scholarly journals Population-level impact of infant 10-valent pneumococcal conjugate vaccination on adult pneumonia hospitalisations in Finland

Thorax ◽  
2017 ◽  
Vol 73 (3) ◽  
pp. 262-269 ◽  
Author(s):  
Omar Okasha ◽  
Hanna Rinta-Kokko ◽  
Arto A Palmu ◽  
Esa Ruokokoski ◽  
Jukka Jokinen ◽  
...  
Keyword(s):  
Negative Binomial ◽  
Negative Binomial Regression ◽  
Population Level ◽  
Interrupted Time Series ◽  
The Elderly ◽  
Negative Binomial Regression Model ◽  
Interrupted Time Series Analysis ◽  
Entire Study ◽  
Before And After ◽  
International Statistical Classification

IntroductionLimited data are available on population-level herd effects of infant 10-valent pneumococcal conjugate vaccine (PCV10) programmes on pneumonia. We assessed national trends in pneumococcal and all-cause pneumonia hospitalisations in adults aged ≥18 years, before and after infant PCV10 introduction in 2010.MethodsMonthly hospitalisation rates of International Statistical Classification of Diseases, 10th revision (ICD-10)-coded primary discharge diagnoses compatible with pneumonia from 2004–2005 to 2014–2015 were calculated with population denominators from the population register. Trends in pneumonia before and after PCV10 introduction were assessed with interrupted time-series analysis. Rates during the PCV10 period were estimated from adjusted negative binomial regression model and compared with those projected as continuation of the pre-PCV10 trend. All-cause hospitalisations were assessed for control purposes.ResultsBefore PCV10, the all-cause pneumonia rate in adults aged ≥18 years increased annually by 2.4%, followed by a 4.7% annual decline during the PCV10 period. In 2014–2015, the overall all-cause pneumonia hospitalisation rate was 109.3/100 000 (95% CI 96.5 to 121.9) or 15.4% lower than the expected rate. A significant 6.7% decline was seen in persons aged ≥65 years (131.5/100 000), which translates to 1456 fewer pneumonia hospitalisations annually. In comparison, hospitalisations other than pneumonia decreased by 3.5% annually throughout the entire study period.ConclusionThese national data suggest that herd protection from infant PCV10 programme has reversed the increasing trend and substantially decreased all-cause pneumonia hospitalisations in adults, particularly the elderly.

scholarly journals Association between the COVID-19 pandemic and pertussis in France using multiple nationwide data sources

2021 ◽  
Author(s):  
Soraya Matczak ◽  
Corinne Levy ◽  
Camille Fortas ◽  
Jeremie F. Cohen ◽  
Stephane Bechet ◽  
...  
Keyword(s):  
Negative Binomial ◽  
Negative Binomial Regression ◽  
Interrupted Time Series ◽  
Mitigation Strategies ◽  
Data Sources ◽  
Mitigation Measures ◽  
Interrupted Time Series Analysis ◽  
National Network ◽  
Vaccination Recommendations ◽  
Quasi Experimental

Background: Interventions to mitigate coronavirus disease 19 (COVID-19) pandemic may impact other respiratory diseases such as pertussis. We aimed to study the course of pertussis in France over an 8-year period and its association with COVID-19 mitigation strategies, using multiple nationwide data sources. Methods: We analyzed the number of French pertussis cases between 2013 and 2020, using the PCR test results from nationwide outpatient laboratories (Source 1) and the pediatric network of 41 hospitals (Source 2), and using the reports of an office-based pediatric national network (Source 3). We conducted a quasi-experimental interrupted time-series analysis, relying on negative binomial regression models. The models accounted for seasonality, longterm cycles, and secular trend, and included a binary variable for the first national lockdown (ordered on March 16, 2021). Results: We identified 19,039 cases of pertussis from the three data sources during the study period. There was a significant decrease of pertussis cases following the implementation of mitigation measures, with adjusted incidence rate ratios of 0.102 (95% CI 0.040-0.256) and 0.216 (95% CI 0.071-0.656) for Source 1 and Source 2, respectively. The association was confirmed in Source 3 (median of 1 [IQR 0-2] vs. 0 [IQR 0-0] pertussis cases per month before and after lockdown, respectively, p=0.0048). Conclusion: The drastic reduction of outpatient and hospitalized cases of pertussis strongly suggests an impact of COVID-19 mitigation measures and their consequences on pertussis epidemiology. Pertussis vaccination recommendations should be carefully followed, and disease monitoring should be continued to detect any resurgence after relaxation of mitigation measures.

