Category: Nevin Manimala

JMIR Ment Health. 2023 Mar 17;10:e39253. doi: 10.2196/39253.

ABSTRACT

BACKGROUND: Postpartum depression (PPD) is treatable and preventable, but most women do not seek professional help for their perinatal depressive symptoms. One increasingly popular approach of improving access to care is the use of web-based intervention programs.

OBJECTIVE: The objective of this study was 2-fold: first, to assess the efficacy of Be a Mom, a brief web-based selective or indicated preventive intervention, in reducing depressive and anxiety symptoms of women at high risk for PPD; and second, to examine mechanisms of change linking modifiable self-regulatory skills (ie, emotion regulation, self-compassion, and psychological flexibility) to improved perinatal mental health outcomes.

METHODS: This 2-arm, open-label randomized controlled trial involved a sample of 1053 perinatal women presenting high risk for PPD who were allocated to the Be a Mom intervention group or a waitlist control group and completed self-report measures at baseline and postintervention assessments. Univariate latent change score models were computed to determine changes over time in adjustment processes and outcomes, with a multigroup-model approach to detect differences between the intervention and control groups and a 2-wave latent change score model to examine whether changes in processes were related to changes in outcomes.

RESULTS: Be a Mom was found to be effective in reducing depressive (intervention group: µΔ=-3.35; P<.001 vs control group: µΔ=-1.48; P<.001) and anxiety symptoms (intervention group: µΔ=-2.24; P<.001 vs control group: µΔ=-0.43; P=.04) in comparison with the control group, where such changes were inexistent or much smaller. All 3 psychological processes under study improved statistically significantly in posttreatment assessments: emotion regulation ability (Δχ²₃=12.3; P=.007) and psychological flexibility (Δχ²₃=34.9; P<.001) improved only in the intervention group, and although self-compassion increased in both groups (Δχ²₃=65.6; P<.001), these improvements were considerably greater in the intervention group.

CONCLUSIONS: These results suggest that Be a Mom, a low-intensity cognitive behavioral therapy program, is a promising first-line intervention for helping perinatal women, particularly those with early-onset PPD symptoms.

TRIAL REGISTRATION: ClinicalTrials.gov NCT03024645; https://clinicaltrials.gov/ct2/show/NCT03024645.

PMID:36930182 | DOI:10.2196/39253

JAMA Health Forum. 2023 Mar 3;4(3):e230106. doi: 10.1001/jamahealthforum.2023.0106.

ABSTRACT

IMPORTANCE: Little is known about primary care physicians who provide contraceptive services to Medicaid beneficiaries. Evaluating this workforce may help explain barriers to accessing these services since contraceptive care access is critical for Medicaid beneficiaries’ health.

OBJECTIVE: To describe the primary care physician workforce that provides contraceptive services to Medicaid beneficiaries and explore the factors associated with their Medicaid contraceptive service provision.

DESIGN, SETTING, AND PARTICIPANTS: This cross-sectional study, conducted from August 1 to October 10, 2022, used data from the Transformed Medicaid Statistical Information System from 2016 for primary care physicians from 4 specialties (family medicine, internal medicine, obstetrics and gynecology [OBGYN], and pediatrics).

MAIN OUTCOMES AND MEASURES: The main outcomes were providing intrauterine devices (IUDs) or contraceptive implants to at least 1 Medicaid beneficiary, prescribing hormonal birth control methods (including a pill, patch, or ring) to at least 1 Medicaid beneficiary, the total number of Medicaid beneficiaries provided IUDs or implants, and the total number Medicaid beneficiaries prescribed hormonal birth control methods in 2016. Physician- and community-level factors associated with contraceptive care provision were assessed using multivariate regression methods.

