Academic literature on the topic 'Establishing the number of extracted factors in EFA'

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Journal articles on the topic "Establishing the number of extracted factors in EFA"

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Iantovics, Laszlo Barna, Corina Rotar, and Florica Morar. "Survey on establishing the optimal number of factors in exploratory factor analysis applied to data mining." Wiley Interdisciplinary Reviews- Data Mining and Knowledge Discovery 9, no. 2 (2019): e1294. https://doi.org/10.1002/widm.1294.

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In many types of researches and studies including those performed by the sciences of agriculture and plant sciences, large quantities of data are frequently obtained that must be analyzed using different data mining techniques. Sometimes data mining involves the application of different methods of statistical data analysis. Exploratory Factor Analysis (EFA) is frequently used as a technique for data reduction and structure detection in data mining. In our survey, we study the EFA applied to data mining, focusing on the problem of establishing of the optimal number of factors to be retained. The number of factors to retain is the most important decision to take after the factor extraction in EFA. Many researchers discussed the criteria for choosing the optimal number of factors. Mistakes in factor extraction may consist in extracting too few or too many factors. An inappropriate number of factors may lead to erroneous conclusions. A comprehensive review of the state-of-the-art related to this subject was made. The main focus was on the most frequently applied factor selection methods, namely Kaiser Criterion, Cattell&#39;s Scree test, and Monte Carlo Parallel Analysis. We have highligted the importance of the analysis in some research, based on the research specificity, of the total cumulative variance explained by the selected optimal number of extracted factors. It is necessary that the extracted factors explain at least a minimum threshold of cumulative variance.&nbsp;<em>ExtrOptFact</em>&nbsp;algorithm presents the steps that must be performed in EFA for the selection of the optimal number of factors.&nbsp;
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Morar, Florica, Laszlo Barna Iantovics, and Adrian Gligor. "Analysis of Phytoremediation Potential of Crop Plants in Industrial Heavy Metal Contaminated Soil in the Upper Mures River Basin." WIREs Data Mining Knowl Discov 9, no. 2 (2018): e1294. https://doi.org/10.3808/jei.201700383.

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In many types of researches and studies including those performed by the sciences&nbsp;of agriculture and plant sciences, large quantities of data are frequently obtainedthat must be analyzed using different data mining techniques. Sometimes data mining&nbsp;involves the application of different methods of statistical data analysis. ExploratoryFactor Analysis (EFA) is frequently used as a technique for data reduction&nbsp;and structure detection in data mining. In our survey, we study the EFA applied todata mining, focusing on the problem of establishing the optimal number of factors to be retained. The number of factors to retain is the most important decisionto take after the factor extraction in EFA. Many researchers discussed the criteria&nbsp;for choosing the optimal number of factors. Mistakes in factor extraction may consistin extracting too few or too many factors. An inappropriate number of factors&nbsp;may lead to erroneous conclusions. A comprehensive review of the state-of-the-artrelated to this subject was made. The main focus was on the most frequently&nbsp;applied factor selection methods, namely Kaiser Criterion, Cattell's Scree test, andMonte Carlo Parallel Analysis. We have highlighted the importance of the analysis in some research, based on the research specificity, of the total cumulative varianceexplained by the selected optimal number of extracted factors. The extracted factors must explain at least a minimum threshold of cumulative variance.The ExtrOptFact algorithm presents the steps that must be performed in EFA for the selection of the optimal number of factors. For validation purposes, a case studywas presented, performed on data obtained in an experimental study that we made on the Brassica napus plant. Applying the ExtrOptFact algorithm for Principal Component&nbsp;Analysis can be decided on the selection of three components that were&nbsp;called Qualitative, Generative, and Vegetative, which explained 92% of the total&nbsp;cumulative variance.
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Candace, Ross R. Descalsota, A. Payo Joan, O. Telanduca Jezsa, Grace R. Ybañez Ailjee, and Gaudencio G. Abellanosa Dr. "DOLE-OUT SATISFACTION MODEL AMONG RECIPIENTS OF GOVERNMENT FINANCIAL ASSISTANCE." International Journal of Engineering Technology Research & Management (IJETRM) 09, no. 05 (2025): 466–77. https://doi.org/10.5281/zenodo.15532108.

