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Waqas M, Xu SH, Hussain S, Aslam MU. Control charts in healthcare quality monitoring: a systematic review and bibliometric analysis. Int J Qual Health Care 2024; 36:mzae060. [PMID: 39018022 DOI: 10.1093/intqhc/mzae060] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/03/2023] [Revised: 06/21/2024] [Accepted: 07/16/2024] [Indexed: 07/18/2024] Open
Abstract
Control charts, used in healthcare operations to monitor process stability and quality, are essential for ensuring patient safety and improving clinical outcomes. This comprehensive research study aims to provide a thorough understanding of the role of control charts in healthcare quality monitoring and future perspectives by utilizing a dual methodology approach involving a systematic review and a pioneering bibliometric analysis. A systematic review of 73 out of 223 articles was conducted, synthesizing existing literature (1995-2023) and revealing insights into key trends, methodological approaches, and emerging themes of control charts in healthcare. In parallel, a bibliometric analysis (1990-2023) on 184 articles gathered from Web of Science and Scopus was performed, quantitatively assessing the scholarly landscape encompassing control charts in healthcare. Among 25 countries, the USA is the foremost user of control charts, accounting for 33% of all applications, whereas among 14 health departments, epidemiology leads with 28% of applications. The practice of control charts in health monitoring has increased by more than one-third during the last 3 years. Globally, exponentially weighted moving average charts are the most popular, but interestingly the USA remained the top user of Shewhart charts. The study also uncovers a dynamic landscape in healthcare quality monitoring, with key contributors, research networks, research hotspot tendencies, and leading countries. Influential authors, such as J.C. Benneyan, W.H. Woodall, and M.A. Mohammed played a leading role in this field. In-countries networking, USA-UK leads the largest cluster, while other clusters include Denmark-Norway-Sweden, China-Singapore, and Canada-South Africa. From 1990 to 2023, healthcare monitoring evolved from studying efficiency to focusing on conditional monitoring and flowcharting, with human health, patient safety, and health surveys dominating 2011-2020, and recent years emphasizing epidemic control, COronaVIrus Disease of 2019 (COVID-19) statistical process control, hospitals, and human health monitoring using control charts. It identifies a transition from conventional to artificial intelligence approaches, with increasing contributions from machine learning and deep learning in the context of Industry 4.0. New researchers and journals are emerging, reshaping the academic context of control charts in healthcare. Our research reveals the evolving landscape of healthcare quality monitoring, surpassing traditional reviews. We uncover emerging trends, research gaps, and a transition in leadership from established contributors to newcomers amidst technological advancements. This study deepens the importance of control charts, offering insights for healthcare professionals, researchers, and policymakers to enhance healthcare quality. Future challenges and research directions are also provided.
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Affiliation(s)
- Muhammad Waqas
- School of Mathematics and Statistics, Xi'an Jiaotong University, XJTU, Xian, Shaanxi 710049, China
- Department of Statistics, University of Wah, Taxila, Punjab 47040, Pakistan
| | - Song Hua Xu
- Department of Health Management & Institute of Medical Artificial Intelligence, The Second Affiliated Hospital, Xi'an Jiaotong University, XJTU, Xian, Shaanxi 710049, China
- Department of Computer Science, Yale University, New Haven, CT 06511, United States
| | - Sajid Hussain
- School of Mathematics and Statistics, Xi'an Jiaotong University, XJTU, Xian, Shaanxi 710049, China
| | - Muhammad Usman Aslam
- School of Mathematics and Statistics, Xi'an Jiaotong University, XJTU, Xian, Shaanxi 710049, China
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Waqas M, Xu SH, Usman Aslam M, Hussain S, Shahzad K, Masengo G. Global contribution of statistical control charts to epidemiology monitoring: A 23-year analysis with optimized EWMA real-life application on COVID-19. Medicine (Baltimore) 2024; 103:e38766. [PMID: 38968501 PMCID: PMC11224875 DOI: 10.1097/md.0000000000038766] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/23/2024] [Accepted: 06/10/2024] [Indexed: 07/07/2024] Open
