Health Services Research and Managerial Epidemiology最新文献

Background: Around half of the world's population is infected with Helicobacter pylori (H. pylori), according to data from a recent systematic review. H. pylori infection is extremely common around the world. It is the most prevalent disease in Ethiopia and contributes to both morbidity and mortality. Patients with gastritis, peptic ulcers, and stomach cancer have been reported to harbor H. pylori.

Objective: The aim of this study was to determine the trends of Helicobacter Pylori infection among patients attending the Bule Hora University Teaching Hospital from 2018-2022, Bule Hora, Ethiopia.

Method: A hospital-based retrospective study design was conducted to recruit 314 sampled data from the logbook, which were five-year data (2018-2022) from the Bule Hora University Teaching Hospitals. Data were extracted using structured checklists. The sample size was calculated using the single-population proportion formula. Study participants were selected using a systematic random sampling technique. Data were entered in EpiData 4.6 and exported to SPSS Version 26 for analysis.

Results: Approximately 314 complete data from selected participants were collected and evaluated for the present study. The mean age was 29.01 (SD ± 4.93). Most of the respondents (39.2%) were in the age group of 21 to 30 years. The general prevalence of H. pylori observed in this study is 28% (95% confidence interval [CI], 23-32.9). The prevalence was higher in women (71.6%) than in men (28.4%). In terms of age category, those over 60 years of age were observed with the highest positiveness for H. pylori with 38.1% and the trend of H. pylori prevalence fluctuated from 2018 to 2022.

Conclusion: In total, 28% of the study participants had H. pylori, but there was variation in the prevalence of H. pylori infection between 2018 and 2022. Compared to other age groups, the 60-year-old age group had a higher prevalence of H. pylori and this prevalence continued to increase annually. The concerned parties must be interested in raising awareness and establishing criteria for the eradication of these bacteria.

Background: Self-scheduling of medical visits is becoming more common but the complexity of applying multiple requirements for self-scheduling has hampered implementation. Mayo Clinic implemented self-scheduling in 2019 and has been increasing its portfolio of self-schedulable visits since then. Our aim was to show measures quantifying the complexity associated with medical visit scheduling and to describe how opportunities and challenges of scheduling complexity apply in self-scheduling.

Methods: We examined scheduled visits from January 1, 2022, through August 24, 2023. For seven visit categories, we counted all unique visit types that were scheduled, for both staff-scheduled and self-scheduled. We examined counts of self-scheduled visit types to identify those with highest uptake during the study period.

Results: There were 9555 unique visit types associated with 20.8 M (million) completed visits. Self-scheduled visit types accounted for 4.0% (838,592/20,769,699) of the completed total visits. Of seven visit categories, self-scheduled established patient visits, testing visits, and procedure visits accounted for 93.5% (784,375/838,592) of all self-scheduled visits. Established patient visits in primary care (10 visit types) accounted for 273,007 (32.6%) of all self-scheduled visits. Testing visits (blood and urine testing, 2 visit types) accounted for 183,870 (21.9%) of all self-scheduled visits. Procedure visits for screening mammograms, bone mineral density, and immunizations (8 visit types) accounted for 147,358 (17.6%) of all self-scheduled visits.

Conclusion: Large numbers of unique visit types comprise a major challenge for self-scheduling. Some visit types are more suitable for self-scheduling. Guideline-based procedure visits such as screening mammograms, bone mineral density exams, and immunizations are examples of visits that have high volumes and can be standardized for self-scheduling. Established patient visits and laboratory testing visits also can be standardized for self-scheduling. Despite the successes, there remain thousands of specific visit types that may need some staff-scheduler intervention to properly schedule.

Background: Self-scheduling of medical visits is becoming available at many medical institutions. We aimed to examine the self-scheduled visit counts and rate of growth of self-scheduled visits in a multispecialty practice.

Methods: For 85 weeks extending from January 1, 2022 through August 24, 2023, we examined self-scheduled visit counts for over 1500 self-scheduled visit types. We compared completed self-scheduled visit counts to all scheduled completed visit counts for the same visit types. We collected counts of the most frequently self-scheduled visit types for each week and examined the change over time. We also determined the proportion that each visit type was self-scheduled.

Results: There were 20,769 699 completed visits during the course of the study that met the criteria for inclusion. Self-scheduled visits accounted for 4.0% of all completed visits (838 592/20,769 699). Over the 85-week span, self-scheduled visits rose from 3.0% to 5.3% of the total. There were 1887 unique visit types that were associated with completed visits. There were just 6 appointment visit types of the total 1887 self-scheduled visit types that accounted for 50.7% of the total 838 592 self-scheduled visits. Those 6 visit types were a lab blood test visit (19.5%, 163 K visits), two Family Medicine office visit types (13.0%, 109 K visits), a screening mammogram visit type (6.6%, 55 K visits), a scheduled express care visit type (6%, 50 K visits) and a COVID immunization visit type (5.7%, 48 K visits). Twenty-one visit types that were self-scheduled accounted for 75% of the total self-scheduled visits. Four seasonal visits, accounting for 10.6% of the total self-scheduled visits, were responsible for almost all the non-linear change in self-scheduling.

Conclusion: Self-scheduling accounted for a small but growing percent of all outpatient scheduled visits in a multispecialty, multisite practice. A wide range of visit types can be successfully self-scheduled.

Objectives: The Kihon Checklist (KCL) is valuable for predicting long-term care (LTC) certification. However, the precise association between KCL scores and the temporal dynamics of LTC need certification remains unclear. This study clarified the characteristic trajectory of KCL scores in individuals certified for LTC need.

