Analysis Of Aggregate Data Research Articles

Abstract PR015: Novel digital PCR ctDNA monitoring assay evaluating exceptional responders to combination immune-oncology (IO) therapy

Abstract Non-invasive monitoring of ctDNA following curative intent IO therapy has been used to build an understanding of the dynamics of clinical response. Defining clinical response for patients identified with stable disease has always been difficult with imaging. To improve the resolution of ctDNA dynamics, we examined the plasma from a set of exceptional responders to combination IO therapy. These patients did not progress by imaging, however, the measurement of ctDNA showed that there was a persistent level of disease burden throughout the time on study. By evaluating these patients, we gained a comprehensive understanding of the response dynamics including the depth of Molecular Response (MR), the level of molecular persistence and the timing of molecular progression. Current NGS based methods for evaluating ctDNA have been shown to have considerable variability particularly at lower Tumor Fractions (TF). MR dynamics of ctDNA utilize a decrease of 50% TF, which may be related to the accuracy and resolution of assay rather than being a biologically relevant limit. We describe a comprehensive monitoring ctDNA platform using a novel digital PCR assay strategy that achieves a dynamic range not achievable by any current assay. METHODS: Plasma samples from the exceptional responders were evaluated using a novel tumor informed ctDNA platform. Personalized panels, targeting 3-5 variants, were selected from patient tumor data. In total, 225 unique timepoints were extracted (avg 25 timepoints per patient). A novel PCR primer algorithm was designed which showed improved amplification for the individual reactions and optimized multiplexing of the assays. Utilizing machine learning methods, the aggregated data analysis calculates quantitative TF and an accurate percent change between each timepoint. RESULTS: By examining the entire treatment cycle of these patients, we found that the patient timeline separated into molecular response, molecular persistence, and molecular progression. The maximum decrease in TF (molecular response) was found by C4D1 with the largest decrease occurring at either C1D1 or C2D1. Molecular persistence was found in one patient to continue for 55 cycles of treatment (1381 days on trial). Molecular progression occurred in 2 of the 3 exceptional responders. One patient had progression at cycle 12, while the second occurred at cycle 43. Progression occurred very rapidly with the TF doubling from baseline value in 28 days. While patient 1 did see a decrease in the TF for 3 cycles, progression was observed, prompting the end of treatment. To our knowledge this is the first study to evaluate the complete treatment cycle of exceptional responders to IO therapy. The ddPCR assay provided a unique and expansive window into the ctDNA dynamics of treatment suggesting that longitudinal monitoring provides valuable insights. Future studies utilizing this assay will allow for better understanding of the complex tumor dynamics during treatment. Citation Format: Daniel Stetson. Novel digital PCR ctDNA monitoring assay evaluating exceptional responders to combination immune-oncology (IO) therapy [abstract]. In: Proceedings of the AACR Special Conference: Liquid Biopsy: From Discovery to Clinical Implementation; 2024 Nov 13-16; San Diego, CA. Philadelphia (PA): AACR; Clin Cancer Res 2024;30(21_Suppl):Abstract nr PR015.

Logistic Regression Model on Nutritional Status Based on Body Mass Index (BMI) in Women of Reproductive Age (WRA) in Indonesia

The aggregate analysis of Riskesdas data in Indonesia revealed that nearly all provinces face a serious double burden of malnutrition. Both undernutrition and overnutrition negatively affect the quality of human resources, especially when they occur in adolescent girls, Women of Reproductive Age (WRA), and pregnant and breastfeeding women, as this can lead to intergenerational nutritional problems. Body Mass Index (BMI) is a nutritional status measurement technique recommended by WHO. Nutritional problems occur when BMI is abnormal or not ideal, categorized as underweight (<18.5 kg/m²), pre-obesity (25-29.9 kg/m²), obesity class I (30-34.9 kg/m²), obesity class II (35-39.9 kg/m²), and obesity class III (>40 kg/m²). Data from laboratory results completed up to 2017, based on selected samples from the 2013 Basic Health Research (Riskesdas), were used to assess the nutritional status of the Indonesian population. This study used 9,418 respondent data samples from 33 provinces in Indonesia. Logistic regression analysis was applied because the response variable in this study is binary, representing ideal BMI (1) and non-ideal BMI (0). The independent variables used in this study include Hemoglobin level, Ferritin level, CRP level, age group, physical activity, living location, marital status, education, and risky food consumption patterns. All independent variables are categorical. The study found that the factors influencing nutritional status based on BMI in WRA are Hemoglobin level, Ferritin level, CRP level, age, and location. The resulting logit model is logit (π)=0.27695-0.1491 (Non-anemic Hemoglobin level)-0.1721 (Normal Ferritin level)+0.741 (Non-infectious CRP level)-0.2933 (Adult age group)-0.0976 (rural location). logit (π)=0.27695-0.1491 (Non-anemic Hemoglobin level)-0.1721 (Normal Ferritin level)+0.741 (Non-infectious CRP level)-0.2933 (Adult age group)-0.0976 (rural location). The largest odds ratio The largest odds ratio was produced by the CRP level variable, indicating that CRP level is the most influential factor affecting nutritional status based on BMI in WRA, where non infectious CRP compared to infectious CRP increases the likelihood of WRA having an ideal BMI.

