Nevin Manimala

J Community Health. 2022 Nov 6. doi: 10.1007/s10900-022-01152-x. Online ahead of print.

ABSTRACT

COVID-19 exacerbated existing disparities in food security in Chicago. Home gardening can improve food security but there are often barriers to participation and the benefits are understudied. Chicago Grows Food (CGF) formed in 2020 to address food insecurity during COVID-19, and created the Grow Your Groceries (GYG) program to provide home gardening kits to families at risk of food insecurity in Chicago. A participatory program evaluation was conducted to better understand the experiences of and benefits to individuals participating in GYG. Program participants shared feedback via focus groups (n = 6) and surveys (n = 72). Qualitative data were analyzed using an iterative coding process. Quantitative data were analyzed using descriptive statistics. Most participants reported confidence in using a grow kit to grow food, increased healthy food consumption, easier access to healthy food, and high likelihood of growing food again. Additionally, participants described increased connections within their communities, increased interaction with their family, and personal growth as benefits of the program. These results demonstrate the benefits of a novel home gardening program that uses fabric grow bags to address food insecurity. A larger scale program evaluation is necessary to better understand the impacts of participating in this home gardening program.

PMID:36336753 | DOI:10.1007/s10900-022-01152-x

Sci Rep. 2022 Nov 6;12(1):18837. doi: 10.1038/s41598-022-23584-0.

ABSTRACT

Growth alterations have been described in patients operated on for oral clefts. The purpose of this work was to analyze the craniofacial and palate morphology and dimensions of young adults operated on for oral clefts in early childhood in Spain. Eighty-three patients from eight different hospitals were divided into four groups based on their type of cleft: cleft lip (CL, n = 6), unilateral cleft lip and palate (UCLP, n = 37), bilateral cleft lip and palate (BCLP, n = 16), and cleft palate only (CPO, n = 24). A control group was formed of 71 individuals. Three-dimensional (3D) digital models were obtained from all groups with an intraoral scanner, together with cephalometries and frontal, lateral, and submental facial photographs. Measurements were obtained and analyzed statistically. Our results showed craniofacial alterations in the BCLP, UCLP, and CPO groups with an influence on the palate, maxilla, and mandible and a direct impact on facial appearance. This effect was more severe in the BCLP group. Measurements in the CL group were similar to those in the control group. Cleft characteristics and cleft type seem to be the main determining factors of long-term craniofacial growth alterations in these patients. Prospective research is needed to clearly delineate the effects of different treatments on the craniofacial appearance of adult cleft patients.

PMID:36336749 | DOI:10.1038/s41598-022-23584-0

Lifetime Data Anal. 2022 Nov 7. doi: 10.1007/s10985-022-09576-2. Online ahead of print.

ABSTRACT

Targeted maximum likelihood estimation (TMLE) provides a general methodology for estimation of causal parameters in presence of high-dimensional nuisance parameters. Generally, TMLE consists of a two-step procedure that combines data-adaptive nuisance parameter estimation with semiparametric efficiency and rigorous statistical inference obtained via a targeted update step. In this paper, we demonstrate the practical applicability of TMLE based causal inference in survival and competing risks settings where event times are not confined to take place on a discrete and finite grid. We focus on estimation of causal effects of time-fixed treatment decisions on survival and absolute risk probabilities, considering different univariate and multidimensional parameters. Besides providing a general guidance to using TMLE for survival and competing risks analysis, we further describe how the previous work can be extended with the use of loss-based cross-validated estimation, also known as super learning, of the conditional hazards. We illustrate the usage of the considered methods using publicly available data from a trial on adjuvant chemotherapy for colon cancer. R software code to implement all considered algorithms and to reproduce all analyses is available in an accompanying online appendix on Github.

PMID:36336732 | DOI:10.1007/s10985-022-09576-2

Obes Surg. 2022 Nov 7. doi: 10.1007/s11695-022-06333-0. Online ahead of print.

ABSTRACT

BACKGROUND: Obesity rates in Hispanics and African Americans (AAs) are higher than in Caucasians in the USA, yet the rate of metabolic and bariatric surgery (MBS) for weight loss remains lower for both Hispanics and AAs.

METHODS: Patient demographics and outcomes of adult AA and Hispanic patients undergoing sleeve gastrectomy (SG) or Roux-en-Y gastric bypass (RYGB) procedures were analyzed using the MBSAQIP dataset [2015-2018] using unmatched and propensity-matched data.