scholarly journals Simple Accurate Regression-Based Forecasting of Intensive Care Unit Admissions due to COVID-19 in Ontario, Canada

2020 ◽  
Author(s):  
David N. Fisman ◽  
Ashleigh R. Tuite
Keyword(s):  
Intensive Care Unit ◽  
Intensive Care ◽  
Convergent Validity ◽  
Negative Binomial ◽  
External Validation ◽  
Negative Binomial Regression ◽  
Population Level ◽  
Negative Binomial Regression Model ◽  
Icu Length Of Stay ◽  
Short Term Forecasting

AbstractThe pandemic caused by SARS-CoV-2 has proven challenging clinically, and at the population level, due to heterogeneity in both transmissibility and severity. Recent case incidence in Ontario, Canada (autumn 2020) has outstripped incidence in seen during the first (spring) pandemic wave; but has been associated with a lower incidence of intensive care unit (ICU) admissions and deaths. We hypothesized that differential ICU burden might be explained by increased testing volumes, as well as the shift in mean case age from older to younger. We constructed a negative binomial regression model using only three covariates, at a 2-week lag: log10(weekly cases); log10(weekly deaths); and mean weekly case age. This model reproduced observed ICU admission volumes, and demonstrated good preliminary predictive validity. Furthermore, when admissions were used in combination with ICU length of stay, our modeled estimates demonstrated excellent convergent validity with ICU occupancy data reported by the Canadian Institute for Health Information. Our approach needs external validation in other settings and at larger and smaller geographic scales, but appears to be a useful short-term forecasting tool for ICU resource demand; we also demonstrate that the virulence of SARS-CoV-2 infection has not meaningfully changed in Ontario between the first and second waves, but the demographics of those infected, and the fraction of cases identified, have.

scholarly journals Psychiatric consequences of a father’s leave policy by nativity: a quasi-experimental study in Sweden

2021 ◽  
pp. jech-2021-217980
Author(s):  
Helena Honkaniemi ◽  
Srinivasa Vittal Katikireddi ◽  
Mikael Rostila ◽  
Sol P Juárez
Keyword(s):  
Mental Health ◽  
Negative Binomial ◽  
Parental Leave ◽  
Negative Binomial Regression ◽  
Interrupted Time Series ◽  
Well Being ◽  
Migrant Health ◽  
Interrupted Time Series Analysis ◽  
Psychiatric Hospitalisation ◽  
Leave Policy

BackgroundParental leave use has been found to promote maternal and child health, with limited evidence of mental health impacts on fathers. How these effects vary for minority populations with poorer mental health and lower leave uptake, such as migrants, remains under-investigated. This study assessed the effects of a Swedish policy to encourage fathers’ leave, the 1995 Father’s quota, on Swedish-born and migrant fathers’ psychiatric hospitalisations.MethodsWe conducted an interrupted time series analysis using Swedish total population register data for first-time fathers of children born before (1992–1994) and after (1995–1997) the reform (n=198 589). Swedish-born and migrant fathers’ 3-year psychiatric hospitalisation rates were modelled using segmented negative binomial regression, adjusting for seasonality and autocorrelation, with stratified analyses by region of origin, duration of residence, and partners’ nativity.ResultsFrom immediately pre-reform to post-reform, the proportion of fathers using parental leave increased from 63.6% to 86.4% of native-born and 37.1% to 51.2% of migrants. Swedish-born fathers exhibited no changes in psychiatric hospitalisation rates post-reform, whereas migrants showed 36% decreased rates (incidence rate ratio (IRR) 0.64, 95% CI 0.47 to 0.86). Migrants from regions not predominantly consisting of Organisation for Economic Cooperation and Development countries (IRR 0.50, 95% CI 0.19 to 1.33), and those with migrant partners (IRR 0.23, 95% CI 0.14 to 0.38), experienced the greatest decreases in psychiatric hospitalisation rates.ConclusionThe findings of this study suggest that policies oriented towards promoting father’s use of parental leave may help to reduce native–migrant health inequalities, with broader benefits for family well-being and child development.

scholarly journals Effect of an Alternative Newborn Naming Strategy on Wrong-Patient Errors: A Quasi-Experimental Study

