NO2 exposure and asthma among over-50s in Ireland: amicrodata analysis

Seán LyonsESRI and Trinity College Dublin

Agenda

1 Introduction and background

2 Example: Local NO2 concentrations and asthma

3 Motivation and methodology

4 Data used in the study

5 Preliminary results

6 What is needed to enable this sort of research

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Introduction and background

Goal: Find causal health effects fromenvironmental factorsIdeal data would combine, for a large, representativesample or population of individuals,

1 Detailed, accurate information on health outcomes2 And specific exposures to environmental pollutantsor amenities

3 And socioeconomic/behaviour information4 Observing same people more than once (i.e.longitudinal)

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What we actually have

Examples of linking

Example: Local NO2 concentrations and asthma

Linking simulated local NO2 exposures to TILDAresidential addresses to assess potential health effects.Primary focus on asthma cases among TILDArespondents.Efforts to circumvent potential bias in self-reporting ofasthma through data on use of obstructive airwaydisease (OAD) medications.

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The team

Dr Anne Nolan, Mr Philip Carthy, Dr Seán Lyons; ESRI & TCD(econometric modelling; knowledge of TILDA data)

Prof. Margaret O’Mahony, Aonghus Ó Domhnaill, Prof. BrianBroderick; TCD. Dr. Aoife Donnelly; TUD. Dr. Owen Naughton;IT Carlow (Local NO2 simulations; knowledge of emissionsprocesses)

Dr. Frank Moriarty, RCSI. (Classification of medications forobstructive airway disease)

Prof. Martina Hennessy, TCD (medicine)

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Motivation

NO2 often used in epidemiological studies as a marker ofcombustion-related outdoor air pollution (Achakulwisut et al. 2019)Also known to be directly associated with asthma and otherrespiratory diseases, particularly in young children (Bowatte et al.2014)Evidence of direct effect in adults is more inconsistent (Guarnieri &Balmes 2014; Le Moual et al. 2013)Area-based studies have difficulty disentangling complex sources ofvariability in populations:A better understanding of the complex relationships betweensocioeconomic, nutritional, lifestyle and environmental conditionsmight help to study their joint and independent roles in asthma (LeMoual et al. 2013)

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Methodology: logistic regression models

Model 1: Self-reported Asthma

P(asthmai = 1 | NO2,X) = Λ(α + β0NO2i + β1High NO2i +∑

βkXki )

Model 2: OAD Medications

P(medicationsi = 1 | NO2,X) = Λ(α+β0NO2i +β1High NO2i +∑

βkXki )

Λ(z) = ez

1+eZ , the c.d.f. of the logistic function.X is a vector of socioeconomic and health factors.We also carry out a number of robustness checks using variousalternative specifications.

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The Irish Longitudinal Study on Ageing (TILDA)

Nationally-representative longitudinal study of theover-50s in IrelandThree modes of data collection: CAPI, SCQ, healthassessmentExtensive data on health status, use of medicationsand socioeconomic characteristicsEach participant’s address is geo-codedHarmonised with SHARE, ELSA, HRSAt baseline (2010), 8,504 over 50s (and partners ofany age) participatedFurther waves in 2012, 2014, 2016 and 2018, with 6thwave planned for 2020

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Asthma Outcomes (TILDA Wave 1)

7226

360360

183

378378

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frac

tion

by O

AD m

edic

atio

n us

e

0 .1 .2 .3 .4 .5 .6 .7 .8 .9 1

fraction by self-reported asthma

No Asthma (W1) Asthma (W1)

Self-reported Asthma (Wave 1)

Some OAD medications used (Wave 1)No OAD medications. used (Wave 1)

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Distribution of NO2 among TILDA respondents

95th Percentile0

.1.2

.3D

ensi

ty

0 5 10 15 20NO2 Concentration (PPB)

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Descriptive Statistics: Socioeconomic Controls

Freq. % Freq. %

Gender Employment StatusMale 3,727 45.75 Employed 2,926 35.92Female 4,419 54.25 Retired 3,032 37.22Age Category Other 2,188 26.8650-64 4,652 57.11 Smoking Status65-74 2,155 26.45 Never 3,556 43.65≥ 75 1,339 16.44 Past 3,104 38.1Income Category Current 1,486 18.240 - 9,999 645 7.92 Educational Attainment10,000 - 19,999 1,656 20.33 Primary/none 2,493 30.620,000 - 39,999 2,699 33.13 Secondary 3,251 39.9140,000 - 69,999 1,554 19.08 Third/higher 2,402 29.49≥ 70,000 699 8.58 Medical CoverNot reported 893 10.96 Not covered 842 10.34Marital Status Medical insurance 3,276 40.22Married 5,616 68.94 Medical card 4,028 49.45Never married 789 9.69 MobilitySep/divorced 551 6.76 No difficulty walking 100m 7,547 92.65Widowed 1,190 14.61 Difficulty walking 100m 599 7.35

