Evaluating the impact of sugar-sweetened beverages tax on overweight, obesity, and type 2 diabetes in an affluent Asian setting: A willingness-to-pay survey and simulation analysis.

BACKGROUND: The potential health effects of taxing sugar-sweetened beverages (SSBs) has been insufficiently examined in Asian contexts. This study aimed to assess the impact of SSB taxation on the prevalence of obesity/overweight and type 2 diabetes mellitus (T2DM) in Hong Kong using a willingness-to-pay (WTP) survey and simulation analysis. METHODS: A random telephone survey was conducted with 1000 adults from May to June 2020. We used a contingent valuation approach to assess individuals' WTP for SSBs under four tax payment scenarios (5%, 10%, 40%, and 50% of the current market price). Based on the WTP, a simulation analysis was conducted to project changes in SSB purchase and associated reductions in the prevalence of obesity/overweight and T2DM over a 10-year simulation period. FINDINGS: When 5% and 10% taxation rates were introduced, approximately one-third of the population were unwilling to maintain their SSB purchase. Our simulation demonstrated a gradual decline in the prevalence of obesity/overweight and diabetes with a more pronounced decrease when higher taxation rates were introduced. 10% taxation resulted in a mean reduction of 1532.7 cases of overweight/obesity per 100 thousand population at the sixth year, while T2DM prevalence decreased by 267.1 (0.3%). CONCLUSIONS: This study underscores the effects of an SSB tax on purchase behaviors and health outcomes in an affluent Asia setting, with a more pronounced influence on adult population. These findings are expected to inform policymakers in making decisions regarding an effective and equitable tax rate on SSBs.

Projection of dengue fever transmissibility under climate change in South and Southeast Asian countries.

Vector-borne infectious disease such as dengue fever (DF) has spread rapidly due to more suitable living environments. Considering the limited studies investigating the disease spread under climate change in South and Southeast Asia, this study aimed to project the DF transmission potential in 30 locations across four South and Southeast Asian countries. In this study, weekly DF incidence data, daily mean temperature, and rainfall data in 30 locations in Singapore, Sri Lanka, Malaysia, and Thailand from 2012 to 2020 were collected. The effects of temperature and rainfall on the time-varying reproduction number (Rt) of DF transmission were examined using generalized additive models. Projections of location-specific Rt from 2030s to 2090s were determined using projected temperature and rainfall under three Shared Socioeconomic Pathways (SSP126, SSP245, and SSP585), and the peak DF transmissibility and epidemic duration in the future were estimated. According to the results, the projected changes in the peak Rt and epidemic duration varied across locations, and the most significant change was observed under middle-to-high greenhouse gas emission scenarios. Under SSP585, the country-specific peak Rt was projected to decrease from 1.63 (95% confidence interval: 1.39-1.91), 2.60 (1.89-3.57), and 1.41 (1.22-1.64) in 2030s to 1.22 (0.98-1.51), 2.09 (1.26-3.47), and 1.37 (0.83-2.27) in 2090s in Singapore, Thailand, and Malaysia, respectively. Yet, the peak Rt in Sri Lanka changed slightly from 2030s to 2090s under SSP585. The epidemic duration in Singapore and Malaysia was projected to decline under SSP585. In conclusion, the change of peak DF transmission potential and disease outbreak duration would vary across locations, particularly under middle-to-high greenhouse gas emission scenarios. Interventions should be considered to slow down global warming as well as the potential increase in DF transmissibility in some locations of South and Southeast Asia.

Predictive evolutionary modelling for influenza virus by site-based dynamics of mutations.

Influenza virus continuously evolves to escape human adaptive immunity and generates seasonal epidemics. Therefore, influenza vaccine strains need to be updated annually for the upcoming flu season to ensure vaccine effectiveness. We develop a computational approach, beth-1, to forecast virus evolution and select representative virus for influenza vaccine. The method involves modelling site-wise mutation fitness. Informed by virus genome and population sero-positivity, we calibrate transition time of mutations and project the fitness landscape to future time, based on which beth-1 selects the optimal vaccine strain. In season-to-season prediction in historical data for the influenza A pH1N1 and H3N2 viruses, beth-1 demonstrates superior genetic matching compared to existing approaches. In prospective validations, the model shows superior or non-inferior genetic matching and neutralization against circulating virus in mice immunization experiments compared to the current vaccine. The method offers a promising and ready-to-use tool to facilitate vaccine strain selection for the influenza virus through capturing heterogeneous evolutionary dynamics over genome space-time and linking molecular variants to population immune response.

