scholarly journals Unraveling the COVID-19 hospitalization dynamics in Spain using publicly available data

2021 ◽  
Author(s):  
Alberto Aleta ◽  
Juan Luis Blas-Laína ◽  
Gabriel Tirado Anglés ◽  
Yamir Moreno
Keyword(s):  
Economic Status ◽  
Healthcare Services ◽  
Model Parameters ◽  
Health Authorities ◽  
Infection Dynamics ◽  
Bayesian Inference Method ◽  
Different Populations ◽  
Autonomous Regions ◽  
The Impact

SummaryBackgroundOne of the main challenges of the ongoing COVID-19 pandemic is to be able to make sense of available, but often heterogeneous and noisy data, to characterize the evolution of the SARS-CoV-2 infection dynamics, with the additional goal of having better preparedness and planning of healthcare services. This contribution presents a data-driven methodology that allows exploring the hospitalization dynamics of COVID-19, exemplified with a study of 17 autonomous regions in Spain.MethodsWe use data on new daily cases and hospitalizations reported by the Ministry of Health of Spain to implement a Bayesian inference method that allows making short and mid-term predictions of bed occupancy of COVID-19 patients in each of the autonomous regions of the country.FindingsWe show how to use given and generated temporal series for the number of daily admissions and discharges from hospital to reproduce the hospitalization dynamics of COVID-19 patients. For the case-study of the region of Aragon, we estimate that the probability of being admitted to hospital care upon infection is 0·090 [0·086-0·094], (95% C.I.), with the distribution governing hospital admission yielding a median interval of 3·5 days and an IQR of 7 days. Likewise, the distribution on the length of stay produces estimates of 12 days for the median and 10 days for the IQR. A comparison between model parameters for the regions analyzed allows to detect differences and changes in policies of the health authorities.InterpretationThe amount of data that is currently available is limited, and sometimes unreliable, hindering our understanding of many aspects of this pandemic. We have observed important regional differences, signaling that to properly compare very different populations, it is paramount to acknowledge all the diversity in terms of culture, socio-economic status and resource availability. To better understand the impact of this pandemic, much more data, disaggregated and properly annotated, should be made available.

scholarly journals Modeling infection dynamics and mitigation strategies to support K-6 in-person instruction during the COVID-19 pandemic

2021 ◽  
Author(s):  
Douglas E. Morrison ◽  
Roch Nianogo ◽  
Vladimir Manuel ◽  
Onyebuchi A. Arah ◽  
Nathaniel Anderson ◽  
...  
Keyword(s):  
School Attendance ◽  
Secondary Infection ◽  
Mitigation Strategies ◽  
Transmission Dynamics ◽  
Model Parameters ◽  
Safety Education ◽  
Vaccination Rates ◽  
Health Authorities ◽  
Infection Dynamics ◽  
Public Health Authorities

AbstractObjectiveTo support safer in-person K-6 instruction during the coronavirus disease 2019 (COVID- 19) pandemic by providing public health authorities and school districts with a practical model of transmission dynamics and mitigation strategies.MethodsWe developed an agent-based model of infection dynamics and preventive mitigation strategies such as distancing, health behaviors, surveillance and symptomatic testing, daily symptom and exposure screening, quarantine policies, and vaccination. The model parameters can be updated as the science evolves and are adjustable via an online user interface, enabling users to explore the effects of interventions on outcomes of interest to states and localities, under a variety of plausible epidemiological and policy assumptions.ResultsUnder default assumptions, secondary infection rates and school attendance are substantially affected by surveillance testing protocols, vaccination rates, class sizes, and effectiveness of safety education.ConclusionsOur model helps policymakers consider how mitigation options and the dynamics of school infection risks affect outcomes of interest. The model’s parameters can be immediately updated in response to changes in epidemiological conditions, science of COVID-19 transmission dynamics, testing and vaccination resources, and reliability of mitigation strategies.

scholarly journals Analysis of The Impact of The Region Expansion on Economic Growth (Case Study: Expansion of North Toraja Regency from Tana Toraja)

Jurnal Office ◽  
2019 ◽  
Vol 5 (1) ◽  
pp. 1
Author(s):  
Samuel Randy Tapparan ◽  
Abdul Wahab
Keyword(s):  
Economic Growth ◽  
Economic Development ◽  
Data Collection ◽  
T Test ◽  
Regional Government ◽  
The People ◽  
Analysis Technique ◽  
Autonomous Regions ◽  
The Impact

