Forecasting Asthma Hospital Admissions from Remotely Sensed

Environmental Data

Rute Almeida

1,2

, Ana Cl

audia Teodoro

, Hern

ani Gonc¸alves

, Alberto Freitas

1,4

, Ana S

a-Sousa

Cristina J

acome

and Jo

ao Fonseca

1,4,5

CINTESIS - Center for Health Technology and Services Research, Faculty of Medicine, University of Porto,

Rua Dr. Pl

acido da Costa 4200-450 Porto, Portugal

CMUP - Centre of Mathematics of the University of Porto, Porto, Portugal

Department of Geoscience, Environment and Land Planning/Institute of Earth Sciences (ICT), Faculty of Sciences,

University of Porto, Rua do Campo Alegre, 4169-007 Porto, Portugal

MEDCIDS - Department of Community Medicine, Information and Health Decision Sciences, Faculty of Medicine,

University of Porto, Rua Dr. Pl

acido da Costa 4200-450 Porto, Portugal

CUF Porto Institute & Hospital, Estrada da Circunvalac¸

ao 14341, 4100-180 Porto, Portugal

Keywords:

Asthma Exacerbation, NDV I, Temperature, NO

, Air Pollution, Meteorological Parameters, Forecasting

System.

Abstract:

Asthma has a major social impact and is prone to exacerbations. It is known that environmental factors, such as

meteorological conditions and air pollutants, have a role over their occurrence. In a previous work, positive as-

sociations were found between hospital admissions due to asthma exacerbation at highly urbanized regions of

Portugal and higher atmospheric NO

levels, lower vegetation density and higher air temperatures, estimated

using remote sensing. In this study we propose the use of georeferenced environmental factors to forecast the

risk of hospital admissions due to asthma exacerbation. We applied linear discriminant analysis using monthly

averages based in 2003–2007 environmental data to forecast positive monthly admission rates in municipal-

ities of Lisboa district (Portugal) during 2008. Space-time estimates of nitrogen dioxide (NO

), vegetation

density from MODIS Normalized Difference Vegetation Index (NDV I) and near-surface air temperature (Ta)

were considered as independent variables. We identiﬁed over 65% of the combinations months/municipalities

having hospital admissions in the testing set, with less than 10% of false positives. These results conﬁrm that

, NDV I and Ta levels obtained from remotely sensed data can be used to predict hospital admissions due

to asthma exacerbation, and may be helpful if applied in warning systems for patients in the future.

1 INTRODUCTION

Asthma is an inﬂammatory disorder of the airways

associated with a hyper-responsiveness that leads to

recurrent episodes of wheezing, breathlessness, chest

tightness, and coughing. It is among the most com-

mon chronic diseases, affecting people of all ages

throughout the world, with increasing prevalence in

many countries, especially among children (GINA,

2016). The Portuguese National Asthma Survey

(2010) found a prevalence of 6.9% (43% uncontrolled

asthma) (S

a-Sousa et al., 2012; Ferreira-Magalh

aes

et al., 2015). Asthma is punctuated by exacerba-

tions, which are characterized by the worsening of

symptoms and increase in reliever medication usage,

which are the main cause for a huge social impact,

by leading to unscheduled healthcare usage, including

hospitalizations, absenteeism and productivity loss at

workplace.

There is evidence that the delivery of healthcare

via information and communication technology has

beneﬁcial effects in chronic diseases management

(Bashshur et al., 2014). Studies with Portuguese

asthma patients showed that they are willing and

ready to use information and communication technol-

ogy to help managing their asthma (Fonseca et al.,

2006; Cruz-Correia et al., 2007). In a Cochrane re-

view of mobile applications to improve asthma symp-

tom control performed in 2013 (Marcano Belisario

et al., 2013), only two randomized control studies

124

Almeida, R., Teodoro, A., Gonçalves, H., Freitas, A., Sa-Sousa, A., Jácome, C. and Fonseca, J.

Forecasting Asthma Hospital Admissions from Remotely Sensed Environmental Data.

DOI: 10.5220/0006385201240130

In Proceedings of the 3rd International Conference on Geographical Information Systems Theory, Applications and Management (GISTAM 2017), pages 124-130

ISBN: 978-989-758-252-3

were found over hundreds of articles and only one

reporting higher asthma-related quality life scores.

