A Role of ß2-Adrenoreceptor Agonists Related to the Development of Parkinson's Disease.

Background: Several studies have suggested the potential protective role of ß2-adrenoreceptor agonist (ß2AR-agonist) on the development of Parkinson's disease (PD). However, those could not reflect a different epidemiologic background in eastern countries. We explored ß2AR-agonist's effect on PD development by controlling for smoking. Materials and Methods: We used the Korean national sample cohort data (from 2002 to 2013) containing 1,025,340 participants (2.2% of the whole population). The subjects over 60 years were included. PD was defined based on the ICD-10 code, which should be diagnosed by neurologists. Atypical Parkinsonisms or ataxic disorders were excluded. We made Set 1 (from 2003 to 2007) and Set 2 (from 2003 to 2008) based on the exposure period for the sensitivity analysis. We observed whether PD had developed during the follow-up periods in each subset. Results: The PD (Set 1, n = 742; Set 2, n = 699) and non-PD group (Set 1, n = 57,645; Set 2, n = 66,586) were collected. Old age, Medicaid, and asthma were risk factors, whereas smoking was a significant protective factor for PD development. The proportion of ß2AR-agonist use was significantly higher in the PD group than in the non-PD group (Set 1, 3.6% vs. 2.4%; Set 2, 4.1% vs. 2.6%). ß2AR-agonist use still was a risk factor in developing PD from the multiple logistic regression analysis. Conclusions: ß2-AR-agonist looked like a risk factor rather than a protective factor for PD development. Well-controlled studies reflecting various epidemiologic backgrounds are required to confirm the role of ß2AR-agonist.

Identifying key hospital service quality factors in online health communities.

BACKGROUND: The volume of health-related user-created content, especially hospital-related questions and answers in online health communities, has rapidly increased. Patients and caregivers participate in online community activities to share their experiences, exchange information, and ask about recommended or discredited hospitals. However, there is little research on how to identify hospital service quality automatically from the online communities. In the past, in-depth analysis of hospitals has used random sampling surveys. However, such surveys are becoming impractical owing to the rapidly increasing volume of online data and the diverse analysis requirements of related stakeholders. OBJECTIVE: As a solution for utilizing large-scale health-related information, we propose a novel approach to identify hospital service quality factors and overtime trends automatically from online health communities, especially hospital-related questions and answers. METHODS: We defined social media-based key quality factors for hospitals. In addition, we developed text mining techniques to detect such factors that frequently occur in online health communities. After detecting these factors that represent qualitative aspects of hospitals, we applied a sentiment analysis to recognize the types of recommendations in messages posted within online health communities. Korea's two biggest online portals were used to test the effectiveness of detection of social media-based key quality factors for hospitals. RESULTS: To evaluate the proposed text mining techniques, we performed manual evaluations on the extraction and classification results, such as hospital name, service quality factors, and recommendation types using a random sample of messages (ie, 5.44% (9450/173,748) of the total messages). Service quality factor detection and hospital name extraction achieved average F1 scores of 91% and 78%, respectively. In terms of recommendation classification, performance (ie, precision) is 78% on average. Extraction and classification performance still has room for improvement, but the extraction results are applicable to more detailed analysis. Further analysis of the extracted information reveals that there are differences in the details of social media-based key quality factors for hospitals according to the regions in Korea, and the patterns of change seem to accurately reflect social events (eg, influenza epidemics). CONCLUSIONS: These findings could be used to provide timely information to caregivers, hospital officials, and medical officials for health care policies.

Investigating the congruence of crowdsourced information with official government data: the case of pediatric clinics.

BACKGROUND: Health 2.0 is a benefit to society by helping patients acquire knowledge about health care by harnessing collective intelligence. However, any misleading information can directly affect patients' choices of hospitals and drugs, and potentially exacerbate their health condition. OBJECTIVE: This study investigates the congruence between crowdsourced information and official government data in the health care domain and identifies the determinants of low congruence where it exists. In-line with infodemiology, we suggest measures to help the patients in the regions vulnerable to inaccurate health information. METHODS: We text-mined multiple online health communities in South Korea to construct the data for crowdsourced information on public health services (173,748 messages). Kendall tau and Spearman rank order correlation coefficients were used to compute the differences in 2 ranking systems of health care quality: actual government evaluations of 779 hospitals and mining results of geospecific online health communities. Then we estimated the effect of sociodemographic characteristics on the level of congruence by using an ordinary least squares regression. RESULTS: The regression results indicated that the standard deviation of married women's education (P=.046), population density (P=.01), number of doctors per pediatric clinic (P=.048), and birthrate (P=.002) have a significant effect on the congruence of crowdsourced data (adjusted R²=.33). Specifically, (1) the higher the birthrate in a given region, (2) the larger the variance in educational attainment, (3) the higher the population density, and (4) the greater the number of doctors per clinic, the more likely that crowdsourced information from online communities is congruent with official government data. CONCLUSIONS: To investigate the cause of the spread of misleading health information in the online world, we adopted a unique approach by associating mining results on hospitals from geospecific online health communities with the sociodemographic characteristics of corresponding regions. We found that the congruence of crowdsourced information on health care services varied across regions and that these variations could be explained by geospecific demographic factors. This finding can be helpful to governments in reducing the potential risk of misleading online information and the accompanying safety issues.