RESULTS: In the sample of 251 017 physicians (54% male; mean [SD] age, 49.17 [12.58] years), 28% were international medical graduates (IMGs) and 70% practiced in a state that had expanded Medicaid in 2016. Of the total physicians, 48% prescribed hormonal birth control methods while 10% provided IUDs or implants. For OBGYN physicians, compared with physicians younger than 35 years, being aged 35 to 44 years (odds ratio [OR], 3.51; 95% CI, 2.93-4.21), 45 to 54 years (OR, 3.01; 95% CI, 2.43-3.72), or 55 to 64 years (OR, 2.27; 95% CI, 1.82-2.83) was associated with higher odds of providing IUDs and implants. However, among family medicine physicians, age groups associated with lower odds of providing IUDs or implants were 45 to 54 years (OR, 0.66; 95% CI, 0.55-0.80), 55 to 64 years (OR, 0.51; 95% CI, 0.39-0.65), and 65 years or older (OR, 0.29; 95% CI, 0.19-0.44). Except for those specializing in OBGYN, being an IMG was associated with lower odds of providing hormonal contraceptive service (family medicine IMGs: OR, 0.80 [95% CI, 0.73-0.88]; internal medicine IMGs: OR, 0.85 [95% CI, 0.77-0.93]; and pediatric IMGs: OR, 0.85 [95% CI, 0.78-0.93]). Practicing in a state that expanded Medicaid by 2016 was associated with higher odds of prescribing hormonal contraception for family medicine (OR 1.50; 95% CI, 1.06-2.12) and internal medicine (OR, 1.71; 95% CI, 1.18-2.48) physicians but not for physicians from other specialties.

CONCLUSIONS AND RELEVANCE: In this cross-sectional study of primary care physicians, physician- and community-level factors, such as specialty, age, and the Medicaid expansion status of their state, were significantly associated with how they provided contraceptive services to Medicaid beneficiaries. However, the existence of associations varied across clinical specialties. Ensuring access to contraception among Medicaid beneficiaries may therefore require policy and program approaches tailored for different physician types.

PMID:36930168 | DOI:10.1001/jamahealthforum.2023.0106

JAMA Netw Open. 2023 Mar 1;6(3):e231887. doi: 10.1001/jamanetworkopen.2023.1887.

ABSTRACT

IMPORTANCE: Type 2 diabetes (T2D) is increasing globally. Diabetic retinopathy (DR) is a leading cause of blindness in adults with T2D; however, the global burden of DR in pediatric T2D is unknown. This knowledge can inform retinopathy screening and treatments to preserve vision in this population.

OBJECTIVE: To estimate the global prevalence of DR in pediatric T2D.

DATA SOURCES: MEDLINE, Embase, the Cumulative Index to Nursing and Allied Health Literature (CINAHL), the Cochrane Library, the Web of Science, and the gray literature (ie, literature containing information that is not available through traditional publishing and distribution channels) were searched for relevant records from the date of database inception to April 4, 2021, with updated searches conducted on May 17, 2022. Searches were limited to human studies. No language restrictions were applied. Search terms included diabetic retinopathy; diabetes mellitus, type 2; prevalence studies; and child, adolescent, teenage, youth, and pediatric.

STUDY SELECTION: Three teams, each with 2 reviewers, independently screened for observational studies with 10 or more participants that reported the prevalence of DR. Among 1989 screened articles, 27 studies met the inclusion criteria for the pooled analysis.

DATA EXTRACTION AND SYNTHESIS: This systematic review and meta-analysis followed the Meta-analysis of Observational Studies in Epidemiology (MOOSE) and the Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) reporting guidelines for systematic reviews and meta-analyses. Two independent reviewers performed the risk of bias and level of evidence analyses. The results were pooled using a random-effects model, and heterogeneity was reported using χ2 and I2 statistics.

MAIN OUTCOMES AND MEASURES: The main outcome was the estimated pooled global prevalence of DR in pediatric T2D. Other outcomes included DR severity and current DR assessment methods. The association of diabetes duration, sex, race, age, and obesity with DR prevalence was also assessed.

RESULTS: Among the 27 studies included in the pooled analysis (5924 unique patients; age range at T2D diagnosis, 6.5-21.0 years), the global prevalence of DR in pediatric T2D was 6.99% (95% CI, 3.75%-11.00%; I2 = 95%; 615 patients). Fundoscopy was less sensitive than 7-field stereoscopic fundus photography in detecting retinopathy (0.47% [95% CI, 0%-3.30%; I2 = 0%] vs 13.55% [95% CI, 5.43%-24.29%; I2 = 92%]). The prevalence of DR increased over time and was 1.11% (95% CI, 0.04%-3.06%; I2 = 5%) at less than 2.5 years after T2D diagnosis, 9.04% (95% CI, 2.24%-19.55%; I2 = 88%) at 2.5 to 5.0 years after T2D diagnosis, and 28.14% (95% CI, 12.84%-46.45%; I2 = 96%) at more than 5 years after T2D diagnosis. The prevalence of DR increased with age, and no differences were noted based on sex, race, or obesity. Heterogeneity was high among studies.