2020 ◽  
Vol 11 (02) ◽  
pp. 235-241 ◽  
Author(s):  
Ethan Pfeifer ◽  
Margaret Lozovatsky ◽  
Joanna Abraham ◽  
Thomas Kannampallil
Keyword(s):  
Experimental Study ◽  
Neonatal Intensive Care ◽  
Interrupted Time Series ◽  
Neonatal Intensive Care Units ◽  
Interrupted Time Series Analysis ◽  
Entire Study ◽  
Before And After ◽  
Quasi Experimental ◽  
Small Set ◽  
Experimental Study Design

Abstract Objectives Newborns are often assigned temporary names at birth. Temporary newborn names—often a combination of the mother's last name and the newborn's gender—are vulnerable to patient misidentification due to similarities with other newborns or between a mother and her newborn. We developed and implemented an alternative distinct naming strategy, and then compared its effectiveness on reducing the number of wrong-patient orders with the standard distinct naming strategy. Methods This study was conducted over a 14-month period in the newborn nursery and neonatal intensive care units of three hospitals that were part of the same health care system. We used a quasi-experimental study design using interrupted time series analysis to compare the differences in wrong-patient orders (an indicator of patient misidentification) before and after the implementation of the alternative distinct naming strategy. Results Overall, there were 25 wrong-patient errors per 10,000 orders during entire study period (36.8 per 10,000 before and 19.6 per 10,000 after). However, there was no statistically significant change in the rate of wrong-patient ordering errors after the transition from the distinct to the alternative distinct naming strategy (β = 0.832, 95% confidence interval [CI] = −0.83 to 2.49, p = 0.326). We also found that, overall, 1.7% of the clinicians contributed to 62% of the wrong-patient errors. Conclusion Although we did not find statistically significant differences in wrong-patient errors, the alternative distinct naming approach provides pragmatic advantages over its predecessors. In addition, the localization of wrong-patient errors within a small set of clinicians highlights the potential for developing strategies for delivering training to clinicians.

An Analysis of State-Level Policies and Opioid Overdose Deaths

2020 ◽  
Vol 42 (7) ◽  
pp. 535-542
Author(s):  
Nicole Adams ◽  
Ellen Gundlach ◽  
Ching-Wei Cheng
Keyword(s):  
Negative Binomial ◽  
State Level ◽  
Negative Binomial Regression ◽  
Interrupted Time Series ◽  
Opioid Overdose ◽  
Interrupted Time Series Analysis ◽  
Community Members ◽  
Minimal Impact ◽  
Regulatory Changes ◽  
Overdose Deaths

Many legislative and regulatory changes have occurred at the state level in response to the opioid crisis in an attempt to decrease overdose deaths. We conducted a negative binomial, regression-based, interrupted time series analysis to evaluate the effects of policies on opioid overdose death counts for 2008–2017 in five Midwestern states: Illinois, Indiana, Kentucky, Michigan, and Ohio. Except for the Good Samaritan laws in Illinois, no single policy change was statistically significant in decreasing overdose deaths. Governmental involvement as a whole was significantly associated with an increase in overdose deaths in Indiana. Policies created to address opioid overdose mortality have had minimal impact in these five Midwestern states. Most of the legislation and regulation that have been created lack educational components for prescribers and community members, which may explain why these policies have not had the intended effect.

scholarly journals Exploring the Built Environment Factors in the Metro That Influence the Ridership and the Market Share of the Elderly and Students

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Jiang Ning ◽  
Tao Lyu ◽  
Yuanqing Wang
Keyword(s):  
Built Environment ◽  
Market Share ◽  
Negative Binomial ◽  
Negative Binomial Regression ◽  
The Elderly ◽  
Negative Binomial Regression Model ◽  
Factors Affecting ◽  
Smart Card Data ◽  
Binomial Regression ◽  
Fractional Response

The metro has developed rapidly in the past two decades and has become one of the crucial patterns of transportation for urban residents in China. Many studies have explored the factors affecting metro ridership, but few have focused on the metro usage of specific groups, such as the elderly and students. This paper uses the negative binomial regression model to explore the relationship between the built environment and the metro ridership of three types of people (adults, the elderly, and students) by using the metro smart card data of Qingdao. We also used the fractional response model to discuss the factors that influence the ridership share for the elderly and students. The results show that most variables promote the metro usage of the three groups of people but have a significantly different effect on the market share of those groups. Specifically, the number of schools, hospitals, supermarkets, squares, parks, and scenic spots near metro stations significantly increases the proportion of the elderly metro usage. The number of bus stops and schools substantially increases the share of metro ridership by students. The research results can provide valuable insights for promoting the metro’s overall ridership and minimizing the gap in allocating public transport resources among different groups.

