Tag: statistics

JMIR Mhealth Uhealth. 2022 May 11;10(5):e15719. doi: 10.2196/15719.

ABSTRACT

BACKGROUND: The prevalence of obesity in India is increasing at an alarming rate. Obesity-related mHealth apps have proffered an exciting opportunity to remotely deliver obesity-related information. This opportunity raises the question of whether such apps are truly effective.

OBJECTIVE: The aim of this study was to identify existing obesity-related mHealth apps in India and evaluate the potential of the apps’ contents to promote health behavior change. This study also aimed to discover the general quality of obesity-related mHealth apps.

METHODS: A systematic search for obesity-related mHealth apps was conducted in both the Google Play Store and the Apple App Store. The features and quality of the sample apps were assessed using the Mobile Application Rating Scale (MARS) and the potential of the sample apps’ contents to promote health behavior change was assessed using the PRECEDE-PROCEED Model (PPM).

RESULTS: A total of 13 apps (11 from the Google Play Store and 2 from the Apple App Store) were considered eligible for the study. The general quality of the 13 apps assessed using MARS resulted in mean scores ranging from 1.8 to 3.7. The bivariate Pearson correlation between the MARS rating and app user rating failed to establish statistically significant results. The multivariate regression analysis result indicated that the PPM factors are significant determinants of health behavior change (F_3,9=63.186; P<.001) and 95.5% of the variance (R²=0.955; P<.001) in the dependent variable (health behavior change) can be explained by the independent variables (PPM factors).

CONCLUSIONS: In general, mHealth apps are found to be more effective when they are based on theory. The presence of PPM factors in an mHealth app can greatly influence the likelihood of health behavior change among users. So, we suggest mHealth app developers consider this to develop efficient apps. Also, mHealth app developers should consider providing health information from credible sources and indicating the sources of the information, which will increase the perceived credibility of the apps among the users. We strongly recommend health professionals and health organizations be involved in the development of mHealth apps. Future research should include mHealth app users to understand better the apps’ effectiveness in bringing about health behavior change.

PMID:35544318 | DOI:10.2196/15719

J Adolesc Young Adult Oncol. 2022 May 11. doi: 10.1089/jayao.2021.0223. Online ahead of print.

ABSTRACT

Purpose: To describe the cancer incidence burden and trends among adolescent and young adults (AYAs) in Alberta, Canada over a 35-year period. Methods: We obtained data from the Alberta Cancer Registry on all first primary cancers, excluding non-melanoma skin cancer, diagnosed at ages 15-39 years among residents in Alberta from 1983 to 2017. Cancers were classified by using Barr’s AYA cancer classification system. Age-standardized incidence rates (ASIR) and the average annual percentage change (AAPC) in incidence rates were calculated. Statistically significant changes in the AAPC during the study period were assessed using Joinpoint regression. Results: Overall, 23,652 incident cases of AYA cancer were diagnosed in Alberta. Females accounted for ∼60% of the diagnoses. AYA cancer increased significantly over the study period overall (AAPC: 0.5%; 95%CI: 0.3%-0.7%), for each sex (AAPC_male: 0.7%; 95%CI: 0.4%-0.9%; AAPC_female: 0.4%; 95%CI: 0.2%-0.6%), and among male and female 20-39 year-olds. Although statistically significant increases were observed in 11 out of 29 cancer sites for at least a portion of the study period, with significant AAPCs ranging from 0.8% (95%CI: 0.01%-1.5%) to 6.6% (95%CI: 4.6%-8.5%), the main driver was thyroid cancer (AAPC: 3.7%; 95%CI: 3.2%-4.2%). Statistically significant decreases were observed for six cancer sites, with AAPCs ranging from -6.4% (95%CI: -8.7% to -4.1%) to -1.1% (95%CI: -1.8% to -0.5%). Conclusions: There is a growing cancer burden among AYAs in Alberta, which is driven primarily by thyroid cancer and early-onset cancers in males. These results highlight the need for etiological studies and tertiary strategies to prevent and mitigate morbidity and mortality in the AYA population.

