scholarly journals 383Pneumococcal conjugate vaccine is effective against hypoxic pneumonia in Laos, Mongolia and Papua New Guinea

2021 ◽  
Vol 50 (Supplement_1) ◽  
Author(s):  
Cattram Nguyen ◽  
Rupert Weaver ◽  
Christopher Blyth ◽  
Claire von Mollendorf ◽  
Kate Britton ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Propensity Score ◽  
Observational Study ◽  
Multiple Imputation ◽  
Papua New Guinea ◽  
Democratic Republic ◽  
New Guinea ◽  
Oxygen Supplementation ◽  
Inverse Probability ◽  
Novel Approach

Abstract Background We describe a novel approach to determine PCV13 effectiveness (VE) against hypoxic pneumonia in children admitted with pneumonia in Lao People’s Democratic Republic (Laos), Mongolia and Papua New Guinea (PNG). Methods A 3-5 year prospective hospital-based observational study of children < =59 months admitted with pneumonia was undertaken. Pneumonia was defined using the 2013 WHO definition. Hypoxia was defined as an oxygen saturation <90% in room air or requiring oxygen supplementation during hospitalisation. PCV13 status was determined by written record. VE was calculated using logistic regression comparing the odds of hypoxia between vaccinated and under-vaccinated pneumonia cases. To handle potential confounding, a propensity score (PS) analysis using inverse probability of treatment weighting (IPW) was used. In Laos, multiple imputation (MI) analysis was undertaken for missing data. Results The VE against hypoxic pneumonia were: in Laos, unadjusted 23% (95% CI: -9, 46%; p = 0·14), IPW adjusted 37% (6, 57%; p = 0.02), MI and IPW adjusted 35% (7, 55%; p = 0.02); in Mongolia, unadjusted 33% (26, 40%; p < 0.001), IPW adjusted 33% (16, 47%; p < 0.001); and in PNG, unadjusted 6% (-15, 24%; p = 0.53), IPW adjusted 36% (17, 51%; p = 0.001). Conclusions Our novel approach shows that PCV13 is effective against hypoxic pneumonia. PCV13 will contribute to reducing child mortality. Key messages We describe a novel, single hospital-based approach for determining VE that can be applied to other similar settings. This is one of the first studies showing PCV13 to be effective against hypoxic pneumonia in children in Asia.

scholarly journals Propensity Score Estimation Using Classification and Regression Trees in the Presence of Missing Covariate Data

2018 ◽  
Vol 7 (1) ◽  
Author(s):  
Bas B.L. Penning de Vries ◽  
Maarten van Smeden ◽  
Rolf H.H. Groenwold
Keyword(s):  
Logistic Regression ◽  
Missing Data ◽  
Propensity Score ◽  
Multiple Imputation ◽  
Incomplete Data ◽  
Regression Trees ◽  
Covariate Data ◽  
Missing Covariate Data ◽  
Classification And Regression ◽  
Cart Algorithm

AbstractData mining and machine learning techniques such as classification and regression trees (CART) represent a promising alternative to conventional logistic regression for propensity score estimation. Whereas incomplete data preclude the fitting of a logistic regression on all subjects, CART is appealing in part because some implementations allow for incomplete records to be incorporated in the tree fitting and provide propensity score estimates for all subjects. Based on theoretical considerations, we argue that the automatic handling of missing data by CART may however not be appropriate. Using a series of simulation experiments, we examined the performance of different approaches to handling missing covariate data; (i) applying the CART algorithm directly to the (partially) incomplete data, (ii) complete case analysis, and (iii) multiple imputation. Performance was assessed in terms of bias in estimating exposure-outcome effects among the exposed, standard error, mean squared error and coverage. Applying the CART algorithm directly to incomplete data resulted in bias, even in scenarios where data were missing completely at random. Overall, multiple imputation followed by CART resulted in the best performance. Our study showed that automatic handling of missing data in CART can cause serious bias and does not outperform multiple imputation as a means to account for missing data.

scholarly journals Spatial prediction of malaria prevalence in Papua New Guinea: a comparison of Bayesian decision network and multivariate regression modelling approaches for improved accuracy in prevalence prediction