Methods: The KCL scores spanning from 2011 to 2019 were obtained from 5630 older individuals, including those certified for LTC need in November 2020, in Iiyama City, Nagano, Japan. We analyzed the KCL score trajectories using a linear mixed model, both before and after propensity score matching.

Results: Throughout the 9-year observation period, the KCL scores consistently remained higher in the certified group compared to the non-certified group. Notably, a significant score increase occurred within the 3 years preceding LTC certification.

Discussion: Our findings highlight the effectiveness of continuous surveillance using the KCL in identifying individuals likely to require LTC within a few years.

Introduction: The volume of prescriptions for proton pump inhibitors (PPIs) remains high, although the literature increasingly points to excessive prescribing in relation to guideline recommendations. No very recent data is available on the specific situation in Germany, particularly on the proportion of PPI consumption from over-the-counter (OTC) sales and self-selection, following PPI down-scheduling. The aim of this study was to determine the actual amount of prescribed and OTC PPIs in Germany.

Methods: For this retrospective study, several IQVIA databases were used, representing all prescriptions billed to statutory and private health insurers in Germany, as well as OTC sales. Analyses were performed for the period November 2020 to October 2021 or partially November 2018 to October 2021 and were descriptive in nature. Mainly, data were collected from IQVIATM PharmaScope National® as well as IQVIA TM DPM® databases.

Results: A total of 2.87 billion PPI tablets were shown to have been sold between November 2020 and October 2021, with most drugs prescribed in the largest packages and strengths. In addition, the OTC PPI market increased by an average of 14% per year over a 3-year period.

Conclusions: The results of this study suggest the substantial size of the PPI market in Germany is based on prescriptions, a consistent increase in OTC PPI purchases and a recent increase in prescriptions.

Introduction: The use of artificial intelligence (AI), which can emulate human intelligence and enhance clinical results, has grown in healthcare decision-making due to the digitalization effects and the COVID-19 pandemic. The purpose of this study was to determine the scope of applications of AI tools in the decision-making process in healthcare service delivery networks.

Materials and methods: This study used a qualitative method to conduct a systematic review of the existing reviews. Review articles published between 2000 and 2024 in English-language were searched in PubMed, Scopus, ProQuest, and Cochrane databases. The CASP (Critical Appraisal Skills Programme) Checklist for Systematic Reviews was used to evaluate the quality of the articles. Based on the eligibility criteria, the final articles were selected and the data extraction was done independently by 2 authors. Finally, the thematic analysis approach was used to analyze the data extracted from the selected articles.

Results: Of the 14 219 identified records, 18 review articles were eligible and included in the analysis, which covered the findings of 669 other articles. The quality assessment score of all reviewed articles was high. And, the thematic analysis of the data identified 3 main themes including clinical decision-making, organizational decision-making, and shared decision-making; which originated from 8 subthemes.

Conclusions: This study revealed that AI tools have been applied in various aspects of healthcare decision-making. The use of AI can improve the quality, efficiency, and effectiveness of healthcare services by providing accurate, timely, and personalized information to support decision-making. Further research is needed to explore the best practices and standards for implementing AI in healthcare decision-making.

Background: Over the past few years, a growing number of studies have explored massage robots. However, to date, a dedicated systematic review focused solely on robot-assisted massage has not been conducted.

Objective: To systematically identify and summarize evidence from studies concerning robot-assisted massage in healthcare settings.

Methods: An extensive literature search, involving electronic databases Ovid and Scopus, was conducted from the inception of the databases up to March 2023. This systematic review adhered to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses statement, and relevant papers were chosen based on the predefined inclusion criteria. Given the substantial methodological diversity among the included studies, a qualitative analysis was conducted.

Results: Seventeen studies met the inclusion criteria, comprising 15 preliminary trials, one quasi-experimental study, and one randomized controlled trial. Approximately 29% of the studies focused on the application of robotic massage for patients, 24% targeted both healthy volunteers and patients, and the remaining 47% were preclinical trials assessing the effectiveness of robotic massage solely on healthy volunteers. Primary interventions included robotic massage for oral rehabilitation, scalp massage, low back massage, shoulder massage, and full-body massage. All studies provided evidence that robotic massage interventions can enhance health and well-being, indicating a promising future for the integration of robotics in the field of massage therapy.

Conclusions: In general, robotic massage interventions offer physical and mental health benefits. Robot-assisted massage may be integrated into care provision as an adjunct to enhance human well-being. Nonetheless, further research is needed to confirm these findings.

The study aimed at applying Multivariate Generalized Linear Mixed Models to examine factors associated with correlation outcomes, in particular, anthropometric measurements among under-five children in Tanzania. Three anthropometric measurements: weight-for-age (WAZ), height-for-age (HAZ), and weight-for-height (WHZ) among under-five children in Tanzania were jointly modeled to identify common factors associated with childhood malnutrition. A total of 9052 children with valid measures of height and weight were processed and analyzed. The results indicate that WAZ was correlated with HAZ (P-value < 2e-16) and WHZ (P-value < 2e-16). The Multivariate Ordered Logit Model has lower AIC = 53213.92 and BIC = 52727.95, indicating better model fit than the Multivariate Ordered Probit Model. In Tanzania, the age of the child, birth order, mother education level, child gender, mother working status, wealth index, marital status, and mother body mass index are important determinants of malnutrition among children under the age of five. Moreover, the common factors were child's age, Birth order, Mother's education attainment, child's sex, Mother working status, wealth index, Marital status, and Mother's Body Mass Index. As a result, emphasis should be placed on analyzing correlated health outcomes in order to draw conclusions about the factors that may have a mutual effect on anthropometric measurements.

[This retracts the article DOI: 10.1177/23333928211053965.][This retracts the article DOI: 10.1177/23333928221103107.][This retracts the article DOI: 10.1177/2333392819841211.].