Health Services Research and Managerial Epidemiology最新文献

{"title":"A Machine Learning Approach to Predicting Higher COVID-19 Care Burden in the Primary Care Safety Net: Hispanic Patient Population Size a Key Factor.","authors":"Evan V Goldstein,&nbsp;Fernando A Wilson","doi":"10.1177/23333928221115894","DOIUrl":"https://doi.org/10.1177/23333928221115894","url":null,"abstract":"<p><strong>Introduction: </strong>The federal government legislated supplemental funding to support community health centers (CHCs) in response to the COVID-19 pandemic. Supplemental funding included standard base payments and adjustments for the number of total and uninsured patients served before the pandemic. However, not all CHCs share similar patient population characteristics and health risks.</p><p><strong>Objective: </strong>To use machine learning to identify the most important factors for predicting whether CHCs had a high burden of patients diagnosed with COVID-19 during the first year of the pandemic.</p><p><strong>Methods: </strong>Our analytic sample included data from 1342 CHCs across the 50 states and D.C. in 2020. We trained a random forest (RF) classifier model, incorporating 5-fold cross-validation to validate the RF model while optimizing the model's hyperparameters. Final performance metrics were calculated following the application of the model that had the best fit to the held-out test set.</p><p><strong>Results: </strong>CHCs with a high burden of COVID-19 had an average of 65.3 patients diagnosed with COVID-19 per 1000 patients in 2020. Our RF model had 80.9% accuracy, 80.1% precision, 25.0% sensitivity, and 98.1% specificity. The percentage of Hispanic patients served in 2020 was the most important feature for predicting whether CHCs had high COVID-19 burden.</p><p><strong>Conclusions: </strong>Findings from our RF model suggest patient population race and ethnicity characteristics were most important for predicting whether CHCs had a high burden of patients diagnosed with COVID-19 in 2020, though sensitivity was low. Enhanced support for CHCs serving large Hispanic patient populations may have an impact on addressing future COVID-19 waves.</p>","PeriodicalId":12951,"journal":{"name":"Health Services Research and Managerial Epidemiology","volume":" ","pages":"23333928221115894"},"PeriodicalIF":1.6,"publicationDate":"2022-08-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ftp.ncbi.nlm.nih.gov/pub/pmc/oa_pdf/2e/1c/10.1177_23333928221115894.PMC9354120.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"40677714","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Survival Status and Predictors of Mortality Among Low Birthweight Neonates Admitted in Amhara Region Referral Hospitals of Ethiopia: Retrospective Follow-Up Study.","authors":"Belete Fenta Kebede,&nbsp;Yalemtsehay Dagnaw Genie,&nbsp;Desta Hailu Aregawi,&nbsp;Behafta Afera Tadele","doi":"10.1177/23333928221117364","DOIUrl":"https://doi.org/10.1177/23333928221117364","url":null,"abstract":"<p><strong>Background: </strong>Being born with low birthweight is a major determinant of perinatal, neonatal, and infant survival. Even though low birthweight-related neonatal mortality is high, there is an information gap regarding the survival status of low birthweight neonates and their predictors of mortality in Ethiopia.</p><p><strong>Objective: </strong>This study was conducted to assess the survival status and predictors of mortality among low birthweight neonates admitted to Amhara region referral hospitals in Ethiopia.</p><p><strong>Methods and materials: </strong>A retrospective follow-up study was conducted on randomly selected low birthweight neonates admitted to the Amhara region referral hospital between January 01-2017 and December 30-2018. Data were entered into Epi-data 4.4.2.1 and exported to Stata 14 for cleaning and analysis. A cox regression model was used to analyze the data. Tables, charts, and text were used to report the results.</p><p><strong>Results: </strong>This study revealed that 35.2% of participants died with incidence rates of 37.86 per 1000 person-day observations (95%CI: 31.79-45.10). Sepsis (AHR:1.72(95% CI: 1.05-2.81), respiratory distress (AHR: 2.03 (95% CI:1.36-3.03), necrotizing enterocolitis (AHR: 2.47 (95% CI: 1.17-5.20), congenital anomalies (AHR:2.37 (95% CI: 1.36-4.13), extreme low birth weight (AHR:2.62 (95% CI:1.54-4.44) and prematurity (AHR: 2.55 (95% CI:1.10-5.92) were independent predictors of mortality.