Health Services Research and Managerial Epidemiology最新文献

{"title":"Small Area Variation of Adherence to Clinical Recommendations: An Example from Switzerland.","authors":"Oliver Gruebner,&nbsp;Wenjia Wei,&nbsp;Agne Ulyte,&nbsp;Viktor von Wyl,&nbsp;Holger Dressel,&nbsp;Beat Brüngger,&nbsp;Caroline Bähler,&nbsp;Eva Blozik,&nbsp;Matthias Schwenkglenks","doi":"10.1177/23333928221097741","DOIUrl":"https://doi.org/10.1177/23333928221097741","url":null,"abstract":"<p><strong>Background: </strong>Unwarranted variation in healthcare utilization can only partly be explained by variation in the health care needs of the population, yet it is frequently found globally. This is the first cross-sectional study that systematically assessed geographic variation in the adherence to clinical recommendations in Switzerland. Specifically, we explored 1) the geographic variation of adherence to clinical recommendations across 24 health services at the sub-cantonal level, 2) assessed and mapped statistically significant spatial clusters, and 3) explored possible influencing factors for the observed geographic variation.</p><p><strong>Methods: </strong>Exploratory spatial analysis using the Moran's I statistic on multivariable multilevel model residuals to systematically identify small area variation of adherence to clinical recommendations across 24 health services.</p><p><strong>Results: </strong>Although there was no overall spatial pattern in adherence to clinical recommendations across all health care services, we identified health services that exhibited statistically significant spatial dependence in adherence. For these, we provided evidence about the locations of local clusters.</p><p><strong>Interpretation: </strong>We identified regions in Switzerland in which specific recommended or discouraged health care services are utilized less or more than elsewhere. Future studies are needed to investigate the place-based social determinants of health responsible for the sub-cantonal variation in adherence to clinical recommendations in Switzerland and elsewhere over time.</p>","PeriodicalId":12951,"journal":{"name":"Health Services Research and Managerial Epidemiology","volume":"9 ","pages":"23333928221097741"},"PeriodicalIF":1.6,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9102215/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10242742","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":"Prenatal Ultrasound Utilization and Its Associated Factors among Pregnant Women in Jimma Town Public Health Institutions, Ethiopia","authors":"A. Yetwale, Tola Kabeto, Tsegaw Biyazin, Belete Fenta","doi":"10.1177/23333928221085881","DOIUrl":"https://doi.org/10.1177/23333928221085881","url":null,"abstract":"Background Ultrasound scanning is an integral part of antenatal care worldwide. However, little is known about the utilization of obstetric ultrasound in Ethiopia. This study aimed to assess prenatal ultrasound utilization and its associated factors among pregnant women attending antenatal care in Jimma town public health care facilities. Methods An institutional-based cross-sectional study was conducted on 303 pregnant women attending antenatal care (ANC) from July to August 2021 in Jimma town public health care facilities. A systematic sampling technique was used to select study participants who attended the ANC service during the data collection period. Logistic regression analysis was performed to determine the association between the explanatory and response variables. The strength of association of dependent and independent variables was presented as crude and adjusted odds ratio (AOR) at a 95% confidence interval. The level of significance was declared at a P-value of less than .05 in multivariable logistic regression. Narratives, figures, and tables were used to obtain the results. Findings The proportion of prenatal ultrasound utilization in this study was 60.7% [(95% CI); (55.4%-66%)]. Residency AOR = 6.09 (95%CI: 2.35-15.78), household monthly income less than 1000 AOR = 0.159(0.035-0.73), mother's history of at least one abortion AOR = 5.78 (95% CI: 1.89– 17.64), and knowledge towards prenatal ultrasound AOR = 15.77 (95% CI: 6.39-38.92) were found statistically significant association with prenatal ultrasound utilization. Conclusions In the current study, the proportion of prenatal ultrasound utilization during pregnancy was lower than the world health organization (WHO) recommendation. Therefore, the author recommended that educating mothers on the purposes of obstetric ultrasound and/ or including a prenatal ultrasound screening as part of antenatal care is needed.","PeriodicalId":12951,"journal":{"name":"Health Services Research and Managerial Epidemiology","volume":"72 1","pages":""},"PeriodicalIF":1.6,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88511613","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":"Utilization of Preconception Care and Its Associated Factors among Pregnant Women of West Guji Zone, Oromia, Ethiopia, 2021: A Community-Based Cross-Sectional Study","authors":"Elias Amaje, Anteneh Fikrie, Takala Utura","doi":"10.1177/23333928221088720","DOIUrl":"https://doi.org/10.1177/23333928221088720","url":null,"abstract":"Background Regardless of its benefit in promoting maternal health, contributing to a healthy pregnancy, little is known concerning the prevalence of utilization of preconception care and its determinant in southern Ethiopia. Hence, this study designed to determine the prevalence of utilization of preconception care and contributing factors among