RESULTS: In total, 173,157 patients were included, of whom 98,185 were AA [56.7%] [21,163-RYGB; 77,022-SG] and 74,972 were Hispanic [43.3%] [20,282-RYGB; 54,690-SG]). Preoperatively, the AA cohort was older, had more females, and higher BMIs with higher rates of all tracked obesity-related medical conditions except for diabetes, venous stasis, and prior foregut surgery. Intra- and postoperatively, AAs were more likely to experience major complications including unplanned ICU admission, 30-day readmission/reintervention, and mortality. After propensity matching, the differences in ED visits, treatment for dehydration, 30-day readmission, 30-day intervention, and pulmonary embolism remained for both SG and RYGB cohorts. Progressive renal insufficiency and ventilator use lost statistical significance in both cohorts. Conversely, 30-day reoperation, postoperative ventilator requirement, unplanned intubation, unplanned ICU admission, and mortality lost significance in the RYGB cohort, but not SG patients.

CONCLUSION: Outcomes for AA patients were worse than for Hispanic patients, even after propensity matching. After matching, differences in major complications and mortality lost significance for RYGB, but not SG. These data suggest that outcomes for RYGB may be driven by the presence and severity of pre-existing patient-related factors.

PMID:36336721 | DOI:10.1007/s11695-022-06333-0

Med Phys. 2022 Nov 6. doi: 10.1002/mp.16087. Online ahead of print.

ABSTRACT

BACKGROUND: The growing adoption of MRI-guided radiation therapy (RT) platforms and a focus on MRI-only RT workflows has brought the technical challenge of synthetic CT (sCT) reconstruction to the forefront. Unpaired-data deep learning-based approaches to the problem offer the attractive characteristic of not requiring paired training data, but the gap between paired- and unpaired-data results can be limiting.

PURPOSE: We present two distinct approaches aimed at improving unpaired-data sCT reconstruction results: a cascade ensemble that combines multiple models and a personalized training strategy originally designed for the paired-data setting.

METHODS: Comparisons are made between the following models: 1) the paired-data fully convolutional DenseNet (FCDN), 2) the FCDN with the Intentional Deep Overfit Learning (IDOL) personalized training strategy, 3) the unpaired-data CycleGAN, 4) the CycleGAN with the IDOL training strategy, and 5) the CycleGAN as an intermediate model in a cascade ensemble approach. Evaluation of the various models over 25 total patients is carried out using a five-fold cross validation scheme, with the patient-specific IDOL models being trained for the five patients of fold 3, chosen at random.

RESULTS: In both the paired- and unpaired-data settings, adopting the IDOL training strategy led to improvements in the mean absolute error (MAE) between true CT images and sCT outputs within the body contour (mean improvement, paired- and unpaired-data approaches, respectively: 38%, 9%) and in regions of bone (52%, 5%), the peak signal to noise ratio (PSNR; 15%, 7%), and the structural similarity index (SSIM; 6%, <1%). The ensemble approach offered additional benefits over the IDOL approach in all three metrics (mean improvement over unpaired-data approach in fold 3; MAE: 20% bone MAE: 16% PSNR: 10% SSIM: 2%), and differences in body MAE between the ensemble approach and the paired-data approach are statistically insignificant.

CONCLUSIONS: We have demonstrated that both a cascade ensemble approach and a personalized training strategy designed initially for the paired-data setting offer significant improvements in image quality metrics for the unpaired-data sCT reconstruction task. Closing the gap between paired- and unpaired-data approaches is a step towards fully enabling these powerful and attractive unpaired-data frameworks. This article is protected by copyright. All rights reserved.

PMID:36336718 | DOI:10.1002/mp.16087

Eur J Hum Genet. 2022 Nov 7. doi: 10.1038/s41431-022-01201-y. Online ahead of print.

ABSTRACT

Genetic prediction of male pattern baldness (MPB) is important in science and society. Previous genetic MPB prediction models were limited by sparse marker coverage, small sample size, and/or data dependency in the different analytical steps. Here, we present novel models for genetic prediction of MPB based on a large set of markers and large independent subsample sets drawn among 187,435 European subjects. We selected 117 SNP predictors within 85 distinct loci from a list of 270 previously MPB-associated SNPs in 55,573 males of the UK Biobank Study (UKBB). Based on these 117 SNPs with and without age as additional predictor, we trained, by use of different methods, prediction models in a non-overlapping subset of 104,694 UKBB males and tested them in a non-overlapping subset of 26,177 UKBB males. Estimates of prediction accuracy were similar between methods with AUC ranges of 0.725-0.728 for severe, 0.631-0.635 for moderate, 0.598-0.602 for slight, and 0.708-0.711 for no hair loss with age, and slightly lower without, while prediction of any versus no hair loss gave 0.690-0.711 with age and slightly lower without. External validation in an early-onset enriched MPB dataset from the Bonn Study (N = 991) showed improved prediction accuracy without considering age such as AUC of 0.830 for no vs. any hair loss. Because of the large number of markers and the large independent datasets used for the different analytical steps, the newly presented genetic prediction models are the most reliable ones currently available for MPB or any other human appearance trait.