Detecting anomalies in currency market of BRICS countries: insights into emerging economies

PurposeThe primary motive of the research paper is to investigate the efficiency of currency market of emerging economies (BRICS countries), by analyzing the presence and consistency of calendar anomalies – specifically the day-of-the-week (DOW) effect, the January effect and the Turn-of-the-month (TOM) effect. Daily data of around 23 years and 4 months were examined for the purpose of study. Segmented data of 8-year intervals were also analyzed to observe the changes in the pattern over a period. The findings are intended to offer insights into market behaviors and potential trading strategies for astute traders.Design/methodology/approachThe study employed a quantitative research design utilizing ordinary least squares regression with dummy variables and Generalized AutoRegressive Conditional Heteroskedasticity (GARCH) (1,1) model. To study the calendar anomalies, the daily closing price of five currency pairs has been taken from Bloomberg’s software. The Kruskal–Wallis test was conducted to validate and check the robustness of analysis.FindingsThere are mixed results regarding calendar anomalies in the BRICS currency markets. Analysis of aggregate data revealed that DOW effect is present in all the currencies except USDZAR, with Monday showing positive return. The segmented analysis showed the presence of DOW effect in USDCNY and USDINR, while other currencies lacked consistent patterns. The January effect was evident for Indian Rupees and Chinese Yuan but not for other currencies. TOM effect was present only in Chinese Yuan. Overall, the Indian Rupee and Chinese Yuan displayed inefficiencies, indicating potential for trading strategies, while other currencies appeared efficient and lacked consistent patterns.Originality/valueThe literature on currency pricing anomalies is sparse, particularly in the context of developing and underdeveloped economies. While some research exists on anomalies within the currencies of developed nations, there is a significant gap in studies focusing on emerging economies. This paper seeks to address this deficiency by investigating the presence of calendar anomalies in the currencies of BRICS countries, providing new insights into the behaviors of these emerging economies.

On aggregation invariance of multinomial processing tree models

Multinomial processing tree (MPT) models are prominent and frequently used tools to model and measure cognitive processes underlying responses in many experimental paradigms. Although MPT models typically refer to cognitive processes within single individuals, they have often been applied to group data aggregated across individuals. We investigate the conditions under which MPT analyses of aggregate data make sense. After introducing the notions of structural and empirical aggregation invariance of MPT models, we show that any MPT model that holds at the level of single individuals must also hold at the aggregate level when it is both structurally and empirically aggregation invariant. Moreover, group-level parameters of aggregation-invariant MPT models are equivalent to the expected values (i.e., means) of the corresponding individual parameters. To investigate the robustness of MPT results for aggregate data when one or both invariance conditions are violated, we additionally performed a series of simulation studies, systematically manipulating (1) the sample sizes in different trees of the model, (2) model parameterization, (3) means and variances of crucial model parameters, and (4) their correlations with other parameters of the respective MPT model. Overall, our results show that MPT parameter estimates based on aggregate data are trustworthy under rather general conditions, provided that a few preconditions are met.

Cholecystectomy and digestive cancer in Chile: Complementary results from interrupted time series and aggregated data analyses.