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This study determines the dole-out satisfaction model among recipients of government financial assistance. AnExploratory Factor Analysis (EFA) was conducted from the survey among 150 dole-out government financialassistance recipients. The Kaiser-Meyer-Olkin (KMO) Measure of Sampling Adequacy and Bartlett&rsquo;s test ofSphericity were used in factor analysis to assess the suitability of the data for factor analysis, and a Scree Plotwas used to graphically identify the optimal number of factors that can be extracted from the survey. Based onthe findings, five factors were determined that influence the satisfaction of the recipients towards the dole-outgovernment financial assistance when using EFA. Particularly, financial impact of the assistance, fairness andtransparency, accessibility and public perception, timeliness and convenience, and the quality of governmentservice delivery are significantly the areas where recipients are satisfied. Together, these factors shape thesatisfaction of the recipients to the assistance offered by the government based on their personal experience.Further, Confirmatory Factor Analysis (CFA) was conducted with another 150 dole-out government assistancerecipients. Among the five factors only two factors remained which are financial impact of the assistance and;fairness and transparency.
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Alam, Md Moddassir, Arun Mittal, and Deepak Chawla. "Patients’ Perception Towards Branded and Generic Medicines in an Emerging Economy: A Scale Development and Validation Study." Global Business Review 20, no. 5 (2019): 1292–310. http://dx.doi.org/10.1177/0972150919846812.

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The study intends to develop and validate a scale to gauge the perception of patients towards branded and generic medicines in an emerging economy like India. Items were generated through literature review and exploratory semi-structured interview with patients and physicians. In the way of establishing generic medicines in the market, patients’ acceptability is very much essential. However, with the advent of information age, the patients are now becoming more conscious and aware regarding the generic medicines, and there is an improvement in the acceptability of generic medicines. Hence, the measurement of their perception towards generic medicines becomes an important issue for various stakeholders of the medical world—physicians, government, pharmaceutical companies and chemists. However, no studies regarding the measurement of perception towards the branded and generic have been conducted to develop and validate measurement scale. The present study is an attempt towards fulfilling this gap. A total of 361 valid responses were obtained using purposive sampling. Exploratory factor analysis (EFA) was carried out through principal component analysis (PCA) followed by confirmatory factor analysis (CFA) to establish the validity of the proposed measurement model. The five factors extracted from EFA were named as quality (Cronbach’s alpha: 0.887), trust (Cronbach’s alpha: 0.919), sustained effectiveness (Cronbach’s alpha: 0.832), reputation (Cronbach’s alpha: 0.881) and psychological benefits (Cronbach’s alpha: 0.737). The obtained factors were found reliable and valid for measuring perception of the patients towards generic and branded medicines in emerging market settings. Convergent and discriminant validity of the scale was also established.
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Sylva, Waribugo, and Anyanwu A.C. Success. "Dimensionalizing the Information and Communication Technology Adoption Construct." INTERNATIONAL JOURNAL OF MANAGEMENT SCIENCE AND BUSINESS ADMINISTRATION 3, no. 1 (2014): 31–40. http://dx.doi.org/10.18775/ijmsba.1849-5664-5419.2014.31.1003.

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This study was conducted to determine the principal components of the information and communication technology (ICT) adoption practices in a developing country. A nineteen- item instrument was developed from extant current literature. Eight hundred and fifty copies of the structured questionnaire were administered to teaching and non-teaching staff of two faculties from the University of Port Harcourt and Rivers State University of Science and Technology, both located in Rivers State. Out of the number distributed, 435 were completed and returned. To determine the dimensions of ICT adoption, an exploratory factor analysis (EFA) was conducted through the application of principal component analysis (PCA). From the analysis, three factors were extracted which account for 68.8% of the total variance. Thus, the researchers named the factors: virtual learning, e-learning and networking practices.
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Chao, Shih-Liang, and Ya-Lan Lin. "Gate automation system evaluation." Maritime Business Review 2, no. 1 (2017): 21–35. http://dx.doi.org/10.1108/mabr-09-2016-0022.