Abstract
Control charts help epidemiologists and healthcare professionals monitor disease incidence and prevalence in real time, preventing outbreaks and health emergencies. However, there remains a notable gap in the comprehensive exploration and application of these techniques, particularly in the context of monitoring and managing disease outbreaks. This study analyses and categorizes worldwide control chart applications from 2000 to 2023 in outbreak monitoring in over 20 countries, focusing on corona-virus (COVID-19), and chooses optimal control charts for monitoring US COVID-19 death waves from February 2020 to December 2023. The systematic literature review analyzes available 35 articles, categorizing data by year, variable, country, study type, and chart design. A selected optimal chart is applied to monitor COVID-19 death patterns and waves in the USA. Control chart adoption in epidemiology monitoring increased during the COVID-19 pandemic, with annual patterns showing a rise in 2021 to 2023 (18%, 36%, 41%). Important variables from 2000 to 2019 include influenza counts, Salmonella cases, and infection rates, while COVID-19 studies focus more on cases, infection rates, symptoms, and deaths. Among 22 countries, the USA (29%) is the top applier of control charts. The monitoring of USA COVID-19 deaths reveals 8 waves with varying severity > > > > > > > . The associated with the JN.1 variant, highlights ongoing challenges. This study emphasizes the significance of control charts in outbreak monitoring for early disease diagnosis and intervention. Control charts help healthcare workers manage epidemics using data-driven methods, improving public health. COVID-19 mortality analysis emphasizes their importance, encouraging worldwide use.
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Affiliation(s)
- Muhammad Waqas
- Department of Statistics, School of Mathematics and Statistics, Xian Jiaotong University, Xian, China
- Department of Statistics, University of WAH, Pakistan
| | - Song Hua Xu
- Department of Health Management & Institute of Medical Artificial Intelligence, The Second Affiliated Hospital, Xi’an Jiaotong University, Xian, China
- Yale University, New Haven, CT
| | - Muhammad Usman Aslam
- Department of Statistics, School of Mathematics and Statistics, Xian Jiaotong University, Xian, China
| | - Sajid Hussain
- Department of Statistics, School of Mathematics and Statistics, Xian Jiaotong University, Xian, China
| | - Khurram Shahzad
- SysReforms International, Department Health Monitoring, Pakistan
- Monitoring and Evaluation Department, Chemonics International Inc., Islamabad, Pakistan
| | - Gilbert Masengo
- Department of Mechanical Engineering, Rwanda Polytechnic/Integrated Polytechnic Regional College Karongi, Kigali, Rwanda
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Chukhrova N, Plate O, Johannssen A. Monitoring epidemic processes under political measures. Stat Med 2024; 43:2122-2160. [PMID: 38487994 DOI: 10.1002/sim.10042] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/24/2022] [Revised: 01/11/2024] [Accepted: 02/06/2024] [Indexed: 05/18/2024]
Abstract
Statistical modeling of epidemiological curves to capture the course of epidemic processes and to implement a signaling system for detecting significant changes in the process is a challenging task, especially when the process is affected by political measures. As previous monitoring approaches are subject to various problems, we develop a practical and flexible tool that is well suited for monitoring epidemic processes under political measures. This tool enables monitoring across different epochs using a single statistical model that constantly adapts to the underlying process, and therefore allows both retrospective and on-line monitoring of epidemic processes. It is able to detect essential shifts and to identify anomaly conditions in the epidemic process, and it provides decision-makers a reliable method for rapidly learning from trends in the epidemiological curves. Moreover, it is a tool to evaluate the effectivity of political measures and to detect the transition from pandemic to endemic. This research is based on a comprehensive COVID-19 study on infection rates under political measures in line with the reporting of the Robert Koch Institute covering the entire period of the pandemic in Germany.