Gallbladder cancer (GBC) mortality in Chile is among the highest worldwide. In 2006, the Chilean government launched a programme guaranteeing access to gallbladder surgery (cholecystectomy) for patients aged 35-49 years. We evaluated the impact of this programme on digestive cancer mortality. After conducting an interrupted time series analysis of hospitalisation and mortality data from 2002 to 2018 publicly available from the Chilean Department of Health Statistics and Information, we calculated the change in the proportion of individuals without gallbladder since 10 years. We then estimated age, gender, region, and calendar-year standardised mortality ratios (SMRs) as a function of the change in the proportion of individuals without gallbladder. The cholecystectomy rate increased by 45 operations per 100,000 persons per year (95%CI 19-72) after the introduction of the health programme. Each 1% increase in the proportion of individuals without gallbladder since 10 years was associated with a 0.73% decrease in GBC mortality (95% CI -1.05% to -0.38%), but the negative correlation was limited to women, southern Chile and age over 60. We also found decreasing mortality rates for extrahepatic bile duct, liver, oesophageal and stomach cancer with increasing proportions of individuals without gallbladder. To conclude, 12 years after its inception, the Chilean cholecystectomy programme has markedly and heterogeneously changed cholecystectomy rates. Results based on aggregate data indicate a negative correlation between the proportion of individuals without gallbladder and mortality due to gallbladder and other digestive cancers, which requires validation using individual-level longitudinal data to reduce the potential impact of ecological bias.

On the choice of weights in aggregate compositional data analysis

On the choice of weights in aggregate compositional data analysis

Use of levothyroxine for euthyroid, thyroid antibody positive women with infertility: Analyses of aggregate data from a survey of European thyroid specialists (Treatment of Hypothyroidism in Europe by Specialists: An International Survey).

The use of levothyroxine (LT4) treatment aiming to improve fertility in euthyroid women with positive thyroid peroxidase antibodies (TPOAb) is not supported by the available evidence. The aim of the study was to document the use of LT4 by European thyroid specialists in such patients. The data presented derive from Treatment of Hypothyroidism in Europe by Specialists, an International Survey (THESIS), a questionnaire conducted between 2019 and 2021 to document the management of hypothyroidism by European thyroid specialists. Here, we report the aggregate results on the use of LT4 in infertile, euthyroid women with positive TPOAb. A total of 2316/5406 (42.8%) respondents stated that LT4 may be indicated in TPOAb positive euthyroid women with infertility. The proportion of those replying positively to this question varied widely across different countries (median 39.4, range 22.9%-83.7%). In multivariate analyses males (OR: 0.8; CI: 0.7-0.9) and respondents >60 years (OR: 0.7; 0.6-0.8) were the least inclined to consider LT4 for this indication. Conversely, respondents managing many thyroid patients ("weekly" [OR: 1.4; CI: 1.0-1.9], "daily" [OR: 1.8; CI: 1.3-2.4]) and practicing in Eastern Europe (OR: 1.5; CI: 1.3-1.9) were most likely to consider LT4. A remarkably high number of respondents surveyed between 2019 and 2021, would consider LT4 treatment in TPOAb positive euthyroid women with infertility. This view varied widely across countries and correlated with sex, ageand workload, potentially influencing patient management. These results raise concerns about potential risks of overtreatment.

More is better? The role of strategic data management in a lean manufacturing process

Purpose Lean manufacturing has been pivotal in emphasizing the reduction of cycle times, minimizing manufacturing costs and diminishing inventories. This research endeavors to formulate a lean data management paradigm, through the design and execution of a strategic edge-cloud data governance approach. This study aims to discern anomalous or unforeseen patterns within data sets, enabling an efficacious examination of product shortcomings within manufacturing processes, while concurrently minimizing the redundancy associated with the storage, access and processing of nonvalue-adding data. Design/methodology/approach Adopting a lean data management framework within both edge and cloud computing contexts, this study ensures the preservation of significant time series sequences, while ascertaining the optimal quantity of normal time series data to retain. The efficacy of detected anomalous patterns, both at the edge and in the cloud, is assessed. A comparative analysis between traditional data management practices and the strategic edge-cloud data governance approach facilitates an exploration into the equilibrium between anomaly detection and space conservation in cloud environments for aggregated data analysis. Findings Evaluation of the proposed framework through a real-world inspection case study revealed its capability to navigate alternative strategies for harmonizing anomaly detection with data storage efficiency in cloud-based analysis. Contrary to the conventional belief that retaining comprehensive data in the cloud maximizes anomaly detection rates, our findings suggest that a strategic edge-cloud data governance model, which retains a specific subset of normal data, can achieve comparable or superior accuracy with less normal data relative to traditional methods. This approach further demonstrates enhanced space efficiency and mitigates various forms of waste, including temporal delays, storage of noncontributory normal data, costs associated with the analysis of such data and excess data transmission. Originality/value By treating inspected normal data as nonvalue-added, this study probes the intricacies of maintaining an optimal balance of such data in light of anomaly detection performance from aggregated data sets. Our proposed methodology augments existing research by integrating a strategic edge-cloud data governance model within a lean data analytical framework, thereby ensuring the retention of adequate data for effective anomaly detection.