Therefore patient self-management using information

and communication technology tools may represent

high value patient care in near future, which potential

is still to be achieved.

It is known that several environmental factors,

such as meteorological conditions and air pollutants,

have a role over exacerbations occurrence. Neverthe-

less, there is no consensus regarding the speciﬁc fac-

tors which should be considered, and attempts to pre-

dict asthma exacerbation from environmental param-

eters have produced inconsistent results (Akinbami

et al., 2010; Delamater et al., 2012; Moustris et al.,

2012; Soyiri et al., 2013; GINA, 2016).

The association between hospital admissions due

to asthma exacerbation and remotely sensed data

(MODIS sensor) for air pollutants NO

and PM

relative humidity (RH), Normalized Difference Veg-

etation Index (NDV I) and near-surface air tempera-

ture (Ta), in Mainland Portugal and considering spa-

tial information, has been recently studied by Ayres-

Sampaio et al (Ayres-Sampaio et al., 2014). In that

work, linear univariate regression analysis and Pear-

son correlation coefﬁcients were used to quantify

separately the association between asthma hospital

admissions (dependent variable) with which one of

the ﬁve environmental variables, considering six-year

(2003-2008) based seasonal averages. A positive as-

sociation between asthma hospitalizations at highly

urbanized regions of Portugal mainland and higher at-

mospheric NO

levels, lower vegetation density and

higher air temperatures.

In the current research we propose that the com-

bined use of georeferenced environmental factors data

are able to forecast the geographical dependent risk

of hospital admissions due to asthma exacerbation. In

this work we explored the potential of the environ-

mental factors previously reported in (Ayres-Sampaio

et al., 2014) as determinants of asthma hospitaliza-

tions due to asthma - Ta, NDV I and NO

- to fore-

cast the positive admission rates by municipality at

the Lisboa district (Portugal).

2 DATA AND METHODS

The study area of this research was the district of Lis-

boa (Figure 1), as it represents more than 45% of the

population living at the Portuguese districts with high

(> 10%) urban coverage and nearly a quarter of all

population living in Portugal, with a population rang-

ing from 2 190 197 to 2 238 484 between 2003 and

2008. Lisboa district has 16 municipalities, which

were considered as separate data points to attend to

spatial dependency of environmental exposition.

The data sources used in this work were the same

used in (Ayres-Sampaio et al., 2014) as well as the

preprocessing of enviromental data. All processing

was performed using ArcGIS 10.0 and MATLAB

R2014a.

2.1 Environmental Data

Several environmental variables have been reported

as associated to asthma hospital admissions. Attend-

ing to the previously found associations between the

admissions due to asthma exacerbation at high urban

coverage districts and NO

, NDV I and Ta (Ayres-

Sampaio et al., 2014), those parameters were chosen

as independent variables in this research.

The air temperature Ta at a given point can be

computed by a linear regression if the lapse rate (I

)

the altitude (H), and the temperature at sea level (T

)

are known. The altitude H was given by Digital Ele-

vation Model (DEM) from the Shuttle Radar Topog-

raphy Mission (SRTM) (Farr et al., 2007) composed

by 23 1X1-degree images with 90m of spatial resolu-

tion and after resampled in a 5 km resolution image

(DEM5); l

and T

were determined using MODIS

temperature proﬁle. MODIS temperature proﬁle was

acquired from MOD07 products. The MODIS Atmo-

spheric Proﬁles product (MOD07) consists of several

parameters, all of than are produced day and night

for Level 2 at 5X 5 1-km pixel resolution. The NDV I

was obtained directly from the MOD13A3 product.

The NDV I assumed values between −1 and +1 and

is computed as:

NDV I = [(ρ

NIR

− ρ

Red

)/(ρ

NIR

+ ρ

Red

)], (1)

where ρ

NIR

and ρ

Red

are respectively the near-infrared

reﬂectance and red reﬂectance. MOD13A3 data are

provided monthly at 1-km spatial resolution. In gen-

erating this monthly product, the algorithm takes all

the 16-day 1-km products that overlap the month.

