21 0 3 6 9 12 15 18 21

234567

April

Levels of PM10

Nicosia

Ayia Marina

C) 

0

1

2

3

4

5

6

7

8

9

10

0 3 6 9 12 15 18 21 0 3 6 9 12 15 18 21 0 3 6 9 12 15 18 21 0 3 6 9 12 15 18 21

28 29 30 31

May

Levels of PM10 on the log-scale

Nicosia

Ayia Marina
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Identification of Figure 4 the likely source of dust storm events

Identification of the likely source of dust storm events. Backwards wind trajectories ending in Nicosia on the day and 

about the time of the first elevated PM10 concentrations of the four suspected dust storm events.
Environmental Health 2008, 7:39 http://www.ehjournal.net/content/7/1/39

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observed only during the warm season. In contrast to the

effects seen with same-day levels of PM10, only lagged

effects were observed with levels of ozone. These associations appeared independent of the effect of levels of PM10

and, perhaps, stronger for cardiovascular causes. More

interestingly, there was also evidence to suggest that risk

of hospitalization was higher on dust storm days. While a

statistically significant association was observed only for

overall admissions, in all cases, effects were at least comparable in magnitude to those seen on non-storm days

with the highest levels of PM10, particularly for cardiovascular causes.

Limitations

Several studies have shown that the effect of air pollution

on hospitalization is stronger for certain cardio-respiratory conditions [24] and among certain sub-groups of the

population, such as children, the elderly or people with a

recent history [25-28]. The small number of daily events

in a city the size of Nicosia provides limited statistical

power to permit a finer age-, sex, or cause-specific analysis.

This is, in fact, portrayed in the often low precision of the

estimates and, thus, wide confidence intervals. Furthermore, multiple testing across sub-groups or different lags

might produce some spurious associations. For this reason, the sub-group analyses were a priori restricted to gender (only)- and age (only)-specific comparisons and

lagged exposure for up to 2 days. Due to lack of statistical

power in a small sample, distributed lag models were not

considered. Models investigating effect modification and

non-linear effects across quartiles are only complimentary

to the main analysis, nevertheless necessary, in the first

case to highlight important differences by season and, in

the case of the latter, to provide a basis of comparison

(and a similar unit of measurement) between dust storm

and non-storm days. The observed patterns appear not

only internally consistent but, as a result of the long

period of investigation (i.e. 10-years), the magnitude of

estimates in a small city the size of Nicosia seem to be in

agreement with those observed in large studies elsewhere.

As with all hospital data, there might be to some extent

misclassification of the cause of admission, particularly in

people with both respiratory and cardiovascular pathologies. However, it is unlikely that such misclassification is

temporally related to levels of air pollutants and, thus, can

only bias our estimates towards the null. With only one

air quality station in Nicosia in operation for the full 10-

year period of investigation, the analyses presented here

are based on a single exposure series. Thus, replacing missing values in about 10% of days (and an additional 2–3%

days purposefully excluded due to availability of less than

12 hourly measurements) was not possible. Nevertheless,

the data series is generally longer than in similar timeseries studies. Furthermore, similar patterns were

observed when analyses were repeated for the reduced

period for which data were available from the background

station. Finally, identification of dust-storm days was

mainly based on visibility observations and coding practices of a meteorological observer. Subjective in nature as

it may be, a series of data-based criteria were used to validate the observations. It is likely that some dust storms,

especially those of milder intensity, might have been

missed out. However, it is unlikely that days with high levels of PM solely due to traffic sources were considered as

dust-storm days. At any rate, the observed effect persisted

Table 4: Calendar of confirmed and suspected dust-storm events as identified by either a meteorology observer at Larnaca airport 

and/or uncharacteristic levels of PM10.