Chickenpox in Poland in 2021.

Chickenpox is a highly contagious disease, but one that can be effectively prevented by vaccination. In Poland, vaccination against the disease is recommended, paid for, and chickenpox remains very common. In recent years, starting in 2002, the upward trend in the incidence of chickenpox has continued, except in 2020. In 2020, there was a decrease in incidence. The aim of this study was to evaluate epidemiological indicators of chickenpox in Poland in 2021 compared to previous years, taking into account the impact of the COVID-19 pandemic. The evaluation of the epidemiological situation of chickenpox in Poland in 2021 was carried out based on the results of the analysis of aggregate data published in the annual bulletins: "Infectious Diseases and Poisons in Poland in 2021" and "Immunization in Poland in 2021". In addition, recommendations from the 2021 Immunization Program are described. 57,669 cases of chickenpox were registered in Poland in 2021, 42% less than in the previous year. The incidence of chickenpox in 2021 was 151.1 per 100,000, which was lower than in 2020, as well as in 2019, when it was 470.6/100,000. The lowest incidence was registered in Lower Silesia Province - 99.2/100,000, while the highest in Silesia Province - 215.8/100,000. The highest incidence was in children aged 0-4 years (18,028). The incidence of chickenpox in males was higher than in females (159.5 vs. 143.3/100 thousand), and urban residents were higher than rural residents (152.1 vs. 149.6/100 thousand). Hospitalization due to chickenpox in 2021 included 210 people, which accounted for 0.36% of the total number of registered cases. In 2021, there was a decrease in the number of chickenpox cases compared to the previous year. The lower incidence may have been the result of a decrease in the transmission of the chickenpox virus, the decrease in the number of cases has to do with, among other things, the restrictions put in place in connection with the COVID-19 pandemic, which result in, among other things, reduced human contact, the wearing of masks and increased social distance.

Reporting of Ethical Considerations in Qualitative Research Utilizing Social Media Data on Public Health Care: Scoping Review.

The internet community has become a significant source for researchers to conduct qualitative studies analyzing users' views, attitudes, and experiences about public health. However, few studies have assessed the ethical issues in qualitative research using social media data. This study aims to review the reportage of ethical considerations in qualitative research utilizing social media data on public health care. We performed a scoping review of studies mining text from internet communities and published in peer-reviewed journals from 2010 to May 31, 2023. These studies, limited to the English language, were retrieved to evaluate the rates of reporting ethical approval, informed consent, and privacy issues. We searched 5 databases, that is, PubMed, Web of Science, CINAHL, Cochrane, and Embase. Gray literature was supplemented from Google Scholar and OpenGrey websites. Studies using qualitative methods mining text from the internet community focusing on health care topics were deemed eligible. Data extraction was performed using a standardized data extraction spreadsheet. Findings were reported using PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews) guidelines. After 4674 titles, abstracts, and full texts were screened, 108 studies on mining text from the internet community were included. Nearly half of the studies were published in the United States, with more studies from 2019 to 2022. Only 59.3% (64/108) of the studies sought ethical approval, 45.3% (49/108) mentioned informed consent, and only 12.9% (14/108) of the studies explicitly obtained informed consent. Approximately 86% (12/14) of the studies that reported informed consent obtained digital informed consent from participants/administrators, while 14% (2/14) did not describe the method used to obtain informed consent. Notably, 70.3% (76/108) of the studies contained users' written content or posts: 68% (52/76) contained verbatim quotes, while 32% (24/76) paraphrased the quotes to prevent traceability. However, 16% (4/24) of the studies that paraphrased the quotes did not report the paraphrasing methods. Moreover, 18.5% (20/108) of the studies used aggregated data analysis to protect users' privacy. Furthermore, the rates of reporting ethical approval were different between different countries (P=.02) and between papers that contained users' written content (both direct and paraphrased quotes) and papers that did not contain users' written content (P<.001). Our scoping review demonstrates that the reporting of ethical considerations is widely neglected in qualitative research studies using social media data; such studies should be more cautious in citing user quotes to maintain user privacy. Further, our review reveals the need for detailed information on the precautions of obtaining informed consent and paraphrasing to reduce the potential bias. A national consensus of ethical considerations such as ethical approval, informed consent, and privacy issues is needed for qualitative research of health care using social media data of internet communities.

