LETTERS TO THE EDITOR 1395

J ALLERGY CLIN IMMUNOL VOLUME 135, NUMBER 5

Jean-Laurent Casanova, MD, PhDa,b,g,h,i Jacinta Bustamante, MD, PhDa,b,j* Antonio Condino-Neto, MD, PhDd*

Environmental and socioeconomic data do not improve the Predicting Asthma Risk in Children (PARC) tool

From athe Laboratory of Human Genetics of Infectious Diseases, Necker Branch, Necker-Enfants-Malades Hospital, Paris, France; bParis Descartes University, Imagine Institute, Paris, France; cthe Department of Public Health and Cellular Biology, University of Rome Tor Vergata, Rome, Italy; dthe Department of Immunology, Institute of Biomedical Sciences, University of S~ao Paulo, S~ao Paulo, Brazil; ethe Bioinformatics Laboratory, Pele Pequeno Principe Research Institute, Curitiba, Brazil; fthe Departments of Pediatrics and Cancer Biology, University of Massachusetts Medical School, Worcester, Mass; gthe Pediatric Hematology-Immunology Unit, Necker-Enfants Malades Hospital, Assistance Publique-H^ opitaux de Paris (AP-HP), Paris, France; hSt Giles Laboratory of Human Genetics of Infectious Diseases, Rockefeller Branch, The Rockefeller University, New York, NY; iHoward Hughes Medical Institute, New York, NY; and jCenter for the Study of Primary Immunodeficiencies, Necker-Enfants Malades Hospital, Assistance Publique-H^opitaux de Paris (AP-HP), Paris, France. E-mail: [email protected]. Or: [email protected]. *These authors contributed equally to this work. The Laboratory of Human Genetics of Infectious Diseases is supported by institutional grants to INSERM and The Rockefeller University, and grants from the French National Research Agency (ANR) (IFNGPHOX-ANR13-ISV3-0001-01), the ‘‘Investments for the Future’’ program (grant no. ANR-10-IAHU-01), Laboratoire d’Excellence Integrative Biology of Emerging Infectious Diseases (grant no. ANR-10-LABX-62-IBEID), the National Center for Research Resources and the National Center for Advancing Translational Sciences, the National Institutes of Health (grant no. 8UL1TR000043), the National Institute of Allergy and Infectious Diseases (grant no. R37AI095983), and the St. Giles Foundation. A.C.-N. was supported by Fundac¸~ao de Amparo a Pesquisa do Estado de S~ao Paulo (grant nos. 2012/ 11757-2, 2010/51814-0, 2012/51094-2, and 2013/50303-0) and Conselho Nacional de Desenvolvimento Cientıfico e Tecnologico (CNPQ grant no. 306902/2013). F.C. was supported by the Department of Public Health and Cellular Biology, University of Rome Tor Vergata. Disclosure of potential conflict of interest: W. C. A. Filho has received research support and travel support from Fundac¸~ao de Amparo a Pesquisa do Estado de S~ao Paulo (FAPESP). J.-L. Casanova has received consultancy fees from Merck, SanofiAventis, Regeneron, Bioaster, Pfizer, and Novaris Pharma and has received lecture fees from Pfizer and BiogenIdec. A. Condino-Neto has received research support from Fundac¸~ao de Amparo a Pesquisa do Estado de S~ao Paulo and is employed by the University of S~ao Paulo. The rest of the authors declare that they have no relevant conflicts of interest.

