Esra Kürüm

The burden of typhoid fever in low- and middle-income countries: A meta-regression approach

Background Upcoming vaccination efforts against typhoid fever require an assessment of the baseline burden of disease in countries at risk. There are no typhoid incidence data from most low- and middle-income countries (LMICs), so model-based estimates offer insights for decision-makers in the absence of readily available data. Methods We developed a mixed-effects model fit to data from 32 population-based studies of typhoid incidence in 22 locations in 14 countries. We tested the contribution of economic and environmental indices for predicting typhoid incidence using a stochastic search variable selection algorithm. We performed out-of-sample validation to assess the predictive performance of the model. Results We estimated that 17.8 million cases of typhoid fever occur each year in LMICs (95% credible interval: 6.9–48.4 million). Central Africa was predicted to experience the highest incidence of typhoid, followed by select countries in Central, South, and Southeast Asia. Incidence typically peaked in the 2–4 year old age group. Models incorporating widely available economic and environmental indicators were found to describe incidence better than null models. Conclusions Recent estimates of typhoid burden may under-estimate the number of cases and magnitude of uncertainty in typhoid incidence. Our analysis permits prediction of overall as well as age-specific incidence of typhoid fever in LMICs, and incorporates uncertainty around the model structure and estimates of the predictors. Future studies are needed to further validate and refine model predictions and better understand year-to-year variation in cases.

Agreeableness and activity engagement in nursing home residents with dementia.

Residents with dementia are the least likely to be engaged in the nursing home and often spend most of their time doing nothing at all. However, resident participation in meaningful activities is important to promote both physical and psychological health. Tailoring activities to individual functional abilities and personality preferences improves both the time and level of participation. This pilot study used an analysis of covariance procedure to test the relationship between the personality trait of agreeableness and engagement when activities are ideally tailored to ability and interest. No significant difference was found between the high and low agreeableness groups, indicating that residents were more engaged when activities were individually tailored, regardless of their agreeableness level. Although low agreeableness may pose a challenge when implementing activities for people with dementia, the results of this study suggest that tailoring activities to functional ability and interest may overcome the effects.

Estimating the population-level impact of vaccines using synthetic controls

Significance Pneumococcus, a bacterial pathogen, is among the most important causes of pneumonia globally. Quantifying the impact of pneumococcal conjugate vaccines (PCVs) on pneumonia is challenging due to time trends unrelated to the vaccine. We use a method developed for website analytics and economics called “synthetic controls” to disentangle changes in pneumonia rates caused by the vaccine from changes caused by unrelated factors. We found that PCVs significantly reduce all-cause pneumonia hospitalizations in young children, and reduce hospitalizations for invasive pneumococcal disease and pneumococcal pneumonia in children and adults. In contrast to previous studies, we did not detect a decline in all-cause pneumonia hospitalizations in older adults in any of the five countries following the introduction of the vaccine in children. When a new vaccine is introduced, it is critical to monitor trends in disease rates to ensure that the vaccine is effective and to quantify its impact. However, estimates from observational studies can be confounded by unrelated changes in healthcare utilization, changes in the underlying health of the population, or changes in reporting. Other diseases are often used to detect and adjust for these changes, but choosing an appropriate control disease a priori is a major challenge. The “synthetic controls” (causal impact) method, which was originally developed for website analytics and social sciences, provides an appealing solution. With this approach, potential comparison time series are combined into a composite and are used to generate a counterfactual estimate, which can be compared with the time series of interest after the intervention. We sought to estimate changes in hospitalizations for all-cause pneumonia associated with the introduction of pneumococcal conjugate vaccines (PCVs) in five countries in the Americas. Using synthetic controls, we found a substantial decline in hospitalizations for all-cause pneumonia in infants in all five countries (average of 20%), whereas estimates for young and middle-aged adults varied by country and were potentially influenced by the 2009 influenza pandemic. In contrast to previous reports, we did not detect a decline in all-cause pneumonia in older adults in any country. Synthetic controls promise to increase the accuracy of studies of vaccine impact and to increase comparability of results between populations compared with alternative approaches.

Challenges to estimating vaccine impact using hospitalization data.

Because the real-world impact of new vaccines cannot be known before they are implemented in national programs, post-implementation studies at the population level are critical. Studies based on analysis of hospitalization rates of vaccine-preventable outcomes are typically used for this purpose. However, estimates of vaccine impact based on hospitalization data are particularly prone to confounding, as hospitalization rates are tightly linked to changes in the quality, access and use of the healthcare system, which often occur simultaneously with introduction of new vaccines. Here we illustrate how changes in healthcare delivery coincident with vaccine introduction can influence estimates of vaccine impact, using as an example reductions in infant pneumonia hospitalizations after introduction of the 10-valent pneumococcal conjugate vaccine (PCV10) in Brazil. To this end, we explore the effect of changes in several metrics of quality and access to public healthcare on trends in hospitalization rates before (2008–09) and after (2011–12) PCV10 introduction in 2010. Changes in infant pneumonia hospitalization rates following vaccine introduction were significantly associated with concomitant changes in hospital capacity and the fraction of the population using public hospitals. Importantly, reduction of pneumonia hospitalization rates after PCV10 were also associated with the expansion of outpatient services in several Brazilian states, falling more sharply where primary care coverage and the number of health units offering basic and emergency care increased more. We show that adjustments for unrelated (non-vaccine) trends commonly employed by impact studies, such as use of single control outcomes, are not always sufficient for accurate impact assessment. We discuss several ways to identify and overcome such biases, including sensitivity analyses using different denominators to calculate hospitalizations rates and methods that track changes in the outpatient setting. Employing these practices can improve the accuracy of vaccine impact estimates, particularly in evolving healthcare settings typical of low- and middle-income countries.