Year Dates Number of days Max number of consecutive days

1995 9 Apr* 1 1

1996 7 Feb#, 8 Feb, 9 Feb 3 3

1997 22 Apr*, 8 Dec 2 1

1998 16 Mar, 27 Mar, 28 Mar, 5 Jul 4 2

1999 17 Feb*, 19 Mar, 30 Mar, 7 May 4 1

2000 4 Apr#, 5 Apr#, 6 Apr#, 13 Apr, 18 Apr, 19 Apr, 18 Nov, 30 Dec* 8 3

2001 27 Feb*, 28 Feb*, 1 Mar, 18 Apr, 19 Apr, 22 Apr, 1 May, 13 May 8 3

2002 31 Mar*, 5 Apr, 6 Apr, 15 Apr, 1 Oct*, 19 Oct, 20 Oct, 10 Nov 8 2

2003 13 Jan, 2 Feb, 18 Feb*, 2 Mar*, 18 Mar, 19 Mar, 3 Apr, 4 Apr, 5 Apr, 6 Apr, 17 Apr*, 

24 Apr, 29 May, 30 May, 11 Sept, 17 Sept

16 4

2004 16 Jan, 22 Jan#, 5 Mar, 27 Mar, 7 May, 10 May, 26 Oct#, 27 Oct# 9 2

Total 63 4

Notes: * Days which were identified as dust storms by the Meteorological services but for which levels of PM10 were not uncharacteristically high 

at either station, n = 11, possibly indicating dust storms of milder intensity # Days which were not identified as dust storms by the Meteorological 

Services but PM10 were uncharacteristically high at both stations, n = 7.
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when restricting inferences to the group of days for which

meteorological observations and measurements at the

three sites were in agreement.

The effect of short-term changes in air pollutants on 

hospitalisation

Even though at limited power due to the small number of

daily events in a city the size of Nicosia, the long period of

investigation has allowed for some positive (and statistically significant) associations between levels of particulate

matter and risk of hospitalization to be observed. Estimates seem consistent with the size of effects seen across

several European cities, particularly for cardiovascular

causes (i.e. 0.4%–1.4% per 10 μg/m3 increase in PM)

[29,30]. Surprisingly, other than the warm season, no

overall effect was observed with respiratory admissions.

While several studies have shown associations between

levels of PM and respiratory mortality [6,10], there have

been some inconsistent results in the case of hospital

admissions [31], with some studies only showing associations in certain sub-groups [32] or for certain respiratory

causes, such as COPD and asthma [33]. Furthermore,

admissions during the colder months are mainly driven

by viral respiratory infections. Even though models

adjusted for the observed seasonal pattern with higher respiratory admissions in the colder season, it is possible that

not explicitly controlling for these causes (due to lack of

data) has masked a possible effect in the colder months,

particularly in children (aged less than 15). It is also

important to note that, in the case of respiratory admissions, a striking weakly pattern was observed with a large

dip in admissions on Tuesdays (2 daily admissions on

average) on either side of high volume on Mondays and

Wednesdays (9 daily admissions). With the exception of

the elderly (aged 65+), similar patterns were observed in

all age and sex groups. While it was not clear whether this

was due to reduced bed availability, there was some indication that people admitted on Monday were more likely

to be discharged before Wednesday (42.5%) while those

admitted on Wednesday were more likely to be kept until

the following Monday (as many as 37% and only 13% for

any longer). The extent to which this atypical weekly patHospital admissions on dust storm days as compared to non-storm days Figure 5

Hospital admissions on dust storm days as compared to non-storm days. Percentage increase (and 95% CI) in all, 

cardiovascular and respiratory admissions across quartiles of days with increasing levels of particulate matter (PM10), compared 

to the quarter of days with the lowest levels, after adjusting for seasonality and the confounding effect of weather. Shown separately, the estimated percentage increase in admissions on dust-storm days (n = 63 all candidate days, 56 confirmed dates 

irrespective of levels of PM and 45 confirmed dates with the highest levels of PM). Note: scales are not comparable across 

graphs.

All-cause

-2

0

2

4

6

8

10

12

Lowest

levels

Quartile 

2

Quartile 

3

Highest

levels

Dust

Storms 

(63 days)

Dust

Storms 

(56 days)

Dust

Storms 

(45 days)

Percentage Increase

Cardiovascular + Respiratory

-5

0

5

10

15

20

25

Lowest

levels

Quartile 

2

Quartile 

3

Highest

levels

Dust

Storms 

(63 days)

Dust

Storms 

(56 days)

Dust

Storms 

(45 days)

Cardiovascular

-15

-10

-5

0

5

10

15

20

25

30

35

40

Lowest

levels

Quartile 

2

Quartile 

3

Highest

levels

Dust

Storms 

(63 days)

Dust

Storms 

(56 days)

Dust

Storms 

(45 days)

Percentage Increase

Respiratory

-15

-10

-5

0

5

10

15

20

25

30

35

40

Lowest

levels

Quartile 

2

Quartile 

3

Highest

levels

Dust

Storms 

(63 days)

Dust

Storms 

(56 days)

Dust

Storms 

(45 days)
Environmental Health 2008, 7:39 http://www.ehjournal.net/content/7/1/39

Page 13 of 16

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tern has produced a discrepancy between actual need and

admission and, thus, contributed to the lack of any strong

association with overall respiratory admissions is not

known.