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Affiliation(s)
- Nataliya Chukhrova
- Faculty of Engineering, The Maersk Mc-Kinney Moller Institute, University of Southern Denmark, Odense, Denmark
| | - Oskar Plate
- Faculty of Business Administration, University of Hamburg, Hamburg, Germany
| | - Arne Johannssen
- Faculty of Business Administration, University of Hamburg, Hamburg, Germany
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Cildoz M, Gaston M, Frias L, Garcia-Vicuña D, Azcarate C, Mallor F. Early detection of new pandemic waves. Control chart and a new surveillance index. PLoS One 2024; 19:e0295242. [PMID: 38346027 PMCID: PMC10861055 DOI: 10.1371/journal.pone.0295242] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2023] [Accepted: 11/20/2023] [Indexed: 02/15/2024] Open
Abstract
The COVID-19 pandemic highlights the pressing need for constant surveillance, updating of the response plan in post-peak periods and readiness for the possibility of new waves of the pandemic. A short initial period of steady rise in the number of new cases is sometimes followed by one of exponential growth. Systematic public health surveillance of the pandemic should signal an alert in the event of change in epidemic activity within the community to inform public health policy makers of the need to control a potential outbreak. The goal of this study is to improve infectious disease surveillance by complementing standardized metrics with a new surveillance metric to overcome some of their difficulties in capturing the changing dynamics of the pandemic. At statistically-founded threshold values, the new measure will trigger alert signals giving early warning of the onset of a new pandemic wave. We define a new index, the weighted cumulative incidence index, based on the daily new-case count. We model the infection spread rate at two levels, inside and outside homes, which explains the overdispersion observed in the data. The seasonal component of real data, due to the public surveillance system, is incorporated into the statistical analysis. Probabilistic analysis enables the construction of a Control Chart for monitoring index variability and setting automatic alert thresholds for new pandemic waves. Both the new index and the control chart have been implemented with the aid of a computational tool developed in R, and used daily by the Navarre Government (Spain) for virus propagation surveillance during post-peak periods. Automated monitoring generates daily reports showing the areas whose control charts issue an alert. The new index reacts sooner to data trend changes preluding new pandemic waves, than the standard surveillance index based on the 14-day notification rate of reported COVID-19 cases per 100,000 population.
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Affiliation(s)
- Marta Cildoz
- Institute of Smart Cities, Public University of Navarre, Campus Arrosadia, Pamplona, Spain
| | - Martin Gaston
- Institute of Smart Cities, Public University of Navarre, Campus Arrosadia, Pamplona, Spain
| | - Laura Frias
- Institute of Smart Cities, Public University of Navarre, Campus Arrosadia, Pamplona, Spain
| | - Daniel Garcia-Vicuña
- Institute of Smart Cities, Public University of Navarre, Campus Arrosadia, Pamplona, Spain
| | - Cristina Azcarate
- Institute of Smart Cities, Public University of Navarre, Campus Arrosadia, Pamplona, Spain
| | - Fermin Mallor
- Institute of Smart Cities, Public University of Navarre, Campus Arrosadia, Pamplona, Spain
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Stanzler M, Figueroa J, Beck AF, McPherson ME, Miff S, Penix H, Little J, Sampath B, Barker P, Hartley DM. Learning from an equitable, data-informed response to COVID-19: Translating knowledge into future action and preparation. Learn Health Syst 2024; 8:e10369. [PMID: 38249853 PMCID: PMC10797568 DOI: 10.1002/lrh2.10369] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2022] [Revised: 04/04/2023] [Accepted: 04/04/2023] [Indexed: 01/23/2024] Open
Abstract
Introduction The COVID-19 pandemic revealed numerous barriers to effectively managing public health crises, including difficulties in using publicly available, community-level data to create learning systems in support of local public health decision responses. Early in the COVID-19 pandemic, a group of health care partners began meeting to learn from their collective experiences. We identified key tools and processes for using data and learning system structures to drive equitable public health decision making throughout different phases of the pandemic. Methods In fall of 2021, the team developed an initial theory of change directed at achieving herd immunity for COVID-19. The theoretical drivers were explored qualitatively through a series of nine 45-min telephonic interviews conducted with 16 public health and community leaders across the United States. Interview responses were analyzed into key themes to inform potential future practices, tools, and systems. In addition to the interviews, partners in Dallas and Cincinnati reflected on their own COVID-19 experiences. Results Interview responses fell broadly into four themes that contribute to effective, community driven responses to COVID-19: real-time, accessible data that are mindful of the tension between community transparency and individual privacy; a continued fostering of public trust; adaptable infrastructures and systems; and creating cohesive community coalitions with shared alignment and goals. These themes and partner experiences helped us revise our preliminary theory of change around the importance of community collaboration and trust building and also helped refine the development of the Community Protection Dashboard tool. Conclusions There was broad agreement amongst public health and community leaders about the key elements of the data and learning systems required to manage public health responses to COVID-19. These findings may be informative for guiding the use of data and learning in the management of future public health crises or population health initiatives.