Meta-Analysis of Data from Four Clinical Trials in the Ivory Coast Assessing the Efficacy of Two Artemisinin-Based Combination Therapies (Artesunate-Amodiaquine and Artemether-Lumefantrine) between 2009 and 2016.

The combinations of artemether-lumefantrine (AL) and artesunate-amodiaquine (ASAQ) are used as first-line treatments for uncomplicated malaria in the Ivory Coast. Different studies document the efficacy of two artemisinin-based combination therapies (ACTs) (AL and ASAQ) in the Ivory Coast. However, there is no meta-analysis examining the data set of these studies. The purpose of this work was to determine the prevalence of malaria treatment failure cases in randomized control trials with two artemisinin-based combination therapies (AL versus ASAQ) in the Ivory Coast between 2009 to 2016. This study is a meta-analysis of data from the results of four previous multicenter, open-label, randomized clinical trial studies evaluating the clinical and parasitological efficacy of artemether-lumefantrine and artesunate-amodiaquine conducted between 2009 and 2016 following World Health Organization (WHO) protocol at sentinel sites in the Ivory Coast. These drug efficacy data collected between 2009 and 2016 were analyzed. During these studies, to distinguish between recrudescence and new infection, molecular genotyping of genes encoding merozoite surface protein 1 and 2 was carried out using nested polymerase chain reaction (PCR). A total of 1575 patients enrolled in the four studies, including 768 in the AL arm and 762 in the ASAQ arm, which were fully followed either for 28 days or 42 days according to WHO protocol. The adequate clinical and parasitological response (ACPR) was higher than 95% in the two groups (intention to treat (ITT): AL = 96.59% and ASAQ = 96.81; Per Protocol (PP): AL = 99.48% and ASAQ = 99.61%) after PCR correction at day 28. Aggregate data analysis (2009-2016) showed that at day 28, the proportions of patients with recurrent infection was higher in the AL group (ITT: 3.79%, PP: 3.9%) than in the ASAQ group (ITT: 2.17%, PP: 2.23%). After PCR correction, most treatment failures were classified as new infections (AL group (ITT: 0.13%, PP: 0.13%); ASAQ group (ITT: 0.39%, PP: 0.39%). The recrudescent infections rate was high, at 0.39% compared to 0.13% for ASAQ and AL, respectively, for both ITT and PP, no significant difference. However, the Kaplan-Meier curve of cumulative treatment success showed a significant difference between the two groups after PCR from 2012-2013 (p = 0.032). Overall, ASAQ and AL have been shown to be effective drugs for the treatment of uncomplicated P. falciparum malaria in the study areas, 14 years after deployment of these drugs.

Building Interest in the Primary Care Specialty Through Enhanced Global Health Experience.