Impact of climate change on dengue fever epidemics in South and Southeast Asian settings: A modelling study.

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 2030s-2090s 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.

A Prism Vote method for individualized risk prediction of traits in genotype data of Multi-population.

Multi-population cohorts offer unprecedented opportunities for profiling disease risk in large samples, however, heterogeneous risk effects underlying complex traits across populations make integrative prediction challenging. In this study, we propose a novel Bayesian probability framework, the Prism Vote (PV), to construct risk predictions in heterogeneous genetic data. The PV views the trait of an individual as a composite risk from subpopulations, in which stratum-specific predictors can be formed in data of more homogeneous genetic structure. Since each individual is described by a composition of subpopulation memberships, the framework enables individualized risk characterization. Simulations demonstrated that the PV framework applied with alternative prediction methods significantly improved prediction accuracy in mixed and admixed populations. The advantage of PV enlarges as genetic heterogeneity and sample size increase. In two real genome-wide association data consists of multiple populations, we showed that the framework considerably enhanced prediction accuracy of the linear mixed model in five-group cross validations. The proposed method offers a new aspect to analyze individual's disease risk and improve accuracy for predicting complex traits in genotype data.

Impact of extreme weather on dengue fever infection in four Asian countries: A modelling analysis.

The rapid spread of dengue fever (DF) infection has posed severe threats to global health. Environmental factors, such as weather conditions, are believed to regulate DF spread. While previous research reported inconsistent change of DF risk with varying weather conditions, few of them evaluated the impact of extreme weather conditions on DF infection risk. This study aims to examine the short-term associations between extreme temperatures, extreme rainfall, and DF infection risk in South and Southeast Asia. A total of 35 locations in Singapore, Malaysia, Sri Lanka, and Thailand were included, and weekly DF data, as well as the daily meteorological data from 2012 to 2020 were collected. A two-stage meta-analysis was used to estimate the overall effect of extreme weather conditions on the DF infection risk. Location-specific associations were obtained by the distributed lag nonlinear models. The DF infection risk appeared to increase within 1-3 weeks after extremely high temperature (e.g. lag week 2: RR = 1.074, 95 % CI: 1.022-1.129, p = 0.005). Compared with no rainfall, extreme rainfall was associated with a declined DF risk (RR = 0.748, 95 % CI: 0.620-0.903, p = 0.003), and most of the impact was across 0-3 weeks lag. In addition, the DF risk was found to be associated with more intensive extreme weathers (e.g. seven extreme rainfall days per week: RR = 0.338, 95 % CI: 0.120-0.947, p = 0.039). This study provides more evidence in support of the impact of extreme weather conditions on DF infection and suggests better preparation of DF control measures according to climate change.

Health Disparity Resulting from the Effect of Built Environment on Temperature-Related Mortality in a Subtropical Urban Setting.

Whereas previous studies have assessed the overall health impact of temperature in Hong Kong, the aim of this study was to investigate whether the health impact is modified by local temperature of small geographic units, which may be related to the diverse socioeconomic characteristics of these units. The effects of local temperature on non-accidental and cause-specific mortality were analyzed using Bayesian spatial models at a small-area level, adjusting for potential confounders, i.e., area-level air pollutants, socioeconomic status, and green space, as well as spatial dependency. We found that a 10% increase in green space density was associated with an estimated 4.80% decrease in non-accidental mortality risk and a 5.75% decrease in cardiovascular disease mortality risk in Hong Kong, whereas variation in local annual temperature did not significantly contribute to mortality. We also found that the spatial variation of mortality within this city could be explained by the geographic distribution of green space and socioeconomic factors rather than local temperature or air pollution. The findings and methodology of this study may help to further understanding and investigation of social and structural determinants of health disparities, particularly place-based built environment across class-based small geographic units in a city, taking into account the intersection of multiple factors from individual to population levels.

Rapid evaluation of COVID-19 vaccine effectiveness against symptomatic infection with SARS-CoV-2 variants by analysis of genetic distance.

