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Record W2074887116 · doi:10.5415/apallergy.2013.3.1.1

Allergies in Asia: are we facing an allergy epidemic?

2013· article· en· W2074887116 on OpenAlexaboutno aff
Meera Thalayasingam, Bee Wah Lee

Bibliographic record

VenueAsia Pacific Allergy · 2013
Typearticle
Languageen
FieldMedicine
TopicAsthma and respiratory diseases
Canadian institutionsnot available
Fundersnot available
KeywordsAsthmaMedicineWesternizationChinaAllergyDemographyPrevalenceEnvironmental healthPopulationGeographyImmunologyEconomic growth

Abstract

fetched live from OpenAlex

Asia is the world’s most populous region consisting of highly diverse populations with respect to ancestry and cultural background. It may not be obvious at the outset, but this region is probably an important resource for lessons in allergic disorders. With growing affluence and westernization, the global trends in allergic diseases have increased at tandem, albeit at different rates. In the 1960’s to 1990’s, several populations [1] including Asian communities [2] have documented increases in asthma prevalence, particularly in children. The International Study of Asthma and Allergies in Childhood (ISAAC), is a highly quoted research initiative that has provided us with a standardized means of comparing global asthma, allergic rhinitis and eczema prevalence. The Phase One ISAAC studies showed us that none of the Asian countries were ranked amongst the top in terms of asthma symptom prevalence [3]. Instead, the United Kingdom, New Zealand, Australia, Republic of Ireland and Canada had the highest prevalence for asthma symptoms. Japan, Thailand, Hong Kong, Philippines, Singapore and Malaysia were ranked midway. Most interestingly, China had one of the lowest prevalence and Indonesia was ranked the lowest for asthma symptom prevalence. The ISAAC Phase 3 study was a repeat of Phase 1 study performed 7 to 10 years later. The data showed that asthma prevalence had plateaued in several populations, with modest increases in some centers. Only 4 of 8 Asia Pacific countries recorded an increase in asthma symptom prevalence, and increases for Hong Kong, Japan and Taiwan were very modest (<1%) [4]. Taken together, the ISAAC Phase 1 and 3 data indicate that childhood asthma prevalence in Asia is likely to have stabilized and would not reach the proportions seen in the western world. These geographical differences in asthma prevalence in children appear to be more related to environment rather than genetics. Migration studies of Asians settling in Australia have shown that Asians born in Australia assume the rates of asthma similar to the local population [5]. Within Asia, Cantonese Chinese children born in Hong Kong have higher prevalence of asthma symptoms compared to ethnically similar children born in the nearby city of Canton China [6]. Likewise, a similar picture is emerging for food epidemiology in Asia. It does not appear likely that Asia will witness a similar surge in peanut and tree nut allergy [7] as it has been documented in North America, United Kingdom and Australia [8]. It is therefore tempting to speculate that the environmental influences in Asia are less conducive for the development of allergic diseases in general. Prospective comparative studies between populations tracking environmental exposures pari passu with serial evaluation of immunological responses may provide clues as to the protective factors in our environment, possibly similar in terms of the protective environment shown in the farming studies of Europe. However despite the relatively lower prevalence of food allergy compared to the West some peculiarities exist and warrant further exploration. Specifically the predominance of shellfish allergy and its varied presentation from isolated oral symptoms akin to pollen allergy syndrome, to life threatening anaphylaxis in certain individuals is intriguing [9]. Further, the emergence of wheat allergy in Korea, Japan and Thailand suggests unique environmental exposures through cultural and cooking practices which are country specific. With economic progress, the future for academic science in Asia is bright. It will be through innovative research that will deliver better understanding of these observations, and ultimately improved strategies for the management of allergic disorders.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.330
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.001

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.023
GPT teacher head0.269
Teacher spread0.246 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations8
Published2013
Admission routes1
Has abstractyes

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