Low-to-moderate atmospheric ozone levels are negatively correlated with hospital visits by asthma patients

Asthma is a chronic illness of the airways that affects approximately 300 million individuals worldwide. While it is commonly accepted that high ozone levels exacerbate asthma symptoms, the impact of low to moderate ozone levels on asthma symptoms has received little attention. The purpose of this research was to determine the relationship between hospital visits by asthma patients showing the severity of their symptoms and moderate ozone levels. Statistical analyses were performed on hospital visit big data for asthma patients in Seoul, Korea, collected between 2013 and 2017. The data set includes outpatient hospital visits (n = 17,787,982), hospital admissions (n = 215,696), and emergency department visits (n = 85,482). The frequency of hospital visits by asthma patients was evaluated in relation to low ozone levels (< 0.03 ppm) and moderate ozone levels (0.03-0.06 ppm) in the Seoul environment. In comparison to low ozone levels, moderate ozone levels resulted in a reduction in outpatient hospital visits (t = 7.052, P < .001). When ozone levels were low to moderate, there was a negative correlation between ozone levels and outpatient visits (r = -0.281, 95% CI: -0.331 to -0.228). Negative associations were also identified between ozone levels and new hospital admissions (t = 2.909, P < .01; r = -0.125, 95% CI: -0.179 to -0.070) and emergency treatments (t = 2.679, P < .01; r = -0.132, 95% CI: -0.186 to -0.076). Additionally, it was verified that moderate ozone levels one day before the visits resulted in a reduction in outpatient visits (t = 5.614, P < .001; r = -0.207, 95% CI: -0.259 to -0.153). A strong relationship was identified between moderate atmospheric ozone levels and a reduction in asthma patient hospital visits.

Machine Learning-Based Analyses of the Effects of Various Types of Air Pollutants on Hospital Visits by Asthma Patients

Asthma is a chronic respiratory disorder defined by airway inflammation, chest pains, wheezing, coughing, and difficulty breathing that affects an estimated 300 million individuals globally. Although various studies have shown an association between air pollution and asthma, few studies have used statistical and machine learning algorithms to investigate the effect of each individual air pollutant on asthma. The purpose of this research was to assess the association between air pollutants and the frequency of hospital visits by asthma patients using three analysis methods: linear correlation analyses were performed by Pearson correlation coefficients, and least absolute shrinkage and selection operator (LASSO) and random forest (RF) models were used for machine learning-based analyses to investigate the effect of air pollutants. This research studied asthma patients using the hospital visit database in Seoul, South Korea, collected between 2013 and 2017. The data set included outpatient hospital visits (n = 17,787,982), hospital admissions (n = 215,696), and emergency department visits (n = 85,482). The daily atmospheric environmental information from 2013 to 2017 at 25 locations in Seoul was evaluated. The three analysis models revealed that NO₂ was the most significant pollutant on average in outpatient hospital visits by asthma patients. For example, NO₂ had the greatest impact on outpatient hospital visits, resulting in a positive association (r=0.331). In hospital admissions of asthma patients, CO was the most significant pollutant on average. It was observed that CO exhibited the most positive association with hospital admissions (I = 3.329). Additionally, a significant time lag was found between both NO₂ and CO and outpatient hospital visits and hospital admissions of asthma patients in the linear correlation analysis. In particular, NO₂ and CO were shown to increase hospital admissions at lag 4 in the linear correlation analysis. This study provides evidence that PM_2.5, PM₁₀, NO₂, CO, SO₂, and O₃ are associated with the frequency of hospital visits by asthma patients.

A risk scoring system to predict clinical events in chronic hepatitis B virus infection: A nationwide cohort study

Many patients with chronic hepatitis B do not receive adequate follow-up. This study aimed to develop a risk score to predict clinical events in patients with chronic hepatitis B virus (HBV) infection at the population level for identifying patients at high risk to warrant regular follow-up. This study analysed population-based data from the nationwide claims database of South Korea obtained between 2005 and 2015. We identified 507,239 non-cirrhotic patients with chronic HBV infection who are not under antiviral treatment. A risk score for predicting clinical events (hepatocellular carcinoma, death or liver transplantation) was developed based on multivariable Cox proportional hazard model in a development cohort (n = 401,745) and validated in a validation cohort (n = 105,494). The cumulative incidence rates of clinical events at 5 years were 2.56% and 2.44% in the development and validation cohorts, respectively. Clinical events in asymptomatic patients with chronic HBV infection (CAP-B) score ranging from 0 to 7.5 points based on age, sex, socioeconomic status, chronic hepatitis C co-infection, diabetes mellitus, statin or antiplatelet exposure, smoking, alcohol consumption, alanine aminotransferase and gamma-glutamyltransferase had good discriminatory accuracy in both the development and validation cohorts (c-indices for 3-, 5- and 10-year risk prediction: all 0.786). The predicted and observed probabilities of clinical events were calibrated in both cohorts. A score of >3.5 points identified subjects at distinctly high risk. The CAP-B score using easily accessible variables can predict clinical events and may allow selection of patients with chronic HBV infection for priority of regular follow-up.