Hourly NO

measurements were collected from the

Portuguese Environmental Agency through an online

database available at http://www.qualar.org/, followed

by the computation of monthly averages from the

daily averages.

The 1-km spatial resolution of the remote sensed

data is enough considering the municipality spatial

unit considered for hospitalizations.

2.2 Hospitalizations Data

Data from hospitalizations is a subset of that used in

(Ayres-Sampaio et al., 2014), which refers to all of the

Forecasting Asthma Hospital Admissions from Remotely Sensed Environmental Data

125

Figure 1: District of Lisboa (Portugal) and its 16 municipalities.

public acute care hospitals of the National Health Ser-

vice as provided by the Ministry’s of Health Central

Authority for Health Services (Administrac¸

ao Central

do Sistema de Sa

ude, ACSS). The database includes

diagnostic codes according to the International Clas-

siﬁcation of Diseases, Ninth Revision, Clinical Modi-

ﬁcation (ICD-9-CM), from which cases with a princi-

pal diagnosis of asthma (code ICD-9-CM 493.x) were

retrieved.

A total of 4 889 admissions in the Lisboa district

in the period from 2003 to 2008 were analized (Ta-

ble 1), which represent over 25% of the total of the

asthma hospital admissions in Portugal during that pe-

riod. Monthly admission rates per 1000 inhabitants

were calculated for each municipality from annual

resident population data obtained from the National

Statistical Institute (Instituto Nacional de Estat

ıstica,

INE).

2.3 Classiﬁcation Strategy

The forecasting of hospital admissions due to asthma

exacerbation was based on the following binary

classes deﬁned for each municipality:

class 0 - no admissions were registered in that month;

class 1 - at least one admission was registered in that

month.

Training set was deﬁned as the reported data from

2003 to 2007. The classiﬁer was constructed by tak-

ing the averages in each month per municipality, both

for the independent variables (T

, NDV I and NO

)

and dependent variable (asthma related monthly ad-

mission rates per 1000 inhabitants).

Supervised classiﬁcation was performed by Lin-

ear Discriminant Analysis (LDA). The LDA classiﬁer

was evaluated over 3 data sets:

i. training data (averages), the monthly 5-years

(2003-2007) based averages for each of the 16 mu-

nicipalities (192 data points);

ii. training data (monthly), the 12 monthly values

for each year from 2003 to 2007 for each of the 16

municipalities (960 data points);

iii. test data (monthly), the 12 monthly values for

2008 for each of the 16 municipalities (192 data

points).

The outcome achieved in a binary classiﬁcation

can be easily displayed as a confusion matrix, which

is a two-by-two table (Table 2). In the confusion ma-

trix, True Negatives (TN) and True Positives (TP) cor-

respond to the number of correct classiﬁcations for re-

spectively classes 0 and 1, while False Positives (FP)

and False Negatives (FN) correspond to the number

of miss-classiﬁcations as class 1 and class 0, respec-

tively. The misclassiﬁcation error rate based on the

training data was quantiﬁed as the apparent error rate:

err = 1/2 (FP/(T N + FP) + FN/(FN + T P)) (2)

where 1/2 corresponds to the prior probabilities for

the groups. Additionally, a 10-fold cross-validation

scheme of training data was used.

GISTAM 2017 - 3rd International Conference on Geographical Information Systems Theory, Applications and Management

126

Table 1: Hospital admissions due to asthma exacerbation, per municipality and year, in the Lisboa district.

Municipality 2003 2004 2005 2006 2007 2008 total

Alenquer 10 9 12 10 8 8 57

Amadora 71 62 86 103 85 55 462

Arruda dos Vinhos 2 0 1 1 1 1 6

Azambuja 5 7 2 2 2 2 20

Cadaval 2 2 2 1 1 2 10

Cascais 49 55 68 78 57 36 343

Lisboa 251 238 224 197 192 222 1324

Loures 67 79 57 74 67 68 412

Lourinh

a 6 7 4 6 3 7 33

Mafra 6 19 12 19 12 18 86

Odivelas 82 87 62 59 78 77 445

Oeiras 74 76 65 57 36 27 335

Sintra 128 159 165 209 182 132 975

Sobral de Monte Agrac¸o 4 5 3 1 1 4 18

Torres Vedras 34 36 24 25 10 22 151

Vila Franca de Xira 44 38 37 34 37 22 212

For all data sets, the performance was also mea-

sured in terms of the sensitivity (S), positive predic-

tive value (P+) and accuracy (A)

S = T P/(T P + FN) (3)

P+ = T P/(T P + FP) (4)

A = (T P + T N)/(T P + FP + FN + T N). (5)

Table 2: Confusion matrix for binary classiﬁcation.