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Affiliation(s)
| | | | - Andrew F. Beck
- Cincinnati Children's Hospital Medical CenterCincinnatiOhioUSA
- University of Cincinnati College of MedicineCincinnatiOhioUSA
| | | | - Steve Miff
- Parkland Center for Clinical Innovation (PCCI)DallasTexasUSA
| | | | | | | | - Pierre Barker
- Institute for Healthcare ImprovementBostonMassachusettsUSA
| | - David M. Hartley
- Cincinnati Children's Hospital Medical CenterCincinnatiOhioUSA
- University of Cincinnati College of MedicineCincinnatiOhioUSA
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Kandeel A, Moatasim Y, Fahim M, Bahaaeldin H, El-Shesheny R, Roshdy WH, Kamel MN, Shawky S, Gomaa M, Naguib A, Guindy NE, Deghedy O, Kamel R, Khalifa M, Galal R, Hassany M, Mahmoud G, Kandeil A, Afifi S, Mohsen A, Fattah MA, Kayali G, Ali MA, Abdelghaffar K. Comparison of SARS-Cov-2 omicron variant with the previously identified SARS-Cov-2 variants in Egypt, 2020-2022: insight into SARS-Cov-2 genome evolution and its impact on epidemiology, clinical picture, disease severity, and mortality. BMC Infect Dis 2023; 23:542. [PMID: 37596534 PMCID: PMC10439637 DOI: 10.1186/s12879-023-08527-y] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/23/2023] [Accepted: 08/10/2023] [Indexed: 08/20/2023] Open
Abstract
BACKGROUND The o severe acute respiratory coronavirus 2 (SARS-CoV-2) pandemic has killed millions of people and caused widespread concern around the world. Multiple genetic variants of SARS-CoV-2 have been identified as the pandemic continues. Concerns have been raised about high transmissibility and lower vaccine efficacy against omicron. There is an urgent need to better describe how omicron will impact clinical presentation and vaccine efficacy. This study aims at comparing the epidemiologic, clinical, and genomic characteristics of the omicron variant prevalent during the fifth wave with those of other VOCs between May 2020 and April 2022. METHODS Epidemiological data were obtained from the National Electronic Diseases Surveillance System. Secondary data analysis was performed on all confirmed COVID-19 patients. Descriptive data analysis was performed for demographics and patient outcome and the incidence of COVID-19 was calculated as the proportion of SARS-CoV-2 confirmed patients out of the total population of Egypt. Incidence and characteristics of the omicron cohort from January- April 2022, were compared to those confirmed from May 2020-December 2021. We performed the whole-genome sequencing of SARS-CoV-2 on 1590 specimens using Illumina sequencing to describe the circulation of the virus lineages in Egypt. RESULTS A total of 502,629 patients enrolled, including 60,665 (12.1%) reported in the fifth wave. The incidence rate of omicron was significantly lower than the mean of incidences in the previous subperiod (60.1 vs. 86.3/100,000 population, p < 0.001). Symptoms were reported less often in the omicron cohort than in patients with other variants, with omicron having a lower hospitalization rate and overall case fatality rate as well. The omicron cohort tended to stay fewer days at the hospital than did those with other variants. We analyzed sequences of 2433 (1590 in this study and 843 were obtained from GISAID platform) Egyptian SARS-CoV-2 full genomes. The first wave that occurred before the emergence of global variants of concern belonged to the B.1 clade. The second and third waves were associated with C.36. Waves 4 and 5 included B.1.617.2 and BA.1 clades, respectively. CONCLUSIONS The study indicated that Omicron-infected patients had milder symptoms and were less likely to be hospitalized; however, patients hospitalized with omicron had a more severe course and higher fatality rates than those hospitalized with other variants. Our findings demonstrate the importance of combining epidemiological data and genomic analysis to generate actionable information for public health decision-making.
