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This study’s objective was to identify, in a statistically valid and efficient manner, the risk factors associated with dental implant failure. We hypothesize that factors exist which can be modified by clinicians to enhance outcome. A retrospective cohort study design was used. Cohort members had ≥ one implant placed. Risk factors were classified as demographic, health status, implant-, anatomic-, or prosthetic-specific, and reconstructive variables. The outcome variable was implant failure. The cohort was composed of 677 patients who had 2349 implants placed. Based on the adjusted multivariate model, factors associated with implant failure were tobacco use, implant length, staging, well size, and immediate implants (p ≤ 0.05). In the setting of correlated survival observations, we recommend adjusting for the correlation of the observations to provide statistically valid and efficient results. Three of the identified factors—tobacco use, immediate implants, and implant staging—potentially may be modified to enhance implant survival.

Because dental implant failure patterns tend to cluster within subjects, we hypothesized that the risk of implant failure varies among subjects. To address this hypothesis in the setting of clustered, correlated observations, we considered a retrospective cohort study where we identified a cohort having at least one implant placed. The cohort was composed of 677 patients who had 2349 implants placed. To test the hypothesis, we applied an innovative analytic method, i.e., the Cox proportional hazards model with frailty, to account for correlation within subjects and the heterogeneity of risk, i.e., frailty, among subjects for implant failure. Consistent with our hypothesis, risk for implant failure among subjects varied to a statistically significantly degree (p = 0.041). In addition, the risk for implant failure is significantly associated with several factors, including tobacco use, implant length, immediate implant placement, staging, well size, and proximity of adjacent implants or teeth.

We examined the relationship between meteorological parameters and hand, foot and mouth disease (HFMD) activity. Meteorological data collected from 2000 to 2004 were tested for correlation with HFMD consultation rates calculated through the sentinel surveillance system in Hong Kong. The regression model constructed was used to predict HFMD consultation rates for 2005–2009. After adjusting for the effect of collinearity, mean temperature, diurnal difference in temperature, relative humidity, and wind speed were positively associated with HFMD consultation rates, and explained HFMD consultation rates well with 2 weeks' lag time (R2=0·119, P=0·010). The predicted HFMD consultation rates were also also well matched with the observed rates (Spearman's correlation coefficient=0·276, P=0·000) in 2005–2009. Sensitivity analysis showed that HFMD consultation rates were mostly affected by relative humidity and least affected by wind speed. Our model demonstrated that climate parameters help in predicting HFMD activity, which could assist in explaining the winter peak detected in recent years and in issuing early warning.

•Global collaboration is feasible and enables comparison of vaccine formulations.•Exposure to pandemic H1N1 vaccines was associated with a slight increased risk of GBS.•Adjuvanted vaccines were not associated with higher GBS risk as compared to non-adjuvanted vaccines.•Results from pooled (mega-analytic) and meta-analytic approaches were consistent.•Low relative incidence of GBS supports recommendations for use of influenza vaccines. The global spread of the 2009 novel pandemic influenza A (H1N1) virus led to the accelerated production and distribution of monovalent 2009 Influenza A (H1N1) vaccines (pH1N1). This pandemic provided the opportunity to evaluate the risk of Guillain–Barré syndrome (GBS), which has been an influenza vaccine safety concern since the swine flu pandemic of 1976, using a common protocol among high and middle-income countries. The primary objective of this project was to demonstrate the feasibility and utility of global collaboration in the assessment of vaccine safety, including countries both with and without an established infrastructure for vaccine active safety surveillance. A second objective, included a priori, was to assess the risk of GBS following pH1N1 vaccination. The primary analysis used the self-controlled case series (SCCS) design to estimate the relative incidence (RI) of GBS in the 42 days following vaccination with pH1N1 vaccine in a pooled analysis across databases and in analysis using a meta-analytic approach. We found a relative incidence of GBS of 2.42 (95% CI 1.58–3.72) in the 42 days following exposure to pH1N1 vaccine in analysis of pooled data and 2.09 (95% CI 1.28–3.42) using the meta-analytic approach. This study demonstrates that international collaboration to evaluate serious outcomes using a common protocol is feasible. The significance and consistency of our findings support a conclusion of an association between 2009 H1N1 vaccination and GBS. Given the rarity of the event the relative incidence found does not provide evidence in contradiction to international recommendations for the continued use of influenza vaccines.

