Strict and repeated lockdowns have caused public fatigue regarding policy compliance and had a large impact on several countries’ economies. We aimed to evaluate the effectiveness of a soft lockdown policy and the strategy of active community screening for controlling COVID-19 in Taiwan. We used village-based daily confirmed COVID-19 statistics in Taipei City and New Taipei City, between May 2, 2021, and July 17, 2021. The temporal Gi* statistic was used to compute the spatiotemporal hotspots. Simple linear regression was used to evaluate the trend of the epidemic, positivity rate from community screening, and mobility changes in COVID-19 cases and incidence before and after a level three alert in both cities. We used a Bayesian hierarchical zero-inflated Poisson model to estimate the daily infection risk. The cities accounted for 11,403 (81.17%) of 14,048 locally confirmed cases. The mean effective reproduction number (Re) surged before the level three alert and peaked on May 16, 2021, the day after the level three alert in Taipei City (Re = 3.66) and New Taipei City (Re = 3.37). Mobility reduction and a lower positive rate were positively associated with a lower number of cases and incidence. In the spatiotemporal view, seven major districts were identified with a radial spreading pattern from one hard-hit district. Villages with a higher inflow degree centrality among people aged ≥ 60 years, having confirmed cases, specific land-use types, and with a higher aging index had higher infection risks than other villages. Early soft lockdown policy and detection of infected patients showed an effective strategy to control COVID-19 in Taiwan.

Background Asthma and obesity are important public health issues around the world. Obesity is considered a risk factor associated with the severity and incidence of asthma. We investigated the relationships between poor pulmonary function (defined by forced vital capacity (FVC) and percentage of predicted FVC (FVC%)) and obesity. Methods This is a retrospective longitudinal study using the MJ health examination database in Taiwan from 2000 to 2015. There were 160,609 participants aged ≥20 years with complete obesity indicators and lung function data, and having at least two visits. A generalized estimation equation (GEE) model was applied to estimate the association between lung function and obesity. Results BMI was the best indicator to predict poor pulmonary function for our participants. Results of BMI are presented as an example: Obesity (body mass index (BMI) ≥27.0 kg/m 2 ) is significantly associated with lower FVC [adjusted coefficients (β) for asthmatics: -0.11 L (95% CI: -0.14, -0.08); adjusted β for non-asthmatics: -0.08 L (-0.09, -0.08)] and FVC% [adjusted β for asthmatics: -1.91% (95% CI: -2.64, -1.19); adjusted β for non-asthmatics: 1.48% (-1.63, -1.33)]. Annual change of BMI (ΔBMI/year) is an independent risk factor for decreased FVC [adjusted β for asthmatics: -0.030 L (-0.048, -0.013); adjusted β for non-asthmatics: -0.019 L (-0.022, -0.016)] and FVC% [adjusted β for non-asthmatics: -0.603% (-1.063, -0.142); adjusted β for non-asthmatics: -0.304% (-0.393, -0.214)], and is significantly associated with accelerated FVC decline [adjusted β of ΔFVC/year and ΔFVC %/year for asthmatics: -0.038 L (-0.054, -0.022) and -0.873% (-1.312, -0.435); adjusted β of ΔFVC/year and ΔFVC %/year for non-asthmatics: -0.033 L (-0.042, -0.024) and -0.889% (-1.326, -0.452)]. Conclusion Obesity is significantly associated with decreased lung function, and asthmatics had a higher risk than non-asthmatics.

Background and objectiveObesity and asthma impose a heavy health and economic burden on millions of people around the world. The complex interaction between genetic traits and phenotypes caused the mechanism between obesity and asthma is still vague. This study investigates the relationship among obesity-related polygenic risk score (PRS), obesity phenotypes and the risk of having asthma.MethodsThis is a matched case–control study, with 4 controls (8288 non-asthmatic) for each case (2072 asthmatic). Data were obtained from the 2008–2015 Taiwan Biobank Database and linked to the 2000–2016 National Health Insurance Research Database. All participants were ≥30 years old with no history of cancer and had a complete questionnaire, as well as physical examination, genome-wide single nucleotide polymorphisms and clinical diagnosis data. Environmental exposure, PM2.5, was also considered. Multivariate adjusted ORs and 95% CIs were calculated using conditional logistic regression stratified by age and sex. Mediation analysis was also assessed, using a generalised linear model.ResultsWe found that the obese phenotype was associated with significantly increased odds of asthma by approximately 26%. Four obesity-related PRS, including body mass index (OR=1.07 (1.01–1.13)), waist circumference (OR=1.10 (1.04–1.17)), central obesity as defined by waist-to-height ratio (OR=1.09 (1.03–1.15)) and general–central obesity (OR=1.06 (1.00–1.12)), were associated with increased odds of asthma. Additional independent risk factors for asthma included lower educational level, family history of asthma, certain chronic diseases and increased PM2.5exposure. Obesity-related PRS is an indirect risk factor for asthma, the link being fully mediated by the trait of obesity.ConclusionsObese phenotypes and obesity-related PRS are independent risk factors for having asthma in adults in the Taiwan Biobank. Overall, genetic risk for obesity increases the risk of asthma by affecting the obese phenotype.