</p><p><strong>Conclusion: </strong>Sepsis, respiratory distress, necrotizing enterocolitis, congenital anomalies, extremely low birth weight, and premature birth were the independent predictors of mortality. Therefore, it is better for all stakeholders to focus more on the early diagnosis and management of low birth weight neonates with the factors associated with mortality.</p>","PeriodicalId":12951,"journal":{"name":"Health Services Research and Managerial Epidemiology","volume":" ","pages":"23333928221117364"},"PeriodicalIF":1.6,"publicationDate":"2022-07-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ftp.ncbi.nlm.nih.gov/pub/pmc/oa_pdf/4e/6c/10.1177_23333928221117364.PMC9340404.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"40667708","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Nature and Composition of Earnings Reported by Health Economists and Related Professionals: Gender, Education, and job Characteristics Matter.","authors":"Ioana Popovici,&nbsp;Manuel J Carvajal,&nbsp;Patti Peeples,&nbsp;Silvia E Rabionet","doi":"10.1177/23333928221106039","DOIUrl":"https://doi.org/10.1177/23333928221106039","url":null,"abstract":"Background Despite the fast growth of the workforce comprising health economics (HE), outcomes research (OR), and market access (MA) professionals, little is known about their earnings determination. Only three studies have examined their earnings and none has considered the number of hours worked, traditionally a critical component of income determination models. Objectives (i) Estimate an indicator of annual earnings of HE/OR/MA professionals, comparing male versus female and U.S. versus non-U.S. earnings levels, and (ii) assess the magnitude of the effect of selected human-capital and job-related covariates on their annual earnings determination. Methods The study used 2019 self-reported survey data from a sample of 304 HE/OR/MA professionals registered in the HealthEconomics.com global subscriber list. A two-way classification model with multiple replications was used to identify and test earnings variations of HE/OR/MA professionals across genders and locations. An earnings determination function using ordinary least squares was used to identify disparities in response to covariates including average workweek, human-capital stock, and job-related variables by gender and location. Results Substantial earning disparities were observed between HE/OR/MA professionals living in the U.S. and those living in other countries. Non-U.S. respondents exhibited earnings gaps of 44.7% in wages/salaries and 46.8% in total earnings relative to their U.S. counterparts with greater gaps for women than men. The female earnings gap outside the U.S. was considerably greater than in the U.S. Holding a graduate degree; working in a pharmaceutical or biotechnology firm; age, a proxy for experience; and working remotely impacted earnings differentials across different subgroups. Conclusions The findings of this paper shed light into the nature and composition of earnings of HE/OR/MA professionals across genders and locations. Exploring the dynamics of earning disparities by gender and location has increased in relevance given the rapidly-changing and uncertain job market environment driven by the COVID-19 pandemic.","PeriodicalId":12951,"journal":{"name":"Health Services Research and Managerial Epidemiology","volume":" ","pages":"23333928221106039"},"PeriodicalIF":1.6,"publicationDate":"2022-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ftp.ncbi.nlm.nih.gov/pub/pmc/oa_pdf/eb/f2/10.1177_23333928221106039.PMC9234843.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"40409645","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Household Dietary Diversity and Associated Factors among Rural Residents of Gomole District, Borena Zone, Oromia Regional State, Ethiopia.","authors":"Alqeer Aliyo,&nbsp;Wako Golicha,&nbsp;Anteneh Fikrie","doi":"10.1177/23333928221108033","DOIUrl":"https://doi.org/10.1177/23333928221108033","url":null,"abstract":"<p><strong>Background: </strong>Low dietary diversity is the main public health issue in developing countries. Despite its burden, there is a paucity of information on the dietary diversity of households and related factors in rural residents of southern Ethiopia. Therefore, this study assessed household dietary diversity and associated factors among rural residents of Gomole district.