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BACKGROUND: There are few studies on the health effects of long-term exposure to neighborhood greenness in a longitudinal setting, especially in Asian countries with high population densities. OBJECTIVES: This study investigates the association between long-term exposure to neighborhood greenness and hypertension among adults in Taiwan. METHODS: We selected 125,537 participants (≥18 years of age) without hypertension from Taiwan who had joined the standard medical examination program between 2001 and 2016. Neighborhood greenness was estimated using the normalized difference vegetation index (NDVI), derived from satellite images at a resolution of 250 m². The 2-y average NDVI value within a 500-m circular buffer around participants' residences was calculated. A time-varying Cox regression model was used to investigate the association between neighborhood greenness and incident hypertension. Mediation analyses were performed to examine whether the association was explained by air pollution, leisure-time physical exercise, or body mass index (BMI). RESULTS: Compared with living in areas within the first quartile of neighborhood greenness, living in areas within the second, third, and fourth quartiles of neighborhood greenness was found to be associated with a lower risk of hypertension, with hazard ratios (HRs) and 95% confidence intervals (CIs) of 0.95 (95% CI: 0.91, 1.00), 0.95 (95% CI: 0.90, 0.99), and 0.93 (95% CI: 0.88, 0.97), respectively. Each 0.1-unit increase in the NDVI was associated with a 24% lower risk of developing hypertension (HR=0.76; 95% CI: 0.66, 0.87), with this associations being stronger among males and those with higher education levels. This association was slightly mediated by BMI but not by air pollution or leisure-time physical exercise. DISCUSSION: Our findings suggest the protective effects of neighborhood greenness on hypertension development, especially in males and well-educated individuals. Our results reinforced the importance of neighborhood greenness for supporting health. [ABSTRACT FROM AUTHOR]

Background: In a new influenza pandemic, travel data such as arrival times of cases seeded by the originating country can be regarded as a combination of the epidemic size and the mobility networks of infections connecting the originating country with other regions. It can be a complete and timely source for estimating the basic reproduction number (R 0 ), a key indicator of disease transmissibility.Method: In this study, we developed a likelihood-based method using arrival times of infected cases in different countries to estimate R 0 for influenza pandemics. A simulation was conducted to assess the performance of the proposed method. We further applied the method to the outbreak of the influenza pandemic A/H1N1 in Mexico.Results: In the numerical application, the estimated R 0 was equal to 1.69 with a 95% confidence interval (1.65, 1.73). For the simulation results, the estimations were robust to the decline of travel rate and other parameter assumptions. Nevertheless, the estimates were moderately sensitive to the assumption of infectious duration. Generally, the findings were in line with other relevant studies.Conclusions: Our approach as well as the estimate is potential to assist officials in planning control and prevention measures. Improved coordination to streamline or even centralize surveillance of imported cases among countries will thus be beneficial to public health. [ABSTRACT FROM AUTHOR]

Background: During the early stages of a new influenza pandemic, travel restriction is an immediate and non-pharmaceutical means of retarding incidence growth. It extends the time frame of effective mitigation, especially when the characteristics of the emerging virus are unknown. In the present study, we used the 2009 influenza A pandemic as a case study to evaluate the impact of regulating air, sea, and land transport. Other government strategies, namely, antivirals and hospitalizations, were also evaluated. Methods: Hong Kong arrivals from 44 countries via air, sea, and land transports were imported into a discrete stochastic Susceptible, Exposed, Infectious and Recovered (SEIR) host-flow model. The model allowed a number of latent and infectious cases to pass the border, which constitutes a source of local disease transmission. We also modeled antiviral and hospitalization prevention strategies to compare the effectiveness of these control measures. Baseline reproduction rate was estimated from routine surveillance data. Results: Regarding air travel, the main route connected to the influenza source area should be targeted for travel restrictions; imposing a 99% air travel restriction delayed the epidemic peak by up to two weeks. Once the pandemic was established in China, the strong land connection between Hong Kong and China rendered Hong Kong vulnerable. Antivirals and hospitalization were found to be more effective on attack rate reductions than travel restrictions. Combined strategies (with 99% restriction on all transport modes) deferred the peak for long enough to establish a vaccination program. Conclusion: The findings will assist policy-makers with decisions on handling similar future pandemics. We also suggest regulating the extent of restriction and the transport mode, once restriction has been deemed necessary for pandemic control. Although travel restrictions have yet to gain social acceptance, they allow time for mitigation response when a new and highly intrusive virus emerges. [ABSTRACT FROM AUTHOR]

We tested seroprevalence of open reading frame 8 antigens to infer the number of unrecognized SARS-CoV-2 Omicron infections in Hong Kong during 2022. We estimate 33.6% of the population was infected, 72.1% asymptomatically. Surveillance and control activities during large-scale outbreaks should account for potentially substantial undercounts. [ABSTRACT FROM AUTHOR]

The potential for dengue fever epidemic due to climate change remains uncertain in tropical areas. This study aims to assess the impact of climate change on dengue fever transmission in four South and Southeast Asian settings. We collected weekly data of dengue fever incidence, daily mean temperature and rainfall from 2012 to 2020 in Singapore, Colombo, Selangor, and Chiang Mai. Projections for temperature and rainfall were drawn for three Shared Socioeconomic Pathways (SSP126, SSP245, and SSP585) scenarios. Using a disease transmission model, we projected the dengue fever epidemics until 2090s and determined the changes in annual peak incidence, peak time, epidemic size, and outbreak duration. A total of 684,639 dengue fever cases were reported in the four locations between 2012 and 2020. The projected change in dengue fever transmission would be most significant under the SSP585 scenario. In comparison to the 2030s, the peak incidence would rise by 1.29 times in Singapore, 2.25 times in Colombo, 1.36 times in Selangor, and >10 times in Chiang Mai in the 2090s under SSP585. Additionally, the peak time was projected to be earlier in Singapore, Colombo, and Selangor, but be later in Chiang Mai under the SSP585 scenario. Even in a milder emission scenario of SSP126, the epidemic size was projected to increase by 5.94%, 10.81%, 12.95%, and 69.60% from the 2030se2090s in Singapore, Colombo, Selangor, and Chiang Mai, respectively. The outbreak durations in the four settings were projected to be prolonged over this century under SSP126 and SSP245, while a slight decrease is expected in 2090s under SSP585. The results indicate that climate change is expected to increase the risk of dengue fever transmission in tropical areas of South and Southeast Asia. Limiting greenhouse gas emissions could be crucial in reducing the transmission of dengue fever in the future. [ABSTRACT FROM AUTHOR]