2021 ◽  
Vol 20 (1) ◽  
Author(s):  
Eimear Cleary ◽  
Manuel W. Hetzel ◽  
Paul Siba ◽  
Colleen L. Lau ◽  
Archie C. A. Clements
Keyword(s):  
Logistic Regression ◽  
Papua New Guinea ◽  
Predictive Performance ◽  
New Guinea ◽  
Malaria Prevalence ◽  
Multilevel Logistic Regression ◽  
Bayesian Decision ◽  
Logistic Regression Models ◽  
Bayesian Decision Network ◽  
Improved Accuracy

Abstract Background Considerable progress towards controlling malaria has been made in Papua New Guinea through the national malaria control programme’s free distribution of long-lasting insecticidal nets, improved diagnosis with rapid diagnostic tests and improved access to artemisinin combination therapy. Predictive prevalence maps can help to inform targeted interventions and monitor changes in malaria epidemiology over time as control efforts continue. This study aims to compare the predictive performance of prevalence maps generated using Bayesian decision network (BDN) models and multilevel logistic regression models (a type of generalized linear model, GLM) in terms of malaria spatial risk prediction accuracy. Methods Multilevel logistic regression models and BDN models were developed using 2010/2011 malaria prevalence survey data collected from 77 randomly selected villages to determine associations of Plasmodium falciparum and Plasmodium vivax prevalence with precipitation, temperature, elevation, slope (terrain aspect), enhanced vegetation index and distance to the coast. Predictive performance of multilevel logistic regression and BDN models were compared by cross-validation methods. Results Prevalence of P. falciparum, based on results obtained from GLMs was significantly associated with precipitation during the 3 driest months of the year, June to August (β = 0.015; 95% CI = 0.01–0.03), whereas P. vivax infection was associated with elevation (β = − 0.26; 95% CI = − 0.38 to − 3.04), precipitation during the 3 driest months of the year (β = 0.01; 95% CI = − 0.01–0.02) and slope (β = 0.12; 95% CI = 0.05–0.19). Compared with GLM model performance, BDNs showed improved accuracy in prediction of the prevalence of P. falciparum (AUC = 0.49 versus 0.75, respectively) and P. vivax (AUC = 0.56 versus 0.74, respectively) on cross-validation. Conclusions BDNs provide a more flexible modelling framework than GLMs and may have a better predictive performance when developing malaria prevalence maps due to the multiple interacting factors that drive malaria prevalence in different geographical areas. When developing malaria prevalence maps, BDNs may be particularly useful in predicting prevalence where spatial variation in climate and environmental drivers of malaria transmission exists, as is the case in Papua New Guinea.

scholarly journals RITUEL INTERAKTION OG ILLUSION: En relationel tilgang

2000 ◽  
Author(s):  
Michael Houseman ◽  
Carlo Severi
Keyword(s):  
Papua New Guinea ◽  
Internal Consistency ◽  
Democratic Republic ◽  
New Guinea ◽  
Ritual Action ◽  
Inherent Nature ◽  
Semantic Properties

By providing an explicitly formal account of three ethnographic examples – the Naven rite of the Iatmul (Papua New Guinea), Amerindian shamanism as illustrated by the Kuna (Panama), and African male initiation among the Wagania (Democratic Republic of Congo) – the authors outline a “relational” approach to the analysis of ritual action. They suggest that the illusion implied by the effectiveness of ritual action derives not from the inherent nature of the items of behaviour involved, but from the particular kind of internal consistency that is imposed by the interactive context in which they occur. Thus, the singular realities constructed through ritual performances are built up and sustained, neither by their functional or semantic properties nor by their syntactic features (for example repetition or fragmentation), nor by qualities depending on pragmatic considerations (performativity, staging procedures, etc.). Rather, they are constructed primarily by the establishment of a particular type of relational configuration.  

scholarly journals A propensity score analysis of the impact of surgical intervention on unexpected 30-day readmission following admission for subdural hematoma

2018 ◽  
Vol 129 (4) ◽  
pp. 1008-1016 ◽  
Author(s):  
Lynze R. Franko ◽  
Kyle M. Sheehan ◽  
Christopher D. Roark ◽  
Jacob R. Joseph ◽  
James F. Burke ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Propensity Score ◽  
Hospital Discharge ◽  
Subdural Hematoma ◽  
Nonoperative Management ◽  
Academic Medical Center ◽  
Operative Management ◽  
Common Disease ◽  
Inverse Probability ◽  
The Impact