pregnant women in West Guji Zone, Southern Ethiopia, 2021. Methods A community-based cross-sectional study was conducted among systematically selected 660 pregnant women in West Guji from June 15 to July 30, 2021. A pretested interviewer-administered structured questionnaire was used to collect the data. Data entry was done in Epidata version3.1 and exported to SPSS version 25 for analysis. Descriptive statistics were used to summarize the data. To identify the factors associated with the utilization of preconception care binary and multivariable logistic regression analysis was performed. Adjusted odds ratios (AOR) with 95% CI were estimated to assess the strength of associations and statistical significance was declared at a p-value < 0.05. Results One hundred-forty seven, 22.3% [95% CI (19.2, 25.4)] of mothers utilized preconception care. Being college and above [(AOR = 5.51 95%CI 91.43-21.19)] and secondary [(AOR = 4.46 95%CI (1.38-14.39)] in educational status, rich [(AOR = 4.23 95%CI (1.32-13.55)], having good knowledge about preconception care [AOR = 2.34 95%CI (1.05-5.28)], having a positive attitude towards preconception care [(AOR = 9.99 95%CI (4.25-23.48)] and deciding with her husband regarding maternal health services [(AOR = 4.71 95%CI (1.91-11.56)] were factors positively affecting utilization of preconception care. Conclusions The utilization of preconception care in the study area is low. Being college and above and secondary in educational status, rich, good knowledge, positive attitude towards preconception care, and deciding with her husband regarding maternal health services were independent factors promoting the utilization of preconception care. Information, education, and communication activities should be strengthened to increase awareness of mothers about preconception care.","PeriodicalId":12951,"journal":{"name":"Health Services Research and Managerial Epidemiology","volume":"9 1","pages":""},"PeriodicalIF":1.6,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74672162","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 Longitudinal Cohort Study of Emergency Room Utilization Following Mifepristone Chemical and Surgical Abortions, 1999-2015.","authors":"James Studnicki, Donna J Harrison, Tessa Longbons, Ingrid Skop, David C Reardon, John W Fisher, Maka Tsulukidze, Christopher Craver","doi":"10.1177/23333928211053965","DOIUrl":"10.1177/23333928211053965","url":null,"abstract":"<p><strong>Introduction: </strong>Existing research on postabortion emergency room visits is sparse and limited by methods which underestimate the incidence of adverse events following abortion. Postabortion emergency room (ER) use since Food and Drug Administration approval of chemical abortion in 2000 can identify trends in the relative morbidity burden of chemical versus surgical procedures.</p><p><strong>Objective: </strong>To complete the first longitudinal cohort study of postabortion emergency room use following chemical and surgical abortions.</p><p><strong>Methods: </strong>A population-based longitudinal cohort study of 423 000 confirmed induced abortions and 121,283 subsequent ER visits occurring within 30 days of the procedure, in the years 1999-2015, to Medicaid-eligible women over 13 years of age with at least one pregnancy outcome, in the 17 states which provided public funding for abortion.</p><p><strong>Results: </strong>ER visits are at greater risk to occur following a chemical rather than a surgical abortion: all ER visits (OR 1.22, CL 1.19-1.24); miscoded spontaneous (OR 1.88, CL 1.81-1.96); and abortion-related (OR 1.53, CL 1.49-1.58). ER visit rates per 1000 abortions grew faster for chemical abortions, and by 2015, chemical versus surgical rates were 354.8 versus 357.9 for all ER visits; 31.5 versus 8.6 for miscoded spontaneous abortion visits; and 51.7 versus 22.0 for abortion-related visits. Abortion-related visits as a percent of total visits are twice as high for chemical abortions, reaching 14.6% by 2015. Miscoded spontaneous abortion visits as a percent of total visits are nearly 4 times as high for chemical abortions, reaching 8.9% of total visits and 60.9% of abortion-related visits by 2015.</p><p><strong>Conclusion: </strong>The incidence and per-abortion rate of ER visits following any induced abortion are growing, but chemical abortion is consistently and progressively associated with more postabortion ER visit morbidity than surgical abortion. There is also a distinct trend of a growing number of women miscoded as receiving treatment for spontaneous abortion in the ER following a chemical abortion.</p>","PeriodicalId":12951,"journal":{"name":"Health Services Research and Managerial Epidemiology","volume":"8 ","pages":"23333928211053965"},"PeriodicalIF":1.6,"publicationDate":"2021-11-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ftp.ncbi.nlm.nih.gov/pub/pmc/oa_pdf/a0/dc/10.1177_23333928211053965.PMC8581786.