PMID:36336714 | DOI:10.1038/s41431-022-01201-y

Malar J. 2022 Nov 6;21(1):319. doi: 10.1186/s12936-022-04323-2.

ABSTRACT

BACKGROUND: Detection of malaria parasitaemia in samples that are negative by rapid diagnostic tests (RDTs) requires resource-intensive molecular tools. While pooled testing using a two-step strategy provides a cost-saving alternative to the gold standard of individual sample testing, statistical adjustments are needed to improve accuracy of prevalence estimates for a single step pooled testing strategy.

METHODS: A random sample of 4670 malaria RDT negative dried blood spot samples were selected from a mass testing and treatment trial in Asembo, Gem, and Karemo, western Kenya. Samples were tested for malaria individually and in pools of five, 934 pools, by one-step quantitative polymerase chain reaction (qPCR). Maximum likelihood approaches were used to estimate subpatent parasitaemia (RDT-negative, qPCR-positive) prevalence by pooling, assuming poolwise sensitivity and specificity was either 100% (strategy A) or imperfect (strategy B). To improve and illustrate the practicality of this estimation approach, a validation study was constructed from pools allocated at random into main (734 pools) and validation (200 pools) subsets. Prevalence was estimated using strategies A and B and an inverse-variance weighted estimator and estimates were weighted to account for differential sampling rates by area.

RESULTS: The prevalence of subpatent parasitaemia was 14.5% (95% CI 13.6-15.3%) by individual qPCR, 9.5% (95% CI (8.5-10.5%) by strategy A, and 13.9% (95% CI 12.6-15.2%) by strategy B. In the validation study, the prevalence by individual qPCR was 13.5% (95% CI 12.4-14.7%) in the main subset, 8.9% (95% CI 7.9-9.9%) by strategy A, 11.4% (95% CI 9.9-12.9%) by strategy B, and 12.8% (95% CI 11.2-14.3%) using inverse-variance weighted estimator from poolwise validation. Pooling, including a 20% validation subset, reduced costs by 52% compared to individual testing.

CONCLUSIONS: Compared to individual testing, a one-step pooled testing strategy with an internal validation subset can provide accurate prevalence estimates of PCR-positivity among RDT-negatives at a lower cost.

PMID:36336700 | DOI:10.1186/s12936-022-04323-2

BMC Med. 2022 Nov 7;20(1):385. doi: 10.1186/s12916-022-02583-y.

ABSTRACT

BACKGROUND: The value of polygenic risk scores (PRSs) towards improving guideline-recommended clinical risk models for coronary artery disease (CAD) prediction is controversial. Here we examine whether an integrated polygenic risk score improves the prediction of CAD beyond pooled cohort equations. METHODS: An observation study of 291,305 unrelated White British UK Biobank participants enrolled from 2006 to 2010 was conducted. A case-control sample of 9499 prevalent CAD cases and an equal number of randomly selected controls was used for tuning and integrating of the polygenic risk scores. A separate cohort of 272,307 individuals (with follow-up to 2020) was used to examine the risk prediction performance of pooled cohort equations, integrated polygenic risk score, and PRS-enhanced pooled cohort equation for incident CAD cases. The performance of each model was analyzed by discrimination and risk reclassification using a 7.5% threshold.

RESULTS: In the cohort of 272,307 individuals (mean age, 56.7 years) used to analyze predictive accuracy, there were 7036 incident CAD cases over a 12-year follow-up period. Model discrimination was tested for integrated polygenic risk score, pooled cohort equation, and PRS-enhanced pooled cohort equation with reported C-statistics of 0.640 (95% CI, 0.634-0.646), 0.718 (95% CI, 0.713-0.723), and 0.753 (95% CI, 0.748-0.758), respectively. Risk reclassification for the addition of the integrated polygenic risk score to the pooled cohort equation at a 7.5% risk threshold resulted in a net reclassification improvement of 0.117 (95% CI, 0.102 to 0.129) for cases and – 0.023 (95% CI, – 0.025 to – 0.022) for noncases [overall: 0.093 (95% CI, 0.08 to 0.104)]. For incident CAD cases, this represented 14.2% correctly reclassified to the higher-risk category and 2.6% incorrectly reclassified to the lower-risk category.

CONCLUSIONS: Addition of the integrated polygenic risk score for CAD to the pooled cohort questions improves the predictive accuracy for incident CAD and clinical risk classification in the White British from the UK Biobank. These findings suggest that an integrated polygenic risk score may enhance CAD risk prediction and screening in the White British population.