PMID:35544316 | DOI:10.1089/jayao.2021.0223

JMIR Form Res. 2022 May 11;6(5):e37737. doi: 10.2196/37737.

ABSTRACT

BACKGROUND: Chinese immigrants bear a high diabetes burden and face significant barriers to accessing diabetes self-management education (DSME) and counseling programs.

OBJECTIVE: The goal of this study was to examine the feasibility and acceptability and to pilot test the potential efficacy of a social media-based DSME intervention among low-income Chinese immigrants with type 2 diabetes (T2D) in New York City.

METHODS: This was a single group pretest and posttest study in 30 Chinese immigrants with T2D. The intervention included 24 culturally and linguistically tailored DSME videos, focusing on diabetes education and behavioral counseling techniques. Over 12 weeks, participants received 2 brief videos each week via WeChat, a free social media app popular among Chinese immigrants. Primary outcomes included the feasibility and acceptability of the intervention. Feasibility was evaluated by recruitment processes, retention rates, and the video watch rate. Acceptability was assessed via a satisfaction survey at 3 months. Secondary outcomes, that is, hemoglobin A_1c (HbA_1c), self-efficacy, dietary intake, and physical activity, were measured at baseline, 3 months, and 6 months. Descriptive statistics and paired 2-sided t tests were used to summarize the baseline characteristics and changes before and after the intervention.

RESULTS: The sample population (N=30) consisted of mostly females (21/30, 70%) who were married (19/30, 63%), with limited English proficiency (30/30, 100%), and the mean age was 61 (SD 7) years. Most reported an annual household income of <US $25,000 (24/30, 80%) and a high school education or less (19/30, 63%). Thirty participants were recruited within 2 months (January and February 2020), and 97% (29/30) of the participants were retained at 6 months. A video watch rate of 92% (28/30) was achieved. The mean baseline HbA_1c level was 7.3% (SD 1.3%), and this level declined by 0.5% (95% CI -0.8% to -0.2%; P=.003) at 6 months. The mean satisfaction score was 9.9 (SD 0.6) out of 10, indicating a high level of satisfaction with the program. All strongly agreed or agreed that they preferred this video-based DSME over face-to-face visits. Compared to baseline, there were significant improvements in self-efficacy, dietary, and physical activity behaviors at 6 months.

CONCLUSIONS: This pilot study demonstrated that a social media-based DSME intervention is feasible, acceptable, and potentially efficacious in a low-income Chinese immigrant population with T2D. Future studies need to examine the efficacy in an adequately powered clinical trial.

PMID:35544298 | DOI:10.2196/37737

JAMA Psychiatry. 2022 May 11. doi: 10.1001/jamapsychiatry.2022.1067. Online ahead of print.

ABSTRACT

IMPORTANCE: Individuals surviving severe COVID-19 may be at increased risk of neuropsychiatric sequelae. Robust assessment of these risks may help improve clinical understanding of the post-COVID syndrome, aid clinical care during the ongoing pandemic, and inform postpandemic planning.

OBJECTIVE: To quantify the risks of new-onset neuropsychiatric conditions and new neuropsychiatric medication prescriptions after discharge from a COVID-19-related hospitalization, and to compare these with risks after discharge from hospitalization for other severe acute respiratory infections (SARI) during the COVID-19 pandemic.

DESIGN, SETTING, AND PARTICIPANTS: In this cohort study, adults (≥18 years of age) were identified from QResearch primary care and linked electronic health record databases, including national SARS-CoV-2 testing, hospital episode statistics, intensive care admissions data, and mortality registers in England, from January 24, 2020, to July 7, 2021.

EXPOSURES: COVID-19-related or SARI-related hospital admission (including intensive care admission).

MAIN OUTCOMES AND MEASURES: New-onset diagnoses of neuropsychiatric conditions (anxiety, dementia, psychosis, depression, bipolar disorder) or first prescription for relevant medications (antidepressants, hypnotics/anxiolytics, antipsychotics) during 12 months of follow-up from hospital discharge. Maximally adjusted hazard ratios (HR) with 95% CIs were estimated using flexible parametric survival models.