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Affiliation(s)
- Amr Kandeel
- Preventive Sector, Ministry of Health and Population, Cairo, Egypt
| | - Yassmin Moatasim
- Centre of Scientific Excellence for Influenza Viruses, National Research Centre, Dokki, Giza, 12622, Egypt
| | - Manal Fahim
- Department of Epidemiology and Surveillance, Preventive Sector, Ministry of Health and Population, Cairo, Egypt
| | - Hala Bahaaeldin
- Department of Epidemiology and Surveillance, Preventive Sector, Ministry of Health and Population, Cairo, Egypt.
| | - Rabeh El-Shesheny
- Centre of Scientific Excellence for Influenza Viruses, National Research Centre, Dokki, Giza, 12622, Egypt
| | - Wael H Roshdy
- Central Public Health Laboratory, Ministry of Health and Population, Cairo, Egypt
| | - Mina N Kamel
- Centre of Scientific Excellence for Influenza Viruses, National Research Centre, Dokki, Giza, 12622, Egypt
| | - Shaymaa Shawky
- Central Public Health Laboratory, Ministry of Health and Population, Cairo, Egypt
| | - Mokhtar Gomaa
- Centre of Scientific Excellence for Influenza Viruses, National Research Centre, Dokki, Giza, 12622, Egypt
| | - Amel Naguib
- Central Public Health Laboratory, Ministry of Health and Population, Cairo, Egypt
| | - Nancy El Guindy
- Central Public Health Laboratory, Ministry of Health and Population, Cairo, Egypt
| | - Ola Deghedy
- Department of Epidemiology and Surveillance, Preventive Sector, Ministry of Health and Population, Cairo, Egypt
| | - Reham Kamel
- Department of Epidemiology and Surveillance, Preventive Sector, Ministry of Health and Population, Cairo, Egypt
| | - Mohamed Khalifa
- Central Public Health Laboratory, Ministry of Health and Population, Cairo, Egypt
| | - Ramy Galal
- Public Health Initiatives, Cairo, 11613, Egypt
| | - Mohamed Hassany
- National Hepatology and Tropical Medicine Research Institute, Ministry of Health and Population, Cairo, 11613, Egypt
| | - Galal Mahmoud
- Central Public Health Laboratory, Ministry of Health and Population, Cairo, Egypt
| | - Ahmed Kandeil
- Centre of Scientific Excellence for Influenza Viruses, National Research Centre, Dokki, Giza, 12622, Egypt
| | - Salma Afifi
- Ministry of Health and Population Consultant, Cairo, Egypt
| | - Amira Mohsen
- Community Medicine Department, National Research Centre, Cairo, Egypt
| | - Mohammad Abdel Fattah
- Preventive Sector, Central Administration for Preventive Affairs, Ministry of Health and Population, Cairo, Egypt
| | | | - Mohamed A Ali
- Centre of Scientific Excellence for Influenza Viruses, National Research Centre, Dokki, Giza, 12622, Egypt
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Waqas M, Xu SH, Anwar SM, Rasheed Z, Shabbir J. The optimal control chart selection for monitoring COVID-19 phases: a case study of daily deaths in the USA. Int J Qual Health Care 2023; 35:mzad058. [PMID: 37552630 DOI: 10.1093/intqhc/mzad058] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2023] [Revised: 07/10/2023] [Accepted: 08/05/2023] [Indexed: 08/10/2023] Open
Abstract
Epidemiologists frequently adopt statistical process control tools, like control charts, to detect changes in the incidence or prevalence of a specific disease in real time, thereby protecting against outbreaks and emergent health concerns. Control charts have proven essential in instantly identifying fluctuations in infection rates, spotting emerging patterns, and enabling timely reaction measures in the context of COVID-19 monitoring. This study aims to review and select an optimal control chart in epidemiology to monitor variations in COVID-19 deaths and understand pandemic mortality patterns. An essential aspect of the present study is selecting an appropriate monitoring technique for distinct deaths in the USA in seven phases, including pre-growth, growth, and post-growth phases. Stage-1 evaluated control chart applications in epidemiology departments of 12 countries between 2000 and 2022. The study assessed various control charts and identified the optimal one based on maximum shift detection using sample data. This study considered at Shewhart ($\bar