CONCLUSIONS AND RELEVANCE: In this study, DR prevalence in pediatric T2D increased significantly at more than 5 years after diagnosis. These findings suggest that retinal microvasculature is an early target of T2D in children and adolescents, and annual screening with fundus photography beginning at diagnosis offers the best assessment method for early detection of DR in pediatric patients.

PMID:36930156 | DOI:10.1001/jamanetworkopen.2023.1887

JAMA Netw Open. 2023 Mar 1;6(3):e233376. doi: 10.1001/jamanetworkopen.2023.3376.

ABSTRACT

IMPORTANCE: Little is known about long-term associations of early-life exposure to extreme temperatures with child health and lung function.

OBJECTIVES: To investigate the association of prenatal and postnatal heat or cold exposure with newborn lung function and identify windows of susceptibility.

DESIGN, SETTING, AND PARTICIPANTS: This population-based cohort study (SEPAGES) recruited pregnant women in France between July 8, 2014, and July 24, 2017. Data on temperature exposure, lung function, and covariates were available from 343 mother-child dyads. Data analysis was performed from January 1, 2021, to December 31, 2021.

EXPOSURES: Mean, SD, minimum, and maximum temperatures at the mother-child’s residence, estimated using a state-of-the-art spatiotemporally resolved model.

MAIN OUTCOMES AND MEASURES: Outcome measures were tidal breathing analysis and nitrogen multiple-breath washout test measured at 2 months of age. Adjusted associations between both long-term (35 gestational weeks and first 4 weeks after delivery) and short-term (7 days before lung function test) exposure to ambient temperature and newborn lung function were analyzed using distributed lag nonlinear models.

RESULTS: A total of 343 mother-child pairs were included in the analyses (median [IQR] maternal age at conception, 32 [30.0-35.2] years; 183 [53%] male newborns). A total of 246 mothers and/or fathers (72%) held at least a master’s degree. Among the 160 female newborns (47%), long-term heat exposure (95th vs 50th percentile of mean temperature) was associated with decreased functional residual capacity (-39.7 mL; 95% CI, -68.6 to -10.7 mL for 24 °C vs 12 °C at gestational weeks 20-35 and weeks 0-4 after delivery) and increased respiratory rate (28.0/min; 95% CI, 4.2-51.9/min for 24 °C vs 12 °C at gestational weeks 14-35 and weeks 0-1 after delivery). Long-term cold exposure (5th vs 50th percentile of mean temperature) was associated with lower functional residual capacity (-21.9 mL; 95% CI, -42.4 to -1.3 mL for 1 °C vs 12 °C at gestational weeks 15-29), lower tidal volume (-23.8 mL; 95% CI, -43.1 to -4.4 mL for 1 °C vs 12 °C at gestational weeks 14-35 and weeks 0-4 after delivery), and increased respiratory rate (45.5/min; 95% CI, 10.1-81.0/min for 1 °C vs 12 °C at gestational weeks 6-35 and weeks 0-1 after delivery) in female newborns as well. No consistent association was observed for male newborns or short-term exposure to cold or heat.

CONCLUSIONS AND RELEVANCE: In this cohort study, long-term heat and cold exposure from the second trimester until 4 weeks after birth was associated with newborn lung volumes, especially among female newborns.

PMID:36930155 | DOI:10.1001/jamanetworkopen.2023.3376

JAMA Netw Open. 2023 Mar 1;6(3):e233391. doi: 10.1001/jamanetworkopen.2023.3391.

ABSTRACT

IMPORTANCE: Artificial intelligence (AI) enables powerful models for establishment of clinical diagnostic and prognostic tools for hip fractures; however the performance and potential impact of these newly developed algorithms are currently unknown.

OBJECTIVE: To evaluate the performance of AI algorithms designed to diagnose hip fractures on radiographs and predict postoperative clinical outcomes following hip fracture surgery relative to current practices.