RESULTS: In this cohort study of data from 8.38 million adults (4.18 million women, 4.20 million men; mean [SD] age 49.18 [18.45] years); 16 679 (0.02%) survived a hospital admission for SARI, and 32 525 (0.03%) survived a hospital admission for COVID-19. Compared with the remaining population, survivors of SARI and COVID-19 hospitalization had higher risks of subsequent neuropsychiatric diagnoses. For example, the HR for anxiety in survivors of SARI was 1.86 (95% CI, 1.56-2.21) and for survivors of COVID-19 infection was 2.36 (95% CI, 2.03-2.74); the HR for dementia for survivors of SARI was 2.55 (95% CI, 2.17-3.00) and for survivors of COVID-19 infection was 2.63 (95% CI, 2.21-3.14). Similar findings were observed for all medications analyzed; for example, the HR for first prescriptions of antidepressants in survivors of SARI was 2.55 (95% CI, 2.24-2.90) and for survivors of COVID-19 infection was 3.24 (95% CI, 2.91-3.61). There were no significant differences observed when directly comparing the COVID-19 group with the SARI group apart from a lower risk of antipsychotic prescriptions in the former (HR, 0.80; 95% CI, 0.69-0.92).

CONCLUSIONS AND RELEVANCE: In this cohort study, the neuropsychiatric sequelae of severe COVID-19 infection were found to be similar to those for other SARI. This finding may inform postdischarge support for people surviving SARI.

PMID:35544272 | DOI:10.1001/jamapsychiatry.2022.1067

JMIR Res Protoc. 2022 May 11;11(5):e37092. doi: 10.2196/37092.

ABSTRACT

BACKGROUND: Hip and knee osteoarthritis is substantially prevalent worldwide, with large numbers of older adults undergoing joint replacement (arthroplasty) every year. A backlog of elective surgery due to the COVID-19 pandemic, and an aging population, has led to substantial issues with access to timely arthroplasty surgery. A potential method to improve the efficiency of arthroplasty services is by increasing the percentage of patients who are listed for surgery from primary care referrals. The use of artificial intelligence (AI) techniques, specifically machine learning, provides a potential unexplored solution to correctly and rapidly select suitable patients for arthroplasty surgery.

OBJECTIVE: This study has 2 objectives: (1) develop a cohort of patients with referrals by general practitioners regarding assessment of suitability for hip or knee replacement from National Health Service (NHS) Grampian data via the Grampian Data Safe Haven and (2) determine the demographic, clinical, and imaging characteristics that influence the selection of patients to undergo hip or knee arthroplasty, and develop a tested and validated patient-specific predictive model to guide arthroplasty referral pathways.

METHODS: The AI to Revolutionise the Patient Care Pathway in Hip and Knee Arthroplasty (ARCHERY) project will be delivered through 2 linked work packages conducted within the Grampian Data Safe Haven and Safe Haven Artificial Intelligence Platform. The data set will include a cohort of individuals aged ≥16 years with referrals for the consideration of elective primary hip or knee replacement from January 2015 to January 2022. Linked pseudo-anonymized NHS Grampian health care data will be acquired including patient demographics, medication records, laboratory data, theatre records, text from clinical letters, and radiological images and reports. Following the creation of the data set, machine learning techniques will be used to develop pattern classification and probabilistic prediction models based on radiological images. Supplemental demographic and clinical data will be used to improve the predictive capabilities of the models. The sample size is predicted to be approximately 2000 patients-a sufficient size for satisfactory assessment of the primary outcome. Cross-validation will be used for development, testing, and internal validation. Evaluation will be performed through standard techniques, such as the C statistic (area under curve) metric, calibration characteristics (Brier score), and a confusion matrix.

