Temperature and humidity associated with increases in tuberculosis notifications: a time-series study in Hong Kong.
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ABSTRACT:
SUBMITTER: Xu M
PROVIDER: S-EPMC8057503 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:BackgroundSeasonality in tuberculosis (TB) has been found in different parts of the world, showing a peak in spring/summer and a trough in autumn/winter. The evidence is less clear which factors drive seasonality. It was our aim to identify and evaluate seasonality in the notifications of TB in Germany, additionally investigating the possible variance of seasonality by disease site, sex and age group.MethodsWe conducted an integer-valued time series analysis using national surveillance data. We analysed the reported monthly numbers of started treatments between 2004 and 2014 for all notified TB cases and stratified by disease site, sex and age group.ResultsWe detected seasonality in the extra-pulmonary TB cases (N = 11,219), with peaks in late spring/summer and troughs in fall/winter. For all TB notifications together (N = 51,090) and for pulmonary TB only (N = 39,714) we did not find a distinct seasonality. Additional stratified analyses did not reveal any clear differences between age groups, the sexes, or between active and passive case finding.ConclusionWe found seasonality in extra-pulmonary TB only, indicating that seasonality of disease onset might be specific to the disease site. This could point towards differences in disease progression between the different clinical disease manifestations. Sex appears not to be an important driver of seasonality, whereas the role of age remains unclear as this could not be sufficiently investigated.
| S-EPMC8025493 | biostudies-literature
Project description:COVID-19 has become a pandemic. The influence of meteorological factors on the transmission and spread of COVID-19 is of interest. This study sought to examine the associations of daily average temperature (AT) and relative humidity (ARH) with the daily counts of COVID-19 cases in 30 Chinese provinces (in Hubei from December 1, 2019 to February 11, 2020 and in other provinces from January 20, 2020 to Februarys 11, 2020). A Generalized Additive Model (GAM) was fitted to quantify the province-specific associations between meteorological variables and the daily cases of COVID-19 during the study periods. In the model, the 14-day exponential moving averages (EMAs) of AT and ARH, and their interaction were included with time trend and health-seeking behavior adjusted. Their spatial distributions were visualized. AT and ARH showed significantly negative associations with COVID-19 with a significant interaction between them (0.04, 95% confidence interval: 0.004-0.07) in Hubei. Every 1 °C increase in the AT led to a decrease in the daily confirmed cases by 36% to 57% when ARH was in the range from 67% to 85.5%. Every 1% increase in ARH led to a decrease in the daily confirmed cases by 11% to 22% when AT was in the range from 5.04 °C to 8.2 °C. However, these associations were not consistent throughout Mainland China.
| S-EPMC7167225 | biostudies-literature
Project description:Statistical models are commonly employed in the estimation of influenza-associated excess mortality that, due to various reasons, is often underestimated by laboratory-confirmed influenza deaths reported by healthcare facilities. However, methodology for timely and reliable estimation of that impact remains limited because of the delay in mortality data reporting. We explored real-time estimation of influenza-associated excess mortality by types/subtypes in each year between 2012 and 2018 in Hong Kong using linear regression models fitted to historical mortality and influenza surveillance data. We could predict that during the winter of 2017/2018, there were ~634 (95% confidence interval (CI): (190, 1033)) influenza-associated excess all-cause deaths in Hong Kong in population ⩾18 years, compared to 259 reported laboratory-confirmed deaths. We estimated that influenza was associated with substantial excess deaths in older adults, suggesting the implementation of control measures, such as administration of antivirals and vaccination, in that age group. The approach that we developed appears to provide robust real-time estimates of the impact of influenza circulation and complement surveillance data on laboratory-confirmed deaths. These results improve our understanding of the impact of influenza epidemics and provide a practical approach for a timely estimation of the mortality burden of influenza circulation during an ongoing epidemic.