No positive effects were observed with levels of PM10 the

2 previous days. While lagged or cumulative effects have

commonly been observed between levels of PM and mortality in several studies [34], it is not uncommon for the

strongest associations to be observed with same-day levels

in the case of hospital admissions [12,35]. Perhaps, even

more so in the case of Cypriot cities where, with no referral system, direct access to specialist health care may be

considerably easier than in larger European or US cities. A

number of person-based studies have now used average

levels of air pollutants the 24 hours preceding an event

(rather than same calendar day which would include

hours after as well as before the event) and, at least

amongst susceptible populations, have shown effects in as

little as 6 hours prior to an arrhythmia and even 2 hours

prior to a myocardial infarction [36-39].

In contrast to the same-day association with levels of PM,

only effects of lagged exposure to ozone were observed.

Negative associations with same-day levels of ozone have

previously been observed in several of the 23 APHEA cities, an effect which persisted even in the summer period

in at least 4 of the cities, namely Rome, Paris, Tel Aviv and

Valencia [40]. While explanations for this are not clear, it

is thought to be a product of the relationship between

ozone (a secondary pollutant produced by photochemical

reactions) and traffic-related primary pollutants such as

nitrogen oxides (NO) and volatile organic compounds

(VOC) which can reduce ozone concentrations at least at

the local scale. Thus, high ozone concentrations in stations located in central parts of cities, such as in this case,

may reflect low levels of local traffic pollutants or good

dispersion conditions, which can lead to negative associations with health indicators, at least in the short-run,

whereas it is common for stronger associations with

ozone to be subsequently observed at lagged intervals. A

similar pattern of negative same-day associations, albeit

much weaker, was observed with levels of ozone as

recorded at the rural station, not influenced by local pollution. Finally, while, there have generally been some

inconsistent findings for the association between cardiovascular morbidity and levels of ozone, with some reporting positive [41], no or even negative associations [42],

effects of a similar magnitude have been previously

reported between levels of ozone two days before admission for cardiovascular causes in London [43]. In general,

neither the fact that stronger associations with lagged

rather than concurrent exposure nor that stronger effects

for cardiovascular than respiratory admissions were

observed seem inconsistent with previous findings [43-

45].

Effect modification by season

Seasonal differential effects of changes in levels of air pollution on the risk of hospitalization have been previously

reported, particularly with respects to stronger effects of

PM on respiratory health during the warm season such as

those observed here [32,43,46]. Other studies, however,

have found little evidence of differential effects by season

[47,48]. It is uncertain whether such differential effects

carry biological plausibility, i.e. a synergistic effect of air

pollution and temperature amplifying people's response

to lower levels of air pollutants than normally, or it simply, reflects an increased proportion of time spent outdoors, and thus higher exposure, on warm days [49]. In

APHEA, for instance, the stronger effects of PM10 on total

mortality observed in the warmer than colder cities persisted when latitude was used instead of actual temperature, thus, it was proposed that ambient measurements in

warmer places may represent the average population

exposure more accurately than in colder places where people do not spend as much time outdoors [21]. Here, other

than some weak evidence of strongest effects on cardiorespiratory morbidity on the warmest days (>30°), generally, more pronounced differential effects were observed

across cold-warm months rather than across cold-warm

days (based on daily mean temperature).

In the light of evidence of ozone effects on mortality

restricted to the warm season across 23 APHEA cities, the

stronger association between cardiovascular admissions

and ozone during the colder season observed here appears

surprising [40]. However, some similar patterns have

been observed elsewhere, particularly amongst the elderly

[50]; possible explanations for this pattern are unclear

and it might simply be a chance finding. Nonetheless, in

a recent cross-sectional study in Korea, it was reported that

measures of blood pressure, a risk factor for cardiovascular disease, were significantly associated with PM10 levels

only in the warm season while the reverse was true for O3,

with associations only during the cold season [51].

The effect of dust storms

Long-range transport of Saharan dust across the Mediterranean into southern Europe [52,53], and less frequently

as far north as the British Isles [54], is well established.