Background An overwhelming majority of matriculating medical students in the USA are keen to deliver quality health care to all people, including the socioeconomically disadvantaged populations in remote, resource-scarce regions nationally and worldwide. Here, we describe a protocol developed to evaluate the interest of our medical students in global health activities. We also examined the relationship between students' interest in global health and readiness to pursue a future career in the primary care specialty. Materials and methods We designed a survey in Qualtrics online software and reached all first-year and third-year medical students between 2019 and 2022 enrolled at the Alabama College of Osteopathic Medicine (ACOM).The survey utilized ordinal scale items to explore the medical students' interest in primary care residency programs, their interest in global health and international travel, and their perceptions of how a range of factors might motivate their desire to participate in global health activities. The study was approved by ACOM's Institutional Review Board (IRB). In order to compare findings from this study withdata from other medical schools, we developed constructs using the national aggregate data, in percentages, from matriculants and graduates of Doctor of Osteopathic Medicine (DO) degree-granting medical schools according to gender, published by the American Association of Colleges of Osteopathic Medicine (AACOM). Statistical analysis of national aggregate data was performed using the unpaired t-test. Results Both female and maleparticipants had lived or traveled abroad before starting medical school.Female (98%, n=249) and male (95%, n=140) participants in the first-year cohorts considered helping the underserved population as important or very important as it is related to a career in medicine. Females in the third-year cohorts (97%, n=71) also ranked this statement as important or very important compared to male cohorts (89%, n=31). Ahigher proportion of females (43%, n=108) compared to males (35%, n=52) in first-year cohorts agreed or strongly agreed that they would likely pursue a residency in primary care.More females (59%, n=43)than males (46%, n=16)in the third-year cohorts agreed or strongly agreed with the same statement. Analysis of the aggregatenational data (2009-2022) revealed that the percentage (actual count not available) of female students who plannedto practice in underserved/shortage areawas higher both at the time of matriculation (M=51%, SD=4%) and before graduation (M=40%, SD=4%) compared to males (matriculation: M=40%, SD=5%; graduation: M=33%, SD=4%) presenting a significant difference (matriculation t(24)=6.7, p<0.0001; graduation t(24)=5.4, p<0.0001). Furthermore, a higher percentage of females at the time of matriculation (M=25%, SD=5%) and graduation (M=40%, SD=6%) plannedto practice in the primary care specialtiescompared to males (matriculation: M=17%, SD=4%; graduation: M=29%, SD=6%) presenting a significant difference (matriculation: t(24)=4.6, p = 0.0001; graduation: t(24)=4.8, p<0.0001). Conclusions Interest in global health activities may be associated with interest in pursuing a future career in the primary care specialty. In this study, more female medical students expressed interestin participating in global healthexperiences, serving the underserved population domesticallyand abroad, andexpressing interest in primary care than males.

Congenital Heart Surgery Machine Learning-Derived In-Depth Benchmarking Tool

BackgroundWe previously showed that machine learning-based methodologies of optimal classification trees (OCTs) can accurately predict risk after congenital heart surgery and assess case-mix–adjusted performance after benchmark procedures. We extend this methodology to provide interpretable, easily accessible, and actionable hospital performance analysis across all procedures. MethodsThe European Congenital Heart Surgeons Association Congenital Cardiac Database data subset of 172,888 congenital cardiac surgical procedures performed in European centers between 1989 and 2022 was analyzed. OCT models (decision trees) were built predicting hospital mortality (area under the curve [AUC], 0.866), prolonged postoperative mechanical ventilatory support time (AUC, 0.851), or hospital length of stay (AUC, 0.818), thereby establishing case-adjusted benchmarking standards reflecting the overall performance of all participating hospitals, designated as the “virtual hospital.” OCT analysis of virtual hospital aggregate data yielded predicted expected outcomes (both aggregate and for risk-matched patient cohorts) for the individual hospital’s own specific case-mix, readily available on-line. ResultsRaw average rates were hospital mortality, 4.9%; mechanical ventilatory support time, 14.5%; and length of stay, 15.0%. Of 146 participating centers, compared with each hospital’s overall case-adjusted predicted hospital mortality benchmark, 20.5% statistically (<90% CI) overperformed and 20.5% underperformed. An interactive tool based on the OCT analysis automatically reveals 14 hospital-specific patient cohorts, simultaneously assessing overperformance or underperformance, and enabling further analysis of cohort strata in any chosen time frame. ConclusionsMachine learning-based OCT benchmarking analysis provides automatic assessment of hospital-specific case-adjusted performance after congenital heart surgery, not only overall but importantly, also by similar risk patient cohorts. This is a tool for hospital self-assessment, particularly facilitated by the user-accessible online-platform.

Digital Patient Reported Outcome Measures Platform for Post-COVID-19 Condition and Other Long-Term Conditions: User-Centered Development and Technical Description.