The increasing number of regions proposing themselves to form new autonomous regions since the enactment of Law Number 32 of 2004 concerning "Regional Government", aims to improve economic development and the welfare of the people in each region. The purpose of this study was to analyze the impact of regional expansion on the economic growth of North Toraja Regency. The Technik of data collection in this study uses the documentary Technik, which is in the form of reports from relevant agencies. The analysis technique used is by using the independent sample T-test. The results of the study show that regional expansion has an impact on the economic growth of North Toraja Regency.

scholarly journals The Impact of Social Status on Language Shift: A Case Study on Family Domain Language in Lembar

2019 ◽  
Vol 6 (3) ◽  
pp. 959
Author(s):  
Nazarudin Sodah
Keyword(s):  
Social Status ◽  
Economic Status ◽  
Influencing Factor ◽  
High Standard ◽  
Upper Class ◽  
Network Mobility ◽  
The People ◽  
Job Opportunities ◽  
The Impact

Every society is stratified in different classes and they are mainly measured through economic conditions. Diversity among the people in terms of their position, status, abilities is a very common phenomenon in this world. Age, gender, nationality, ethnicity, power, economy are a few influencing factors which are promoting divisions among the group. This research is about social status which trigers lexicon shifts on nucluer family of lembar society. This aims at finding out factors which lead to lexicon shift as well where the shifts mainly occur. The participants were 20 from low socio-economic status with span of age 20 to 50; no particular gender takes into account. Data obtained from this research clearly shows that peoples’ inclination towards prestigious variety comes after their desire to be upper class like. People’s social network/mobility is one of the influencing factor determines people to shift the language. People who possess good education, job opportunities and wealth obviously influence low economic people to use high standard language.

OP37 Impact On Uncertainty Of Disaggregating Cost Data

2019 ◽  
Vol 35 (S1) ◽  
pp. 8-9
Author(s):  
Conor Teljeur ◽  
Paul Carty ◽  
Máirín Ryan
Keyword(s):  
Cost Utility ◽  
Cost Data ◽  
Model Parameters ◽  
Robot Assisted ◽  
Total Cost ◽  
Uncertainty Set ◽  
The Cost ◽  
The Impact ◽  
Cost Components

IntroductionEconomic models contain several parameters ordinarily subject to uncertainty. Unlike most other model parameters, costs can constitute numerous distinct components. For example, a surgical intervention can require a variety of disposables and reusable equipment. A micro-costing output may be disaggregated or presented as a total cost. Uncertainty could be applied to individual cost components or to total cost. We aimed to explore how disaggregation of cost data may impact on uncertainty using a case study.MethodsA set of simulations using hypothetical scenarios were developed with uncertainty set at ± 20 percent. The simulations investigated the impact of number of cost components, balance between components, and correlation between them. A cost-utility model from an assessment of robot-assisted radical prostatectomy was analyzed; procedure cost was divided into 32 individual cost components or treated as a total cost.ResultsBased on five equal cost components, uncertainty reduces from ± 20 percent for correlated variables to ± 9 percent for uncorrelated variables. With increasing numbers of uncorrelated cost components, the uncertainty in the total cost decreases markedly. The uncertainty around total robot-assisted surgery procedure equipment costs was ± 19.7 percent when components were correlated and ± 9.4 percent when uncorrelated. The impact on uncertainty in the incremental cost effectiveness ratio (ICER) was negligible but the ranking of parameters in the univariate sensitivity analysis changed.ConclusionsAnalyzing uncertainty by aggregated or disaggregated costs can have implications for presenting uncertainty in costs to decision makers. Applying uncertainty to aggregated costs essentially implies that variation in the cost of individual components is perfectly correlated. By disaggregating cost components they are being treated as uncorrelated, which can substantially reduce uncertainty in the total cost. In this case study we found that although the reduction in uncertainty could be clearly seen in the cost parameter, it had a negligible impact on uncertainty in the ICER.

Understanding the Impact of Childhood Sexual Abuse on Women's Sexuality

2012 ◽  
Vol 34 (1) ◽  
pp. 14-37 ◽  
Author(s):  
James Colangelo ◽  
Kathleen Keefe-Cooperman
Keyword(s):  
Sexual Abuse ◽  
Childhood Sexual Abuse ◽  
Mental Health Counselors ◽  
Women's Sexuality ◽  
Negative Consequences ◽  
Women’S Sexuality ◽  
Different Populations ◽  
Therapeutic Services ◽  
The Impact

The relationship between child sexual abuse and adult sexual functioning is well-established. Given the documented high incidence of childhood sexual abuse (CSA) and negative consequences for adult sexuality, many mental health counselors will encounter and provide therapeutic services to members of this population. Counselors must have a good understanding of how sexual victimization during childhood impacts a woman's sexuality and sex life. We discuss the prevalence of CSA among women in different populations and the significant impact it has on women's sexuality. Generalized practice issues are applied using a case study and phase-oriented approach.

