Audit of the impact of the integrated psychological medicine service (IPMS) on service utilisation

AimsAn audit to assess the impact of an Integrated Psychological Medicine Service (IPMS) on healthcare utilization pre & post intervention. We hypothesized that an IPMS approach would reduce healthcare utilization.BackgroundThe IPMS focusses on integrating biopsychosocial assessments into physical healthcare pathways. It has developed in stages as opportunities presented in different specialities leading to a heterogeneous non-standardised service. The key aim is involvement of mental health practitioners, psychologists & psychiatrists in complex patients with comorbidity or functional presentations in combination with the specialty MDT. This audit is the first attempt to gather data across all involved specialities and complete a randomised deep dive into cases.MethodReferrals into IMPS from July 2019 to June 2020 pulled 129 referrals, of which a 10% randomised sample of 13 patients was selected to analyse. 5 patients had one year of data either side of the duration of the IPMS intervention (excluding 8 patients with incomplete data sets).We analysed; the duration & nature of the IPMS intervention, the number, duration & speciality of inpatient admissions & short stays, outpatient attendances, non-attendances & patient cancellations. Psychosocial information was also gathered. One non-randomised patient was analysed as a comparative case illustration.ResultRandomised patients; patient 78's utilisation remained static, patient 71 post-referral engaged with health psychology & reduced healthcare utilisation. Patient 7 increased healthcare utilisation post-referral secondary to health complications. Patient 54 did not attend & increased healthcare utilisation post-referral. Patient 106 had increased healthcare utilisation post-referral from a new health condition. The randomised sample identified limitations of using healthcare utilisation as an outcome measure when contrasted to the non-randomised case (which significantly reduced healthcare utilisation post-referral).ConclusionCorrelation only can be inferred from the data due to sample size, limitations & confounding factors e.g. psycho-social life events, acquired illness. Alternative outcome measurements documented (e.g PHQ9/GAD7) were not reliably recorded across pathways.The results evidenced that single cases can demonstrate highly desirable effects of a biopsychosocial approach but they can also skew data sets if results are pooled due to the small sample size & heterogeneous interventions. With some patients an increase in healthcare utilisation was appropriate for an improved clinical outcome. This audit identified that utilising healthcare utilisation as an outcome measure is a crude tool with significant limitations & the need to agree tailored outcome measures based on the type of intervention to assess the impact of IPMS.

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Impact of sample size on principal component analysis ordination of an environmental data set: effects on eigenstructure

Ekológia (Bratislava) ◽

10.1515/eko-2016-0014 ◽

2016 ◽

Vol 35 (2) ◽

pp. 173-190 ◽

Cited By ~ 13

Author(s):

S. Shahid Shaukat ◽

Toqeer Ahmed Rao ◽

Moazzam A. Khan

Keyword(s):

Principal Component Analysis ◽

Sample Size ◽

Principal Component ◽

Component Analysis ◽

Small Sample ◽

Environmental Data ◽

Data Matrix ◽

Data Sets ◽

Data Set ◽

The Impact

AbstractIn this study, we used bootstrap simulation of a real data set to investigate the impact of sample size (N = 20, 30, 40 and 50) on the eigenvalues and eigenvectors resulting from principal component analysis (PCA). For each sample size, 100 bootstrap samples were drawn from environmental data matrix pertaining to water quality variables (p = 22) of a small data set comprising of 55 samples (stations from where water samples were collected). Because in ecology and environmental sciences the data sets are invariably small owing to high cost of collection and analysis of samples, we restricted our study to relatively small sample sizes. We focused attention on comparison of first 6 eigenvectors and first 10 eigenvalues. Data sets were compared using agglomerative cluster analysis using Ward’s method that does not require any stringent distributional assumptions.

