National and regional hospitalization rates for allergic disorders in the United States : a 17 year time-trend analysis

Abstrakt

Background: There is a paucity of data on national trends in hospitalizations for allergic disorders. The aim of this study was to present recent trends in common allergic disorders. Methods: We used the National Inpatient Sample for the period 1998-2014 to analyze 130 million hospital discharge records of all age groups from four U.S. census regions for the following allergic disorders: allergic conjunctivitis, allergic rhinitis, anaphylaxis, angioedema, asthma, atopic eczema/dermatitis, drug allergy, food allergy, urticaria and venom allergy. Yearly age- and sex-standardized hospitalization rates and trends over the 17-year period we calculated using segmented generalized linear regression. The trends were reported as annual percentage change (APC) for one segment or average annual percentage change (AAPC) for multiple segments. Results: Increases in hospitalization rates at national and regional levels were observed for anaphylaxis (APC 2.1%, CI: 0.1,4.2) and angioedema (APC 3.3%, CI: 1.8,4.8). In contrast, decreases were observed in drug allergy (APC -6%, CI: -9.4,-3.1), urticaria (APC -3.3%, CI: -6.3,-0.5) and asthma (AAPC -1.8%, CI: -2.2,-1.4). Non-primary diagnoses, defined as up to 14 diagnoses following the primary diagnosis, showed some contrary trends, such as increases in asthma (AAPC 4.3%, CI: 4.1,4.5) and additional trends, such as increases in allergic rhinitis (AAPC 9.7%, CI: 8.8,10.6) and atopic eczema/dermatitis (APC 4.2%, CI: 3.6,4.6). Allergic conjunctivitis, food allergy, and venom allergy showed no change. Conclusions: Analysis of recent national hospitalization data for allergic disorders reveals a complex picture with some conditions showing increased hospitalization rates whilst others showed no change or a decline.

Tip publikacije
Publikacija
Allergy, 75(5), pp. 1243-1247
Primož Kocbek
Primož Kocbek
Doktorski študent

Moji raziskovalni interesi vključujejo statistične modele in metode strojnega učenja z aplikacijami v zdravstvu. Specifična področja, ki me zanimajo, vključujejo časovno analizo podatkov, interpretacijo napovednih modelov, stabilnost algoritmov, napredne metode strojnega učenja na masivnih podatkovjih, npr. globoke nevronske mreže.

Nino Fijačko
Nino Fijačko
Doktorski študent

Moji raziskovalni interesi vključujejo sodobnejše pedagoške pristope na različnih področjih zdravstva. Specifično raziskujem kako resne igre in igrifikacija vplivajo na raven fizioloških in psiholoških lastnosti posameznih oseb v različnih situacijah, kot je na primer kardiopulmonalno oživljanje.

Bright I Nwaru
Bright I Nwaru
Universitetslektor
Paige Wickner
Paige Wickner
Assistant Professor of Medicine
Aziz Sheikh
Aziz Sheikh
Chair of Primary Care Research and Development
Gregor Štiglic
Gregor Štiglic
Izredni profesor in predstojnik raziskovalnega inštituta

Moji raziskovalni interesi vključujejo tehnike strojnega učenja z uporabo v zdravstvu. Specifična področja, ki me zanimajo, vključujejo razumljivost napovednih modelov, klasifikacija, ki temelji na človeški interakciji, stabilnost algoritmov za izbiro lastnosti, meta učenje in odkrivanje longitudinalnih pravil.