Multilevel analysis of clinical parameters in chronic periodontitis after root planing/scaling, surgery, and systemic and local antibiotics: 2-year results

Aim: Find the periodontal treatment that best maintained clinical results over time evaluated by changes in pocket depth (PD) and clinical attachment level (CAL). Methods: 229 patients with chronic periodontitis from USA (n=134) and Sweden (n=95) were randomly assigned to eight groups receiving 1 scaling+root planing (SRP) alone or combined with 2 surgery (SURG)+systemic amoxicillin (AMOX)+systemic metronidazole (MET); 3 SURG+local tetracycline (TET); 4 SURG; 5 AMOX+MET+TET; 6 AMOX+MET; 7 TET; and 8 SURG+AMOX+MET+TET. Antibiotics were given immediately after SRP. Plaque, gingival redness, bleeding on probing, suppuration, PD, and CAL were recorded at baseline and after 3, 6, 12, 18, and 24 months. Treatment effects were evaluated by linear multilevel regression and logistic multilevel regression models. We considered only data from sites with a baseline PD of at least 5 mm of 187 patients completing the study. Results: Surgically treated patients experienced most CAL loss. Adjunctive therapy including SURG was most effective in reducing PD. Combining SURG with AMOX, MET, and TET gave significant clinical benefits. Past and current smoking habits were significant predictors of deeper PD. Only current smoking was a significant predictor of CAL loss. Bleeding, accumulation of plaque, gingival redness, and suppuration were significant predictors of further CAL loss and deeper PD. Conclusions: Both surgical and non-surgical therapies can be used to arrest chronic periodontitis. SURG+AMOX+MET+TET gave best maintenance of clinical results

Effect of Vaccines and Antivirals during the Major 2009 A(H1N1) Pandemic Wave in Norway – And the Influence of Vaccination Timing

To evaluate the impact of mass vaccination with adjuvanted vaccines (eventually 40% population coverage) and antivirals during the 2009 influenza pandemic in Norway, we fitted an age-structured SEIR model using data on vaccinations and sales of antivirals in 2009/10 in Norway to Norwegian ILI surveillance data from 5 October 2009 to 4 January 2010. We estimate a clinical attack rate of approximately 30% (28.7–29.8%), with highest disease rates among children 0–14 years (43–44%). Vaccination started in week 43 and came too late to have a strong influence on the pandemic in Norway. Our results indicate that the countermeasures prevented approximately 11–12% of potential cases relative to an unmitigated pandemic. Vaccination was found responsible for roughly 3 in 4 of the avoided infections. An estimated 50% reduction in the clinical attack rate would have resulted from vaccination alone, had the campaign started 6 weeks earlier. Had vaccination been prioritized for children first, the intervention should have commenced approximately 5 weeks earlier in order to achieve the same 50% reduction. In comparison, we estimate that a non-adjuvanted vaccination program should have started 8 weeks earlier to lower the clinical attack rate by 50%

Dynamic modelling of costs and health consequences of school closure during an influenza pandemic

Background The purpose of this article is to evaluate the cost-effectiveness of school closure during a potential influenza pandemic and to examine the trade-off between costs and health benefits for school closure involving different target groups and different closure durations. Methods We developed two models: a dynamic disease model capturing the spread of influenza and an economic model capturing the costs and benefits of school closure. Decisions were based on quality-adjusted life years gained using incremental cost-effectiveness ratios. The disease model is an age-structured SEIR compartmental model based on the population of Oslo. We studied the costs and benefits of school closure by varying the age targets (kindergarten, primary school, secondary school) and closure durations (1–10 weeks), given pandemics with basic reproductive number of 1.5, 2.0 or 2.5. Results The cost-effectiveness of school closure varies depending on the target group, duration and whether indirect costs are considered. Using a case fatality rate (CFR) of 0.1-0.2% and with current cost-effectiveness threshold for Norway, closing secondary school is the only cost-effective strategy, when indirect costs are included. The most cost-effective strategies would be closing secondary schools for 8 weeks if R 0 =1.5, 6 weeks if R 0 =2.0, and 4 weeks if R 0 = 2.5. For severe pandemics with case fatality rates of 1-2%, similar to the Spanish flu, or when indirect costs are disregarded, the optimal strategy is closing kindergarten, primary and secondary school for extended periods of time. For a pandemic with 2009 H1N1 characteristics (mild severity and low transmissibility), closing schools would not be cost-effective, regardless of the age target of school children. Conclusions School closure has moderate impact on the epidemic’s scope, but the resulting disruption to society imposes a potentially great cost in terms of lost productivity from parents’ work absenteeism

A compelling demonstration of why traditional statistical regression models cannot be used to identify risk factors from case data on infectious diseases: a simulation study

Background Regression models are often used to explain the relative risk of infectious diseases among groups. For example, overrepresentation of immigrants among COVID-19 cases has been found in multiple countries. Several studies apply regression models to investigate whether different risk factors can explain this overrepresentation among immigrants without considering dependence between the cases. Methods We study the appropriateness of traditional statistical regression methods for identifying risk factors for infectious diseases, by a simulation study. We model infectious disease spread by a simple, population-structured version of an SIR (susceptible-infected-recovered)-model, which is one of the most famous and well-established models for infectious disease spread. The population is thus divided into different sub-groups. We vary the contact structure between the sub-groups of the population. We analyse the relation between individual-level risk of infection and group-level relative risk. We analyse whether Poisson regression estimators can capture the true, underlying parameters of transmission. We assess both the quantitative and qualitative accuracy of the estimated regression coefficients. Results We illustrate that there is no clear relationship between differences in individual characteristics and group-level overrepresentation —small differences on the individual level can result in arbitrarily high overrepresentation. We demonstrate that individual risk of infection cannot be properly defined without simultaneous specification of the infection level of the population. We argue that the estimated regression coefficients are not interpretable and show that it is not possible to adjust for other variables by standard regression methods. Finally, we illustrate that regression models can result in the significance of variables unrelated to infection risk in the constructed simulation example (e.g. ethnicity), particularly when a large proportion of contacts is within the same group. Conclusions Traditional regression models which are valid for modelling risk between groups for non-communicable diseases are not valid for infectious diseases. By applying such methods to identify risk factors of infectious diseases, one risks ending up with wrong conclusions. Output from such analyses should therefore be treated with great caution