During the early phase of the influenza pandemic in 2009, all cases of laboratory-confirmed pandemic (H1N1) 2009 (pH1N1) infection required compulsory isolation in hospital. These cases were offered oseltamivir treatment and only allowed to be discharged from the hospital when three consecutive respiratory specimens were negative for the virus by reverse transcription–polymerase chain reaction (RT–PCR). We reviewed the case records of these patients to assess the viral shedding kinetics of the pH1N1 virus. We defined viral shedding duration as the interval from illness onset date to the date of collection of the last positive specimen from the patients. Fifty-six patients were included in the study, of whom 96% received oseltamivir. The median viral shedding duration of pH1N1 virus by viral culture and RT–PCR were 3 days and 4 days, respectively. Patients who started oseltamivir treatment >48 h after onset had a significantly longer median viral shedding duration by viral culture than those who started treatment within 48 h of onset (4 days vs . 2 days, P =0·014).

The study's purposes were to estimate dental implant survival in a statistically valid manner and to compare three models for estimating survival. We estimated survival using three different statistical models: (1) randomly selecting one implant per patient; (2) utilizing all implants, assuming independence among implants from the same subject; and (3) utilizing all implants, assuming dependence among implants from the same subject. The cohort was composed of 660 patients who had 2286 implants placed. Due to the high success rates of implants, the five-year survival point and standard error estimates varied little among the three models. Patients at high risk for implant failure (smokers) manifested greater variation in the standard error estimates among the three models, 8.2%, 4.0%, and 5.6%, respectively. To obtain statistically valid survival confidence intervals when performing Kaplan-Meier survival analyses, we recommend adjusting for dependence when there are multiple observations within the same subject.

We conducted a 31-month retrospective review of all laboratory-confirmed Streptococcus suis infections admitted to public hospitals in Hong Kong. Strain identification, serotyping and antibiotic susceptibility tests were conducted on S. suis isolates. Twenty-one sporadic cases were identified, comprising 18 (86%) males and 3 (14%) females. About half were patients aged ≥65 years. More cases occurred during summer. Occupational exposure was documented in five (24%) cases. The estimated annual incidence was 0·09/100 000 in the general population and 32/100 000 in people with occupational exposure to pigs and raw pork. The primary clinical manifestations were meningitis (48%), septicaemia (38%) and endocarditis (14%). The case-fatality rate was 5%. All available isolates from 15 patients were serotype 2, sensitive to penicillin, ampicillin, ceftriaxone, but resistant to tetracycline. Injury prevention and proper handling of pigs or raw pork should be advocated to both at-risk occupational groups and the general population.

In mid-June 2009, an outbreak of pandemic (H1N1) 2009 (pH1N1) infection occurred in a secondary school in Hong Kong. We carried out an epidemiological investigation to delineate the characteristics of the outbreak, gauge the extent of secondary household transmission, and assess the protective role of oseltamivir in household contacts. We interviewed pH1N1-confirmed cases using a standardized questionnaire. Sixty-five of 511 students in the school were affected. Of the 205 household contacts identified, 12 were confirmed as cases. All cases recovered. The estimated secondary household attack rate was 5·9% (95% CI 2·7–9·1). Household contacts aged <18 years were about 15 times more likely to be infected than older contacts. Household contacts who had received oseltamivir prophylaxis were less likely to acquire a secondary infection than those who had not (odds ratio=0). The estimated mean household serial interval of pH1N1 virus was 2·8 days (95% CI 2·1–3·4 days).

The analyses of clustered survival observations within the same subject are challenging. This study's purpose was to compare and contrast predicted dental implant survival estimates assuming the independence or dependence of clustered observations. Using a retrospective cohort composed of 677 patients (2349 implants), we applied an innovative analytic marginal approach to produce point and variance estimates of survival predictions given the covariates smoking status, implant staging, and timing of placement adjusted for clustered observations (dependence method). We developed a second model assuming independence of the clustered observations (naïve method). The 95% confidence intervals for survival prediction point estimates given the naive method were 5.9% to 14.3% more narrow than the dependence method estimates, resulting in an increased risk for type I error and erroneous rejection of the null hypothesis. To obtain statistically valid confidence intervals for survival prediction of the Aalen-Breslow estimates, we recommend adjusting for dependence among clustered survival observations.