Tag: nevin manimala

Fam Med. 2023 Jul 24. doi: 10.22454/FamMed.2023.755040. Online ahead of print.

ABSTRACT

BACKGROUND AND OBJECTIVES: The COVID-19 pandemic impacted the volume and nature of pediatric primary care visits nationwide. This study aimed to identify trends in pediatric visits at our institution during the pandemic to reveal opportunities to improve care of children and adolescents.

METHODS: We performed a retrospective chart review of all pediatric visits conducted at a single family medicine clinic within a large academic medical center in Northern California from January 1, 2019, through September 30, 2021. Data collected for each visit included age, sex, type of visit (preventive or problem-focused), reason for visit (if problem-focused), and mode of visit (in-person or telehealth). We analyzed data using descriptive statistics and χ2 tests.

RESULTS: A total of 4,844 pediatric visits occurred during the study period. Visit volume dropped 9% from 2019 to 2020 and recovered to prepandemic levels in 2021. During the study period from 2019 to 2021, the percentage of problem-focused visits increased from 30% to 37% (P=.008) among adolescents, driven largely by an increase in the percentage of behavioral health visits from 14% to 29% (P<.001). We found no significant changes in the age or sex of patients seen. Telemedicine visit volume decreased from 2020 to 2021 in all age categories except for adolescents, which remained stable at 43% of all visits.

CONCLUSIONS: A sharp increase in behavioral health concerns among adolescents stands out as the most notable impact of COVID-19 on pediatric care at our institution. Our findings raise questions about how behavioral health care can be optimized for adolescents in the postpandemic era.

PMID:37540534 | DOI:10.22454/FamMed.2023.755040

Fam Med. 2023 Jul 24. doi: 10.22454/FamMed.2023.794972. Online ahead of print.

ABSTRACT

BACKGROUND AND OBJECTIVES: Mentorship is critical to physician recruitment, career development, and retention. Many underrepresented in medicine (URiM) physicians experience minority taxes that can undermine their professional objectives. Use of cross-cultural mentoring skills to navigate differences between non-URiM and URiM physicians can make mentorship relationships with URiM physicians more effective. This survey examined military family physician demographics and mentorship practices.

METHODS: Design and Setting: Cross-sectional study using voluntary, anonymous data from the 2021 Uniformed Services Academy of Family Physicians (USAFP) Annual Meeting Omnibus Survey.

STUDY POPULATION: USAFP Members attending 2021 Virtual Annual Meeting.

INTERVENTION: None.

STATISTICAL ANALYSIS: Descriptive statistics and χ2 tests.

RESULTS: The response rate to the omnibus survey was 52.9%, n=258. More than half of respondents did not have a URiM mentee and had not collaborated with a URiM colleague on a scholarly activity within the last 3 years. Only 54.7% of respondents could recognize and address minority taxes. URiM physicians were more likely to have a URiM mentee (65.4% vs 44.4%, P=.042) and to recognize and address minority taxes (84.6% vs 51.3%, P=.001). They also were more confident (84.6% vs 60.3%, P=.015) and more skilled in discussing racism (80.8% vs 58.2%, P=.026).

CONCLUSIONS: Structured programs are needed to improve knowledge and skills to support cross-cultural mentorship. Additional studies are needed to further evaluate and identify implementation strategies.

PMID:37540532 | DOI:10.22454/FamMed.2023.794972

JAMA Netw Open. 2023 Aug 1;6(8):e2327326. doi: 10.1001/jamanetworkopen.2023.27326.

ABSTRACT

IMPORTANCE: Direct-acting antiviral (DAA) treatment for hepatitis C virus (HCV) infection is highly effective but remains underused. Understanding disparities in the delivery of DAAs is important for HCV elimination planning and designing interventions to promote equitable treatment.

OBJECTIVE: To examine variations in the receipt of DAA in the 6 months following a new HCV diagnosis.

DESIGN, SETTING, AND PARTICIPANTS: This retrospective cohort study used national Medicaid claims from 2017 to 2019 from 50 states, Washington DC, and Puerto Rico. Individuals aged 18 to 64 years with a new diagnosis of HCV in 2018 were included. A new diagnosis was defined as a claim for an HCV RNA test followed by an International Statistical Classification of Diseases and Related Health Problems, Tenth Revision (ICD-10) diagnosis code, after a 1-year lookback period.