Timely evaluation of the protective effects of Coronavirus Disease 2019 (COVID-19) vaccines against severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variants of concern is urgently needed to inform pandemic control planning. Based on 78 vaccine efficacy or effectiveness (VE) data from 49 studies and 1,984,241 SARS-CoV-2 sequences collected from 31 regions, we analyzed the relationship between genetic distance (GD) of circulating viruses against the vaccine strain and VE against symptomatic infection. We found that the GD of the receptor-binding domain of the SARS-CoV-2 spike protein is highly predictive of vaccine protection and accounted for 86.3% (P = 0.038) of the VE change in a vaccine platform-based mixed-effects model and 87.9% (P = 0.006) in a manufacturer-based model. We applied the VE-GD model to predict protection mediated by existing vaccines against new genetic variants and validated the results by published real-world and clinical trial data, finding high concordance of predicted VE with observed VE. We estimated the VE against the Delta variant to be 82.8% (95% prediction interval: 68.7-96.0) using the mRNA vaccine platform, closely matching the reported VE of 83.0% from an observational study. Among the four sublineages of Omicron, the predicted VE varied between 11.9% and 33.3%, with the highest VE predicted against BA.1 and the lowest against BA.2, using the mRNA vaccine platform. The VE-GD framework enables predictions of vaccine protection in real time and offers a rapid evaluation method against novel variants that may inform vaccine deployment and public health responses.

Risk Assessment of CHD Using Retinal Images with Machine Learning Approaches for People with Cardiometabolic Disorders.

BACKGROUND: Coronary heart disease (CHD) is the leading cause of death worldwide, constituting a growing health and social burden. People with cardiometabolic disorders are more likely to develop CHD. Retinal image analysis is a novel and noninvasive method to assess microvascular function. We aim to investigate whether retinal images can be used for CHD risk estimation for people with cardiometabolic disorders. METHODS: We have conducted a case-control study at Shenzhen Traditional Chinese Medicine Hospital, where 188 CHD patients and 128 controls with cardiometabolic disorders were recruited. Retinal images were captured within two weeks of admission. The retinal characteristics were estimated by the automatic retinal imaging analysis (ARIA) algorithm. Risk estimation models were established for CHD patients using machine learning approaches. We divided CHD patients into a diabetes group and a non-diabetes group for sensitivity analysis. A ten-fold cross-validation method was used to validate the results. RESULTS: The sensitivity and specificity were 81.3% and 88.3%, respectively, with an accuracy of 85.4% for CHD risk estimation. The risk estimation model for CHD with diabetes performed better than the model for CHD without diabetes. CONCLUSIONS: The ARIA algorithm can be used as a risk assessment tool for CHD for people with cardiometabolic disorders.

Spatiotemporal relationship between temperature and non-accidental mortality: Assessing effect modification by socioeconomic status.

BACKGROUND: Most published studies have assessed the overall health impact of temperature by using one-station or multiple-station averaged meteorological and air quality data. Concern has arisen about whether the temperature health impact is homogeneous across the whole territory geographically, since green space and socioeconomic factors may modify the impact. OBJECTIVE: This study aims at investigating how small-area mortality is modified by local temperature and other meteorological, air quality, green space, and socioeconomic factors of small geographic units in a subtropical urban setting. METHODS: Data on meteorological, air pollutants, and non-accidental mortality count in Hong Kong during 2006-2016 were obtained. Combined with green space and socioeconomic data, spatiotemporal analysis using Generalized Additive Mixed Models was conducted to examine the temperature-mortality relationship, adjusted for seasonality, long-term trend, other meteorological factors, pollutants, socioeconomic characteristics and green space. RESULTS: Socioeconomic status was found to modify the temporal temperature-mortality relationship. A J-shape association was identified for most areas in Hong Kong, where a sharp increase of mortality was observed when daily minimum temperature dropped lower than the turning point. However, for people living in the most affluent areas, after the initial increase there was a decrease of mortality for colder days. Besides, when comparing the two spatiotemporal models (i.e. using nearby or central temperature monitoring station), while leaving the other predictors unchanged, this study showed that there was little difference in the overall model performances. CONCLUSION: This study indicated that the daily fluctuation of mortality was associated with daily temperature, while the spatial variation of mortality within this city could be explained by the geographical distribution of green space and socioeconomic factors. Since people living in affluent areas were found to be more tolerant of cold temperatures, it would be more efficient to tailor cold temperature health education and warning information for socioeconomically deprived communities.

Clustering of Socioeconomic Data in Hong Kong for Planning Better Community Health Protection.