To the Editor: Many preschool children present to their doctor with respiratory symptoms, but not all of them develop asthma. Prediction tools can help to distinguish children with a high risk of developing asthma from children whose risk is low. A good prediction tool selects children who need therapeutic intervention and reassures parents whose children have transient problems. Five tools to predict school-age asthma in symptomatic preschool children are currently available. All predict only _0.43 or area under the receiver moderately well (Youden index < _0.74).1-5 They include the operating characteristics curve [AUC] < asthma predictive index,4 the Prevention and Incidence of Asthma and Mite Allergy (PIAMA) prediction tool,3 and the Predicting Asthma Risk in Children (PARC) tool,2 which we developed previously. The latter consists of 10 clinical predictors, including current respiratory symptoms. To develop the PARC tool, we deliberately included only those predictors that can be easily assessed clinically. Thus, we considered neither physiological measurements nor environmental or socioeconomic factors. These factors might not be generalizable to other populations. Nonetheless, environmental and socioeconomic factors, such as second-hand tobacco smoke or house pets, have been associated with respiratory symptoms in children.6,7 Some asthma prediction tools for children and young adults do include such exposures, namely, maternal smoking or parental education.3,5 In this study, we test whether the addition of environmental exposures and socioeconomic factors improves the predictive performance of the PARC tool. Our study population was the same that we used to develop the PARC tool. We used questionnaire data from a population-based cohort from Leicestershire, United Kingdom, described in detail elsewhere.8 We included children aged 1 to 3 years at baseline (in 1998) with parent-reported wheeze or chronic cough (cough without colds, or cough at night) who visited their doctor for wheeze or cough at least once during the past 12 months. The outcome ‘‘any asthma’’ was assessed 5 years later, at the age of 6 to 8 years. Any asthma was defined as current wheeze plus use of asthma medication within the past 12 months. For each child, we calculated the PARC tool risk score for developing asthma (range of score is 0-15).2 We then investigated whether the following environmental and socioeconomic factors, assessed at baseline, improved the accuracy of the score’s prediction: nursery care, number of older siblings, heating or cooking with gas, pet ownership (cat, dog, other furry pets, bird), mother smoking during pregnancy, exposure to environmental tobacco smoke (mother or other persons in the household smoking), duration of breastfeeding, ethnicity (white vs South Asian), crowding, single parenthood, parental education, Townsend deprivation index,9 living in an urban area, and self-reported traffic density at home address. As when we developed the PARC tool, we used least absolute shrinkage and selection operator–penalized logistic regression to identify important predictors without overfitting the data.10 The penalty for the regression coefficients is set using the penalization parameter l. For large values of l, no predictors enter the model. With decreasing l, more predictors enter the model, in order of their added predictive value. For our final model, we set l to a value that maximized the AUC of resulting predictions in 10fold cross-validation.

REFERENCES 1. Casanova JL, Abel L. Genetic dissection of immunity to mycobacteria: the human model. Annu Rev Immunol 2002;20:581-620. 2. Bustamante J, Boisson-Dupuis S, Abel L, Casanova JL. Mendelian susceptibility to mycobacterial disease: genetic, immunological, and clinical features of inborn errors of IFN-g immunity. Semin Immunol 2014;26:454-70. 3. Bustamante J, Arias AA, Vogt G, Picard C, Galicia LB, Prando C, et al. Germline CYBB mutations that selectively affect macrophages in kindreds with X-linked predisposition to tuberculous mycobacterial disease. Nat Immunol 2011;12:213-21. 4. Bustamante J, Aksu G, Vogt G, de Beaucoudrey L, Genel F, Chapgier A, et al. BCG-osis and tuberculosis in a child with chronic granulomatous disease. J Allergy Clin Immunol 2007;120:32-8. 5. Deffert C, Cachat J, Krause KH. Phagocyte NADPH oxidase, chronic granulomatous disease and mycobacterial infections. Cell Microbiol 2014;16:1168-78. 6. Condino-Neto A, Newburger PE. Interferon-gamma improves splicing efficiency of CYBB gene transcripts in an interferon-responsive variant of chronic granulomatous disease due to a splice site consensus region mutation. Blood 2000;95: 3548-54. 7. Vulcano M, Dusi S, Lissandrini D, Badolato R, Mazzi P, Riboldi E, et al. Toll receptor-mediated regulation of NADPH oxidase in human dendritic cells. J Immunol 2004;173:5749-56. 8. Casbon AJ, Long ME, Dunn KW, Allen LA, Dinauer MC. Effects of IFN-gamma on intracellular trafficking and activity of macrophage NADPH oxidase flavocytochrome b558. J Leukoc Biol 2012;92:869-82. 9. Trivedi A, Singh N, Bhat SA, Gupta P, Kumar A. Redox biology of tuberculosis pathogenesis. Adv Microb Physiol 2012;60:263-324. Available online December 24, 2014. http://dx.doi.org/10.1016/j.jaci.2014.11.004