scholarly journals Association between fatality rate of COVID-19 and selenium deficiency in China

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Hai-Yang Zhang ◽  
An-Ran Zhang ◽  
Qing-Bin Lu ◽  
Xiao-Ai Zhang ◽  
Zhi-Jie Zhang ◽  
...  
Keyword(s):  
Negative Binomial ◽  
Viral Infections ◽  
Case Fatality ◽  
Selenium Deficiency ◽  
Negative Binomial Regression ◽  
Negative Binomial Regression Model ◽  
Fatality Rate ◽  
Selenium Content ◽  
Individual Level ◽  
Binomial Regression

Abstract Background COVID-19 has impacted populations around the world, with the fatality rate varying dramatically across countries. Selenium, as one of the important micronutrients implicated in viral infections, was suggested to play roles. Methods An ecological study was performed to assess the association between the COVID-19 related fatality and the selenium content both from crops and topsoil, in China. Results Totally, 14,045 COVID-19 cases were reported from 147 cities during 8 December 2019–13 December 2020 were included. Based on selenium content in crops, the case fatality rates (CFRs) gradually increased from 1.17% in non-selenium-deficient areas, to 1.28% in moderate-selenium-deficient areas, and further to 3.16% in severe-selenium-deficient areas (P = 0.002). Based on selenium content in topsoil, the CFRs gradually increased from 0.76% in non-selenium-deficient areas, to 1.70% in moderate-selenium-deficient areas, and further to 1.85% in severe-selenium-deficient areas (P < 0.001). The zero-inflated negative binomial regression model showed a significantly higher fatality risk in cities with severe-selenium-deficient selenium content in crops than non-selenium-deficient cities, with incidence rate ratio (IRR) of 3.88 (95% CIs: 1.21–12.52), which was further confirmed by regression fitting the association between CFR of COVID-19 and selenium content in topsoil, with the IRR of 2.38 (95% CIs: 1.14–4.98) for moderate-selenium-deficient cities and 3.06 (1.49–6.27) for severe-selenium-deficient cities. Conclusions Regional selenium deficiency might be related to an increased CFR of COVID-19. Future studies are needed to explore the associations between selenium status and disease outcome at individual-level.

scholarly journals Statistical models for analyzing count data: predictors of length of stay among HIV patients in Portugal using a multilevel model

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Ahmed Nabil Shaaban ◽  
Bárbara Peleteiro ◽  
Maria Rosario O. Martins
Keyword(s):  
Length Of Stay ◽  
Regression Model ◽  
Random Effects ◽  
Count Data ◽  
Negative Binomial ◽  
Negative Binomial Regression ◽  
Comprehensive Approach ◽  
Negative Binomial Regression Model ◽  
Hiv Patients ◽  
Binomial Regression

Abstract Background This study offers a comprehensive approach to precisely analyze the complexly distributed length of stay among HIV admissions in Portugal. Objective To provide an illustration of statistical techniques for analysing count data using longitudinal predictors of length of stay among HIV hospitalizations in Portugal. Method Registered discharges in the Portuguese National Health Service (NHS) facilities Between January 2009 and December 2017, a total of 26,505 classified under Major Diagnostic Category (MDC) created for patients with HIV infection, with HIV/AIDS as a main or secondary cause of admission, were used to predict length of stay among HIV hospitalizations in Portugal. Several strategies were applied to select the best count fit model that includes the Poisson regression model, zero-inflated Poisson, the negative binomial regression model, and zero-inflated negative binomial regression model. A random hospital effects term has been incorporated into the negative binomial model to examine the dependence between observations within the same hospital. A multivariable analysis has been performed to assess the effect of covariates on length of stay. Results The median length of stay in our study was 11 days (interquartile range: 6–22). Statistical comparisons among the count models revealed that the random-effects negative binomial models provided the best fit with observed data. Admissions among males or admissions associated with TB infection, pneumocystis, cytomegalovirus, candidiasis, toxoplasmosis, or mycobacterium disease exhibit a highly significant increase in length of stay. Perfect trends were observed in which a higher number of diagnoses or procedures lead to significantly higher length of stay. The random-effects term included in our model and refers to unexplained factors specific to each hospital revealed obvious differences in quality among the hospitals included in our study. Conclusions This study provides a comprehensive approach to address unique problems associated with the prediction of length of stay among HIV patients in Portugal.

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