DATA SOURCES: A systematic review of the literature was performed using the MEDLINE, Embase, and Cochrane Library databases for all articles published from database inception to January 23, 2023. A manual reference search of included articles was also undertaken to identify any additional relevant articles.

STUDY SELECTION: Studies developing machine learning (ML) models for the diagnosis of hip fractures from hip or pelvic radiographs or to predict any postoperative patient outcome following hip fracture surgery were included.

DATA EXTRACTION AND SYNTHESIS: This study followed the Preferred Reporting Items for Systematic Reviews and Meta-analyses and was registered with PROSPERO. Eligible full-text articles were evaluated and relevant data extracted independently using a template data extraction form. For studies that predicted postoperative outcomes, the performance of traditional predictive statistical models, either multivariable logistic or linear regression, was recorded and compared with the performance of the best ML model on the same out-of-sample data set.

MAIN OUTCOMES AND MEASURES: Diagnostic accuracy of AI models was compared with the diagnostic accuracy of expert clinicians using odds ratios (ORs) with 95% CIs. Areas under the curve for postoperative outcome prediction between traditional statistical models (multivariable linear or logistic regression) and ML models were compared.

RESULTS: Of 39 studies that met all criteria and were included in this analysis, 18 (46.2%) used AI models to diagnose hip fractures on plain radiographs and 21 (53.8%) used AI models to predict patient outcomes following hip fracture surgery. A total of 39 598 plain radiographs and 714 939 hip fractures were used for training, validating, and testing ML models specific to diagnosis and postoperative outcome prediction, respectively. Mortality and length of hospital stay were the most predicted outcomes. On pooled data analysis, compared with clinicians, the OR for diagnostic error of ML models was 0.79 (95% CI, 0.48-1.31; P = .36; I2 = 60%) for hip fracture radiographs. For the ML models, the mean (SD) sensitivity was 89.3% (8.5%), specificity was 87.5% (9.9%), and F1 score was 0.90 (0.06). The mean area under the curve for mortality prediction was 0.84 with ML models compared with 0.79 for alternative controls (P = .09).

CONCLUSIONS AND RELEVANCE: The findings of this systematic review and meta-analysis suggest that the potential applications of AI to aid with diagnosis from hip radiographs are promising. The performance of AI in diagnosing hip fractures was comparable with that of expert radiologists and surgeons. However, current implementations of AI for outcome prediction do not seem to provide substantial benefit over traditional multivariable predictive statistics.

PMID:36930153 | DOI:10.1001/jamanetworkopen.2023.3391

JAMA Netw Open. 2023 Mar 1;6(3):e233413. doi: 10.1001/jamanetworkopen.2023.3413.

ABSTRACT

IMPORTANCE: Firearm homicides are a major public health concern; lack of timely mortality data presents considerable challenges to effective response. Near real-time data sources offer potential for more timely estimation of firearm homicides.

OBJECTIVE: To estimate near real-time burden of weekly and annual firearm homicides in the US.

DESIGN, SETTING, AND PARTICIPANTS: In this prognostic study, anonymous, longitudinal time series data were obtained from multiple data sources, including Google and YouTube search trends related to firearms (2014-2019), emergency department visits for firearm injuries (National Syndromic Surveillance Program, 2014-2019), emergency medical service activations for firearm-related injuries (biospatial, 2014-2019), and National Domestic Violence Hotline contacts flagged with the keyword firearm (2016-2019). Data analysis was performed from September 2021 to September 2022.

MAIN OUTCOMES AND MEASURES: Weekly estimates of US firearm homicides were calculated using a 2-phase pipeline, first fitting optimal machine learning models for each data stream and then combining the best individual models into a stacked ensemble model. Model accuracy was assessed by comparing predictions of firearm homicides in 2019 to actual firearm homicides identified by National Vital Statistics System death certificates. Results were also compared with a SARIMA (seasonal autoregressive integrated moving average) model, a common method to forecast injury mortality.