The concept of socioeconomic vulnerability has made a substantial contribution to the understanding and conceptualization of health risk. To assess the spatial distribution of multi-dimensional socioeconomic vulnerability in an urban context, a vulnerability assessment scheme was proposed to guide decision-making in disaster resilience and sustainable urban development to reduce health risk. A two-stage approach was applied in Hong Kong to identify subgroups among Tertiary Planning Units (TPU) (i.e., the local geographic areas) with similar characteristics. In stage 1, principal components analysis was used for dimension reduction and to de-noise the socioeconomic data for each TPU based on the variables selected, while in stage 2, Gaussian mixture modeling was used to partition all the TPUs into different subgroups based on the results of stage 1. This study summarized socioeconomic-vulnerability-related data into five principal components, including indigenous degree, family resilience, individual productivity, populous grassroots, and young-age. According to these five principal components, all TPUs were clustered into five subgroups/clusters. Socioeconomic vulnerability is a concept that could be used to help identify areas susceptible to health risk, and even identify susceptible groups in affluent areas. More attention should be paid to areas with high populous grassroots scores and low young-age score since they were associated with a higher mortality rate.

Superspreading and heterogeneity in transmission of SARS, MERS, and COVID-19: A systematic review.

BACKGROUND: Severe acute respiratory syndrome (SARS), Middle East respiratory syndrome (MERS), and coronavirus disease 2019 (COVID-19) have caused substantial public health burdens and global health threats. Understanding the superspreading potentials of these viruses are important for characterizing transmission patterns and informing strategic decision-making in disease control. This systematic review aimed to summarize the existing evidence on superspreading features and to compare the heterogeneity in transmission within and among various betacoronavirus epidemics of SARS, MERS and COVID-19. METHODS: PubMed, MEDLINE, and Embase databases were extensively searched for original studies on the transmission heterogeneity of SARS, MERS, and COVID-19 published in English between January 1, 2003, and February 10, 2021. After screening the articles, we extracted data pertaining to the estimated dispersion parameter (k) which has been a commonly-used measurement for superspreading potential. FINDINGS: We included a total of 60 estimates of transmission heterogeneity from 26 studies on outbreaks in 22 regions. The majority (90%) of the k estimates were small, with values less than 1, indicating an over-dispersed transmission pattern. The point estimates of k for SARS and MERS ranged from 0.12 to 0.20 and from 0.06 to 2.94, respectively. Among 45 estimates of individual-level transmission heterogeneity for COVID-19 from 17 articles, 91% were derived from Asian regions. The point estimates of k for COVID-19 ranged between 0.1 and 5.0. CONCLUSIONS: We detected a substantial over-dispersed transmission pattern in all three coronaviruses, while the k estimates varied by differences in study design and public health capacity. Our findings suggested that even with a reduced R value, the epidemic still has a high resurgence potential due to transmission heterogeneity.

Long-term effectiveness of elderly health care voucher scheme strategies: a system dynamics simulation analysis.

BACKGROUND: The elderly healthcare voucher (EHCV) scheme is expected to lead to an increase in the number of elderly people selecting private primary healthcare services and reduce reliance on the public sector in Hong Kong. However, studies thus far have reported that this scheme has not received satisfactory responses. In this study, we examined changes in the ratio of visits between public and private doctors in primary care (to measure reliance on the public sector) for different strategic scenarios in the EHCV scheme. METHODS: Based on comments from an expert panel, a system dynamics model was formulated to simulate the impact of various enhanced strategies in the scheme: increasing voucher amounts, lowering the age eligibility, and designating vouchers for chronic conditions follow-up. Data and statistics for the model calibration were collected from various sources. RESULTS: The simulation results show that the current EHCV scheme is unable to reduce the utilization of public healthcare services, as well as the ratio of visits between public and private primary care among the local aging population. When comparing three different tested scenarios, even if the increase in the annual voucher amount could be maintained at the current pace or the age eligibility can be lowered to include those aged 60 years, the impact on shifts from public-to-private utilization were insignificant. The public-to-private ratio could only be marginally reduced from 0.74 to 0.64 in the first several years. Nevertheless, introducing a chronic disease-oriented voucher could result in a significant drop of 0.50 in the public-to-private ratio during the early implementation phase. However, the effect could not be maintained for an extended period. CONCLUSIONS: Our findings will assist officials in improving the design of the EHCV scheme, within the wider context of promoting primary care among the elderly. We suggest that an additional chronic disease-oriented voucher can serve as an alternative strategy. The scheme must be redesigned to address more specific objectives or provide a separate voucher that promotes under-utilized healthcare services (e.g., preventive care), instead of services designed for unspecified reasons, which may lead to concerns regarding exploitation.

Clinical study of diabetic peripheral neuropathy screening by retinal vascular geometric parameters.