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MHealth Applications for People with Cognitive Impairment and Their Caregivers: A Scoping Review (Preprint)

10.2196/preprints.22474 ◽

2020 ◽

Author(s):

Qing Zhao ◽

Pei Chen ◽

Yu Zhang ◽

Haining Liu ◽

Xianwen Li

Keyword(s):

Cognitive Impairment ◽

Sample Size ◽

Research Design ◽

Scoping Review ◽

Life Support ◽

Small Sample ◽

Mobile Technologies ◽

Control Group ◽

Caregiver Support ◽

The Impact

BACKGROUND Mobile health application has become an important tool for healthcare systems. One such tool is the delivery of assisting in people with cognitive impairment and their caregivers. OBJECTIVE This scoping review aims to explore and evaluate the existing evidence and challenges on the use of mHealth applications that assisting in people with cognitive impairment and their caregivers. METHODS Nine databases, including PubMed, EMBASE, Cochrane, PsycARTICLES, CINAHL, Web of Science, Applied Science & Technology Source, IEEE Xplore and the ACM Digital Library were searched from inception through June 2020 for the studies of mHealth applications on people with cognitive impairment and their caregivers. Two reviewers independently extracted, checked synthesized data independently. RESULTS Of the 6101 studies retrieved, 64 studies met the inclusion criteria. Three categories emerged from this scoping review. These categories are ‘application functionality’, ‘evaluation strategies’, ‘barriers and challenges’. All the included studies were categorized into 7 groups based on functionality: (1) cognitive assessment; (2) cognitive training; (3) life support; (4) caregiver support; (5) symptom management; (6) reminiscence therapy; (7) exercise intervention. The included studies were broadly categorized into four types: (1) Usability testing; (2) Pilot and feasibility studies; (3) Validation studies; and (4) Efficacy or Effectiveness design. These studies had many defects in research design such as: (1) small sample size; (2) deficiency in active control group; (3) deficiency in analyzing the effectiveness of intervention components; (4) lack of adverse reactions and economic evaluation; (5) lack of consideration about the education level, electronic health literacy and smartphone proficiency of the participants; (6) deficiency in assessment tool; (7) lack of rating the quality of mHealth application. Some progress should be improved in the design of smartphone application functionality, such as: (1) the design of cognitive measurements and training game need to be differentiated; (2) reduce the impact of the learning effect. Besides this, few studies used health behavior theory and performed with standardized reporting. CONCLUSIONS Preliminary results show that mobile technologies facilitate the assistance in people with cognitive impairment and their caregivers. The majority of mHealth application interventions incorporated usability outcome and health outcomes. However, these studies have many defects in research design that limit the extrapolation of research. The content of mHealth application is urgently improved to adapt to demonstrate the real effect. In addition, further research with strong methodological rigor and adequate sample size are needed to examine the feasibility, effectiveness, and cost-effectiveness of mHealth applications for people with cognitive impairment and their caregivers.

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Understanding and responding to COVID-19 in Wales: protocol for a privacy-protecting data platform for enhanced epidemiology and evaluation of interventions

BMJ Open ◽

10.1136/bmjopen-2020-043010 ◽

2020 ◽

Vol 10 (10) ◽

pp. e043010

Author(s):

Jane Lyons ◽

Ashley Akbari ◽

Fatemeh Torabi ◽

Gareth I Davies ◽

Laura North ◽

...

Keyword(s):