MAIN OUTCOMES AND MEASURES: Outcome was receipt of a DAA prescription within 6 months of diagnosis. Logistic regression was used to examine demographic factors and ICD-10-identified comorbidities associated with treatment initiation.

RESULTS: Among 87 652 individuals, 43 078 (49%) were females, 12 355 (14%) were age 18 to 29 years, 35 181 (40%) age 30 to 49, 51 282 (46%) were non-Hispanic White, and 48 840 (49%) had an injection drug use diagnosis. Of these individuals, 17 927 (20%) received DAAs within 6 months of their first HCV diagnosis. In the regression analyses, male sex was associated with increased treatment initiation (OR, 1.24; 95% CI, 1.16-1.33). Being age 18 to 29 years (OR, 0.65; 95% CI, 0.50-0.85) and injection drug use (OR, 0.84; 95% CI, 0.75-0.94) were associated with decreased treatment initiation. After adjustment for state fixed effects, Asian race (OR, 0.50; 95% CI, 0.40-0.64), American Indian or Alaska Native race (OR, 0.68; 95% CI, 0.55-0.84), and Hispanic ethnicity (OR, 0.81; 95% CI, 0.71-0.93) were associated with decreased treatment initiation. Adjustment for state Medicaid policy did not attenuate the racial or ethnic disparities.

CONCLUSIONS: In this retrospective cohort study, HCV treatment initiation was low among Medicaid beneficiaries and varied by demographic characteristics and comorbidities. Interventions are needed to increase HCV treatment uptake among Medicaid beneficiaries and to address disparities in treatment among key populations, including younger individuals, females, individuals from minoritized racial and ethnic groups, and people who inject drugs.

PMID:37540513 | DOI:10.1001/jamanetworkopen.2023.27326

JAMA Netw Open. 2023 Aug 1;6(8):e2327421. doi: 10.1001/jamanetworkopen.2023.27421.

ABSTRACT

IMPORTANCE: Early-life socioeconomic adversity may be associated with poor cognitive health over the life course.

OBJECTIVE: To examine the association of childhood and midlife neighborhood socioeconomic position (nSEP) with cognitive decline.

DESIGN, SETTING, AND PARTICIPANTS: This cohort study included 5711 men and women enrolled in the community-based Atherosclerosis Risk in Communities (ARIC) Study with repeated cognitive data measured over a median 27.0 years (IQR, 26.0-27.9 years) (1990-2019). Statistical analysis was performed from December 2022 through March 2023.

EXPOSURE: Residence addresses for ARIC Study cohort participants were obtained at midlife (1990-1993) and as recalled addresses at 10 years of age (childhood). A composite nSEP z score was created as a sum of z scores for US Census-based measures of median household income; median value of owner-occupied housing units; percentage of households receiving interest, dividend, or net rental income; percentage of adults with a high school degree; percentage of adults with a college degree; and percentage of adults in professional, managerial, or executive occupations. Childhood nSEP and midlife nSEP were modeled as continuous measures and discretized into tertiles.

MAIN OUTCOMES AND MEASURES: A factor score for global cognition was derived from a battery of cognitive tests administered at 5 in-person visits from baseline to 2019. The rate of cognitive decline from 50 to 90 years of age was calculated by fitting mixed-effects linear regression models with age as the time scale and adjusted for race, sex, birth decade, educational level, and presence of the apolipoprotein E ε4 allele.

RESULTS: Among 5711 ARIC Study participants (mean [SD] baseline age, 55.1 [4.7] years; 3372 women [59.0%]; and 1313 Black participants [23.0%]), the median rate of cognitive decline was -0.33 SDs (IQR, -0.49 to -0.20 SDs) per decade. In adjusted analyses, each 1-SD-higher childhood nSEP score was associated with a slower (β, -9.2%; 95% CI, -12.1% to -6.4%) rate of cognitive decline relative to the sample median. A comparable association was observed when comparing the highest tertile with the lowest tertile of childhood nSEP (β, -17.7%; 95% CI, -24.1% to -11.3%). Midlife nSEP was not associated with the rate of cognitive decline.