Background Uncontrolled asthma in adults leads to poor clinical outcome, while the clinical heterogeneity of phenotypes interferes the applicable genetic determinants. This study aimed to identify phenotypes and genetic impact on poorly-controlled asthma to optimize individualized treatment strategies. Methods This propensity score-matched case-control study included 340 and 1020 asthmatics with poorly-controlled asthma and well-controlled asthma, respectively. Data were obtained from the 2008–2015 Taiwan Biobank Database and linked to the National Health Insurance Research Database. All asthmatics were aged ≥30 years, without cancer history, and each completed a questionnaire, physical examination, and genome-wide single nucleotide polymorphisms (SNPs). Multivariate adjusted odds ratios (ORs) for genetic risk scores were calculated using conditional logistic regression, stratified by age and sex. A model integrating obesity- and asthma-associated phenotypes and genotypes was applied for poorly-controlled asthma risk prediction. Results General obesity with body mass index (BMI) ≥27 kg/m 2 (OR:1.49, 95% confidence interval (CI) 1.09–2.03), central obesity with waist-to-height ratio (WHtR) ≥0.5 (OR:1.62, 95% CI 1.22–2.15), and parental history of asthma (OR:1.65, and 1.68; for BMI model and WHtR model, respectively) were significantly associated with poorly-controlled asthma in adults, and the combination effect of both obesity phenotypes was 1.66 (95% CI 1.17–2.35). A total of 16 obesity-associated SNPs and 9 asthma-associated SNPs were converted into genetic scores, and the aforementioned phenotypes were incorporated into the risk prediction model for poorly-controlled asthma, with an area under curve 0.72 in the receiver operating characteristic curve. The potential biological functions of genes are involved in immunity pathways. Conclusion The prediction model integrating obesity-asthma phenotypes and genotypes for poorly-controlled asthma can facilitate the prediction of high-risk asthma and provide potential targets for novel treatment.

Background Kidney function is associated with clinical outcomes in patients with cancer. Objectives This study aimed to assess the association between kidney function decline and cancer‐related mortality among community‐dwelling elderly individuals. Design This was a retrospective longitudinal cohort study. Participants The 61,988 participants were from an elderly health examination database in Taipei City from 2005 to 2012. Measurements Multivariable logistic regression was used to assess the association between baseline covariates and rapidly deteriorating estimated glomerular filtration rate (eGFR). In addition, Cox proportional hazards model and the Fine–Gray model were used to quantify the effects of covariates on total cancer mortality and six specific cancer mortalities. Results During the follow‐up period, 1482 participants died of cancer. Their baseline average eGFR was 73.8 ± 19.9 mL/min/1.73 m 2 , and 18.3% had rapid renal function decline (≥5 mL/min/1.73 m 2 per year). Rapid renal function decline was positively related to age, baseline eGFR, proteinuria, hypertension, waist circumferences, high log triglyceride levels, and diabetes mellitus (DM) history. In Cox proportional hazard models, participants with rapid eGFR decline had an increased risk of cancer mortality [hazard ratio (95% CI): 1.97 (1.73, 2.24); p < 0.001] compared to those without rapid eGFR decline. In the analysis of site‐specific cancer mortality risk, rapid eGFR decline was associated with six site‐specific cancer mortality, namely gastrointestinal tract, hepatobiliary, lung, prostate, urinary tract, and hematological malignancies. Conclusions Elderly individuals with rapid kidney function decline had higher cancer mortality risks. Serial assessments of dynamic changes in eGFR might provide information relevant for cancer prognosis.