OBJECTIVESubdural hematoma (SDH) is a common disease that is increasingly being managed nonoperatively. The all-cause readmission rate for SDH has not previously been described. This study seeks to describe the incidence of unexpected 30-day readmission in a cohort of patients admitted to an academic neurosurgical center. Additionally, the relationship between operative management, clinical outcome, and unexpected readmission is explored.METHODSThis is an observational study of 200 consecutive adult patients with SDH admitted to the neurosurgical ICU of an academic medical center. Demographic information, clinical characteristics, and treatment strategies were compared between readmitted and nonreadmitted patients. Multivariable logistic regression, weighted by the inverse probability of receiving surgery using propensity scores, was used to evaluate the association between operative management and unexpected readmission.RESULTSOf 200 total patients, 18 (9%) died during hospitalization and were not included in the analysis. Overall, 48 patients (26%) were unexpectedly readmitted within 30 days. Sixteen patients (33.3%) underwent SDH evacuation during their readmission. Factors significantly associated with unexpected readmission were nonoperative management (72.9% vs 54.5%, p = 0.03) and female sex (50.0% vs 32.1%, p = 0.03). In logistic regression analysis weighted by the inverse probability of treatment and including likely confounders, surgical management was not associated with likelihood of a good outcome at hospital discharge, but was associated with significantly reduced odds of unexpected readmission (OR 0.19, 95% CI 0.08–0.49).CONCLUSIONSOver 25% of SDH patients admitted to an academic neurosurgical ICU were unexpectedly readmitted within 30 days. Nonoperative management does not affect outcome at hospital discharge but is significantly associated with readmission, even when accounting for the probability of treatment by propensity score weighted logistic regression. Additional research is needed to validate these results and to further characterize the impact of nonoperative management on long-term costs and clinical outcomes.

scholarly journals The Impact of Propofol- and Inhalant-Based Anesthesia on Acute Kidney Injury After Major Abdominal Surgery: A Retrospective Observational Study

2020 ◽  
Author(s):  
Bo Rim Kim ◽  
Susie Yoon ◽  
Gyu Young Song ◽  
Seohee Lee ◽  
Jae-Hyon Bahk ◽  
...  
Keyword(s):  
Acute Kidney Injury ◽  
Logistic Regression ◽  
Propensity Score ◽  
Abdominal Surgery ◽  
Kidney Injury ◽  
Major Abdominal Surgery ◽  
Regression Analyses ◽  
Inverse Probability ◽  
The Impact ◽  
Postoperative Aki

Abstract Background: The optimal anesthetic for preventing postoperative acute kidney injury (AKI) remains unclear, and few studies on this topic have been conducted in the context of non-cardiac surgery. The purpose of this retrospective study was to compare propofol- and inhalant-based anesthesia in terms of the risk of AKI after open major abdominal surgery (MAS).Methods: Adult patients who underwent open MAS (gastrectomy, hepatectomy, colectomy, or pancreatectomy) at our institute from January 2016 to December 2018 were included. Using multivariable logistic regression, the risk of postoperative AKI was compared between patients who underwent propofol-based anesthesia (propofol group) and those who received inhalant-based anesthesia (inhalant group). Additional logistic regression analyses were performed after propensity score matching and inverse probability of treatment weighting (IPTW).Results: In total, 3,616 patients were analyzed. The incidence of postoperative AKI was 5.0% (77/1546) and 7.8% (161/2070) in the propofol and inhalant groups, respectively. The risk of AKI was significantly higher in the inhalant group (adjusted odds ratio [aOR], 1.69; 95% confidence interval [CI], 1.23–2.30; P= 0.001) than the propofol group. In the propensity score-matched cohort, the inhalant group had a higher risk of AKI than the propofol group (aOR, 1.68; 95% CI, 1.21–2.34; P= 0.002), and the logistic regression with IPTW showed similar results (OR, 1.74; 95% CI 1.14–1.66; P< 0.001).Conclusion: The risk of AKI after open MAS may differ significantly according to the anesthetic used. Patients receiving inhalant-based anesthesia may have a greater risk of postoperative AKI than those anaesthetized with propofol.

scholarly journals Analysis of Clinical Characteristics and Prognosis of Traumatic Brain Injury in Papua New Guinea

2021 ◽  
Vol 2021 ◽  
pp. 1-7
Author(s):  
Peng Chen ◽  
Yongbing Deng ◽  
Xing Yu ◽  
Tao Huang ◽  
Jingrui Huang
Keyword(s):  
Logistic Regression ◽  
General Hospital ◽  
Papua New Guinea ◽  
Clinical Characteristics ◽  
Statistical Significance ◽  
New Guinea ◽  
Regression Analyses ◽  
Intracranial Infection ◽  
Severe Tbi ◽  
Port Moresby