1396 LETTERS TO THE EDITOR

J ALLERGY CLIN IMMUNOL MAY 2015

TABLE I. Characteristics of the study population (children seeing a doctor for wheeze or cough at age 1 to 3 years, by asthma outcome at age 6 to 8 years; N 5 1226)

TABLE I. (Continued ) 5 y later Asthma (n 5 345)

5 y later

Characteristic

Demographic factors Sex: male Age (y) 1 2 3 Ethnicity White South Asian Current wheeze and total asthma prediction score Current wheeze PARC tool score , mean 6 SD Environmental exposures Nursery care Older siblings 0 1 or 2 >2 Heating Central heating only Gas, coal, other Cooking fuel Electrical stove only Gas, other Pet ownership Cat Dog Other furry pet Bird Mother smoking during pregnancy Mother smoking (number of cigarettes/d) 1-10 >10 Other person smoking in household (number of cigarettes/d) 1-10 >10 Breast-fed (mo) 6 Self-reported traffic density (at home) Low Moderate High Socioeconomic factors Crowding (persons/room) < _1 1.1-1.5 >1.5 Single parents

Asthma (n 5 345)

No asthma (n 5 881)

n

n

%

%

Characteristic P value*

224

64.9

454

51.5

_1 >10 Other person smoking in household (number of cigarettes/d) > _1 >10 Breast-fed (mo) Any duration (vs no breast-feeding) > _1 > _4 >6 Self-reported traffic density (at home) At least moderate High Socioeconomic factors Crowding (persons/room) >1 >1.5 Single parents High parental education Townsend deprivation index* More affluent Affluent Deprived More deprived Living in an urban area 