Policy Development ◽

Healthcare Utilisation ◽

Population Data ◽

Population Level ◽

Risk Groups ◽

Service Utilisation ◽

Natural Experiments ◽

Spread Of Infection ◽

The Impact

IntroductionThe emergence of the novel respiratory SARS-CoV-2 and subsequent COVID-19 pandemic have required rapid assimilation of population-level data to understand and control the spread of infection in the general and vulnerable populations. Rapid analyses are needed to inform policy development and target interventions to at-risk groups to prevent serious health outcomes. We aim to provide an accessible research platform to determine demographic, socioeconomic and clinical risk factors for infection, morbidity and mortality of COVID-19, to measure the impact of COVID-19 on healthcare utilisation and long-term health, and to enable the evaluation of natural experiments of policy interventions.Methods and analysisTwo privacy-protecting population-level cohorts have been created and derived from multisourced demographic and healthcare data. The C20 cohort consists of 3.2 million people in Wales on the 1 January 2020 with follow-up until 31 May 2020. The complete cohort dataset will be updated monthly with some individual datasets available daily. The C16 cohort consists of 3 million people in Wales on the 1 January 2016 with follow-up to 31 December 2019. C16 is designed as a counterfactual cohort to provide contextual comparative population data on disease, health service utilisation and mortality. Study outcomes will: (a) characterise the epidemiology of COVID-19, (b) assess socioeconomic and demographic influences on infection and outcomes, (c) measure the impact of COVID-19 on short -term and longer-term population outcomes and (d) undertake studies on the transmission and spatial spread of infection.Ethics and disseminationThe Secure Anonymised Information Linkage-independent Information Governance Review Panel has approved this study. The study findings will be presented to policy groups, public meetings, national and international conferences, and published in peer-reviewed journals.

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The importance of input data quality and quantity in climate field reconstructions – results from the assimilation of various tree-ring collections

Climate of the Past ◽

10.5194/cp-16-1061-2020 ◽

2020 ◽

Vol 16 (3) ◽

pp. 1061-1074 ◽

Cited By ~ 2

Author(s):

Jörg Franke ◽

Veronika Valler ◽

Stefan Brönnimann ◽

Raphael Neukom ◽

Fernando Jaume-Santero

Keyword(s):

Sample Size ◽

Tree Ring ◽

Input Data ◽

Small Sample ◽

Screening Methods ◽

Temperature Sensitive ◽

Data Sets ◽

Large Sample Size ◽

Spatial Coverage ◽

Reconstruction Quality

Abstract. Differences between paleoclimatic reconstructions are caused by two factors: the method and the input data. While many studies compare methods, we will focus in this study on the consequences of the input data choice in a state-of-the-art Kalman-filter paleoclimate data assimilation approach. We evaluate reconstruction quality in the 20th century based on three collections of tree-ring records: (1) 54 of the best temperature-sensitive tree-ring chronologies chosen by experts; (2) 415 temperature-sensitive tree-ring records chosen less strictly by regional working groups and statistical screening; (3) 2287 tree-ring series that are not screened for climate sensitivity. The three data sets cover the range from small sample size, small spatial coverage and strict screening for temperature sensitivity to large sample size and spatial coverage but no screening. Additionally, we explore a combination of these data sets plus screening methods to improve the reconstruction quality. A large, unscreened collection generally leads to a poor reconstruction skill. A small expert selection of extratropical Northern Hemisphere records allows for a skillful high-latitude temperature reconstruction but cannot be expected to provide information for other regions and other variables. We achieve the best reconstruction skill across all variables and regions by combining all available input data but rejecting records with insignificant climatic information (p value of regression model >0.05) and removing duplicate records. It is important to use a tree-ring proxy system model that includes both major growth limitations, temperature and moisture.

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The impact of visual impairment on nutritional status: A systematic review

British Journal of Visual Impairment ◽

10.1177/0264619617730860 ◽

2018 ◽

Vol 36 (1) ◽

pp. 17-30 ◽

Cited By ~ 5

Author(s):

Nabila Jones ◽

Hannah Bartlett

Keyword(s):

Nutritional Status ◽

Visual Impairment ◽

Sample Size ◽

Visually Impaired ◽

Small Sample Size ◽

Small Sample ◽

Control Group ◽

Visually Impaired People ◽

Impaired People ◽

The Impact

The aim of this review was to evaluate the literature that has investigated the impact of visual impairment on nutritional status. We identified relevant articles through a multi-staged systematic approach. Fourteen articles were identified as meeting the inclusion criteria. The sample size of the studies ranged from 9 to 761 participants. It was found that visual impairment significantly affects nutritional status. The studies reported that visually impaired people have an abnormal body mass index (BMI); a higher prevalence of obesity and malnutrition was reported. Visually impaired people find it difficult to shop for, eat, and prepare meals. Most studies had a small sample size, and some studies did not include a study control group for comparison. The limitations of these studies suggest that the findings are not conclusive enough to hold true for only those who are visually impaired. Further studies with a larger sample size are required with the aim of developing interventions.