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10270531","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":"Data Versus Truth in the Midst of the COVID-19 Pandemic.","authors":"Jim Rohrer","doi":"10.1177/2333392820970681","DOIUrl":"https://doi.org/10.1177/2333392820970681","url":null,"abstract":"Perhaps at no time in history has more data about a global pandemic been so rapidly and freely available. Anyone with a computer can download current data and analyze it independently. Several forecasting models have been developed and their differing projections are easily found on government websites. Uncounted scientific articles have been published about the pandemic. Missing from all this information and analysis is frank recognition of the uncertainty in the assumptions upon which data analysis and forecasts are based. State mitigation strategies are based partly on guidance from the Centers for Disease Control and Prevention (CDC) and partly on local politics. The effectiveness of different mitigation strategies is not strongly supported by population-based evidence, yet television news programs constantly bring out ‘experts’ who insist that if we only did this or that, pandemic deaths would have been avoided. Consider the situation in three contiguous states: Iowa, Minnesota, and Wisconsin. Figure 1 shows the latest data from CDC on deaths per 100,000 in those three states compared to the United States as a whole. The first conclusion we reach is that all three states have lower deaths per 100,000 than the US. Wisconsin is the lowest, but a recent surge in cases might cause that line to shift above Minnesota and Iowa. We can only wait and see what happens. However, if Wisconsin moves upward, that will make their trend line even closer to the lines for Minnesota and Iowa. In sum, we might argue that the three states are more similar to each other than they are to the US average. However, the governors of these states have followed different mitigation strategies. Iowa has been the least restrictive of the three. Closures occurred at different rates in different localities. State policy now is to be reopened. In contrast, Minnesota moved more aggressively toward locking down the state and was more cautious about reopening. Wisconsin seems to have been a mixture. They reopened the state but have imposed new restrictions. The politics of the three states explain these differences in policy. Iowa was a GOP state in 2016. Minnesota seems firmly in the blue-state column. Wisconsin is generally classed as a battleground state. Despite their differences in mitigation policy, deaths per hundred thousand in all three states have been lower than the national rate. Why might this be so? Some demographic information is presented in the Table 1. In general, we can safely say that all three states are less urban than the US overall, have lower population densities, and have total populations that are modest. Demographically, they are similar. Quantifying the specific effects of demographic variables on COVID-19 deaths per 100,000 is not yet possible. However, we might wonder if demographics have more to do with pandemic mortality than state government policy. Why might state policies not be as effective in practice than they are in theory? As researchers","PeriodicalId":12951,"journal":{"name":"Health Services Research and Managerial Epidemiology","volume":"7 ","pages":"2333392820970681"},"PeriodicalIF":1.6,"publicationDate":"2020-11-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1177/2333392820970681","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"38626438","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":"Covid19 Outbreak in Victoria, Australia Update August 1, 2020.","authors":"Alberto Boretti","doi":"10.1177/2333392820960350","DOIUrl":"https://doi.org/10.1177/2333392820960350","url":null,"abstract":"<p><p>The Victoria Covid19 outbreak is well explained by the data represented in Figure 1. To August 1, 10,931 have tested positive for a coronavirus after more than 1,633,900 tests were performed. 116 people have died from coronavirus in Victoria. The number of infected, tests performed, their ratio, and the number of fatalities as communicated daily by ¹ are proposed vs. the number of days since May 31st.</p>","PeriodicalId":12951,"journal":{"name":"Health Services Research and Managerial Epidemiology","volume":"7 ","pages":"2333392820960350"},"PeriodicalIF":1.6,"publicationDate":"2020-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1177/2333392820960350","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"38528937","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":"Machine Learning Approach to Predict Risk of 90-Day Hospital Readmissions in Patients With Atrial Fibrillation: Implications for Quality Improvement in Healthcare.","authors":"Man Hung,&nbsp;Eric S Hon,&nbsp;Evelyn Lauren,&nbsp;Julie Xu,&nbsp;Gary Judd,&nbsp;Weicong Su","doi":"10.1177/2333392820961887","DOIUrl":"https://doi.org/10.1177/2333392820961887","url":null,"abstract":"<p><strong>Background: </strong>Atrial fibrillation (AF) in the elderly population is projected to increase over the next several decades. Catheter ablation shows promise as a treatment option and is becoming increasingly available. We examined 90-day hospital readmission for AF patients undergoing catheter ablation and utilized machine learning methods to explore the risk factors associated with these readmission trends.</p><p><strong>Methods: </strong>Data from the 2013 Nationwide Readmissions Database on AF cases were used to predict 90-day readmissions for AF with catheter ablation. Multiple machine learning methods such as k-Nearest Neighbors, Decision Tree, and Support Vector Machine were employed to determine variable importance and build risk prediction models. Accuracy, precision, sensitivity, specificity, and area under the curve were compared for each model.</p><p><strong>Results: </strong>The 90-day hospital readmission rate was 17.6%; the average age of the patients was 64.9 years; 62.9% of patients were male. Important variables in predicting 90-day hospital readmissions in patients with AF undergoing catheter ablation included the age of the patient, number of diagnoses on the patient's record, and the total number of discharges from a hospital. The k-Nearest Neighbor had the best performance with a prediction accuracy of 85%. This was closely followed by Decision Tree, but Support Vector Machine was less ideal.