CONCLUSIONS AND RELEVANCE: In this cohort study of contextual factors associated with cognitive decline, childhood nSEP was inversely associated with trajectories of cognitive function throughout adulthood.

PMID:37540511 | DOI:10.1001/jamanetworkopen.2023.27421

Cancer Epidemiol Biomarkers Prev. 2023 Aug 4:EPI-23-0132. doi: 10.1158/1055-9965.EPI-23-0132. Online ahead of print.

ABSTRACT

BACKGROUND: Cervical cancer disparities exist in the United States with the highest incidence in Hispanic women and the highest mortality in Black women. Effective control of cervical cancer in the population requires targeted interventions tailored to community composition in terms of race, ethnicity, and social determinants of health (SDOH).

METHODS: Using cancer registry and SDOH data, geospatial hot spot analyses were carried out to identify statistically significant neighborhood clusters with high numbers of cervical cancer cases within the catchment area of an NCI-designated cancer center. The locations, racial and ethnic composition, and SDOH resources of these hot spots were used by the center’s community outreach and engagement office to deploy mobile screening units (MSU) for intervention in communities with women facing heightened risk for cervical cancer.

RESULTS: Neighborhood hot spots with high numbers of cervical cancer cases in South Florida largely overlap with locations of poverty. Cervical cancer hot spots are associated with a high percentage of Hispanic cases and low SDOH status, including low income, housing tenure, and education attainment.

CONCLUSIONS: A geospatially referenced cancer surveillance platform integrating cancer registry, SDOH, and cervical screening data can effectively identify targets for cervical cancer intervention in neighborhoods experiencing disparities.

IMPACT: Guided with a data-driven surveillance system, MSUs proactively bringing prevention education and cervical screening to communities with more unscreened, at-risk women are an effective means for addressing disparities associated with cervical cancer control.

PMID:37540496 | DOI:10.1158/1055-9965.EPI-23-0132

Vet Res Commun. 2023 Aug 4. doi: 10.1007/s11259-023-10183-1. Online ahead of print.

ABSTRACT

Leptospirosis is a zoonotic disease with significant public health and economic impact worldwide. Rapid and accurate diagnosis is essential for effective prevention and treatment. This study optimized a loop-mediated isothermal amplification (LAMP) assay using BFo isothermal DNA polymerase with different colorimetric indicators. LAMP was able to detect DNA from pathogenic and intermediate leptospires, while non-pathogenic leptospires and other non-leptospiral microorganisms were negative. LAMP assay combined with calcein showed a tenfold higher limit of detection (1 ng of leptospiral DNA per reaction) than LAMP combined with hydroxynaphthol blue or end-point PCR lipL32 (10 ng of DNA per reaction). Animal samples were collected from infected and non-infected Golden Syrian hamsters (Mesocricetus auratus) to evaluate and compare the performance of LAMP and PCR. These techniques showed a substantial agreement according to Cohen’s kappa statistic, being both useful techniques for detecting leptospiral DNA in clinical samples. Overall, this study demonstrates that the LAMP assay is a sensitive, specific, rapid, and simple tool for the detection of leptospiral DNA. It has the potential to facilitate the diagnosis of leptospirosis, particularly in low-income regions with limited diagnosis resources.

PMID:37540477 | DOI:10.1007/s11259-023-10183-1

J Epidemiol Glob Health. 2023 Aug 4. doi: 10.1007/s44197-023-00141-0. Online ahead of print.

ABSTRACT

BACKGROUND: Evaluating mortality effects of the COVID-19 pandemic using all-cause mortality data for national populations is inevitably associated with the risk of masking important subnational differentials and hampering targeted health policies. This study aims at assessing simultaneously cause-specific, spatial and seasonal mortality effects attributable to the pandemic in Germany in 2020.

METHODS: Our analyses rely on official cause-of-death statistics consisting of 5.65 million individual death records reported for the German population during 2015-2020. We conduct differential mortality analyses by age, sex, cause, month and district (N = 400), using decomposition and standardisation methods, comparing each strata of the mortality level observed in 2020 with its expected value, as well as spatial regression to explore the association of excess mortality with pre-pandemic indicators.