Association between the prolonged use of magnesium sulfate for tocolysis and fracture risk among infants

In 2013, the U.S. Food and Drug Administration issued a safety warning that cautioned against using magnesium sulfate (MgSO4) injections for more than 5 to 7 days to stop preterm delivery due to the bone problems subsequently observed in infants. However, the warning was mainly based on case reports, and further investigation is necessary to determine whether prolonged MgSO4 use increased infant fractures.To evaluate whether prolonged MgSO4 use for tocolysis increased the risk of subsequent fractures among infants.A retrospective population-based cohort study was conducted with a new-user study design using the National Health Insurance Database in Taiwan. We included pregnant women aged between 12 and 55 years old who delivered a live-born singleton. The enrollment period was from January 1, 2012 to December 31, 2014. The exposure group was defined as pregnant women who received MgSO4 injection for >5 days during pregnancy, while those not receiving any tocolytics were the reference group. The outcome was any bone fracture among the infants during the 2-year follow-up period. Propensity score matching and Cox proportional hazards regression models were used to estimate the hazard of fractures. We further studied the effect of MgSO4 treatment with varied dosages and durations of treatment in the sensitivity analyses.Among the 4092 pregnant women in the database, 693 (16.9%) of them were included in the exposure group. The hazard ratio of infant fractures among prolonged MgSO4 users was not significantly different from that of tocolytic nonusers in adjusted models (adjusted hazard ratio (aHR) = 1.48; 95% confidence interval (CI) = 0.59-3.71). A similar lack of significance was found in the sensitivity analyses (aHR = 1.45; 95% CI = 0.40-5.28 for larger treatment dosage; aHR = 2.52; 95% CI = 0.49-12.98 for longer treatment duration).Prolonged MgSO4 tocolysis use did not increase the risk of infant fractures. Our findings reconfirmed the safety of MgSO4 as a tocolytic treatment.

Postpartum Depression and Subsequent Autoimmune Diseases in Taiwan

Postpartum depression (PPD) is one of the most common examples of postnatal morbidity, but the subsequent risks of autoimmune diseases in patients with PPD have yet to be fully investigated. This nationwide population-based study utilized data of the National Health Insurance Research Database of Taiwan for the period from 1996 to 2013. In total, 45,451 women with primiparity were identified. Among them, 542 patients with PPD were enrolled as a study group while 2165 matched patients without PPD were enrolled as a control group. The demographic characteristics and comorbidities of the patients were analyzed, and Cox regression analysis was applied to calculate the hazard ratios for the risk of autoimmune diseases. Of the 2707 women enrolled in this study, 469 (17.3%) patients with newly diagnosed autoimmune diseases were identified, including 123 (22.7%) in the PPD group and 346 (16%) in the non-PPD group. After adjusting for confounding factors, it was determined that the patients with PPD had a significantly higher risk of subsequent autoimmune diseases (adjusted hazard ratio (aHR): 1.61, 95% confidence interval (CI): 1.30–1.99; p < 0.001). Specifically, increased risks of pernicious anemia (aHR: 3.85, 95% CI: 2.06–7.22), rheumatoid arthritis (aHR: 2.62, 95% CI: 1.28–5.39), and Graves’ disease (aHR: 1.57, 95% CI: 1.05–2.33) were observed in the PPD group. This study demonstrated that patients with PPD have higher risks of subsequent autoimmune diseases, especially pernicious anemia, rheumatoid arthritis, and Graves’ disease. This useful information provides physicians with clues regarding the associations between autoimmune diseases and PPD.

Understanding the productive author who published papers in medicine using National Health Insurance Database: A systematic review and meta-analysis

Many researchers used National Health Insurance database to publish medical papers which are often retrospective, population-based, and cohort studies. However, the author's research domain and academic characteristics are still unclear.By searching the PubMed database (Pubmed.com), we used the keyword of [Taiwan] and [National Health Insurance Research Database], then downloaded 2913 articles published from 1995 to 2017. Social network analysis (SNA), Gini coefficient, and Google Maps were applied to gather these data for visualizing: the most productive author; the pattern of coauthor collaboration teams; and the author's research domain denoted by abstract keywords and Pubmed MESH (medical subject heading) terms.Utilizing the 2913 papers from Taiwan's National Health Insurance database, we chose the top 10 research teams shown on Google Maps and analyzed one author (Dr. Kao) who published 149 papers in the database in 2015. In the past 15 years, we found Dr. Kao had 2987 connections with other coauthors from 13 research teams. The cooccurrence abstract keywords with the highest frequency are cohort study and National Health Insurance Research Database. The most coexistent MESH terms are tomography, X-ray computed, and positron-emission tomography. The strength of the author research distinct domain is very low (Gini < 0.40).SNA incorporated with Google Maps and Gini coefficient provides insight into the relationships between entities. The results obtained in this study can be applied for a comprehensive understanding of other productive authors in the field of academics.

Asthma associated with chronic rhinosinusitis: a population-based study

BACKGROUND

Few studies have investigated the relationship between asthma and chronic rhinosinusitis (CRS). The present study investigated the association between asthma and the risk of CRS in a large national sample.

METHODS

Patients newly diagnosed with asthma (International Classification of Diseases, Ninth Revision [ICD-9], Clinical Modification code 493) between 2000 and 2008 were identified from the Taiwan National Health Insurance Research Database. The cases were compared with sex-, age-, residence-, and insurance premium-matched controls, and both groups were followed until the end of 2009 for incidences of CRS with or without nasal polyps (CRSwNP or CRSsNP, respectively). Competing risk-adjusted Cox regression analyses were performed after adjustment for sex, age, residence, insurance premium, steroid use, hyperlipidemia, diabetes, hypertension, coronary artery disease, Charlson comorbidity index score, and mortality. We also performed a case-control study to determine the association between asthma and CRS.

RESULTS

The cohort study analysis examined 81,462 patients with a mean ± standard deviation (SD) follow-up period of 5.8 ± 2.4 years. Asthma was an independent predictor of CRSsNP (hazard ratio = 2.58; 95% confidence interval [CI], 2.20 to 3.03; p < 0.001) in the fully adjusted models. In the case-control analysis, both CRSwNP and CRSsNP were associated with asthma in the fully adjusted models.

CONCLUSION

Asthma was associated with increased risks of CRSwNP and CRSsNP, independent of several potential confounding factors.