X$, $R$, $C$) control charts and exponentially weighted moving average (EWMA) control chart with smoothing parameters λ = 0.25, 0.5, 0.75, and 1 were all investigated in this study. In Stage-2, we applied the EWMA control chart for monitoring because of its outstanding shift detection capabilities and compatibility with the present data. Daily deaths have been monitored from March 2020 to February 2023. Control charts in epidemiology show growing use, with the USA leading at 42% applications among top countries. During the application on COVID-19 deaths, the EWMA chart accurately depicted mortality dynamics from March 2020 to February 2022, indicating six distinct stages of death. The third and fifth waves were extremely catastrophic, resulting in a considerable loss of life. Significantly, a persistent sixth wave appeared from March 2022 to February 2023. The EWMA map effectively determined the peaks associated with each wave by thoroughly examining the time and amount of deaths, providing vital insights into the pandemic's progression. The severity of each wave was measured by the average number of deaths $W5(1899)\,\gt\,W3(1881)\,\gt\,W4(1393)\,\gt\,W1(1036)\,\gt\,W2(853)\,\gt\,(W6(473)$. The USA entered a seventh phase (6th wave) from March 2022 to February 2023, marked by fewer deaths. While reassuring, it remains crucial to maintain vaccinations and pandemic control measures. Control charts enable early detection of daily COVID-19 deaths, providing a systematic strategy for government and medical staff. Incorporating the EWMA chart for monitoring immunizations, cases, and deaths is recommended.
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Affiliation(s)
- Muhammad Waqas
- School of Mathematics and Statistics, Xi'an Jiaotong University, Xi'an, Shaanxi 710049, China
- Department of Statistics, University of WAH, Taxila 47040, Pakistan
| | - Song Hua Xu
- School of Mathematics and Statistics, Xi'an Jiaotong University, Xi'an, Shaanxi 710049, China
- Institute of Medical Artificial Intelligence the Second Affiliated Hospital XJTU, Shaanxi, China
| | - Syed Masroor Anwar
- Department of Statistics, University of Azad Jammu and Kashmir, Muzaffarabad 13100, Pakistan
| | - Zahid Rasheed
- School of Mathematics and Statistics, Xi'an Jiaotong University, Xi'an, Shaanxi 710049, China
- Department of Mathematics, Women University of Azad Jammu and Kashmir, Bagh, AJK 12500, Pakistan
| | - Javid Shabbir
- Department of Statistics, University of WAH, Taxila 47040, Pakistan
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Sofikitou EM, Markatou M, Koutras MV. Multivariate semiparametric control charts for mixed-type data. Stat Methods Med Res 2023; 32:671-690. [PMID: 36788007 DOI: 10.1177/09622802221142528] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/16/2023]
Abstract
A useful tool that has gained popularity in the Quality Control area is the control chart which monitors a process over time, identifies potential changes, understands variations, and eventually improves the quality and performance of the process. This article introduces a new class of multivariate semiparametric control charts for monitoring multivariate mixed-type data, which comprise both continuous and discrete random variables (rvs). Our methodology leverages ideas from clustering and Statistical Process Control to develop control charts for MIxed-type data. We propose four control chart schemes based on modified versions of the KAy-means for MIxed LArge KAMILA data clustering algorithm, where we assume that the two existing clusters represent the reference and the test sample. The charts are semiparametric, the continuous rvs follow a distribution that belongs in the class of elliptical distributions. Categorical scale rvs follow a multinomial distribution. We present the algorithmic procedures and study the characteristics of the new control charts. The performance of the proposed schemes is evaluated on the basis of the False Alarm Rate and in-control Average Run Length. Finally, we demonstrate the effectiveness and applicability of our proposed methods utilizing real-world data.