</p><p><strong>Methods: </strong>A community-based cross-sectional study design was conducted from November 1 to December 30, 2020, among 554 randomly selected rural households. Data were collected by using pretested structured face-to-face interviews about dietary intake by any household members within the past 24 h. The data were coded and entered into Epi data 3.1 and analyzed using Statistical Package for Social Sciences version 23. Both descriptive and analytic statistics were employed. Adjusted odds ratios (AORs) together with 95% confidence intervals and p values of less than 0.05 were used to declare statistical significance.</p><p><strong>Results: </strong>Out of the total, 19% (95%; CI = 15.7-22.2) of households had adequate dietary diversity scores. The mean (±SD) dietary diversity score was 5.12 (±1.6). The most consumed food groups in the study area were cereals/grains (96.4%), condiments (salt and beverages (tea)) (84.3%) and milk and milk products (80%). The sex of the household head (AOR = 2.31, 95% CI: 1.04-5.13), bank account ownership (AOR = 2.18, 95% CI: 1.04-4.59), and livestock ownership (AOR = 4.75, 95% CI: 1.35-16.73) were positively associated with dietary diversity. However, distance to the marketplace (AOR = 0.11, 95% CI: 0.02-0.56) was negatively associated with adequate dietary diversity.</p><p><strong>Conclusion: </strong>There is low dietary diversity among households of study area. Improving livestock rearing and bank accounts should be strengthened through strong intersectional collaboration to alleviate the problem.</p>","PeriodicalId":12951,"journal":{"name":"Health Services Research and Managerial Epidemiology","volume":" ","pages":"23333928221108033"},"PeriodicalIF":1.6,"publicationDate":"2022-06-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ftp.ncbi.nlm.nih.gov/pub/pmc/oa_pdf/1b/69/10.1177_23333928221108033.PMC9201295.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"40013705","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Clinical Use of an Electronic Pre-Visit Questionnaire Soliciting Patient Visit Goals and Interim History: A Retrospective Comparison Between Safety-net and Non-Safety-net Clinics.","authors":"Hannah Shucard,&nbsp;Emily Muller,&nbsp;Joslyn Johnson,&nbsp;Jan Walker,&nbsp;Joann G Elmore,&nbsp;Thomas H Payne,&nbsp;Jacob Berman,&nbsp;Sara L Jackson","doi":"10.1177/23333928221080336","DOIUrl":"https://doi.org/10.1177/23333928221080336","url":null,"abstract":"<p><strong>Introduction/objectives: </strong>We examined an initial step towards co-generation of clinic notes by inviting patients to complete a pre-visit questionnaire that could be inserted into clinic notes by providers and describe the experience in a safety-net and non-safety-net clinic.</p><p><strong>Methods: </strong>We sent an electronic pre-visit questionnaire on visit goals and interim history to patients at a safety-net clinic and a non-safety-net clinic before clinic visits. We compared questionnaire utilization between clinics during a one-year period and performed a chart review of a sample of patients to examine demographics, content and usage of patient responses to the questionnaire.</p><p><strong>Results: </strong>While use was low in both clinics, it was lower in the safety-net clinic (3%) compared to the non-safety-net clinic (10%). We reviewed a sample of respondents and found they were more likely to be White compared to the overall clinic populations (<i>p</i> < 0.05). There were no statistically significant differences in patient-typed notes (word count and number of visit goals) between the safety-net and non-safety-net samples however, patients at the safety-net clinic were less likely to have all of their goals addressed within the PCP documentation, compared to the non-safety-net clinic.</p><p><strong>Conclusions: </strong>Given potential benefits of this questionnaire as a communication tool, addressing barriers to use of technology among vulnerable patients is needed, including access to devices and internet, and support from caregivers or culturally concordant peer navigators.