Score-adjusted models

Full model

OR

95% CI

P value

OR

95% CI

P value

OR

95% CI

P value

0.79 0.86

0.59-1.06 0.67-1.11

.113 .250

1.25 0.69

0.90-1.75 0.52-0.92

.186 .011

1.55 0.66

0.97-2.46 0.49-0.89

.065 .007

1.06 1.25

0.81-1.38 0.83-1.90

.692 .283

0.95 1.03

0.70-1.29 0.64-1.65

.735 .911

0.95 1.15

0.68-1.32 0.67-1.96

.762 .607

1.07

0.81-1.41

.623

1.13

0.83-1.54

.445

1.15

0.83-1.60

.397

0.69

0.52-0.91

.008

0.91

0.66-1.25

.548

0.82

0.58-1.16

.268

1.00 1.13 1.47 0.87 1.14

0.72-1.38 0.82-1.55 0.99-2.18 0.46-1.65 0.80-1.62

.996 .469 .058 .668 .463

0.90 1.05 1.12 0.80 0.97

0.62-1.30 0.73-1.50 0.71-1.77 0.38-1.67 0.65-1.45

.567 .802 .626 .554 .900

0.91 1.05 1.19 0.74 0.70

0.62-1.35 0.71-1.58 0.73-1.96 0.34-1.61 0.37-1.30

.651 .796 .484 .449 .255

1.39 1.65

1.03-1.89 1.09-2.49

.034 .019

1.15 1.57

0.81-1.64 0.97-2.53

.419 .067

1.33 1.70

0.74-2.38 0.85-3.39

.345 .131

0.84 1.10

0.63-1.14 0.73-1.65

.267 .657

0.91 1.13

0.65-1.27 0.71-1.78

.570 .613

0.76 1.39

0.47-1.21 0.73-2.63

.247 .318

0.79 0.76 0.83 0.88

0.62-1.02 0.59-0.98 0.63-1.09 0.63-1.22

.070 .031 .185 .443

0.92 0.85 0.95 1.01

0.70-1.29 0.64-1.13 0.70-1.30 0.70-1.46

.546 .254 .754 .947

1.09 0.70 1.13 1.06

0.66-1.80 0.40-1.24 0.65-1.95 0.62-1.82

.744 .221 .664 .820

0.91 0.74

0.71-1.17 0.47-1.15

.473 .183

0.86 0.87

0.64-1.14 0.53-1.43

.295 .591

0.85 0.93

0.62-1.17 0.55-1.59

.314 .794

0.81 0.71 1.32 1.02

0.60-1.10 0.39-1.28 0.89-1.95 0.79-1.32

.179 .253 .172 .864

0.77 0.88 0.87 1.13

0.55-1.09 0.46-1.69 0.55-1.36 0.85-1.51

.146 .701 .534 .402

0.67 1.04 0.90 1.15

0.43-1.04 0.49-2.19 0.54-1.51 0.84-1.58

.076 .919 .699 .388

0.94 0.88 1.15 1.00 0.97

0.69-1.28 0.68-1.13 0.89-1.49 0.73-1.36 0.76-1.25

.677 .311 .277 .977 .817

0.92 0.88 1.21 0.93 1.10

0.65-1.31 0.66-1.17 0.90-1.62 0.65-1.33 0.83-1.46

.655 .363 .206 .708 .508

1.01 0.97 1.48 0.76 1.20

0.64-1.60 0.62-1.52 0.92-2.40 0.46-1.24 0.83-1.73

.969 .900 .108 .264 .327

OR, Odds ratio. *The categories are cutoffs between the following Townsend deprivation index intervals: (25.522 to 22.981), (22.886 to 21.264), (21.250 to 0.908), (0.909 to 4.403), and (4.418 to 11.072).  Living in Leicester postcode areas LE1 to LE5.

1397.e3 LETTERS TO THE EDITOR

J ALLERGY CLIN IMMUNOL MAY 2015

TABLE E2. Associations of environmental and socioeconomic factors with symptoms included in the asthma prediction tool Environmental/socioeconomic factor

Wheeze-related symptoms included in the prediction tool

Townsend deprivation index (most deprived group compared with the rest) Wheeze without colds >3 attacks of wheeze Interference with daily activity Wheeze causing shortness of breath

Any A lot At least sometimes Always

Exercise-related wheeze/cough Aeroallergen-related wheeze/cough Mother smoking (number of cigarettes/d) > _1

Wheeze without colds >3 attacks of wheeze Interference with daily activity Wheeze causing shortness of breath

>10

Exercise-related wheeze/cough Aeroallergen-related wheeze/cough Wheeze without colds >3 attacks of wheeze Interference with daily activity Wheeze causing shortness of breath

Any A lot At least sometimes Always

Any A lot At least sometimes Always

Exercise-related wheeze/cough Aeroallergen-related wheeze/cough Pet ownership Cat

Wheeze without colds >3 attacks of wheeze Interference with daily activity Wheeze causing shortness of breath

Dog

Exercise-related wheeze/cough Aeroallergen-related wheeze/cough Wheeze without colds >3 attacks of wheeze Interference with daily activity Wheeze causing shortness of breath

Other furry pet

Exercise-related wheeze/cough Aeroallergen-related wheeze/cough Wheeze without colds >3 attacks of wheeze Interference with daily activity Wheeze causing shortness of breath Exercise-related wheeze/cough Aeroallergen-related wheeze/cough

Symptoms during the last 12 mo. Towsend deprivation index interval of the most deprived group: (4.418-11.072). OR, Odds ratio.

Any A lot At least sometimes Always

Any A lot At least sometimes Always

Any A lot At least sometimes Always

P value

OR

95% CI

1.09 0.67 1.34 3.63 0.95 1.06 1.33 1.38

0.75-1.58 0.47-0.97 1.00-1.79 1.66-7.96 0.70-1.28 0.59-1.91 1.00-1.76 0.83-2.28

.640 .033 .046 .001 .726 .844 .052 .214

2.23 1.30 1.56 1.01 1.50 1.20 1.51 0.71 2.26 1.27 1.46 0.89 1.11 1.55 1.58 0.91

1.60-3.10 0.94-1.81 1.16-2.08 0.38-2.71 1.12-2.01 0.68-2.14 1.13-2.01 0.39-1.30 1.45-3.51 0.81-2.01 0.98-2.19 0.21-3.81 0.73-1.68 0.75-3.21 1.06-2.35 0.41-2.01

Environmental and socioeconomic data do not improve the Predicting Asthma Risk in Children (PARC) tool.

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