Post-COVID-19 condition (PCC), colloquially known as long COVID, is a multisystem condition characterized by persistent symptoms beyond 4 weeks after the SARS-CoV-2 infection. More than 60 million people with PCC worldwide need prompt assessment, diagnosis, and monitoring, with many requiring specialist help from a multidisciplinary team of health care professionals (HCPs). Consequently, a scalable digital system is required for both people with PCC and HCPs to capture the breadth of symptoms and their impact on health, using patient-reported outcome measures (PROMs) and patient-reported experience measures (PREMs). We aim to develop and implement a novel PCC digital PROM (DPROM) platform for (1) securely collecting PROM and PREM data from people with PCC, (2) enabling users to monitor symptoms longitudinally and assess response to treatment, (3) generating reports for the electronic health records (EHRs), (4) providing summary reports on PCC services based on national requirements, and (5) facilitating the sharing of relevant data with authorized research teams to accelerate our understanding of this new condition and evaluate new strategies to manage PCC. We (1) undertook requirement analysis with people with PCC, HCPs, and researchers to identify the needs of the DPROM platform and determine its required functionalities; (2) designed and developed a clinically useful web portal for staff and a mobile app for patients, with a web-based alternative app to improve patient and staff choice, limit the risk of digital exclusion, and account for variability across services; (3) determined the PROMs and PREMs that PCC services would prefer to use on the platform; and (4) designed the summary report function that can be generated for each user for the EHR and for reporting to national health authorities. A DPROM platform to record PCC symptom profile, condition severity, functional disability, and quality of life, based on the C19-YRS (Yorkshire Rehabilitation Scale) and other PROMs and PREMs, was developed. Individual-level medical information and details on the COVID-19 illness can be captured systematically. The platform generates easy-to-understand scores, radar plots and line graphs for people with PCC to self-monitor their condition and for HCPs to assess the natural course of the condition and the response to interventions. Clinics can configure a suite of PROMs and PREMs based on their local and national service and commissioning requirements and support research studies which require large-scale data collection on PROMs. The DPROM platform enables automatic aggregate data analysis for services to undertake service evaluation and cost-effectiveness analysis. The DPROM platform generated summary report can be uploaded to the EHRs of people with PCC. A multifunctional DPROM platform to assess, grade, and monitor PCC has been developed. Future research will analyze the system's usability in specialist PCC clinical services and other long-term conditions.

Gavin Jones’ scholarship on divorce in Asia: understanding trends, patterns, and implications

ABSTRACT This paper highlights Professor Gavin Jones' seminal work on Asian divorce patterns, with a particular focus on Southeast Asia's Muslim communities. Beginning in the 1980s, Jones addressed the challenges of fragmented and often unreliable demographic data on divorce. With careful compilation and analysis of aggregate data, paired with firsthand surveys and ethnographic studies, he set a foundation for examining shifting divorce trends in Southeast Asia. His findings challenged prevailing ethnocentric perspectives and questioned the notion that modernisation invariably leads to rising divorce rates. Jones stressed the importance of understanding divorce within the broader social, cultural, and historical contexts of the respective societies. Beyond his individual research, he was instrumental in cultivating a community of scholars studying marriage and divorce in Asia and fostering collaborations. Jones' insights into Asia's demographic shifts have been invaluable, and his legacy will continue to influence demographers for generations to come.

P-079 Impact of treatment effect on measurable residual disease (MRD) and progression-free survival (PFS): an aggregate data analysis from randomized clinical trials in multiple myeloma (MM)

P-079 Impact of treatment effect on measurable residual disease (MRD) and progression-free survival (PFS): an aggregate data analysis from randomized clinical trials in multiple myeloma (MM)

Impact of treatment effect on MRD and PFS: an aggregate data analysis from randomized clinical trials in multiple myeloma

Impact of treatment effect on MRD and PFS: an aggregate data analysis from randomized clinical trials in multiple myeloma

Enhancing groundwater management using aggregated-data analysis and segmented robust regression: A case study on spatiotemporal changes in water quality

Groundwater quality management, crucial for ensuring sustainable water resources and public health, is the scope of this study. Our objective is to demonstrate the significance of secondary data analysis for the spatiotemporal characterization of groundwater quality. To this end, we develop and employ a robust trend analysis method, in tandem with a spatiotemporal data aggregation method, to accurately identify shifts in groundwater quality over time, even in the face of inflection points or breakpoints. The methods and results reveal diverse trends and characteristics in water quality over space and time across the entire dataset from selected regions in South Korea, emphasizing the importance of analyzing aggregated data beyond individual business locations. The conclusions indicate that this study contributes to the development of more reliable and effective groundwater quality management strategies by addressing gaps in traditional monitoring methods and the challenges of limited monitoring resources and uneven data quality. Future research directions include the application of the developed methods to other regions and data sources, opening avenues for further advances in groundwater quality management.