</p>","PeriodicalId":12951,"journal":{"name":"Health Services Research and Managerial Epidemiology","volume":" ","pages":"23333928221080336"},"PeriodicalIF":1.6,"publicationDate":"2022-02-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ftp.ncbi.nlm.nih.gov/pub/pmc/oa_pdf/2e/2a/10.1177_23333928221080336.PMC8859650.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"39659310","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Cardiovascular Disease Risk Factors in Hypertensive Patients: A Case Study of Jimma University Medical Center.","authors":"Habtamu Ayele,&nbsp;Akalu Banbeta,&nbsp;Abiyot Negash","doi":"10.1177/23333928221078601","DOIUrl":"https://doi.org/10.1177/23333928221078601","url":null,"abstract":"<p><strong>Background: </strong>Cardiovascular diseases are a group of disorders of the heart and blood vessels. Globally an estimated 17.9 million people died from cardiovascular diseases, which covers 31% of all global deaths, and the three quarters taking place in developing countries. Hypertension is the major cause of cardiovascular diseases. Its influence is high with other risk factors. This study aimed to determine the major risk factors of cardiovascular disease among hypertensive patients at Jimma University Medical Center.</p><p><strong>Methods: </strong>Using December to January 2017 hypertension-related report of Jimma University Medical Center (JUMC), a retrospective cohort study type was conducted on purposively selected 343 patients. Three nurses from JUMC participated in the data collection, and the data were fitted using the Cox-Proportional Hazard (Cox-PH) model.</p><p><strong>Results: </strong>About 138 (40.23%) patients were experienced cardiovascular disease at 28 months median time. From the Cox-PH model, the hazard ratio and 95% CI of age (HR = 1.0495, 95% CI: 1.0250-1.0747), urban (HR = 2.1225, 95% CI: 1.3813-3.2613), diabetes mellitus (HR = 1.702, 95% CI: 1.0082-2.8731), proteinuria (HR = 1.8749, 95% CI: 1.2675-2.7734), two drug users (HR = 0.2533, 95% CI: 0.1376-0.4662), systolic blood pressure (HR = 1.0343, 95% CI: 1.0147-1.0542) and pulse rate (HR = 1.0111, 95% CI: 0.9933-1.0293).</p><p><strong>Conclusion: </strong>The presence of proteinuria, diabetes mellitus, and being an urban resident had a great impact on the cardiovascular diseases of hypertensive patients.</p>","PeriodicalId":12951,"journal":{"name":"Health Services Research and Managerial Epidemiology","volume":" ","pages":"23333928221078601"},"PeriodicalIF":1.6,"publicationDate":"2022-02-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ftp.ncbi.nlm.nih.gov/pub/pmc/oa_pdf/14/e8/10.1177_23333928221078601.PMC8848038.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"39637332","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Pre-Anesthetic Medical Evaluations: Criteria Considerations for Telemedicine Alternatives to Face to Face Visits.","authors":"Kushal D Khera,&nbsp;Joseph D Blessman,&nbsp;Mark E Deyo-Svendsen,&nbsp;Nathaniel E Miller,&nbsp;Kurt B Angstman","doi":"10.1177/23333928221074895","DOIUrl":"https://doi.org/10.1177/23333928221074895","url":null,"abstract":"<p><strong>Background: </strong>The number of pre-anesthetic medical evaluations (PAMEs) being conducted in primary care is increasing. Due to the COVID-19 pandemic, the use of telemedicine has surged, providing a feasible way to conduct some of these visits. This study aimed to identify patient-related factors where a face to face (FTF) evaluation is indicated, measured by the need for pre-operative testing.</p><p><strong>Methods: </strong>A retrospective chart review was conducted on patients age ≥ 18 years who had a PAME between January 2019-June 2020 at a rural primary care clinic in Southeast Minnesota. Data collected included age, gender, Charlson Comorbidity Index Score, medications, revised cardiac risk index (RCRI), smoking status, exercise capacity, body mass index, and pre-operative testing. Logistical regression modeling for odds ratios of outcomes was performed.</p><p><strong>Results: </strong>254 patients were included, with an average age of 64.1 years; 43.7% were female. Most were obese (mean BMI 31.6), non-smoking (93.7%) with excellent functional capacity (87.8% ≥ 5 METs). 