</p><p><strong>Conclusions: </strong>Machine learning methods can produce accurate models in predicting hospital readmissions for patients with AF. The likelihood of readmission to the hospital increases as the patient age, total number of hospital discharges, and total number of patient diagnoses increase. Findings from this study can inform quality improvement in healthcare and in achieving patient-centered care.</p>","PeriodicalId":12951,"journal":{"name":"Health Services Research and Managerial Epidemiology","volume":"7 ","pages":"2333392820961887"},"PeriodicalIF":1.6,"publicationDate":"2020-09-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1177/2333392820961887","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"38518910","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":"Ethics and Economics of the COVID-19 Pandemic in the United States.","authors":"Peter Hilsenrath,&nbsp;Tyrone Borders","doi":"10.1177/2333392820957661","DOIUrl":"https://doi.org/10.1177/2333392820957661","url":null,"abstract":"<p><p>The Covid-19 experience provides a natural experiment in personal and social ethics. Difficult decisions are routinely made to optimize lives and livelihoods. This commentary provides background and insight into the ethical and economic foundations underpinning dilemmas of this historic pandemic.</p>","PeriodicalId":12951,"journal":{"name":"Health Services Research and Managerial Epidemiology","volume":"7 ","pages":"2333392820957661"},"PeriodicalIF":1.6,"publicationDate":"2020-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1177/2333392820957661","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"38527604","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":"Helpfulness of Clinical Visit Summary Content From Multi-Specialty Care: A Mixed-Methods Assessment.","authors":"Priya Ramar,&nbsp;Daniel L Roellinger,&nbsp;Roma F Merrick,&nbsp;Jon O Ebbert,&nbsp;Lindsey M Philpot","doi":"10.1177/2333392820950909","DOIUrl":"https://doi.org/10.1177/2333392820950909","url":null,"abstract":"<p><strong>Objective: </strong>We surveyed patients who visited multiple outpatient specialty practices to understand what summary content was most helpful with the goal of optimizing meaningful outpatient clinical visit summary content.</p><p><strong>Materials and methods: </strong>We constructed a survey instrument to measure delivery, use, and contents of clinical visit summaries. We surveyed patients who visited with at least 2 different outpatient medical specialties to understand preferences.</p><p><strong>Results: </strong>Most patients in our sample valued the summary information they received, and retained it as healthcare documentation (84%) and/or quick reference in supporting self-care (70%). Patients most commonly reported that information on results of completed tests (91%) and treatment plan instructions (89%) were very helpful. Additionally, patients expressed the importance of online access to clinical visit summary information.</p><p><strong>Discussion: </strong>Most patients used the clinical visit summary as healthcare documentation, and valued online availability of their summary information. Patients most often reported that information on results of recently completed tests and specific instructions on treatment plan were very helpful. Patients who sought further information after their visit most often looked to a provider and/or online.</p><p><strong>Conclusions: </strong>Patients valued clinical visit summary accessibility and as a reference tool to summarize care and provide next steps. Optimal clinical visit summaries might collate and integrate assessments and recommendations from multiple specialties into coherent care plans for patients.</p>","PeriodicalId":12951,"journal":{"name":"Health Services Research and Managerial Epidemiology","volume":"7 ","pages":"2333392820950909"},"PeriodicalIF":1.6,"publicationDate":"2020-08-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1177/2333392820950909","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"38374709","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":"Understanding the Difference Between Healthcare Informatics and Healthcare Data Analytics in the Present State of Health Care Management.","authors":"Thomas Wan, Varadraj Gurupur","doi":"","DOIUrl":"","url":null,"abstract":"<p><p>The purpose of this article is to perform a scientific analysis of the definitions associated with healthcare informatics and healthcare data analytics. Additionally, the authors attempt to redefine the scientific pursuit of healthcare informatics and healthcare data analytics. This commentary can assist the thinking of informaticians and data analysts working in healthcare management and practice. The authors also provide a brief insight on the possible future direction of informatics and analytics associated with healthcare.</p>","PeriodicalId":12951,"journal":{"name":"Health Services Research and Managerial Epidemiology","volume":"7 ","pages":"2333392820952668"},"PeriodicalIF":1.6,"publicationDate":"2020-08-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ftp.ncbi.nlm.nih.gov/pub/pmc/oa_pdf/f7/c0/10.1177_2333392820952668.PMC7450285.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"38374707","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}