Classiﬁer

class 0 class 1

Truth

class 0 TN FP

class 1 FN TP

3 RESULTS AND DISCUSSION

The frequency distributions of hospital admission

rates in each data set are represented in Figure 2. As

consequence of averaging, a single admission within

the 5-year period (2003-2007) is sufﬁcient to produce

non-zero mean. Thus, the class 0 (no admissions)

in the monthly averages is less represented that in

monthly data.

Considering performance evaluation over training

data (averages), the apparent error rate was 24% and

a 36% error was found using 10-fold cross-validation.

The confusion matrices considering training and test-

ing data are presented in Table 3, while sensitivity

(S), positive predictivity (P+) and accuracy (A) values

can be found in Table 4. Notice that the LDA classi-

ﬁer was able to correctly identify roughly 2/3 of the

combinations months/municipalities having hospital

admissions (with S = 65% in testing data), while the

fraction of false positive identiﬁcations was always

below 15%. For test data, less than 10% of the pos-

itive forecastings would be false alarms for hospital

admissions in that month for the speciﬁc municipal-

ity. The results obtained conﬁrm that Ta, NDV I and

levels based on remotely sensed data have the

ability to predict existence of hospital admissions due

to asthma exacerbation, using simple linear methods,

which do not consider any possible nonlinear depen-

dencies.

Table 3: Confusion matrices for training and testing data.

Training data Classiﬁer

(averages) no admissions admissions

Truth

no admissions 19 2

admissions 64 105

Total 83 107

Training data Classiﬁer

(monthly) no admissions admissions

Truth

no admissions 238 64

admissions 179 479

Total 417 543

Testing data Classiﬁer

(monthly) no admissions admissions

Truth

no admissions 55 8

admissions 44 85

Total 99 93

It is important to mention that many of asthma ex-

acerbation episodes does not require healthcare atten-

tion and from those requiring it, only a small fraction

results on hospitalization. Thus, hospital admissions

are not a good indicator of mild asthma exacerbation,

quantifying only the most severe cases. Furthermore

only admissions with principal diagnosis of asthma

Forecasting Asthma Hospital Admissions from Remotely Sensed Environmental Data

127

0 0.02 0.04 0.06 0.08 0.1 0.12 0.14 0.16 0.18 0.2

2003−2007 based monthly averages

Absolute frequency

0 0.02 0.04 0.06 0.08 0.1 0.12 0.14 0.16 0.18 0.2

100

150

200

250

300

350

2003−2007 monthly values

Absolute frequency

0 0.02 0.04 0.06 0.08 0.1 0.12 0.14 0.16 0.18 0.2

2008 monthly values

Admissions rate per 1000 inhabitants

Absolute frequency

Figure 2: Hospital admission rates per 1000 inhabitants,

with a principal diagnosis of asthma, in the district of Lis-

boa, considering the training set with 192 points (top plot),

the training set with 960 points (middle plot) and the testing

set with 192 points (lower plot).

Table 4: Performance evaluation based on Sensitivity (S),

Positive Predictivity (P+) and Accuracy (A), expressed in

S P+ A

training data

averages 63 98 66

monthly 73 88 75

testing data monthly 66 91 73

were retrieved, excluding the cases of hospital admis-

sions in which other diseases (co-morbidities) were

classiﬁed as primary diagnosis in spite of asthma ex-

acerbation were also occurring (e.g. hospital admis-

sions during asthma exacerbation but with concomi-

tant pneumonia). This constitutes a main limitation

of this work and alternative indicators and sources

of data for less severe exacerbation should be pur-

sued. Even using the most populated district of Por-

tugal, hospital admissions per municipality were not

very high, and the data size is likely to limit the per-

formance of discriminant analysis. Additionally, the

loss of time reference within the month can intro-

duce spurious information, as an admission at the ﬁrst

days of a particular month will surely not depend on

the future environmental exposition during the whole

month. Future studies could therefore include a larger

dataset, for example by including more districts, and

analyze shorter time periods (e.g. weekly data).