| S-EPMC6627011 | biostudies-literature
Project description:BackgroundBefore major non-pharmaceutical interventions were implemented, seasonal incidence of influenza in Hong Kong showed a rapid and unexpected reduction immediately following the early spread of COVID-19 in mainland China in January 2020. This decline was presumably associated with precautionary behavioral changes (e.g., wearing face masks and avoiding crowded places). Knowing their effectiveness on the transmissibility of seasonal influenza can inform future influenza prevention strategies.MethodsWe estimated the effective reproduction number (R t ) of seasonal influenza in 2019/20 winter using a time-series susceptible-infectious-recovered (TS-SIR) model with a Bayesian inference by integrated nested Laplace approximation (INLA). After taking account of changes in underreporting and herd immunity, the individual effects of the behavioral changes were quantified.FindingsThe model-estimated mean R t reduced from 1.29 (95%CI, 1.27-1.32) to 0.73 (95%CI, 0.73-0.74) after the COVID-19 community spread began. Wearing face masks protected 17.4% of people (95%CI, 16.3-18.3%) from infections, having about half of the effect as avoiding crowded places (44.1%, 95%CI, 43.5-44.7%). Within the current model, if more than 85% of people had adopted both behaviors, the initial R t could have been less than 1.ConclusionOur model results indicate that wearing face masks and avoiding crowded places could have potentially significant suppressive impacts on influenza.
| S-EPMC9728392 | biostudies-literature
Project description:There is a large gap between the number of people who develop tuberculosis (TB) and those who are diagnosed, treated and notified, with only an estimated 71% of people with TB notified globally in 2019. Implementing better TB case finding strategies is necessary to close this gap. In Cameroon, 1,597 healthcare workers at 725 health facilities were trained and engaged to intensively screen and test people for TB, then follow-up to link people to appropriate care. Primary care centers were linked to TB testing through a locally-tailored specimen referral network. This intervention was implemented across 6 regions of the country, with a population of 16 million people, while the remaining 4 regions in the country, with 7.3 million people, served as a control area. Controlled interrupted time series analyses were used to compare routinely-collected programmatic TB case notification rates in the intervention versus control area for 12 quarters prior to (2016–2018) and for 8 quarters after the start of the intervention (2019–2020). In 2019–2020, a total of 167,508 people were tested for TB at intervention sites, including 52,980 people attending primary care facilities that did not previously provide organized TB services. The number of people tested for TB increased by 45% during the intervention as compared to prior to the intervention. The controlled interrupted time series analyses showed that after two years of the intervention, the all-forms TB case notification rate in the intervention population increased by 9% (ratio of case notification rate ratios = 1.09, 95% CI 1.06 to 1.12), as compared with the counterfactual estimated from pre-intervention trends. This increase was observed even during a negative national impact on case finding from the COVID-19 pandemic. These results support the use of this health-facility based intervention to improve access to TB testing and care in this setting.
| S-EPMC10021155 | biostudies-literature
Project description:BackgroundRecent studies showed increased mortality risks after hot nights, but their effect on hospitalizations, especially in vulnerable populations, remains under-studied.MethodsDaily hospitalization, meteorological (including hourly), and air pollution data were collected for the hot seasons (May-October) of 2000-19 in Hong Kong. We derived three hot-night metrics: HNday28 °C, daily minimum temperature ≥28 °C, the governmental definition of hot nights; HNe, hot night excess calculated by summing heat excess of hourly temperatures above 28 °C at night; and HNday90th, hot nights classified using the 90th percentile HNe (17.7 °C⋅h) as a cutoff. We fitted time-series regression with distributed lag nonlinear models to examine the associations of hot-night metrics with various hospitalizations.FindingsDuring the 3680 study days, 5,002,114 non-cancer non-external (NCNE) hospitalizations were recorded. Half (1874) of the days experienced excess nighttime heat (HNe>0) with a mean (SD) of 8.0 (6.8) °C⋅h; 499 and 187 hot nights were identified by HNday28 °C and HNday90th, respectively. Extreme HNe (99th percentile vs 0 °C⋅h) was significantly associated with increased NCNE hospitalizations over lag 0-4 days by 3.1% [95% confidence interval: 1.5%, 4.8%] overall, with enhanced effects in elderly (5.3% [3.2%, 7.4%]), low-SES individuals (5.3% [2.8%, 8.0%]), and circulatory admissions (3.4% [0.2%, 6.8%]). HNday90th, reflecting extreme HNe, better identified hazardous hot nights than the official HNday28 °C.InterpretationExcessive nighttime heat is significantly associated with increased hospitalizations, particularly affecting the elderly and socioeconomically disadvantaged individuals. Nighttime heat intensity should be incorporated in defining hot nights with public health relevance.FundingBritish Heart Foundation.