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Affiliation(s)
- Elisavet M Sofikitou
- Department of Biostatistics, School of Public Health & Health Professions, State University of New York at Buffalo, Buffalo, NY, USA
| | - Marianthi Markatou
- Department of Biostatistics, School of Public Health & Health Professions, State University of New York at Buffalo, Buffalo, NY, USA
| | - Markos V Koutras
- Department of Statistics & Insurance Science, School of Finance & Statistics, 69000University of Piraeus, Pireas, Greece
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Milanesi S, Rosset F, Colaneri M, Giordano G, Pesenti K, Blanchini F, Bolzern P, Colaneri P, Sacchi P, De Nicolao G, Bruno R. Early detection of variants of concern via funnel plots of regional reproduction numbers. Sci Rep 2023; 13:1052. [PMID: 36658143 PMCID: PMC9852294 DOI: 10.1038/s41598-022-27116-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2022] [Accepted: 12/26/2022] [Indexed: 01/20/2023] Open
Abstract
Early detection of the emergence of a new variant of concern (VoC) is essential to develop strategies that contain epidemic outbreaks. For example, knowing in which region a VoC starts spreading enables prompt actions to circumscribe the geographical area where the new variant can spread, by containing it locally. This paper presents 'funnel plots' as a statistical process control method that, unlike tools whose purpose is to identify rises of the reproduction number ([Formula: see text]), detects when a regional [Formula: see text] departs from the national average and thus represents an anomaly. The name of the method refers to the funnel-like shape of the scatter plot that the data take on. Control limits with prescribed false alarm rate are derived from the observation that regional [Formula: see text]'s are normally distributed with variance inversely proportional to the number of infectious cases. The method is validated on public COVID-19 data demonstrating its efficacy in the early detection of SARS-CoV-2 variants in India, South Africa, England, and Italy, as well as of a malfunctioning episode of the diagnostic infrastructure in England, during which the Immensa lab in Wolverhampton gave 43,000 incorrect negative tests relative to South West and West Midlands territories.
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Affiliation(s)
- Simone Milanesi
- Department of Mathematics, University of Pavia, Pavia, Italy
| | - Francesca Rosset
- Department of Mathematics, Computer Science and Physics, University of Udine, Udine, Italy
| | - Marta Colaneri
- Division of Infectious Diseases I, Fondazione IRCCS Policlinico San Matteo, Pavia, Italy
| | - Giulia Giordano
- Department of Industrial Engineering, University of Trento, Trento, Italy
| | - Kenneth Pesenti
- Department of Surgical Medical and Health Sciences, University of Trieste, Trieste, Italy
| | - Franco Blanchini
- Department of Mathematics, Computer Science and Physics, University of Udine, Udine, Italy
| | - Paolo Bolzern
- Department of Electronics, Information and Bioengineering, Politecnico di Milano, Milan, Italy
| | - Patrizio Colaneri
- Department of Electronics, Information and Bioengineering, Politecnico di Milano, Milan, Italy
- Institute of Electronics, Information Engineering and Telecommunication (IEIIT), Italian National Research Council (CNR), Turin, Italy
| | - Paolo Sacchi
- Division of Infectious Diseases I, Fondazione IRCCS Policlinico San Matteo, Pavia, Italy
| | - Giuseppe De Nicolao
- Division of Infectious Diseases I, Fondazione IRCCS Policlinico San Matteo, Pavia, Italy.
- Department of Electrical, Computer and Biomedical Engineering, University of Pavia, Pavia, Italy.