Total 8,146 100

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Preliminary modelling results

Dep. Var: P(Self-reported Asthma (W1)) Sign and significance†

NO2 Exposure NO2 (PPB) ++NO2 > P95 +

Gender Male [ref.]Female + + +

Age Category 50-64 [ref.]65-74>75 − − −

Smoking Status Never [ref.]Past + + +Current −

Medical Cover Not covered [ref.]Medical insuranceMedical card + + +

Mobility No difficulty walking 100m [ref.]Difficulty walking 100m + + +

N 8,146† Significance levels: +p<0.1; ++p<0.05; +++p<0.01

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Robustness checks

NO2 exposure coefficients in self-reported asthma andmedication use models very similarPollutant coefficients stable across TILDA wavesMarginal effect on High NO2 dummy variablesgenerally about 10x larger than PPB coefficients,though statistical significance of High NO2 term variesImplied scale of pollution effect big enough to bepolicy-relevant

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What is needed to enable this sort of research

All datasets must have detailed spatial identifiers; e.g.TILDA locations are geocoded due to how samplingwas doneAccess protocols to allow spatial linking under secureconditionsUse environmental exposure variables in a format thatdoes not increase risk of disclosiveness

E.g. code variables as quantiles of exposure rather than continuousvariables; integer levels NO2 exposure with top coding

Protocol to allow researchers access to the linkedanonymised data

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References

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References I

Achakulwisut, P., Brauer, M., Hystad, P., & Anenberg, S. C. (2019).Global, national, and urban burdens of paediatric asthma incidenceattributable to ambient NO2 pollution: Estimates from globaldatasets. The Lancet Planetary Health, 3(4), e166–e178.doi:10.1016/s2542-5196(19)30046-4

Bowatte, G., Lodge, C., Lowe, A. J., Erbas, B., Perret, J.,Abramson, M. J., . . . Dharmage, S. C. (2014). The influence ofchildhood traffic-related air pollution exposure on asthma, allergyand sensitization: A systematic review and a meta-analysis of birthcohort studies. Allergy, 70(3), 245–256. doi:10.1111/all.12561

Bowatte, G., Lodge, C. J., Knibbs, L. D., Lowe, A. J., Erbas, B.,Dennekamp, M., . . . Dharmage, S. C. (2017). Traffic-related airpollution exposure is associated with allergic sensitization, asthma,and poor lung function in middle age. Journal of Allergy and ClinicalImmunology, 139(1), 122–129.e1. doi:10.1016/j.jaci.2016.05.008

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References II

Fuertes, E., Heinrich, J., Bowatte, G., Lodge, C. J., Lowe, A. J., Erbas, B.,. . . Dharmage, S. C. (2015). The influence of childhoodtraffic-related air pollution exposure on asthma, allergy andsensitization. Allergy, 70(10), 1350–1352. doi:10.1111/all.12611

Gauderman, W. J., Avol, E., Lurmann, F., Kuenzli, N., Gilliland, F.,Peters, J., & McConnell, R. (2005). Childhood asthma and exposureto traffic and nitrogen dioxide. Epidemiology, 737–743.doi:10.1097/01.ede.0000181308.51440.75

Guarnieri, M., & Balmes, J. R. (2014). Outdoor air pollution and asthma.The Lancet, 383(9928), 1581–1592.doi:10.1016/s0140-6736(14)60617-6

McConnell, R., Berhane, K., Yao, L., Jerrett, M., Lurmann, F.,Gilliland, F., . . . Peters, J. (2006). Traffic, susceptibility, andchildhood asthma. Environmental Health Perspectives, 114(5),766–772. doi:10.1289/ehp.8594

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References III

Moual, N. L., Jacquemin, B., Varraso, R., Dumas, O., Kauffmann, F., &Nadif, R. (2013). Environment and asthma in adults. La PresseMédicale, 42(9), e317–e333. doi:10.1016/j.lpm.2013.06.010

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