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P033: To choose or not to choose: evaluating the impact of a Choosing Wisely knowledge translation initiative on urban and rural emergency physician guideline awareness

CJEM ◽

10.1017/cem.2017.235 ◽

2017 ◽

Vol 19 (S1) ◽

pp. S89

Author(s):

K. Chandra ◽

P.R. Atkinson ◽

J. Fraser ◽

H. Chatur ◽

C. Adams

Keyword(s):

Knowledge Translation ◽

Sample Size ◽

Teaching Hospital ◽

Small Sample ◽

Choosing Wisely ◽

Rural Setting ◽

Rural Site ◽

Urban Site ◽

Post Intervention ◽

The Impact

Introduction: Choosing Wisely is an innovative approach to address physician and patient attitudes towards low value medical tests; however, a knowledge translation (KT) gap exists. We aimed to quantify the baseline familiarity of emergency medicine (EM) physicians with the Choosing Wisely Canada (CWC) EM recommendations. We then assessed whether a structured KT initiative affected knowledge and awareness. Methods: Physicians working in urban (tertiary teaching hospital, Saint John, NB) and rural (community teaching hospital, Waterville, NB) emergency departments were asked to participate in a survey assessing awareness and knowledge of the first five CWC EM recommendations before an educational intervention. The intervention consisted of a 1-hour seminar reviewing the recommendations, access to a video cast and departmental posters. Knowledge was assessed by asking respondents to identify 80% or more of the recommendations correctly. Physicians were surveyed again at a 6-month follow up period. The Fisher exact test was used for statistical analyses. A sample size of 36 was required to detect a 30% change with an alpha of 0.05 and a power of 80%. Results: At the urban site, 16 of 25 (64%) physicians responded to the pre- and 14 of 26 (53.8%) responded to the post-intervention survey. Awareness of the EM recommendations did not increase significantly (81.3% pre; 95% CI 56.2-94.2 vs. 92.9% post; 66.4-99.9; p=0.60). There was a weak trend towards improved knowledge with 62.5% (38.5-81.6) of physicians responding correctly initially, and 85.7% (58.8-97.2; p=0.23) after the intervention. At the rural site, 8 of 11 (72.7%) physicians responded to the pre- and post-intervention survey. There was a trend towards improved awareness, (25% pre; 6.3-59.9 vs. 75% post; 40.1-93.7; p=0.13), with 50% (21.5-78.5) responding correctly pre, and 87.5% (50.8-99.9; p=0.28) after the intervention. Conclusion: We have described the current awareness and knowledge of the CWC EM recommendations. Limited by our small sample size, we report a trend towards increased awareness and knowledge at 6 months following our KT initiative in a rural setting where there was a low baseline awareness. At the urban site where baseline knowledge was high, changes seen were less significant. Further work will look at the effectiveness of our initiative on physician practice.

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Role of an extended pathology exam in risk classification of testicular germ cell tumors (GCTs).

Journal of Clinical Oncology ◽

10.1200/jco.2012.30.15_suppl.e15032 ◽

2012 ◽

Vol 30 (15_suppl) ◽

pp. e15032-e15032

Author(s):

Mihai Vasile Marinca ◽

Irina Draga Caruntu ◽

Ludmila Liliac ◽

Simona Eliza Giusca ◽

Andreea Marinca ◽

...