Objective. To evaluate the clinical characteristics and prognosis of TBI patients from 2016 to 2019 admitted to Port Moresby General Hospital (PMGH) of Papua New Guinea (PNG) and compare the results with previous researches to analyze current clinical characteristics and prognosis. Methods. A retrospective study was performed on 389 TBI patients in Port Moresby General Hospital (PMGH) over a 48-month period (from January 2016 to December 2019). The clinical and radiographic data were collected. Patients were followed up for at least 3 months, and outcomes were assessed using the Glasgow Outcome Scale (GOS). Univariate and multivariate logistic regressions were performed to analyze the prognosis and intracranial infection of patients, as well as the effect of surgery on the prognosis of TBI patients. Results. The average age of the 389 TBI patients was 24.9 years old, and the most common age was 18-40 years old, accounting for 55.5%. The proportion of male patients was 79.4%, and the proportion of juvenile patients (≤18 years) was 30.8%. The most primary cause of injury was fighting and brawling (38.0%). At admission, patients had an average GCS score of 9.1, and patients with severe TBI accounted for 46.8%. Overall, 32.1% of the patients had a good prognosis, with a mortality rate of 13.9% (54 cases). Analyzing the relationship between surgical treatment and prognosis in 303 patients with moderate or severe TBI, there was no statistical significance. Univariate and logistic regression analyses for poor prognosis included gender, GCS, multiple injuries, Rotterdam CT scores, and intracranial infection. Univariate and logistic regression analyses for intracranial infection included GCS, open brain trauma, and postoperative drainage time. Conclusion. Despite there has been a secular trend towards reduced incidence of TBI, the prognosis of moderate or severe TBI patients who received surgery showed no significant improvement, indicating that PNG, as a backward developing country, faced a huge problem in TBI prevention and control.

scholarly journals Propensity score analysis with partially observed covariates: How should multiple imputation be used?

2017 ◽  
Vol 28 (1) ◽  
pp. 3-19 ◽  
Author(s):  
Clémence Leyrat ◽  
Shaun R Seaman ◽  
Ian R White ◽  
Ian Douglas ◽  
Liam Smeeth ◽  
...  
Keyword(s):  
Propensity Score ◽  
Multiple Imputation ◽  
Treatment Effect ◽  
Propensity Scores ◽  
Propensity Score Analysis ◽  
Imputation Model ◽  
Complete Case ◽  
Inverse Probability ◽  
Score Analysis ◽  
Partially Observed

Inverse probability of treatment weighting is a popular propensity score-based approach to estimate marginal treatment effects in observational studies at risk of confounding bias. A major issue when estimating the propensity score is the presence of partially observed covariates. Multiple imputation is a natural approach to handle missing data on covariates: covariates are imputed and a propensity score analysis is performed in each imputed dataset to estimate the treatment effect. The treatment effect estimates from each imputed dataset are then combined to obtain an overall estimate. We call this method MIte. However, an alternative approach has been proposed, in which the propensity scores are combined across the imputed datasets (MIps). Therefore, there are remaining uncertainties about how to implement multiple imputation for propensity score analysis: (a) should we apply Rubin’s rules to the inverse probability of treatment weighting treatment effect estimates or to the propensity score estimates themselves? (b) does the outcome have to be included in the imputation model? (c) how should we estimate the variance of the inverse probability of treatment weighting estimator after multiple imputation? We studied the consistency and balancing properties of the MIte and MIps estimators and performed a simulation study to empirically assess their performance for the analysis of a binary outcome. We also compared the performance of these methods to complete case analysis and the missingness pattern approach, which uses a different propensity score model for each pattern of missingness, and a third multiple imputation approach in which the propensity score parameters are combined rather than the propensity scores themselves (MIpar). Under a missing at random mechanism, complete case and missingness pattern analyses were biased in most cases for estimating the marginal treatment effect, whereas multiple imputation approaches were approximately unbiased as long as the outcome was included in the imputation model. Only MIte was unbiased in all the studied scenarios and Rubin’s rules provided good variance estimates for MIte. The propensity score estimated in the MIte approach showed good balancing properties. In conclusion, when using multiple imputation in the inverse probability of treatment weighting context, MIte with the outcome included in the imputation model is the preferred approach.

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