RESULTS: The study was funded by the Chief Scientist Office Scotland as part of a Clinical Research Fellowship that runs from August 2021 to August 2024. Approval from the North Node Privacy Advisory Committee was confirmed on October 13, 2021. Data collection started in May 2022, with the results expected to be published in the first quarter of 2024. ISRCTN registration has been completed.

CONCLUSIONS: This project provides a first step toward delivering an automated solution for arthroplasty selection using routinely collected health care data. Following appropriate external validation and clinical testing, this project could substantially improve the proportion of referred patients that are selected to undergo surgery, with a subsequent reduction in waiting time for arthroplasty appointments.

TRIAL REGISTRATION: ISRCTN Registry ISRCTN18398037; https://www.isrctn.com/ISRCTN18398037.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): PRR1-10.2196/37092.

PMID:35544289 | DOI:10.2196/37092

Turk J Anaesthesiol Reanim. 2022 Apr;50(2):135-141. doi: 10.5152/TJAR.2021.1356.

ABSTRACT

OBJECTIVE: Arterial pulse-derived cardiac output monitors are routinely employed to guide hemodynamic management during liver transplant surgery. In this study, we sought to assess the reliability by evaluating the agreement of the cardiac output measured by the FloTrac Vigileo versus pulmonary artery catheter (continuous cardiac output) at specified times during liver transplant.

METHODS: Liver transplant database with cardiac output values measured by FloTrac Vigileo and continuous cardiac output was analyzed retrospectively at a tertiary care hospital. Data were compared at T0: baseline, T1: 1 hour in dissection phase, T2: anhepatic phase, T3: portosystemic shunt, T4: reperfusion, T5: 1 hour after reperfusion, and T6: skin closure. Statistical analysis was done using Bland-Altman analysis and percentage error (<30%) to assess the agreement between cardiac output measured by 2 techniques, Lin’s concordance correlation coefficient for quantifying the agreement and 4-quadrant plots to compare the trends of cardiac output.

RESULTS: Bland-Altman analysis showed mean cardiac output ± standard deviation L min-1 (95% CI) at T0: 0.2 ± 2.09 (-3.9 to 4.3), T1: 0.53 ± 3.0 (-5.4 to 6.4), T2: 0.47 ± 2.1(-3.7 to 4.6), T3: 0.31 ± 1.9 (-3.4 to 4.0), T4: 0.44 ± 2.15 (-3.8 to 4.7), T 5:0.69 ± 1.9. (-2.9 to 4.3), and at T6: 0.43 ± 2.25 (-4.0 to 4.8). Percentage error was 44%-72% and concordance correlation coefficient was poor (<0.65) at all points.

CONCLUSIONS: There is poor agreement between the cardiac output measured by FloTrac and pulmonary artery catheter among liver transplant recipients. The need for superior hemodynamic monitoring is mandated in liver transplant.

PMID:35544253 | DOI:10.5152/TJAR.2021.1356

J Nutr. 2022 May 11:nxac076. doi: 10.1093/jn/nxac076. Online ahead of print.

ABSTRACT

BACKGROUND: Structural racism and economic marginalization shape dietary patterns in complex ways. Most research examining race and income inequities discount their interactions in shaping dietary intakes. An intersectional approach is needed to identify interconnected sources of social inequities and to more precisely locate dietary inequities.

OBJECTIVES: We examined whether racial identity and perceived income adequacy independently and jointly shape dietary quality, defined using the Healthy Eating Index (HEI) 2015, among a large sample of adults in Canada.

METHODS: Cross-sectional data from 2540 adults (≥18 years of age) in Canada who participated in the 2019 International Food Policy Study were analyzed. Multivariable linear regression models were executed to test the independent associations and interactions between racial identity and perceived income adequacy with HEI-2015 scores. Models were constructed to examine HEI-2015 total and component scores, adjusting for age, gender, and education.