Keyword(s):

Sample Size ◽

Small Sample Size ◽

Germ Cell Tumors ◽

Disease Free Survival ◽

Developed Countries ◽

Small Sample ◽

P Value ◽

Testicular Germ Cell ◽

Therapeutic Decisions ◽

The Impact

e15032 Background: The 1997 IGCCCG Consensus classification provides clinicians with enough information to efficiently choose between treatment options for most GCT patients. Nevertheless, therapy is ineffective in 5-10% of cases (even more in less developed countries), and about the same numbers experience severe side effects. This exploratory study aims to assess the impact of more rigorous and detailed pathology examination on improving the assignation of these patients to prognostic groups and, consequently, making optimal therapeutic decisions. Methods: Predefined features were reviewed on histology slides from 39 GCT patients followed-up for a median of 48.28 months. We designed a uniform pathology protocol, focused on identifying potential new prognostic factors. Categorical and continuous variables were quantified using light microscopy and computer-aided morphometry and, due to the small sample size, their statistical correlation was analyzed by exact tests and Spearman’s rho, respectively. Significant (2-sided p-value <0.05, under sample size reserve) coefficient values were entered in hierarchical cluster analysis (HCA). Results: Favorable IGCCCG group, presence of seminoma, glandular tissue pattern, presence and histoarchitecture of lymphocytic infiltrate associated better survival rates and lower risk of progression. Invasion of the epididymis and spermatic cord, presence of teratoma, choriocarcinoma and yolk-sac elements, papillary pattern and cell pleomorphism predicted poorer outcomes. HCA yielded 2 significantly distinct patient groups in terms of overall survival (p=0.018) and time to progression (p=0.080), but not disease-free survival (p=0.614). Conclusions: Quantification of tumor subtypes and other histology features of GCTs (e.g. necrosis, tissue patterns, inflammation) is feasible and, if standardized, may prove useful in optimal selection of risk groups, when performed by an experienced pathologist.

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Influence of Pilot and Small Trials in Meta-Analyses of Behavioral Interventions: A Meta-epidemiological Study

10.21203/rs.3.rs-46722/v1 ◽

2020 ◽

Author(s):

Michael W. Beets ◽

R. Glenn Weaver ◽

John P.A. Ioannidis ◽

Alexis Jones ◽

Lauren von Klinggraeff ◽

...

Keyword(s):

Sample Size ◽

Behavioral Interventions ◽

Meta Analysis ◽

Statistical Significance ◽

Feasibility Studies ◽

Small Sample ◽

Effect Sizes ◽

Absolute Difference ◽

Meta Analyses ◽

The Impact

Abstract Background: Pilot/feasibility or studies with small sample sizes may be associated with inflated effects. This study explores the vibration of effect sizes (VoE) in meta-analyses when considering different inclusion criteria based upon sample size or pilot/feasibility status. Methods: Searches were conducted for meta-analyses of behavioral interventions on topics related to the prevention/treatment of childhood obesity from 01-2016 to 10-2019. The computed summary effect sizes (ES) were extracted from each meta-analysis. Individual studies included in the meta-analyses were classified into one of the following four categories: self-identified pilot/feasibility studies or based upon sample size (N≤100, N>100, and N>370 the upper 75th of sample size). The VoE was defined as the absolute difference (ABS) between the re-estimations of summary ES restricted to study classifications compared to the originally reported summary ES. Concordance (kappa) of statistical significance between summary ES was assessed. Fixed and random effects models and meta-regressions were estimated. Three case studies are presented to illustrate the impact of including pilot/feasibility and N≤100 studies on the estimated summary ES.Results: A total of 1,602 effect sizes, representing 145 reported summary ES, were extracted from 48 meta-analyses containing 603 unique studies (avg. 22 avg. meta-analysis, range 2-108) and included 227,217 participants. Pilot/feasibility and N≤100 studies comprised 22% (0-58%) and 21% (0-83%) of studies. Meta-regression indicated the ABS between the re-estimated and original summary ES where summary ES were comprised of ≥40% of N≤100 studies was 0.29. The ABS ES was 0.46 when summary ES comprised of >80% of both pilot/feasibility and N≤100 studies. Where ≤40% of the studies comprising a summary ES had N>370, the ABS ES ranged from 0.20-0.30. Concordance was low when removing both pilot/feasibility and N≤100 studies (kappa=0.53) and restricting analyses only to the largest studies (N>370, kappa=0.35), with 20% and 26% of the originally reported statistically significant ES rendered non-significant. Reanalysis of the three case study meta-analyses resulted in the re-estimated ES rendered either non-significant or half of the originally reported ES. Conclusions: When meta-analyses of behavioral interventions include a substantial proportion of both pilot/feasibility and N≤100 studies, summary ES can be affected markedly and should be interpreted with caution.