The Fit Between Spatial Configuration and Idealized Flows: Mapping Flows in Surgical Facilities as Part of Case Study Visits

2020 ◽  
pp. 193758672092835
Author(s):  
Herminia Machry ◽  
Anjali Joseph ◽  
Deborah Wingler
Keyword(s):  
Design Process ◽  
Spatial Configuration ◽  
Healthcare Services ◽  
Design Teams ◽  
Flow Mapping ◽  
Healthcare Facilities ◽  
Mapping Tool ◽  
Mapping Approach ◽  
The Impact

Purpose: This study proposes a flow mapping approach for surgical facilities that can be implemented by design teams as a component of case study tours. Background: The provision of healthcare services involves simultaneous and closely coupled flows of people, objects, and information, and the efficiency of these flows is influenced by the spatial configuration of the buildings where these services are housed. Many architecture firms conduct case study tours to inform their design process. However, these tours often lack a structured way of documenting different flows and interpreting observations. A structured approach is needed during the design process to understand the impact of spatial configuration on healthcare flows. Method: Site tours were conducted at four surgery centers to develop and test an evidence-based flow mapping approach. Idealized flows within surgical facilities were first identified from the literature, followed by the development of a data collection tool aimed at documenting these flows in each case study through a pre-assessment questionnaire, a physical assessment, and interviews with staff. Results: The flow mapping tool kit was effective in allowing the design team to systematically understand the physical configuration of surgical flows across case studies. The tool also allowed the team to identify spatial configuration characteristics acting as barriers and facilitators to idealized flows. Conclusions: The flow mapping approach was able to provide structure for conducting these short tours more effectively via observations and staff inquiry, enabling design teams to draw more meaningful conclusions from case study tours and conduct comparisons between healthcare facilities visited.

scholarly journals Impact of Artificial Intelligence Research on Politics of the European Union Member States: The Case Study of Portugal

2020 ◽  
Vol 12 (17) ◽  
pp. 6708 ◽  
Author(s):  
João Reis ◽  
Paula Santo ◽  
Nuno Melão
Keyword(s):  
Artificial Intelligence ◽  
European Union ◽  
Data Science ◽  
Healthcare Services ◽  
Future Research ◽  
The European Union ◽  
Multimethod Research ◽  
Holistic Understanding ◽  
The Impact

Currently, artificial intelligence (AI) is at the center of academic and public debate. However, its implications on politics remain little understood. To understand the impact of the AI phenomenon on politics of the European Union (EU), we have carried out qualitative multimethod research by performing a systematic literature review and a case study. The first method was performed according to the preferred reporting items for systematic reviews and meta-analyses (PRISMA), in order to report the state-of-the-art in the existing literature and explore the most relevant research areas. The second method contained contributions from experts in data science and AI of the Portuguese scientific community. The results showed that solutions such as intelligent decision support systems are improving the political decision-making process and impacting the Portuguese society at local, regional, and national levels. We also found that practitioners and scientists are currently shifting their interests from environmental and biological sciences to healthcare services, which is bringing new challenges in terms of protecting patient/citizen data and growing concerns about handling of critical information. Future research may focus on comparative studies with other EU States to obtain a comprehensive and holistic understanding of the AI phenomenon.

scholarly journals When time-dependence in disease outcome risk is not captured by impact evaluation modeling studies: a measles vaccination case study

2021 ◽  
Author(s):  
Allison Portnoy ◽  
Yuli Lily Hsieh ◽  
Kaja Abbas ◽  
Petra Klepac ◽  
Heather Santos ◽  
...  
Keyword(s):  
Vaccine Coverage ◽  
Disease Incidence ◽  
Estimation Methods ◽  
Model Parameters ◽  
Time Varying ◽  
Modeling Studies ◽  
Measles Vaccination ◽  
Secondary Outcomes ◽  
The Impact