Only environmental factors for which the monthly

base seasonal average showed relevant correlation

with admissions due to asthma in (Ayres-Sampaio

et al., 2014) were considered in this work. Nev-

ertheless PM

and RH exposition has been related

with asthma exacerbation in the past (Akinbami et al.,

2010; Delamater et al., 2012). It is that any possible

effect of those factors was diluted by the month-based

averages. Furthermore, the environmental effects on

asthma are likely to be more immediate (weekly or

even daily) possibly non visible using month-based

values. In particular, with respect to air temperatures

and pollutants, the intrinsic daily variability and ex-

position to extreme values which were not considered

in this work, can matter. Also, in the present work we

considered the time range from 2003 to 2008 because

we used environmental factors data already processed

in a previous work ((Ayres-Sampaio et al., 2014)). In

the near future we will consider a wider and more re-

cent temporal window and include these alternative

variables in the analysis, possibly using weekly data.

The implemented strategy only considers global

geographically dependent risk, thus other personal ex-

posure factors such as indoor air pollution, time spent

outdoors, passive smoking, allergen avoidance behav-

ior, and viral infections were not considered. Also if

a patient moves across several municipalities (multi

GISTAM 2017 - 3rd International Conference on Geographical Information Systems Theory, Applications and Management

128

locations exposure), both locations should be consid-

ered.

All these previously mentioned particularities and

limitations of the present work, namely using rough

temporal scales and not considering a personalized

approach, might explain the lower sensitivity values

compared to the overall accuracy. Still, regarding the

interest for asthma self-management tools, the classi-

ﬁcation obtained can be used as geographical depen-

dent risk indicator, in spite of the above listed limita-

tions.

4 CONCLUSIONS

The classiﬁer developed in this work allowed to fore-

cast asthma related admissions with good accuracy

levels. The reduced rate of false positive is important

if it is to be included in information and communi-

cation technology tools for patient self-management.

It can be used as a risk warning tool, to be combined

with individual monitoring factors. Despite all the en-

vironmental variables have been processed and ana-

lyzed in a GIS software, in the future a deeper analysis

using a GIS approach and considering other factors,

not considered in this work will improve the informa-

tion on the spatial distribution of asthma hospitaliza-

tions and their relationship with the environment.

ACKNOWLEDGEMENTS

This article was supported by the Project NORTE-

01-0145-FEDER-000016 (NanoSTIMA), ﬁnanced

by the North Portugal Regional Operational Pro-

gramme (NORTE 2020), under the PORTUGAL

2020 Partnership Agreement, and through the

European Regional Development Fund (ERDF).

Hern

ani Gonc¸alves is ﬁnanced by a post-doctoral

grant (SFRH/BPD/69671/2010) from the Fundac¸

para a Ci

encia e a Tecnologia (FCT), Portugal.

The MATLAB licenses used in this work were

supported by Portuguese funds through CMUP

UID/MAT/00144/2013, funded by the Portuguese

Foundation for Science and Technology (FCT -

Fundac¸

ao para a Ci

encia e a Tecnologia). The authors

wish to thank the Portuguese Ministry’s of Health Au-

thority for Health Services (Administrac¸

ao Central do

Sistema de Sa

ude, ACSS) for providing access to na-

tional hospital admissions data and to Diogo Ayres

Sampaio by the initial preprocessing of the data.

REFERENCES

Akinbami, L. J., Lynch, C. D., Parker, J. D., and Woodruff,

T. J. (2010). The association between childhood

asthma prevalence and monitored air pollutants in

metropolitan areas, United States, 2001-2004. Envi-

ronmental Research, 110(3):294 – 301.

Ayres-Sampaio, D., Teodoro, A. C., Sillero, N., Santos, C.,

Fonseca, J., and Freitas, A. (2014). An investigation

of the environmental determinants of asthma hospital-

izations: An applied spatial approach. Applied Geog-

raphy, 47:10 – 19.