76.8% of the planned surgeries were intermediate or high risk. 35.0% (<i>n</i> = 89) of visits resulted in medication adjustments and 76.7% (<i>n</i> = 195) in pre-operative testing. Age ≥ 65 years, ≥7 current medications, and diabetes all significantly increased the odds of requiring pre-operative testing (<i>P</i> < .05).</p><p><strong>Conclusions: </strong>This study was able to identify patient-related factors that increased the likelihood of requiring pre-operative testing. Patients who are age ≥ 65 years, ≥7 current medications, and those with diabetes could be scheduled for a FTF evaluation. Others could be scheduled for a telemedicine visit to minimize health-care exposures.</p>","PeriodicalId":12951,"journal":{"name":"Health Services Research and Managerial Epidemiology","volume":" ","pages":"23333928221074895"},"PeriodicalIF":1.6,"publicationDate":"2022-01-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ftp.ncbi.nlm.nih.gov/pub/pmc/oa_pdf/a4/ec/10.1177_23333928221074895.PMC8785288.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"39963978","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"We Live in Interesting Times: How Health Services Research and Managerial Epidemiology Helps Point the Way Forward","authors":"Gregory M. Garrison","doi":"10.1177/23333928221083061","DOIUrl":"https://doi.org/10.1177/23333928221083061","url":null,"abstract":"While big data promises to revolutionize and personalize healthcare, the push of technology has caused medical care to become more fragmented and specialized. Additionally, the COVID-19 pandemic has accelerated change with new sweeping public health guidance and a rapid adoption of tele-healthcare or virtual visits. In this rapidly changing environment, it is more important than ever to scientifically consider the impact of these changes on quality of care, access to care, value of care, and overall health. Last year, Ghosh et.al found a worrisome widening disparity in hospital length-of-stay between black and white patients following implementation of the Hospital Readmission Reduction Project. Studnicki et.al noted that chemical abortions were associated with more emergency department visit morbidity than surgical abortions. And Boretti raised important questions about an alarming rise in COVID-19 cases coinciding with vaccine introduction in Israel. While seemingly unconnected, these studies, all published in this journal, indicate the need to objectively evaluate interventions for potential unintended effects. Healthcare is a complex adaptive system, and as such, outcomes are not necessarily linear and cannot be determined by examining only the components. This journal, Health Services Research & Managerial Epidemiology, is uniquely positioned to study the complex effects of these changes. By applying case-control and cohort studies, the tools of analytic epidemiology, to healthcare service and management issues, we can objectively determine effects and prove or disprove hypotheses. This is a continuation of the vision Dr James Rohrer expressed while founding this journal. As your new editor-in-chief, I am grateful for the foundation that Dr Rohrer developed. A study by Khera et.al, reported in this issue, is a wonderful example of the science this journal can advance. Using a retrospective cohort, his team identified patient characteristics which could be used to determine eligibility for virtual pre-anesthetic medical exams. In 2022, I look forward to publishing more works like this which use the techniques of epidemiology to examine all aspects of healthcare delivery. Ironically, the first known reference of the quote was attributed to Sir Austin Chamberlain, a British statesman and halfbrother to the Prime Minister, during the leadup to World War II. Despite inaccurate and hyperbolic political comparisons to World War II, we are lucky COVID-19 does not match the devastation of a world war which killed 3% of the world’s population or more than 70 million people. While we mourn for those lost to COVID-19, we also recognize it has altered our lives dramatically and changed healthcare delivery suddenly. We are indeed living thru interesting times; it would be a shame not to study them objectively and scientifically.","PeriodicalId":12951,"journal":{"name":"Health Services Research and Managerial Epidemiology","volume":"1 1","pages":""},"PeriodicalIF":1.6,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84460939","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Proposed