Background: The influenza surveillance has been received much attention in public health area. For the cases with excessive zeroes, the zero-inflated Poisson process is widely used. However, the traditional control charts based on zero-inflated Poisson model, ignore the association between influenza cases and risk factors, and thus may lead to unexpected mistakes when implementing monitoring charts.Method: In this paper, we proposed risk-adjusted zero-inflated Poisson cumulative sum control charts, in which the risk factors were put to adjust the risk of influenza and the adjustment was made by zero-inflated Poisson regression. We respectively proposed the control chart monitoring the parameters individually and simultaneously.Results: The performance of our proposed risk-adjusted zero-inflated Poisson cumulative sum control chart was evaluated and compared with the unadjusted standard cumulative sum control charts in simulation studies. The results show that for different distribution of impact factors and different coefficients, the risk-adjusted cumulative sum charts can generate much less false alarm than the standard ones. Finally, the influenza surveillance data from Hong Kong is used to illustrate the application of the proposed chart.Conclusions: Our results suggest that the adjusted cumulative sum control chart we proposed is more accurate and credible than the unadjusted standard control charts because of the lower false alarm rate of the adjusted ones. Even the unadjusted control charts may signal a little faster than the adjusted ones, the alarm they raise may have low credibility since they also raise alarm frequently even the processes are in control. Thus we suggest using the risk-adjusted cumulative sum control charts to monitor the influenza surveillance data to alert accurately, credibly and relatively quickly. [ABSTRACT FROM AUTHOR]

Motivation: Increasing amounts of whole exome or genome sequencing data present the challenge of analysing rare variants with extremely small minor allele frequencies. Various statistical tests have been proposed, which are specifically configured to increase power for rare variants by conducting the test within a certain bin, such as a gene or a pathway. However, a gene may contain from several to thousands of markers, and not all of them are related to the phenotype. Combining functional and non-functional variants in an arbitrary genomic region could impair the testing power. Results: We propose a Zoom-Focus algorithm (ZFA) to locate the optimal testing region within a given genomic region. It can be applied as a wrapper function in existing rare variant association tests to increase testing power. The algorithm consists of two steps. In the first step, Zooming, a given genomic region is partitioned by an order of two, and the best partition is located. In the second step, Focusing, the boundaries of the zoomed region are refined. Simulation studies showed that ZFA substantially increased the statistical power of rare variants' tests, including the SKAT, SKAT-O, burden test and the W-test. The algorithm was applied on real exome sequencing data of hypertensive disorder, and identified biologically relevant genetic markers to metabolic disorders that were undetectable by a gene-based method. The proposed algorithm is an efficient and powerful tool to enhance the power of association study for whole exome or genome sequencing data. [ABSTRACT FROM AUTHOR]

Background Cardiovascular disease (CVD) is a major cause of death in China. Despite government efforts, the majority of hypertensive and diabetic patients in China do not receive proper treatment. Reducing CVD events requires long-term care that is proactive, patient-centred, community-based, and sustainable. We have designed a package of interventions for patients at high risk of CVD to be implemented by family doctors based in township hospitals (providers of primary care) in rural Zhejiang, China. This trial aims to determine whether the systematic CVD risk reduction package results in reduced CVD events among patients at risk of CVD compared with usual care, and whether the package is cost-effective and suitable for routine implementation and scale-up. Methods/Design This is a prospective, open-label, cluster randomized controlled trial (RCT) with blinded data analysis. The trial will randomize 67 township hospitals with 31,708 participants in three counties in Zhejiang Province. Participants will be identified from existing health records and will comprise adults aged 50 to 74 years, with a calculated 10-year CVD risk of 20% or higher, or diabetes. In the intervention arm, participants will receive a package of interventions including: 1) healthy lifestyle counseling (smoking cessation, and salt, oil, and alcohol reduction); 2) prescription of a combination of drugs (antihypertensives, aspirin, and statin); and 3) adherence support for drug compliance and healthy lifestyle change. In the control arm, participants will receive usual care for hypertension and diabetes management at individual clinicians' discretion. The primary outcome is the incidence of severe CVD events over 24 months of follow-up. All CVD events will be defined according to the World Health Organization (WHO) monitoring of trends and determinants in cardiovascular disease (MONICA) definitions, diagnosed at the county hospital or higher level, and reported by the Zhejiang surveillance system. Secondary outcomes include: mean systolic and diastolic blood pressure, blood glucose, serum total cholesterol (TC), and adherence to appointments, and drugs and lifestyle changes. Discussion This trial focuses on risk reduction of CVD rather than specific diseases. It is not designed to compare therapeutic and healthy lifestyle interventions, but rather their combined effects in primary care settings. Through the trial, we intend to understand the effectiveness of the comprehensive CVD reduction package in routine practice. We also intend to understand the barriers and facilitators to implementing the package, and thus to advise on policy and practice change. Trial registration Current Controlled Trials: ISRCTN58988083 [ABSTRACT FROM AUTHOR]