RESULTS: Perceived income adequacy, but not racial identity, was independently associated with HEI-2015 total scores. The interaction between racial identity and perceived income adequacy was significantly associated with HEI-2015 scores. Compared to the reference group (individuals identifying as White and reporting income adequacy), those identifying as Black and reporting income adequacy were associated with lower HEI-2015 scores (β, -7.30; 95% CI, -13.07 to -1.54) and those identifying as Black and reporting income inadequacy were associated with lower HEI-2015 scores (β, -6.37; 95% CI, -12.13 to -0.60). Individuals who identified as indigenous and reported neither income adequacy nor inadequacy had lower HEI-2015 scores (β, -8.50; 95% CI, -13.82 to -3.18) compared to the reference group.

CONCLUSIONS: Findings suggest that racial identity and perceived income adequacy jointly shape dietary quality. Inequities in dietary quality may be missed when intersecting racial identities and socioeconomic positions are not explicitly investigated. To support healthier dietary patterns, strategies must reduce socioeconomic barriers that impose dietary constraints on some racialized groups.

PMID:35544238 | DOI:10.1093/jn/nxac076

CBE Life Sci Educ. 2022 Jun;21(2):ar25. doi: 10.1187/cbe.21-09-0237.

ABSTRACT

Community colleges have an opportunity to promote achievement of more science, technology, engineering, and mathematics (STEM) students and meet larger goals of scientific advancement and educational equity. Understanding community college students’ needs and backgrounds is key to increasing students’ success in STEM fields and realizing this potential. The objective of this paper is to use data from the U.S. Department of Education’s National Center for Education Statistics and other sources to characterize community college students and their academic achievement and to offer equity-based approaches to increase success, particularly in STEM. Here, I document that community college students, who constitute approximately one-third of U.S. undergraduates, are a unique population with greater proportions of underrepresented STEM minorities, parents, and students requiring developmental education. They are also more likely to be older, working, part-time, low-income, and first-generation students and more likely to differ demographically from faculty. I also found lower rates of academic achievement among community college students, including lower rates of retention and STEM degree attainment with evidence of even lower achievement for STEM underrepresented groups. The data point to the need for equity-based strategies to address achievement disparities for STEM community college students, including increasing community college faculty diversity and sensitivity to diverse students’ needs and experiences; adopting inclusive, active-learning pedagogies; and reforming developmental education.

PMID:35544203 | DOI:10.1187/cbe.21-09-0237

JAMA Psychiatry. 2022 May 11. doi: 10.1001/jamapsychiatry.2022.1017. Online ahead of print.

ABSTRACT

IMPORTANCE: Psychiatric and cognitive phenotypes have been associated with a range of specific, rare copy number variants (CNVs). Moreover, IQ is strongly associated with CNV risk scores that model the predicted risk of CNVs across the genome. But the utility of CNV risk scores for psychiatric phenotypes has been sparsely examined.

OBJECTIVE: To determine how CNV risk scores, common genetic variation indexed by polygenic scores (PGSs), and environmental factors combine to associate with cognition and psychopathology in a community sample.

DESIGN, SETTING, AND PARTICIPANTS: The Philadelphia Neurodevelopmental Cohort is a community-based study examining genetics, psychopathology, neurocognition, and neuroimaging. Participants were recruited through the Children’s Hospital of Philadelphia pediatric network. Participants with stable health and fluency in English underwent genotypic and phenotypic characterization from November 5, 2009, through December 30, 2011. Data were analyzed from January 1 through July 30, 2021.

EXPOSURES: The study examined (1) CNV risk scores derived from models of burden, predicted intolerance, and gene dosage sensitivity; (2) PGSs from genomewide association studies related to developmental outcomes; and (3) environmental factors, including trauma exposure and neighborhood socioeconomic status.

MAIN OUTCOMES AND MEASURES: The study examined (1) neurocognition, with the Penn Computerized Neurocognitive Battery; (2) psychopathology, with structured interviews based on the Schedule for Affective Disorders and Schizophrenia for School-Age Children; and (3) brain volume, with magnetic resonance imaging.