RESULTS: Both individual and ensemble models yielded highly accurate estimates of firearm homicides. Individual models’ mean error for weekly estimates of firearm homicides (root mean square error) varied from 24.95 for emergency department visits to 31.29 for SARIMA forecasting. Ensemble models combining data sources had lower weekly mean error and higher annual accuracy than individual data sources: the all-source ensemble model had a weekly root mean square error of 24.46 deaths and full-year accuracy of 99.74%, predicting the total number of firearm homicides in 2019 within 38 deaths for the entire year (compared with 95.48% accuracy and 652 deaths for the SARIMA model). The model decreased the time lag of reporting weekly firearm homicides from 7 to 8 months to approximately 6 weeks.

CONCLUSIONS AND RELEVANCE: In this prognostic study of diverse secondary data on machine learning, ensemble modeling produced accurate near real-time estimates of weekly and annual firearm homicides and substantially decreased data source time lags. Ensemble model forecasts can accelerate public health practitioners’ and policy makers’ ability to respond to unanticipated shifts in firearm homicides.

PMID:36930150 | DOI:10.1001/jamanetworkopen.2023.3413

JAMA Netw Open. 2023 Mar 1;6(3):e233502. doi: 10.1001/jamanetworkopen.2023.3502.

ABSTRACT

IMPORTANCE: Early detection of attention-deficit/hyperactivity disorder (ADHD) and sleep problems is paramount for children’s mental health. Interview-based diagnostic approaches have drawbacks, necessitating the development of an evaluation method that uses digital phenotypes in daily life.

OBJECTIVE: To evaluate the predictive performance of machine learning (ML) models by setting the data obtained from personal digital devices comprising training features (ie, wearable data) and diagnostic results of ADHD and sleep problems by the Kiddie Schedule for Affective Disorders and Schizophrenia Present and Lifetime Version for Diagnostic and Statistical Manual of Mental Disorders, 5th edition (K-SADS) as a prediction class from the Adolescent Brain Cognitive Development (ABCD) study.

DESIGN, SETTING, AND PARTICIPANTS: In this diagnostic study, wearable data and K-SADS data were collected at 21 sites in the US in the ABCD study (release 3.0, November 2, 2020, analyzed October 11, 2021). Screening data from 6571 patients and 21 days of wearable data from 5725 patients collected at the 2-year follow-up were used, and circadian rhythm-based features were generated for each participant. A total of 12 348 wearable data for ADHD and 39 160 for sleep problems were merged for developing ML models.

MAIN OUTCOMES AND MEASURES: The average performance of the ML models was measured using an area under the receiver operating characteristics curve (AUC), sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV). In addition, the Shapley Additive Explanations value was used to calculate the importance of features.

RESULTS: The final population consisted of 79 children with ADHD problems (mean [SD] age, 144.5 [8.1] months; 55 [69.6%] males) vs 1011 controls and 68 with sleep problems (mean [SD] age, 143.5 [7.5] months; 38 [55.9%] males) vs 3346 controls. The ML models showed reasonable predictive performance for ADHD (AUC, 0.798; sensitivity, 0.756; specificity, 0.716; PPV, 0.159; and NPV, 0.976) and sleep problems (AUC, 0.737; sensitivity, 0.743; specificity, 0.632; PPV, 0.036; and NPV, 0.992).

CONCLUSIONS AND RELEVANCE: In this diagnostic study, an ML method for early detection or screening using digital phenotypes in children’s daily lives was developed. The results support facilitating early detection in children; however, additional follow-up studies can improve its performance.

PMID:36930149 | DOI:10.1001/jamanetworkopen.2023.3502

Transl Vis Sci Technol. 2023 Mar 1;12(3):16. doi: 10.1167/tvst.12.3.16.

ABSTRACT

PURPOSE: Our goal was to build a system that combined deep convolutional neural networks (DCNNs) and feature extraction algorithms, which automatically extracted and quantified vascular abnormalities in posterior pole retinal images of full-term infants clinically diagnosed with mild familial exudative retinopathy (FEVR).

METHODS: Using posterior pole retinal images taken from 4628 full-term infants with a total of 9256 eyes, we created data sets, trained DCNNs, and performed tests and comparisons. With the segmented images, our system extracted peripapillary vascular densities, mean tortuosities, and maximum diameter ratios within the region of interest. We also compared them with normal eyes statistically.