To investigate the relationship between geometrical changes of retinal vessels and diabetic peripheral neuropathy (DPN), and to determine the effectiveness of retinal vascular geometry analysis and vibration perception threshold (VPT) for DPN assessment. Type 2 diabetes patients (n = 242) were categorized by stage of DPN. VPT and fundus photography was performed to obtain retinal vascular geometry parameters. The risk factors for DPN and the correlation between DPN stages were analyzed. The efficiency of the retinal vascular geometric parameters obtained with VPT as a diagnostic tool for DPN was examined. Stages of DPN showed a linear correlation with VPT (r = 0.818), central retinal vein equivalent (CRVE) (r = 0.716), and fractal dimension arterioles (DFa) (r = - 0.769). VPT, CRVE, DFa, and fractal dimension veins (DFv) showed high sensitivity (80%, 55%, 82%, and 67%, respectively) and specificity (92%, 93%, 82%, and 80%, respectively) for DPN diagnosis. Good agreement was observed between combined use of geometric parameters (CRVE, DFa and DFv) and VPT (Kappa value 0.430). The detection rate of DPN with combined use of geometric parameters of retinal vessels (64.88%) was significantly higher than that with use of VPT (47.52%). Retinal vascular geometry changes demonstrated significant correlation with DPN severity. VPT, CRVE, DFa, and DFv may provide insights for understanding DPN.

Clinical significance of site-specific metastases in pancreatic cancer: a study based on both clinical trial and real-world data.

Background: There is limited consensus on whether metastatic patterns are correlated with prognosis and treatment efficacy in pancreatic cancer. A better understanding of clinical implication of the metastatic patterns is pivotal for therapeutic decision-making and drug development. Methods: This study included 977 patients with metastatic pancreatic cancer (MPC) in three cohorts. The training cohort included 273 patients from clinical trial NCT00574275 and 367 patients from clinical trial NCT01124786. As the validation cohort, 337 patients from Changzhou No.2 People's Hospital and Shanghai General Hospital were enrolled. The correlations between different patterns of metastases and clinicopathological characteristics were investigated with the Pearson Chi-Square test. Kaplan-Meier analysis and log-rank test were applied to analyze the survival outcomes among groups with different metastatic patterns. The prognostic value of the number of metastatic sites and other variables was evaluated using the Cox regression model. Results: MPC patients aged ≥65 years had a higher rate of lung metastasis and those with liver metastasis were prone to have a high level of carbohydrate antigen 19-9 (CA19-9). Additionally, patients with isolated lung metastasis had much better overall survival (OS) than those with isolated liver or peritoneum metastasis. Cox regression analyses showed that the number of metastatic sites was an independent prognostic factor for OS in patients with MPC. Furthermore, for patients with one-site or two-site metastasis, there was a significant difference in OS among patients receiving no chemotherapy, monotherapy and combination therapy. However, for patients with more than two metastatic sites, receiving combination therapy or monotherapy showed limited superiority in OS over receiving no chemotherapy. Conclusion: MPC patients with isolated lung metastasis had better OS than those with isolated liver or peritoneum metastasis. Moreover, the number of metastatic sites showed prognostic and predictive value in patients with MPC.

In silico prediction of influenza vaccine effectiveness by sequence analysis.

The effectiveness of seasonal influenza vaccines varies with the matching of vaccine strains to circulating strains. Based on the genetic distance of hemagglutinin and neuraminidase gene of the influenza viruses to vaccine strains, we statistically quantified the relationship between the genetic mismatch and vaccine effectiveness (VE) for influenza A/H1N1pdm09, A/H3N2 and B. We also proposed a systematic approach to integrate multiple genes and influenza types for overall VE estimation. Evident linear relationships were identified and validated in independent data. The modelling framework may enable in silico prediction for VE on a real-time basis and inform the influenza vaccine selection strategy.

Independent association between meteorological factors, PM2.5, and seasonal influenza activity in Hangzhou, Zhejiang province, China.