RESULTS: Participants included 9498 youths aged 8 to 21 years; 4906 (51.7%) were female, and the mean (SD) age was 14.2 (3.7) years. After quality control, 18 185 total CNVs greater than 50 kilobases (10 517 deletions and 7668 duplications) were identified in 7101 unrelated participants genotyped on Illumina arrays. In these participants, elevated CNV risk scores were associated with lower overall accuracy on cognitive tests (standardized β = 0.12; 95% CI, 0.10-0.14; P = 7.41 × 10-26); lower accuracy across a range of cognitive subdomains; increased overall psychopathology; increased psychosis-spectrum symptoms; and higher deviation from a normative developmental model of brain volume. Statistical models of developmental outcomes were significantly improved when CNV risk scores were combined with PGSs and environmental factors.

CONCLUSIONS AND RELEVANCE: In this study, elevated CNV risk scores were associated with lower cognitive ability, higher psychopathology including psychosis-spectrum symptoms, and greater deviations from normative magnetic resonance imaging models of brain development. Together, these results represent a step toward synthesizing rare genetic, common genetic, and environmental factors to understand clinically relevant outcomes in youth.

PMID:35544191 | DOI:10.1001/jamapsychiatry.2022.1017

Lancet Microbe. 2021 Apr;2(4):e141-e150. doi: 10.1016/S2666-5247(21)00009-4. Epub 2021 Mar 2.

ABSTRACT

BACKGROUND: The epidemiology and severity of non-falciparum malaria in endemic settings has garnered little attention. We aimed to characterise the prevalence, interaction, clinical risk factors, and temporal trends of non-falciparum Plasmodium species among symptomatic individuals presenting at health-care facilities in endemic settings of Kenya.

METHODS: We diagnosed and analysed infecting malaria species (Plasmodium falciparum, Plasmodium ovale curtisi, Plasmodium ovale wallikeri, and Plasmodium malariae) via PCR in clinical samples collected between March 1, 2008, and Dec 31, 2016, from six hospitals located in different regions of Kenya. We recruited patients aged 6 months or older who presented at outpatient departments with symptoms of malaria or tested positive for uncomplicated malaria by malaria rapid diagnostic test. Descriptive statistics were used to describe the prevalence and distribution of Plasmodium species. A statistical model was designed and used for estimating the frequency of Plasmodium species and assessing interspecies interactions. Mixed-effect linear regression models with random slopes for each location were used to test for change in prevalence over time.

FINDINGS: Samples from 2027 symptomatic participants presenting at care facilities were successfully analysed for all Plasmodium species. 1469 (72·5%) of the samples were P falciparum single-species infections, 523 (25·8%) were mixed infections, and only 35 (1·7%) were single non-falciparum species infections. 452 (22·3%) were mixed infections containing P ovale spp. A likelihood-based model calculation of the population frequency of each species estimated a significant within-host interference between P falciparum and P ovale curtisi. Mixed-effect logistic regression models identified a significant increase in P ovale wallikeri (2·1% per year; p=0·043) and P ovale curtisi (0·7% per year; p=0·0002) species over time, with a reciprocal decrease in P falciparum single-species infections (2·5% per year; p=0·0065). The frequency of P malariae infections did not significantly change over time. Risk of P falciparum infections presenting with fever was lower if co-infected with P malariae (adjusted odds ratio 0·43, 95% CI 0·25-0·74; p=0·0023).

INTERPRETATION: Our results show a prevalence of non-falciparum species infections of 27·5% among symptomatic individuals presenting at care facilities, which is higher than expected from previous cross-sectional surveys. The proportion of infections with P ovale wallikeri and P ovale curtisi was observed to significantly increase over the period of study, which could be due to attenuated responsiveness of these species to malaria drug treatment. The increase in frequency of P ovale spp could threaten the malaria control efforts in Kenya and pose increased risk of malaria to travellers.

FUNDING: Armed Forces Health Surveillance Branch and its Global Emerging Infections Surveillance Section.

PMID:35544189 | DOI:10.1016/S2666-5247(21)00009-4