Dataset Information

Seasonal patterns of Asthma medication fills among diverse populations of the United States.

ABSTRACT: OBJECTIVE:Nonadherence to controller and overuse of reliever asthma medications are associated with exacerbations. We aimed to determine patterns of seasonal asthma medication use and to identify time period(s) during which interventions to improve medication adherence could reduce asthma morbidity. METHODS:We conducted a retrospective cohort study of asthmatics 4-50 years of age and enrolled in three diverse health insurance plans. Seasonal patterns of medications were reported by monthly prescription fill rates per 1000 individuals with asthma from 1998 to 2013, and stratified by healthcare plan, sex, and age. RESULTS:There was a distinct and consistent seasonal fill pattern for all asthma medications. The lowest fill rate was observed in the month of July. Fills increased in the autumn and remained high throughout the winter and spring. Compared with the month of May with high medication fills, July represented a relative decrease of fills ranging from 13% (rate ratio, RR: 0.87, 95% confidence interval, 95%CI: 0.72-1.04) for the combination of inhaled corticosteroids (ICS) + long acting beta agonists (LABA) to 45% (RR: 0.55, 95%CI: 0.49-0.61) for oral corticosteroids. Such a seasonal pattern was observed each year across the 16-year study period, among healthcare plans, sexes, and ages. LABA containing control medication (ICS+LABA and LABA) fill rates were more prevalent in older asthmatics, while leukotriene receptor antagonists were more prevalent in the younger population. CONCLUSIONS:A seasonal pattern of asthma medication fill rates likely represents a reactive response to a loss of disease control and increased symptoms. Adherence to and consistent use of asthma medications among individuals who use medications in reaction to seasonal exacerbations might be a key component in reducing the risk of asthma exacerbations.

SUBMITTER: Turi KN

PROVIDER: S-EPMC6200335 | biostudies-literature | 2018 Jul

REPOSITORIES: biostudies-literature

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Seasonal patterns of Asthma medication fills among diverse populations of the United States.

Turi Kedir N KN Gebretsadik Tebeb T Lee Rees L RL Hartert Tina V TV Evans Amber M AM Stone Cosby C Sicignano Nicholas M NM Wu Ann C AC Iribarren Carlos C Butler Melissa G MG Mitchel Edward E Morrow James J Larkin Emma K EK Wu Pingsheng P

The Journal of asthma : official journal of the Association for the Care of Asthma 20170907 7

<h4>Objective</h4>Nonadherence to controller and overuse of reliever asthma medications are associated with exacerbations. We aimed to determine patterns of seasonal asthma medication use and to identify time period(s) during which interventions to improve medication adherence could reduce asthma morbidity.<h4>Methods</h4>We conducted a retrospective cohort study of asthmatics 4-50 years of age and enrolled in three diverse health insurance plans. Seasonal patterns of medications were reported b ...[more]

PMID: 28881155

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Project description:BackgroundAgencies such as the Centers for Disease Control and Prevention (CDC) currently release influenza-like illness incidence data, along with descriptive summaries of simple spatio-temporal patterns and trends. However, public health researchers, government agencies, as well as the general public, are often interested in deeper patterns and insights into how the disease is spreading, with additional context. Analysis by domain experts is needed for deriving such insights from incidence data.ObjectiveOur goal was to develop an automated approach for finding interesting spatio-temporal patterns in the spread of a disease over a large region, such as regions which have specific characteristics (eg, high incidence in a particular week, those which showed a sudden change in incidence) or regions which have significantly different incidence compared to earlier seasons.MethodsWe developed techniques from the area of transactional data mining for characterizing and finding interesting spatio-temporal patterns in disease spread in an automated manner. A key part of our approach involved using the principle of minimum description length for representing a given target set in terms of combinations of attributes (referred to as clauses); we considered both positive and negative clauses, relaxed descriptions which approximately represent the set, and used integer programming to find such descriptions. Finally, we designed an automated approach, which examines a large space of sets corresponding to different spatio-temporal patterns, and ranks them based on the ratio of their size to their description length (referred to as the compression ratio).ResultsWe applied our methods using minimum description length to find spatio-temporal patterns in the spread of seasonal influenza in the United States using state level influenza-like illness activity indicator data from the CDC. We observed that the compression ratios were over 2.5 for 50% of the chosen sets, when approximate descriptions and negative clauses were allowed. Sets with high compression ratios (eg, over 2.5) corresponded to interesting patterns in the spatio-temporal dynamics of influenza-like illness. Our approach also outperformed description by solution in terms of the compression ratio.ConclusionsOur approach, which is an unsupervised machine learning method, can provide new insights into patterns and trends in the disease spread in an automated manner. Our results show that the description complexity is an effective approach for characterizing sets of interest, which can be easily extended to other diseases and regions beyond influenza in the US. Our approach can also be easily adapted for automated generation of narratives.

Dataset Information

Seasonal patterns of Asthma medication fills among diverse populations of the United States.

Publications

Seasonal patterns of Asthma medication fills among diverse populations of the United States.

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