Risk conditions in children hospitalized with influenza in Norway, 2017–2019

Background Norwegian children are more frequently hospitalized with influenza than adults. Little is known about the characteristics of these children. Our aim was to investigate the presence of pre-existing risk conditions and to determine the duration of influenza hospitalizations in children during two influenza seasons. Methods The Norwegian Patient Registry holds data on all hospitalized patients in Norway. We included all patients younger than 18 years hospitalized with a diagnosis of influenza during the influenza seasons 2017–18 and 2018–19. Pre-existing risk conditions for influenza were identified by ICD-10 diagnoses in the Norwegian Patient Registry. In addition, information on asthma diagnoses were also retrieved from the Norwegian Registry for Primary Health Care. To estimate the prevalence of risk conditions in the child population, we obtained diagnoses on all Norwegian children in a two-year period prior to each influenza season. We calculated age-specific rates for hospitalization and risk for being hospitalized with influenza in children with risk conditions. Results In total, 1013 children were hospitalized with influenza during the two influenza seasons. Children younger than 6 months had the highest rate of hospitalization, accounting for 13.5% of all admissions (137 children). Hospitalization rates decreased with increasing age. Among children hospitalized with influenza, 25% had one or more pre-existing risk conditions for severe influenza, compared to 5% in the general population under 18 years. Having one or more risk conditions significantly increased the risk of hospitalization, (Odds Ratio (OR) 6.1, 95% confidence interval (CI) 5.0–7.4 in the 2017–18 season, and OR 6.8, 95% CI 5.4–8.4 in the 2018–19 season). Immunocompromised children and children with epilepsy had the highest risk of hospitalization with influenza, followed by children with heart disease and lung disease. The average length of stay in hospital were 4.6 days, and this did not differ with age. Conclusion Children with pre-existing risk conditions for influenza had a higher risk of hospitalization for influenza. However, most children (75%) admitted to hospital with influenza in Norway during 2017–2019 did not have pre-existing risk conditions. Influenza vaccination should be promoted in particular for children with risk conditions and pregnant women to protect new-borns

Methicillin-resistant Staphylococcus aureus (MRSA) is increasing in Norway: a time series analysis of reported MRSA and methicillin-sensitive S. aureus cases, 1997-2010.

BACKGROUND: Accurate estimates of the incidence and prevalence of methicillin-resistant Staphylococcus aureus (MRSA) infections are needed to inform public health policies. In Norway, where both MRSA infection and carriage are notifiable conditions, the reported incidence of MRSA is slowly increasing. However, the proportion of MRSA in relation to all S. aureus isolates is unknown, making it difficult to determine if the rising incidence is real or an artifact of an increasing number of tests performed. AIM: To characterize recent trends in MRSA infections and obtain a more complete understanding of the MRSA level in Norway. METHODS: All reported cases of MRSA and methicillin-sensitive S. aureus (MSSA) from Oslo County (1997-2010) and Health Region East (2008-2008), representing approximately 11% and 36% of the Norwegian population, respectively, were analyzed using a stochastic time series analysis to characterize trends. RESULTS: In Oslo County, the proportion of methicillin-resistant cases increased from 0.73% to 3.78% during the study period and was well modeled by an exponential growth with a doubling constant of 5.7 years (95% CI 4.5-7.4 years). In Health Region East, the proportion of MRSA cases increased from 0.4% to 2.1% from 2002 to 2008, with a best-fitting linear increase of 0.26% (95% CI 0.21-0.30%) per year. In both cases, the choice of a linear or exponential model for the time trend produced only marginally different model fits. We found no significant changes due to revised national MRSA guidelines published in June 2009. Significant variations in the increasing time trend were observed in the five hospitals within the region. The yearly reported incidence of MSSA was relatively stable in both study areas although we found seasonal patterns with peaks in August. CONCLUSION: The level of MRSA is increasing in Norway, and the proportion of methicillin resistance in all S. aureus isolates are higher than the reported proportion of MRSA in invasive infections

Model fit of the proportion of methicillin-resistant <i>Staphylococcus aureus</i> for Oslo County and Health Region East to exponential time trend (A), linear time trend (B) and power law time trend (C) with sum of squared errors for three different fit functions and time t in months.

1<p>Dataset I.</p>2<p>Dataset II.</p

The monthly proportion of MRSA cases in Oslo County: September 1997–2010.

<p>Thick black curve: based on exponential LSF; Blue curve: ; Black curve: based on linear LSF; Red curve: Linear LSF through June 2009; Green curve: Linear LSF after June 2009; Red dashed curve: 95% confidence bounds on exponential and linear LSF curve; ▪: Data, □: Stochastic simulation (run) .</p

The monthly number of MRSA cases in Health Region East: 2002–2008.

<p>▪: Data, ☆: Stochastic simulation based on the beta binomial distribution. Black curve: Exponential LSF; Black dashed curve: Linear LSF.</p