RESULTS: In the test data set, the trained system obtained a sensitivity of 0.78 and a specificity of 0.98 for vascular segmentation, with 0.94 and 0.99 for optic disc, respectively. While in the comparison data set, compared with normal, we found a significant increase in vascular densities in retinal images with mild FEVR (5.3211% ± 0.7600% vs. 4.5998% ± 0.6586%) and a significant increase in the maximum diameter ratios (1.8805 ± 0.3197 vs. 1.5087 ± 0.2877), while the mean tortuosities significantly decreased (2.1018 ± 0.2933 [104 cm-3] vs. 3.3344 ± 0.3890 [104 cm-3]). All values were statistically significantly different.

CONCLUSIONS: Our system could automatically segment the posterior pole retinal images and extract from vascular features associated with mild FEVR. Quantitative analysis of these parameters may help ophthalmologists in the early detection of FEVR.

TRANSLATIONAL RELEVANCE: This system may contribute to the early detection of FEVR and facilitate the promotion of artificial intelligence-assisted diagnostic techniques in clinical applications.

PMID:36930137 | DOI:10.1167/tvst.12.3.16

Medicine (Baltimore). 2023 Mar 17;102(11):e33247. doi: 10.1097/MD.0000000000033247.

ABSTRACT

BACKGROUND: Congenital heart disease (CHD), the most common heart defect in children, refers to congenital disease with abnormal development of the heart or large blood vessels during the fetal period. The researchers suggest that children with CHD show more obvious neurodevelopmental disorders than children with normal development, and children with CHD may have a higher risk of social interaction and communication disorders. This is similar to the characteristics of children with autism spectrum disorder (ASD). However, the association between type of CHD and ASD is not well understood. This systematic review and meta-analysis will reveal the relationship between type of CHD and ASD.

METHODS: We will search the Cochrane Library, Embase, PubMed, China National Knowledge Infrastructure, Wanfang, Chinese Scientific Journals Full text, and China Biology Medicine disc databases using relevant subject terms and free words. We will use a fixed effects model or random effects model for meta-analysis. The risk of bias will be assessed by the Newcastle-Ottawa Scale and the agency for health care research and quality. Heterogeneity will be tested by Q statistics and I² values. Publication bias will be detected by funnel plots and Egger test. Subgroup analyses and sensitivity analyses will also be used to explore and interpret the heterogeneity.

RESULTS: The study will afford additional insight into the investigation the association between type of CHD and ASD.

CONCLUSIONS: The results will provide evidence for the early identification and early intervention of ASD in children with CHD, which may contribute to improving the neurodevelopmental outcome of children with CHD.

PMID:36930132 | DOI:10.1097/MD.0000000000033247

Medicine (Baltimore). 2023 Mar 17;102(11):e33262. doi: 10.1097/MD.0000000000033262.

ABSTRACT

BACKGROUND: We aimed to find out whether the combined treatment of acupuncture and oral medication is more effective than sole oral medication in reducing pain and improving knee function at the end of treatment and after short-term period (4-6 weeks after treatment). Second, if it is effective, we investigated whether the effect surpasses the minimal clinically important difference.

METHODS: Articles published between January 1, 1992, and August 31, 2022, were searched in PubMed, Cochrane, and Embase. The PICO (population, intervention, comparison, and outcome) of this study are as follows: Population: knee osteoarthritis patients; Intervention: acupuncture (non-sham acupuncture) + oral medication (analgesic or non-steroidal anti-inflammatory drugs); Comparison: oral medication (analgesic or non-steroidal anti-inflammatory drugs); Outcome: visual analog scale (VAS) or Western Ontario and McMaster University (WOMAC) osteoarthritis index.

RESULTS: The combined treatment of oral medication and adjuvant acupuncture showed statistically significant improvement in VAS and WOMAC scores at the end of acupuncture treatment and short-term follow-up time (between 4 and 6 weeks after acupuncture). In addition, the degree of improvement of VAS and WOMAC index showed effects beyond minimal clinically important differences compared to pretreatment at both the end of acupuncture treatment and the short-term follow-up of acupuncture treatment.

CONCLUSION: The existing evidence suggests that adjuvant acupuncture may play a role in the treatment of knee osteoarthritis. However, physicians should be aware of adverse effects such as hematoma in adjuvant acupuncture treatment.

PMID:36930121 | DOI:10.1097/MD.0000000000033262