| | - Raffaele Bruno
- Division of Infectious Diseases I, Fondazione IRCCS Policlinico San Matteo, Pavia, Italy
- Department of Clinical, Surgical, Diagnostic, and Pediatric Sciences, University of Pavia, Pavia, Italy
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10
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Ramaswamy R, Ramaswamy V, Holly M, Bartels S, Barach P. Building local decision-making competencies during COVID-19: Accelerating the transition from learning healthcare systems to learning health communities. Learn Health Syst 2022; 7:e10337. [PMID: 36247203 PMCID: PMC9538137 DOI: 10.1002/lrh2.10337] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2022] [Revised: 08/01/2022] [Accepted: 08/08/2022] [Indexed: 12/15/2022] Open
Abstract
Introduction The persisting and evolving COVID-19 pandemic has made apparent that no singular policy of mitigation at a regional, national or global level has achieved satisfactory and universally acceptable results. In the United States, carefully planned and executed pandemic policies have been neither effective nor popular and COVID-19 risk management decisions have been relegated to individual citizens and communities. In this paper, we argue that a more effective approach is to equip and strengthen community coalitions to become local learning health communities (LLHCs) that use data over time to make adaptive decisions that can optimize the equity and well-being in their communities. Methods We used data from the North Carolina (NC) county and zip code levels from May to August 2020 to demonstrate how a LLHC could use statistical process control (SPC) charts and simple statistical analysis to make local decisions about how to respond to COVID-19. Results We found many patterns of COVID-19 progression at the local (county and zip code) levels during the same time period within the state that were completely different from the aggregate NC state level data used for policy making. Conclusions Systematic approaches to learning from local data to support effective decisions have promise well beyond the current pandemic. These tools can help address other complex public health issues, and advance outcomes and equity. Building this capacity requires investment in data infrastructure and the strengthening of data competencies in community coalitions to better interpret data with limited need for advanced statistical expertise. Additional incentives that build trust, support data transparency, encourage truth-telling and promote meaningful teamwork are also critical. These must be carefully designed, contextually appropriate and multifaceted to motivate citizens to create and sustain an effective learning system that works for their communities.
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Affiliation(s)
- Rohit Ramaswamy
- Cincinnati Children's Hospital Medical CenterJames M Anderson Center for Health Systems ExcellenceCincinnatiOhioUSA
| | | | - Margaret Holly
- Department of Health Policy and ManagementUniversity of North Carolina at Chapel Hill Gillings School of Global Public HealthChapel HillNorth CarolinaUSA
| | - Sophia Bartels
- Department of Health BehaviorUniversity of North Carolina at Chapel Hill Gillings School of Global Public HealthChapel HillNorth CarolinaUSA
| | - Paul Barach
- College of Population HealthThomas Jefferson UniversityPhiladelphiaPennsylvaniaUSA
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Letshedi TI, Malela-Majika JC, Shongwe SC. New extended distribution-free homogenously weighted monitoring schemes for monitoring abrupt shifts in the location parameter. PLoS One 2022; 17:e0261217. [PMID: 35061667 PMCID: PMC8782475 DOI: 10.1371/journal.pone.0261217] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2021] [Accepted: 11/27/2021] [Indexed: 11/30/2022] Open
Abstract
A homogeneously weighted moving average (HWMA) monitoring scheme is a recently proposed memory-type scheme that gained its popularity because of its simplicity and superiority over the exponentially weighted moving average (EWMA) and cumulative sum (CUSUM) schemes in detecting small disturbances in the process. Most of the existing HWMA schemes are designed based on the assumption of normality. It is well-known that the performance of such monitoring schemes degrades significantly when this assumption is violated. Therefore, in this paper, three distribution-free monitoring schemes are developed based on the Wilcoxon rank-sum W statistic. First, the HWMA W scheme is introduced. Secondly, the double HWMA (DHWMA) W scheme is proposed to improve the ability of the HWMA W scheme in detecting very small disturbances in the location parameter and at last, the hybrid HWMA (HHWMA) W scheme is also proposed because of its flexibility and better performance in detecting shifts of different sizes. The zero-state performances of the proposed schemes are investigated using the characteristics of the run-length distribution. The proposed schemes outperform their existing competitors, i.e. EWMA, CUSUM and DEWMA W schemes, in many situations, and particularly the HHWMA W scheme is superior to these competitors regardless of the size of the shift in the location parameter. Real-life data are used to illustrate the implementation and application of the new monitoring schemes.
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Affiliation(s)
- Tokelo Irene Letshedi
- Department of Statistics, College of Science, Engineering and Technology, University of South Africa, Pretoria, South Africa
| | - Jean-Claude Malela-Majika
- Department of Statistics, Faculty of Natural and Agricultural Sciences, University of Pretoria, Hatfield, South Africa
| | - Sandile Charles Shongwe
- Department of Mathematical Statistics and Actuarial Science, Faculty of Natural and Agricultural Sciences, University of the Free State, Bloemfontein, South Africa
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