Consistent with a recent examination of these events [23],

our analysis also suggests that there has been a rise in their

frequency, at least half of which seem to occur during the

spring months and can last for as many as four consecutive days. Unlike the mineral and chemical composition

as well as transport patterns of Saharan dust, the possible

health effects of these natural events have not been extensively studied. There is some modest evidence from Tai-
Environmental Health 2008, 7:39 http://www.ehjournal.net/content/7/1/39

Page 14 of 16

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wan and Korea of adverse effects of wind-blown dust from

the Mongolia/China desert on both cardiovascular and

respiratory health, some times lasting for up 3 days after

the event [55-57]; reported associations were, however,

not always statistically significant.

In contrast, a study of 17 episodes with a high concentration of coarse (crustal-derived) but not fine particles during a six-year period in Spokane, Washington, revealed no

evidence of an increased risk of death on dust days [14].

Similarly, an investigation into an unusual event of transported dust over the Atlantic to Greater Vancouver, Canada, has shown no effect on hospital admissions [58].

Some studies have, in fact, reported strengthening of the

observed health effects when windblown dust days were

excluded from the analyses [59] or reduced health effects

on windy days [60], in both cases suggestive that crustalderived particles are more benign that those from anthropogenic processes. In our analyses, we have not observed

an attenuation of effects by including dust-storm days. On

the contrary, we found evidence of increased admissions

on dust-storm days of similar magnitude to the effects

seen on non-storm days with the highest concentrations.

Of course, the possibility of air-borne dust containing particles of anthropogenic sources can not be excluded since

mega-cities, such as Cairo, are commonly on the path of

these events. Alternatively, some support for the adverse

effects of particles due to natural sources observed here

comes from a recent study of the biological content in

dust transferred to Haifa, Israel during similar events in

2004–2005 that revealed both an increase in the concentration of airborne microorganisms and a change in the

usual content of fungal population in PM samples, both

which are thought to affect human health [19]. Similar

findings have been reported from aeromicrobiological

analyses of samples on the Turkish coastal town of

Erdemli [61].

Conclusion

We observed an increased risk of hospitalization at elevated levels of particulate matter and ozone generally consistent with the magnitude seen across several European

cities. Interestingly, we also observed an increased risk of

hospitalization on dust storm days, particularly for cardiovascular causes. While inference from these associations

is limited due to the small number of dust storm days in

the study period, these effects did not appear to be an artifact of including days of high traffic pollution. While these

represent non-preventable events, with a magnitude of

effects at least comparable to those on days with the highest levels of PM10 from traffic sources, such events may

merit special health warnings directed to the most vulnerable population groups.

List of abbreviations

μg/m3: Micrograms per cubic metre; AIC: Akaike information criterion; APHEA: The Air Pollution and Health: a

European Approach Project; CI: Confidence Interval; ICD:

International Classification of Diseases; HYSPLIT: Hybrid

Single-Particle Lagrangian Integrated Trajectory; GAM:

Generalized Additive Models; NMMAPS: The National

Morbidity, Mortality and Air Pollution Study; NOAA:

National Oceanic and Atmospheric Administration; O3:

Ozone; PM10: Particulate matter with aerodynamic diameter less than 10 microns; ppb: parts per billion; SD:

Standard deviation; TEOM: Tapered Element Oscilling

Micro-balance; UBRE: Unbiased Risk Estimator.

Competing interests

The authors declare that they have no competing interests.

Authors' contributions

PY, PD and PK conceived and secured funding for the

study from the Cyprus Ministry of Health. PY, NM and OK

collected the data. NM compiled the data and performed

the statistical analyses. PK, JS and DD provided guidance

and statistical advice. All authors assisted in the interpretation of results. NM wrote the first draft of this paper. All

authors contributed towards the final version. All authors

read and approved the final manuscript.

Acknowledgements

We wish to acknowledge the Cyprus Ministry of Health for financial assistance for this project. Also, the Cyprus Statistical Services, the Air Quality 

Section (Cyprus Ministry of Labour) and the Meteorological Services of 

Cyprus for providing us with the admissions, air pollution and meteorological data respectively. In particular, Mr Stelios Pashiardis (Meteorological 

Services) for the support and helpful discussions. Finally, Dr Meredith Franklin for advice on statistical matters.

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