LABA/LAMA versus LABA/ICS fixed-dose combinations in the prevention of COPD exacerbations: a modeling analysis of literature aggregate data.

This study aimed to quantitatively compare the efficacy and safety of long-acting β2-agonist (LABA)/long-acting muscarinic antagonist (LAMA) and LABA/inhaled corticosteroid (ICS) fixed-dose combinations (FDCs) in preventing moderate or severe chronic obstructive pulmonary disease (COPD) exacerbations. A literature search was performed using public databases. The time course characteristics of the probability of a moderate or severe exacerbation in stable COPD patients treated with LABA/LAMA and LABA/ICS FDCs were described by the parametric survival function. A random-effects model in a single-arm meta-analysis was used to analyze the incidence of serious adverse events (SAEs) and pneumonia. Twenty studies including 23,955 participants were included. The proportion of participants with a history of COPD exacerbation (%) in the previous year and the postbronchodilator forced expiratory volume in the first second (FEV1) (%predicted) were important factors affecting drug efficacy. After adjusting the above factors to median levels of 100% and 45.5%, respectively, the moderate or severe exacerbation rates at 52weeks for olodaterol/tiotropium, formoterol/budesonide, indacaterol/glycopyrronium, formoterol/glycopyrronium, vilanterol/fluticasone, salmeterol/fluticasone, and vilanterol/umeclidinium were 38.3%, 41.0%, 42.6%, 47.0%, 47.5%, 47.9%, and 53.0%, respectively. In terms of safety, significant differences were observed among drugs containing different LABA/LAMA FDCs. This study showed that not all LABA/LAMA FDCs were superior to LABA/ICS FDCs in safety and in preventing moderate or severe exacerbations in patients with stable COPD, providing important quantitative information for COPD-related guidelines.

Partial Pressure of Arterial Oxygen in Healthy Adults at High Altitudes

With increasing altitude, the partial pressure of inspired oxygen decreases and, consequently, the Pao2 decreases. Even though this phenomenon is well known, the extent of the reduction as a function of altitude remains unknown. To calculate an effect size estimate for the decrease in Pao2 with each kilometer of vertical gain among healthy unacclimatized adults and to identify factors associated with Pao2 at high altitude (HA). A systematic search of PubMed and Embase was performed from database inception to April 11, 2023. Search terms included arterial blood gases and altitude. A total of 53 peer-reviewed prospective studies in healthy adults providing results of arterial blood gas analysis at low altitude (<1500 m) and within the first 3 days at the target altitude (≥1500 m) were analyzed. Primary and secondary outcomes as well as study characteristics were extracted from the included studies, and individual participant data (IPD) were requested. Estimates were pooled using a random-effects DerSimonian-Laird model for the meta-analysis. Mean effect size estimates and 95% CIs for reduction in Pao2 at HA and factors associated with Pao2 at HA in healthy adults. All of the 53 studies involving 777 adults (mean [SD] age, 36.2 [10.5] years; 510 men [65.6%]) reporting 115 group ascents to altitudes between 1524 m and 8730 m were included in the aggregated data analysis; 13 of those studies involving 305 individuals (mean [SD] age, 39.8 [13.6] years; 185 men [60.7%]) reporting 29 ascents were included in the IPD analysis. The estimated effect size of Pao2 was -1.60 kPa (95% CI, -1.73 to -1.47 kPa) for each 1000 m of altitude gain (τ2 = 0.14; I2 = 86%). The Pao2 estimation model based on IPD data revealed that target altitude (-1.53 kPa per 1000 m; 95% CI, -1.63 to -1.42 kPa per 1000 m), age (-0.01 kPa per year; 95% CI, -0.02 to -0.003 kPa per year), and time spent at an altitude of 1500 m or higher (0.16 kPa per day; 95% CI, 0.11-0.21 kPa per day) were significantly associated with Pao2. In this systematic review and meta-analysis, the mean decrease in Pao2 was 1.60 kPa per 1000 m of vertical ascent. This effect size estimate may improve the understanding of physiological mechanisms, assist in the clinical interpretation of acute altitude illness in healthy individuals, and serve as a reference for physicians counseling patients with cardiorespiratory disease who are traveling to HA regions.