Gender Differences in Factors Associated With Delirium Severity in Older Adults With Dementia

The purpose of this descriptive correlational study was to explore potential gender differences in the relationship of dementia severity, age, APOE status, cognitive reserve and co-morbidity (two potentially modifiable factors), to delirium severity in older adults. Baseline data from an ongoing clinical trial and a Poisson regression procedure were used in the analyses. Participants were 148 elderly individuals with dementia and delirium admitted to post-acute care. In women, delirium severity was related to dementia severity (p=0.002) and co-morbidity moderated that effect (p=0.03). In men, education was marginally associated with delirium severity (p=0.06). Implications for research are discussed.

Limited reliability of radiographic assessment of spinal progression in ankylosing spondylitis

Objectives Conventional radiography is key to assessing AS-related spinal involvement and has become increasingly important given that spinal fusion may continue under biologic therapy. We aimed to compare the reliability of radiographic scoring of the spine by using different approaches to understand how different readers agree on overall scores and on individual findings. Method Six investigators scored 68 plain radiographs of the cervical and lumbar spine of 34 patients with a 2-year interval, for erosions, sclerosis, squaring, syndesmophytes and ankyloses using the Spondyloarthritis Radiography (SPAR) module. The intraclass correlation coefficients were calculated compared with two gold standards. The reproducibility of each finding in 1632 vertebral corners and new syndesmophytes in each corner was calculated by kappa analysis and positive agreement rates. Results The intraclass correlation coefficients mostly revealed good to excellent agreement with the gold standards (0.69-0.95). The kappa analysis showed worse agreement, being relatively higher for syndesmophytes (0.163-0.559) and ankylosis (0.48-0.95). Positive agreement rates showed that erosions were never detected at the same vertebral corner by two readers (positive agreement rate: 0%). The mean (range) positive agreement rates were 10.1% (0-27.7%) for sclerosis and 19.2% (0-59.7%) for squaring, and were higher for syndesmophytes [38.8% (21.4-62.5%)] and ankylosis [77.3% (64-95.3%)]. Conclusion Our results show that there is a poor agreement on the presence of grade 1 lesions included in the Modified Stoke Ankylosing Spondylitis Spine Score-mostly for erosions and sclerosis-which may increase the measurement error. The currently used definitions of reliability have a risk of overestimating reproducibility.

A preliminary study showing that ultrasonography cannot differentiate between psoriatic arthritis and nodal osteoarthritis based on enthesopathy scores

Author(s): Yumusakhuylu, Yasemin; Kasapoglu-Gunal, Esen; Murat, Sadiye; Kurum, Esra; Keskin, Havva; Icagasioglu, Afitap; McGonagle, Dennis; Zehra Aydin, Sibel

Impact of Pneumococcal Conjugate Vaccines on Pneumonia Hospitalizations in High- and Low-Income Subpopulations in Brazil

The results in this article suggest that PCVs have an important impact against hospitalizations for all-cause pneumonia in both low- and high-income populations.

Causal mediation analysis with multiple mediators in the presence of treatment noncompliance

Randomized experiments are often complicated because of treatment noncompliance. This challenge prevents researchers from identifying the mediated portion of the intention-to-treated (ITT) effect, which is the effect of the assigned treatment that is attributed to a mediator. One solution suggests identifying the mediated ITT effect on the basis of the average causal mediation effect among compliers when there is a single mediator. However, considering the complex nature of the mediating mechanisms, it is natural to assume that there are multiple variables that mediate through the causal path. Motivated by an empirical analysis of a data set collected in a randomized interventional study, we develop a method to estimate the mediated portion of the ITT effect when both multiple dependent mediators and treatment noncompliance exist. This enables researchers to make an informed decision on how to strengthen the intervention effect by identifying relevant mediators despite treatment noncompliance. We propose a nonparametric estimation procedure and provide a sensitivity analysis for key assumptions. We conduct a Monte Carlo simulation study to assess the finite sample performance of the proposed approach. The proposed method is illustrated by an empirical analysis of JOBS II data, in which a job training intervention was used to prevent mental health deterioration among unemployed individuals.

A copula model for joint modeling of longitudinal and time-invariant mixed outcomes: Joint modeling of longitudinal and time-invariant mixed outcomes

Motivated by a preclinical study in a mouse model of breast cancer, we suggest a joint modeling framework for outcomes of mixed type and measurement structures (longitudinal versus single time/time-invariant). We present an approach based on the time-varying copula models, which is used to jointly model longitudinal outcomes of mixed types via a time-varying copula, and extend the scope of these models to handle outcomes with mixed measurement structures. Our framework allows the parameters corresponding to the longitudinal outcome to be time varying and thereby enabling researchers to investigate how the response-predictor relationships change with time. We investigate the finite sample performance of this new approach via a Monte Carlo simulation study and illustrate its usefulness by an empirical analysis of the motivating preclinical study, comparing the effect of various treatments on tumor volume (longitudinal continuous response) and the number of days until tumor volume triples (time-invariant count response). Through the real-life application and the simulation study, we demonstrate that, compared with marginal modeling, the joint modeling framework offers more precision in the estimation of model parameters.