Background: In modeling studies that evaluate the effects of health programs, the risk of secondary outcomes attributable to infection can vary with underlying disease incidence. Consequently, the impact of interventions on secondary outcomes would not be proportional to incidence reduction. Here we use a case study on measles vaccine program to demonstrate how failure to capture this non-linear relationship can lead to over- or under-estimation. Methods: We used a published model of measles CFR that depends on incidence and vaccine coverage to illustrate the effects of: (1) assuming higher CFR in 'no-vaccination' scenarios; (2) time-varying CFRs over the past; and (3) time-varying CFRs in future projections on measles impact estimation. We evaluated how different assumptions on vaccine coverage, measles incidence, and CFR levels in 'no-vaccination' scenarios affect estimation of future deaths averted by measles vaccination. Results: Compared to constant CFRs, aligning both 'vaccination' and 'no-vaccination' scenarios with time variant measles CFR estimates led to larger differences in mortality in historical years and lower in future years. Conclusions: To assess consequences of interventions, impact estimates should consider the effect of 'no-intervention' scenario assumptions on model parameters to project estimated impact for alternative scenarios according to intervention strategies and investment decisions.

scholarly journals The impact of accreditation on healthcare quality improvement: a qualitative case study

2016 ◽  
Vol 30 (8) ◽  
pp. 1242-1258 ◽  
Author(s):  
Sara Melo
Keyword(s):  
Patient Safety ◽  
Quality Improvement ◽  
Large Scale ◽  
Positive Impact ◽  
Healthcare Quality ◽  
Healthcare Services ◽  
Content Type ◽  
Healthcare Quality Improvement ◽  
The Impact

Purpose Research on accreditation has mostly focused on assessing its impact using large scale quantitative studies, yet little is known on how quality is improved in practice through an accreditation process. Using a case study of an acute teaching hospital in Portugal, the purpose of this paper is to explore the dynamics through which accreditation can lead to an improvement in the quality of healthcare services provided. Design/methodology/approach Data for the case study was collected through 46 in-depth semi-structured interviews with 49 clinical and non-clinical members of staff. Data were analyzed using a framework thematic analysis. Findings Interviewees felt that hospital accreditation contributed to the improvement of healthcare quality in general, and more specifically to patient safety, as it fostered staff reflection, a higher standardization of practices, and a greater focus on quality improvement. However, findings also suggest that the positive impact of accreditation resulted from the approach the hospital adopted in its implementation as well as the fact that several of the procedures and practices required by accreditation were already in place at the hospital, albeit often in an informal way. Research limitations/implications The study was conducted in only one hospital. The design of an accreditation implementation plan tailored to the hospital’s context can significantly contribute to positive outcomes in terms of quality and patient safety improvements. Originality/value This study provides a better understanding of how accreditation can contribute to healthcare quality improvement. It offers important lessons on the factors and processes that potentiate quality improvements through accreditation.

scholarly journals Missing Diagnoses during the COVID-19 Pandemic: A Year in Review

2021 ◽  
Vol 18 (10) ◽  
pp. 5335
Author(s):  
Héctor Pifarré i Arolas ◽  
Josep Vidal-Alaball ◽  
Joan Gil ◽  
Francesc López ◽  
Catia Nicodemo ◽  
...  
Keyword(s):  
Health System ◽  
Healthcare Services ◽  
Evidence Based ◽  
Administrative Records ◽  
The Public ◽  
Primary Health ◽  
Health Authorities ◽  
Different Types ◽  
Icd 10 ◽  
The Impact

The COVID-19 pandemic has had major impacts on population health not only through COVID-positive cases, but also via the disruption of healthcare services, which in turn has impacted the diagnosis and treatment of all other diseases during this time. We study changes in all new registered diagnoses in ICD-10 groups during 2020 with respect to a 2019 baseline. We compare new diagnoses in 2019 and 2020 based on administrative records of the public primary health system in Central Catalonia, Spain, which cover over 400,000 patients and 3 million patient visits. We study the ratio of new diagnoses between 2019 and 2020 and find an average decline of 31.1% in new diagnoses, with substantial drops in April (61.1%), May (55.6%), and November (52%). Neoplasms experience the largest decline (49.7%), with heterogeneity in the magnitudes of the declines across different types of cancer diagnoses. While we find evidence of temporal variation in new diagnoses, reductions in diagnoses early in the year are not recouped by the year end. The observed decline in new diagnoses across all diagnosis groups suggest a large number of untreated and undetected cases across conditions. Our findings provide a year-end summary of the impact of the pandemic on healthcare activities and can help guide health authorities to design evidence-based plans to target under-diagnosed conditions in 2021.

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