RESULTS: The spatial analyses of excess mortality reveal a very heterogenous pattern, even within federal states. The coastal areas in the north were least affected, while the south of eastern Germany experienced the highest levels. Excess mortality in the most affected districts, with standardised mortality ratios reaching up to 20%, is driven widely by older ages and deaths reported in December, particularly from COVID-19 but also from cardiovascular and mental/nervous diseases.

CONCLUSIONS: Our results suggest that increased psychosocial stress influenced the outcome of excess mortality in the most affected areas during the second lockdown, thus hinting at possible adverse effects of strict policy measures. It is essential to accelerate the collection of detailed mortality data to provide policymakers earlier with relevant information in times of crisis.

PMID:37540473 | DOI:10.1007/s44197-023-00141-0

Behav Res Methods. 2023 Jul 24. doi: 10.3758/s13428-023-02132-2. Online ahead of print.

ABSTRACT

Outcome reporting bias (ORB) refers to the biasing effect caused by researchers selectively reporting outcomes within a study based on their statistical significance. ORB leads to inflated effect size estimates in meta-analysis if only the outcome with the largest effect size is reported due to ORB. We propose a new method (CORB) to correct for ORB that includes an estimate of the variability of the outcomes’ effect size as a moderator in a meta-regression model. An estimate of the variability of the outcomes’ effect size can be computed by assuming a correlation among the outcomes. Results of a Monte-Carlo simulation study showed that the effect size in meta-analyses may be severely overestimated without correcting for ORB. Estimates of CORB are close to the true effect size when overestimation caused by ORB is the largest. Applying the method to a meta-analysis on the effect of playing violent video games on aggression showed that the effect size estimate decreased when correcting for ORB. We recommend to routinely apply methods to correct for ORB in any meta-analysis. We provide annotated R code and functions to help researchers apply the CORB method.

PMID:37540470 | DOI:10.3758/s13428-023-02132-2

Behav Res Methods. 2023 Jul 19. doi: 10.3758/s13428-023-02108-2. Online ahead of print.

ABSTRACT

pyWitness is a python toolkit for recognition memory experiments, with a focus on eyewitness identification (ID) data analysis and model fitting. The current practice is for researchers to use different statistical packages to analyze a single dataset. pyWitness streamlines the process. In addition to conducting key data analyses (e.g., receiver operating characteristic analysis, confidence accuracy characteristic analysis), statistical comparisons, signal-detection-based model fits, simulated data generation, and power analyses are also possible. We describe the package implementation and provide detailed instructions and tutorials with datasets so that users can follow. There is also an online manual that is regularly updated. We developed pyWitness to be user-friendly, reduce human interaction with pre-processing and processing of data and model fits, and produce publication-ready plots. All pyWitness features align with open science practices, such that the algorithms, fits, and methods are reproducible and documented. While pyWitness is a python toolkit, it can also be used from R for users more accustomed to this environment.

PMID:37540469 | DOI:10.3758/s13428-023-02108-2

Behav Res Methods. 2023 Aug 1. doi: 10.3758/s13428-023-02117-1. Online ahead of print.

ABSTRACT

Multiple Imputation (MI) is one of the most popular approaches to addressing missing values in questionnaires and surveys. MI with multivariate imputation by chained equations (MICE) allows flexible imputation of many types of data. In MICE, for each variable under imputation, the imputer needs to specify which variables should act as predictors in the imputation model. The selection of these predictors is a difficult, but fundamental, step in the MI procedure, especially when there are many variables in a data set. In this project, we explore the use of principal component regression (PCR) as a univariate imputation method in the MICE algorithm to automatically address the many-variables problem that arises when imputing large social science data. We compare different implementations of PCR-based MICE with a correlation-thresholding strategy through two Monte Carlo simulation studies and a case study. We find the use of PCR on a variable-by-variable basis to perform best and that it can perform closely to expertly designed imputation procedures.

PMID:37540467 | DOI:10.3758/s13428-023-02117-1