PMID:36336692 | DOI:10.1186/s12916-022-02583-y

BMC Geriatr. 2022 Nov 7;22(1):838. doi: 10.1186/s12877-022-03529-y.

ABSTRACT

BACKGROUND: The oldest-old (aged 80 or older) are the most rapidly growing age group, and they are more likely to suffer from cognitive impairment, leading to severe medical and economic burdens. The influence of intergenerational relationships on cognition among Chinese oldest-old adults is not clear. We aim to examine the association of intergenerational relationships with cognitive impairment among Chinese adults aged 80 or older.

METHODS: This was a prospective cohort study, and data were obtained from the Chinese Longitudinal Healthy Longevity Survey, 14,180 participants aged 80 or older with at least one follow-up survey from 1998 to 2018. Cognitive impairment was assessed by the Chinese version of Mini Mental State Examination, and intergenerational relationships were assessed by getting main financial support from children, living with children or often being visited by children, and doing housework or childcare. We used time-varying Cox proportional hazards models to estimate hazard ratios (HRs) and 95% confidence intervals (CIs) of associations between intergenerational relationships and cognitive impairment.

RESULTS: We identified 5443 incident cognitive impairments in the 24-cut-off MMSE cohort and 4778 in the 18-cut-off MMSE cohort between 1998 and 2018. After adjusting for a wide range of confounders, the HR was 2.50 (95% CI: 2.31, 2.72) in the old who received main financial support from children, compared with those who did not. The HR was 0.89 (95% CI: 0.83, 0.95) in the oldest-old who did housework or childcare, compared with those who did not. However, there were no significant associations between older adults’ cognitive impairments and whether they were living with or often visited by their children. Our findings were consistent in two different MMSE cut-off values (24 vs. 18) for cognitive impairment.

CONCLUSIONS: Sharing housework or childcare for children showed a protective effect on older adults’ cognitive function, whereas having children provide primary financial support could increase the risk for cognitive impairments. Our findings suggest that governments and children should pay more attention to older adults whose main financial sources from their children. Children can arrange some easy tasks for adults 80 years of age or older to prevent cognitive impairments.

PMID:36336683 | DOI:10.1186/s12877-022-03529-y

J Cardiovasc Magn Reson. 2022 Nov 7;24(1):57. doi: 10.1186/s12968-022-00891-z.

ABSTRACT

BACKGROUND: Computational fluid dynamics (CFD) is increasingly used for the assessment of blood flow conditions in patients with congenital heart disease (CHD). This requires patient-specific anatomy, typically obtained from segmented 3D cardiovascular magnetic resonance (CMR) images. However, segmentation is time-consuming and requires expert input. This study aims to develop and validate a machine learning (ML) method for segmentation of the aorta and pulmonary arteries for CFD studies.

METHODS: 90 CHD patients were retrospectively selected for this study. 3D CMR images were manually segmented to obtain ground-truth (GT) background, aorta and pulmonary artery labels. These were used to train and optimize a U-Net model, using a 70-10-10 train-validation-test split. Segmentation performance was primarily evaluated using Dice score. CFD simulations were set up from GT and ML segmentations using a semi-automatic meshing and simulation pipeline. Mean pressure and velocity fields across 99 planes along the vessel centrelines were extracted, and a mean average percentage error (MAPE) was calculated for each vessel pair (ML vs GT). A second observer (SO) segmented the test dataset for assessment of inter-observer variability. Friedman tests were used to compare ML vs GT, SO vs GT and ML vs SO metrics, and pressure/velocity field errors.

RESULTS: The network’s Dice score (ML vs GT) was 0.945 (interquartile range: 0.929-0.955) for the aorta and 0.885 (0.851-0.899) for the pulmonary arteries. Differences with the inter-observer Dice score (SO vs GT) and ML vs SO Dice scores were not statistically significant for either aorta or pulmonary arteries (p = 0.741, p = 0.061). The ML vs GT MAPEs for pressure and velocity in the aorta were 10.1% (8.5-15.7%) and 4.1% (3.1-6.9%), respectively, and for the pulmonary arteries 14.6% (11.5-23.2%) and 6.3% (4.3-7.9%), respectively. Inter-observer (SO vs GT) and ML vs SO pressure and velocity MAPEs were of a similar magnitude to ML vs GT (p > 0.2).

CONCLUSIONS: ML can successfully segment the great vessels for CFD, with errors similar to inter-observer variability. This fast, automatic method reduces the time and effort needed for CFD analysis, making it more attractive for routine clinical use.

PMID:36336682 | DOI:10.1186/s12968-022-00891-z