Multi-Criteria Optimization Approach to Enhance Clinical Outcomes Evaluation for Diabetes Care: A Commentary","authors":"T. Wan, Sarah D. Matthews, H. Luh, Yong Zeng, Zhibo Wang, Lin Yang","doi":"10.1177/23333928221089125","DOIUrl":"https://doi.org/10.1177/23333928221089125","url":null,"abstract":"There are several challenges in diabetes care management including optimizing the currently used therapies, educating patients on selfmanagement, and improving patient lifestyle and systematic healthcare barriers. The purpose of performing a systems approach to implementation science aided by artificial intelligence techniques in diabetes care is two-fold: 1) to explicate the systems approach to formulate predictive analytics that will simultaneously consider multiple input and output variables to generate an ideal decision-making solution for an optimal outcome; and 2) to incorporate contextual and ecological variations in practicing diabetes care coupled with specific health educational interventions as exogenous variables in prediction. A similar taxonomy of modeling approaches proposed by Brennon et al (2006) is formulated to examining the determinants of diabetes care outcomes in program evaluation. The discipline-free methods used in implementation science research, applied to efficiency and quality-of-care analysis are presented. Finally, we illustrate a logically formulated predictive analytics with efficiency and quality criteria included for evaluation of behavioralchange intervention programs, with the time effect included, in diabetes care and research.","PeriodicalId":12951,"journal":{"name":"Health Services Research and Managerial Epidemiology","volume":"5 1","pages":""},"PeriodicalIF":1.6,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82022316","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Risk Factors for COVID-19 Hospitalization in School-Age Children","authors":"L. O'Neill, N. Chumbler","doi":"10.1177/23333928221104677","DOIUrl":"https://doi.org/10.1177/23333928221104677","url":null,"abstract":"Introduction With the recent emergence of the Omicron variant, there has been a rapid and alarming increase in the number of COVID-19 cases among pediatric populations. Yet few US pediatric cohort studies have characterized the clinical features of children with severe COVID-19. The objective of this study was to identify those chronic comorbidities that increase the risk of hospitalization for pediatric populations with severe COVID-19. Methods A retrospective cohort study that utilized the Texas Inpatient Public Use Data file was conducted. The study included 1187 patients (ages 5 to 19) from 164 acute-care Texas hospitals with the primary or secondary ICD-10CM diagnosis code U07.1 (COVID-19, virus identified). The baseline comparison group included 38 838 pediatric patients who were hospitalized in 2020. Multivariable binary logistic regression, controlling for patient characteristics, sociodemographic factors, and health insurance, was used to estimate the adjusted risk of hospitalization for COVID-19. Results Obesity and type 1 diabetes increased the risk of hospitalization with COVID-19 among both children (5-12 years) and adolescents (13-19 years). Adolescents with morbid obesity were 10 times more likely to have severe COVID-19 (p < 0.001). Regardless of age, single-parent households (%) for the patient's zip code was associated with an increased risk of hospitalization with COVID-19 (AOR = 1.02, CI: 1.01-1.03, p < 0.01). Other risk factors included chronic kidney disease (p < 0.05), male gender (p < 0.001), Medicaid (p < 0.001), and charity care (p < 0.001). Conclusion Pediatric providers and public health officials should consider the need to tailor clinical management and mitigation efforts for pediatric populations with identifiable risk factors for severe COVID-19. These findings can be used to improve risk communication with families of children with underlying medical conditions and to prioritize prevention measures, including vaccinations.","PeriodicalId":12951,"journal":{"name":"Health Services Research and Managerial Epidemiology","volume":"32 1","pages":""},"PeriodicalIF":1.6,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74270692","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}