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Are methylation beta-values simplex distributed?

10.1101/753459 ◽

2019 ◽

Author(s):

Lara Nonell ◽

Juan R González

Keyword(s):

Dna Methylation ◽

Sample Size ◽

Microarray Data ◽

Regression Models ◽

Real Data ◽

Small Sample ◽

Beta Regression ◽

Data Sets ◽

Methylation Data ◽

Simplex Distribution

AbstractDNA methylation plays an important role in the development and progression of disease. Beta-values are the standard methylation measures. Different statistical methods have been proposed to assess differences in methylation between conditions. However, most of them do not completely account for the distribution of beta-values. The simplex distribution can accommodate beta-values data. We hypothesize that simplex is a quite flexible distribution which is able to model methylation data.To test our hypothesis, we conducted several analyses using four real data sets obtained from microarrays and sequencing technologies. Standard data distributions were studied and modelled in comparison to the simplex. Besides, some simulations were conducted in different scenarios encompassing several distribution assumptions, regression models and sample sizes. Finally, we compared DNA methylation between females and males in order to benchmark the assessed methodologies under different scenarios.According to the results obtained by the simulations and real data analyses, DNA methylation data are concordant with the simplex distribution in many situations. Simplex regression models work well in small sample size data sets. However, when sample size increases, other models such as the beta regression or even the linear regression can be employed to assess group comparisons and obtain unbiased results. Based on these results, we can provide some practical recommendations when analyzing methylation data: 1) use data sets of at least 10 samples per studied condition for microarray data sets or 30 in NGS data sets, 2) apply a simplex or beta regression model for microarray data, 3) apply a linear model in any other case.

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Reproducibility of R-fMRI Metrics on the Impact of Different Strategies for Multiple Comparison Correction and Sample Sizes

10.1101/128645 ◽

2017 ◽

Cited By ~ 2

Author(s):

Xiao Chen ◽

Bin Lu ◽

Chao-Gan Yan

Keyword(s):

Sex Differences ◽

Sample Size ◽

Small Sample ◽

Multiple Comparison ◽

Sample Sizes ◽

Retest Reliability ◽

Test Retest Reliability ◽

The Impact ◽

Correction Strategies ◽

Multiple Comparison Correction

ABSTRACTConcerns regarding reproducibility of resting-state functional magnetic resonance imaging (R-fMRI) findings have been raised. Little is known about how to operationally define R-fMRI reproducibility and to what extent it is affected by multiple comparison correction strategies and sample size. We comprehensively assessed two aspects of reproducibility, test-retest reliability and replicability, on widely used R-fMRI metrics in both between-subject contrasts of sex differences and within-subject comparisons of eyes-open and eyes-closed (EOEC) conditions. We noted permutation test with Threshold-Free Cluster Enhancement (TFCE), a strict multiple comparison correction strategy, reached the best balance between family-wise error rate (under 5%) and test-retest reliability / replicability (e.g., 0.68 for test-retest reliability and 0.25 for replicability of amplitude of low-frequency fluctuations (ALFF) for between-subject sex differences, 0.49 for replicability of ALFF for within-subject EOEC differences). Although R-fMRI indices attained moderate reliabilities, they replicated poorly in distinct datasets (replicability < 0.3 for between-subject sex differences, < 0.5 for within-subject EOEC differences). By randomly drawing different sample sizes from a single site, we found reliability, sensitivity and positive predictive value (PPV) rose as sample size increased. Small sample sizes (e.g., < 80 (40 per group)) not only minimized power (sensitivity < 2%), but also decreased the likelihood that significant results reflect “true” effects (PPV < 0.26) in sex differences. Our findings have implications for how to select multiple comparison correction strategies and highlight the importance of sufficiently large sample sizes in R-fMRI studies to enhance reproducibility.

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