| S-EPMC11367509 | biostudies-literature
Project description:BackgroundThe influence of meteorological factors on the transmission and spread of COVID-19 is of interest and has not been investigated.ObjectiveThis study aimed to investigate the associations between meteorological factors and the daily number of new cases of COVID-19 in 9 Asian cities.MethodsPearson correlation and generalized additive modeling (GAM) were performed to assess the relationships between daily new COVID-19 cases and meteorological factors (daily average temperature and relative humidity) with the most updated data currently available.ResultsThe Pearson correlation showed that daily new confirmed cases of COVID-19 were more correlated with the average temperature than with relative humidity. Daily new confirmed cases were negatively correlated with the average temperature in Beijing (r=-0.565, P<.001), Shanghai (r=-0.47, P<.001), and Guangzhou (r=-0.53, P<.001). In Japan, however, a positive correlation was observed (r=0.416, P<.001). In most of the cities (Shanghai, Guangzhou, Hong Kong, Seoul, Tokyo, and Kuala Lumpur), GAM analysis showed the number of daily new confirmed cases to be positively associated with both average temperature and relative humidity, especially using lagged 3D modeling where the positive influence of temperature on daily new confirmed cases was discerned in 5 cities (exceptions: Beijing, Wuhan, Korea, and Malaysia). Moreover, the sensitivity analysis showed, by incorporating the city grade and public health measures into the model, that higher temperatures can increase daily new case numbers (beta=0.073, Z=11.594, P<.001) in the lagged 3-day model.ConclusionsThe findings suggest that increased temperature yield increases in daily new cases of COVID-19. Hence, large-scale public health measures and expanded regional research are still required until a vaccine becomes widely available and herd immunity is established.
| S-EPMC7836910 | biostudies-literature
Project description:ImportanceWith immune recovery following early initiation of antiretroviral therapy (ART), the risk of tuberculosis (TB) reactivation among individuals with HIV could be reduced. The current strategy of annual latent TB infection (LTBI) testing should be revisited to increase cost-effectiveness and reduce the intensity of testing for individuals.ObjectiveTo analyze the cost-effectiveness of LTBI testing strategies for individuals in Hong Kong with HIV who had negative LTBI test results at baseline.Design, setting, and participantsThis decision analytical model study using a cost-effectiveness analysis included 3130 individuals with HIV in Hong Kong, China, which has an intermediate TB burden and a low incidence of HIV-TB coinfection. A system dynamics model of individuals with HIV attending a major HIV specialist clinic in Hong Kong was developed and parameterized by longitudinal clinical and LTBI testing records of patients during a 15-year period. The study population was stratified by age group, CD4 lymphocyte level, ART status, and right of abode. Alternative strategies for LTBI testing after a baseline test were compared with annual testing under different coverages of ART, LTBI testing, and LTBI treatment scenarios in the model. An annual discounting rate of 3.5% was used in cost-effectiveness analysis.Main outcomes and measuresProportion of new TB cases averted above base case scenario, discounted quality-adjusted life-years gained (QALYG), incremental cost, and incremental cost-effectiveness ratios in 2017 to 2023.ResultsA total of 3130 patients with HIV (2740 [87.5%] male and 2800 [89.5%] younger than 50 years at HIV diagnosis) with 16?630 person-years of follow-up data from 2002 to 2017 were analyzed. Of these, 94 patients (0.67 [95% CI, 0.51-0.91] per 100 person-years) developed TB. Model estimates of cumulative number of TB cases would reach 146 by 2023, with the annual number of new TB diagnoses ranging from 6 to 8. For patients who had negative LTBI test results at baseline, subsequent LTBI testing strategies were ranked by ascending effectiveness as follows: (1) no testing, (2) test by risk factors, (3) biennial testing for all, (4) up to 3 tests for all, and (5) annual testing for all. Applying a willingness-to-pay threshold of $50?000 per QALYG, none of the subsequent testing strategies were cost-effective. Test by risk factors and up to 3 tests for all were cost-effective only if the willingness-to-pay threshold was increased to $100?000 per QALYG and $200?000 per QALYG, respectively. More new TB cases would be averted by expanding LTBI testing and/or treatment coverage.Conclusions and relevanceChanging the current testing strategy to less intense testing strategies is likely to be cost-effective in the presence of an increased coverage of baseline LTBI testing and/or treatment.
| S-EPMC6735410 | biostudies-literature
Project description:Dark fermentation batch experimental 16S rRNA sequencing data Genome sequencing and assembly
| PRJNA980953 | ENA
Project description:Raw sequence reads of rRNA of Chaetoceros, Pseudo-nitzschia, and Thalassiosira in Hong Kong coastal waters
| PRJNA1138948 | ENA