Bashshur, R. L., Shannon, G. W., Smith, B. R., Alverson,

D. C., Antoniotti, N., Barsan, W. G., Bashshur, N.,

Brown, E. M., Coye, M. J., Doarn, C. R., Ferguson,

S., Grigsby, J., Krupinski, E. A., Kvedar, J. C., Link-

ous, J., Merrell, R. C., Nesbitt, T., Poropatich, R.,

Rheuban, K. S., Sanders, J. H., Watson, A. R., We-

instein, R. S., and Yellowlees, P. (2014). The Em-

pirical Foundations of Telemedicine Interventions for

Chronic Disease Management. Telemedicine and e-

Health, 20(9):769–800.

Cruz-Correia, R., Fonseca, J., Amaro, M. J., Lima, L.,

Ara

ujo, L., Delgado, L., Grac¸a Castel-Branco, M.,

Costa-Pereira, A., Jo

ao, R., and Correia, C. (2007).

Web-based or paper-based self- management tools for

Asthma patients’ opinions and quality of data in a ran-

domized crossover study. Studies in health technology

and informatics.

Delamater, P. L., Finley, A. O., and Banerjee, S. (2012). An

analysis of asthma hospitalizations, air pollution, and

weather conditions in los angeles county, california.

Science of The Total Environment, 425:110 – 118.

Farr, T. G., Rosen, P. A., Caro, E., Crippen, R., Duren, R.,

Hensley, S., Kobrick, M., Paller, M., Rodriguez, E.,

Roth, L., Seal, D., Shaffer, S., Shimada, J., Umland,

J., Werner, M., Oskin, M., Burbank, D., and Alsdorf,

D. (2007). The Shuttle Radar Topography Mission.

Reviews of Geophysics, 45(2):RG2004.

Ferreira-Magalh

aes, M., Pereira, A. M., Sa-Sousa, A.,

Morais-Almeida, M., Azevedo, I., Azevedo, L. F., and

Fonseca, J. A. (2015). Asthma control in children is

associated with nasal symptoms, obesity, and health

insurance: a nationwide survey. Pediatric Allergy and

Immunology, 26(5):466–473.

Fonseca, J. A., Costa-Pereira, A., Delgado, L., Fernandes,

L., and Castel-Branco, M. G. (2006). Asthma patients

are willing to use mobile and web technologies to sup-

port self-management. Allergy, 61(3):389–390.

GINA (2016). Global strategy for asthma manage-

ment and prevention. Technical report, Global

Initiative for Asthma. http://ginasthma.org/2016-

gina-report-global-strategy-for-asthma-management-

and-prevention/.

Marcano Belisario, J. S., Huckvale, K., Greenﬁeld, G., Car,

J., and Gunn, L. H. (2013). Smartphone and tablet

self management apps for asthma. In Marcano Belis-

ario, J. S., editor, Cochrane Database of Systematic

Reviews, number 11, page CD010013. John Wiley &

Sons, Ltd, Chichester, UK.

Moustris, K. P., Douros, K., Nastos, P. T., Larissi, I. K.,

Anthracopoulos, M. B., Paliatsos, A. G., and Priftis,

Forecasting Asthma Hospital Admissions from Remotely Sensed Environmental Data

129

K. N. (2012). Seven-days-ahead forecasting of child-

hood asthma admissions using artiﬁcial neural net-

works in athens, greece. International Journal of En-

vironmental Health Research, 22(2):93–104. PMID:

21854178.

a-Sousa, A., Morais-Almeida, M., Azevedo, L. F., Car-

valho, R., Jacinto, T., Todo-Bom, A., Loureiro, C.,

Bugalho-Almeida, A., Bousquet, J., and Fonseca,

J. A. (2012). Prevalence of asthma in portugal -

the portuguese national asthma survey. Clinical and

Translational Allergy, 2(1):15.

Soyiri, I. N., Reidpath, D. D., and Sarran, C. (2013). Fore-

casting asthma-related hospital admissions in london

using negative binomial models. Chronic Respiratory

Disease, 10(2):85–94. PMID: 23620439.

GISTAM 2017 - 3rd International Conference on Geographical Information Systems Theory, Applications and Management

130