BACKGROUND: Due to variations in climatic conditions, the effects of meteorological factors and PM2.5 on influenza activity, particularly in subtropical regions, vary in existing literature. In this study, we examined the relationship between influenza activity, meteorological parameters, and PM2.5 . METHODS: A total of 20 165 laboratory-confirmed influenza cases in Hangzhou, Zhejiang province, were documented in our dataset and aggregated into weekly counts for downstream analysis. We employed a combination of the quasi-Poisson-generalized additive model and the distributed lag non-linear model to examine the relationship of interest, controlling for long-term trends, seasonal trends, and holidays. RESULTS: A hockey-stick association was found between absolute humidity and the risk of influenza infections. The overall cumulative adjusted relative risk (ARR) was statistically significant when weekly mean absolute humidity was low (<10 µg/m3 ) and high (>17.5 µg/m3 ). A slightly higher ARR was observed when weekly mean temperature reached over 30.5°C. A statistically significantly higher ARR was observed when weekly mean relative humidity dropped below 67%. ARR increased statistically significantly with increasing rainfall. For PM2.5 , the ARR was marginally statistically insignificant. In brief, high temperature, wet and dry conditions, and heavy rainfall were the major risk factors associated with a higher risk of influenza infections. CONCLUSIONS: The present study contributes additional knowledge to the understanding of the effects of various environmental factors on influenza activities. Our findings shall be useful and important for the development of influenza surveillance and early warning systems.

Monitoring disease transmissibility of 2019 novel coronavirus disease in Zhejiang, China.

Owing to the frequent travel connections between Wuhan and Zhejiang, Zhejiang was the third worst-affected province in China with 1,205 cases confirmed before 26 February 2020. The transmissibility of the 2019 novel coronavirus disease was monitored in Zhejiang, accounting for the transmissions from imported cases. Even though Zhejiang was one of the worst-affected provinces, an interruption of disease transmission (i.e. instantaneous reproduction numbers <1) was observed in early/mid-February after a comprehensive set of interventions combating the outbreak.

Epidemiological changes in measles infections in southern China between 2009 and 2016: a retrospective database analysis.

BACKGROUND: The incidence rate of measles in China reached a nadir in 2012 after 2 supplementary immunization activities (SIAs) were undertaken in 2009 and 2010. However, the disease began re-emerging in 2013, with a high prevalence rate observed in 2013-2014 in the southern province of Guangdong. In this study, we assessed the changes that occurred in measles epidemiology during 2009-2016, particularly between 2009 and 2011 (when the influence of the SIAs were in full effect) and between 2012 and 2016 (when this influence subsided). METHODS: Data from 22,362 patients with measles diagnosed between 2009 and 2016, and whose diagnoses were confirmed clinically and/or with laboratory testing, were extracted from the National Infectious Disease Monitoring Information System. Descriptive analyses were performed, and changes in epidemiological characteristics between 2009 and 2011 and 2012-2016 were compared. RESULTS: There was a substantial surge in 0-8-month-old patients after 2012; the incidence rate increased from 4.0 per 100,000 population in 2011 (10.3% of the total) to 280 per 100,000 population in 2013 (32.8% of the total). Patients aged 0-6 years represented 73.4% of the total increase between 2011 and 2013. Compared with 2009-2011, adults aged ≥25 years accounted for a higher proportion of patients in 2013 and after (p < 0.01), and were highest in 2016 (31% of the patient total). CONCLUSION: Despite the remarkable results achieved by SIAs in terms of providing herd immunity, the 2013 resurgence of measles revealed insufficient immunization coverage among children. Therefore routine immunization programs should be strengthened, and supplementary vaccinations targeting adults should also be contemplated.

Were infections in migrants associated with the resurgence of measles epidemic during 2013-2014 in southern China? A retrospective data analysis.

OBJECTIVE: The 2009 province-wide and 2010 nationwide supplementary immunization activities (SIAs) greatly reduced measles prevalence in Guangdong, a province in southern China with the largest migrant population. However, during 2013-2014, Guangdong experienced a resurgence of the measles epidemic. This study was performed to examine the association between infections in migrants and the resurgence of the measles epidemic. METHODS: The records of 22 362 clinically and laboratory-confirmed measles cases from the years 2009 to 2014 were extracted from the National Infectious Disease Monitoring Information System. The epidemiological characteristics of infections in migrants during 2009-2012 were compared to those during 2013-2014. RESULTS: Infections in migrants were not significantly associated with the resurgence of the measles epidemic in 2013-2014 (p=0.98). Nevertheless, for infections among locals and migrants during 2009-2012 and 2013-2014, substantial increases in the proportion of infection were detected among children aged <8 months and the unvaccinated population (p<0.01). CONCLUSIONS: The study findings suggest that infections in migrants might not have been the major cause of the epidemic resurgence of measles. Instead, the resurgence was likely due to infections among children aged <8 months and the unvaccinated. Thus, officials are advised to give higher priority to appropriate populations when formulating control measures, and to strengthen routine surveillance of vaccination coverage among them.