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Purpose This study has two purposes. The first is to identify the determinants influencing the selection of a container number recognition system via a quantitative method to thereby establish an evaluation structure. The second purpose is to conduct an empirical study to determine the weights of the criteria and alternatives. Design/methodology/approach The exploratory factor analysis (EFA) and fuzzy analytic hierarchy process (AHP) were applied to determine the evaluation structure and weights of the criteria and alternatives, respectively. Findings An empirical study based on a dedicated terminal at Keelung Port is conducted. The result demonstrates that the radio-frequency identification (RFID) system is a suitable system for the terminal under consideration in this study. Originality/value The value of this study is twofold. First, EFA was applied to extract common factors from a wide questionnaire survey, thereby establishing a hierarchical analysis structure. This method and comprehensive evaluation structure are useful references for both practitioners and researchers to deal with problems of gate automation. Second, fuzzy AHP was used to decide the weights of the hierarchical structure. The weights obtained by this method are more objective and rational as the imprecision expressions in returned samples have been considered and dealt with.
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Musharaf, Quratulain, Nazia Jahangir, Naimal Ehsan, Saba Ashraf, and Sadia Abid. "Translation and Validation of Social Media Addiction Scale for Students." Journal of Policy Research 9, no. 2 (2023): 848–66. http://dx.doi.org/10.61506/02.00031.

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The focus of the current study was to translate and Validate Social Media Addiction Scale (SMAS) into Urdu. Study was based to establish the psychometric properties of SMAS in Pakistani Culture. Translation and Adoption were accomplished with forward and backward translation method for SMAS. After that it was scientifically pre-tested to validate its constructs and it was found significant. In pre- testing, minimum rating for the equivalence of both English and Urdu version of SMAS with Mean 84.8 and standard deviation 12.617. In the next step, adapted Urdu version was administered on the sample of large population of 516 students with age between 15 to 22 years using purposive sampling technique (M = 81.70 and SD = 15.666). Cronbach’s alpha, exploratory factor analysis (EFA) and confirmatory factor analysis (CFA) were computed by using SPSS software (version .25) and AMOS software (version .21). Results showed that Cronbach’s Alpha ₌ 0.87. The item total correlation of Urdu SMAS was greater than 0.3. Exploratory Factor Analysis and Confirmatory Factor Analysis done to check the number of factors in SMAS. The EFA extracted four factors for SMAS Urdu version. After testing the four-factor model in CFA, the analysis showed good fitness indexes (comparative fit index = 0.95; goodness of fit index = 0.97). SAAS Urdu version has high internal consistency and reliability and can be used for research and evaluation objectives in clinical settings in Pakistani society.
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Yu, Tianzuo, Weiwei Shang, Shaoxue Liu, and Jiabin Zhu. "How to Assess Generic Competencies: From Sustainable Development Needs among Engineering Graduates in Industry." Sustainability 14, no. 15 (2022): 9270. http://dx.doi.org/10.3390/su14159270.

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Achieving many of the UN’s 17 Sustainable Development Goals requires the active contribution of skilled engineers. Globally, however, there appears to be a mismatch between the sustainable competencies that engineering graduates possess and those required by industry. Closing this gap requires a reliable and valid means of establishing which competencies are of greatest importance to engineering practitioners. In this research, we developed a model of generic engineering competency and designed a scale comprising 55 skills in total. This instrument was then used to survey two samples of engineering graduates working in the Chinese industry, with 746 in the first round of surveys, and 1183 in the second. Using exploratory factor analysis (EFA), seven subscales were extracted from the data: (1) leadership, (2) engineering design, (3) professionalism, (4) problem solving, (5) lifelong learning, (6) technical theory, and (7) communication. Confirmatory factor analysis (CFA) demonstrated that the total number of generic engineering competencies was represented by a second-order, single-factor model that adequately fitted the data. Further, the Cronbach’s alpha values and composite reliability of the scale indicate its reliability. Overall, the evidence shows that the instrument offers a valid and reliable means of researching and assessing engineering education practices.
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Mya, Kyaw Swa, Ko Ko Zaw, and Khay Mar Mya. "Developing and validating a questionnaire to assess an individual’s perceived risk of four major non-communicable diseases in Myanmar." PLOS ONE 16, no. 4 (2021): e0234281. http://dx.doi.org/10.1371/journal.pone.0234281.

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Adopting healthy lifestyles is greatly influenced by an individual’s perceived risk of developing non-communicable diseases (NCDs). This study aimed to develop and validate a questionnaire that can assess an individual’s perceived risk of developing four major NCDs. We used the exploratory sequential mixed methods design. The qualitative part developed a questionnaire by two rounds of Delphi expert panels. The quantitative part validated the questionnaire using both exploratory (EFA) and confirmatory factor analysis (CFA). We used separate samples for EFA (n = 150) and CFA (n = 210). The participants were aged between 25–60 years of both sexes with no known history of NCDs, and face-to-face interviews were conducted. First, we generated an 86-item questionnaire based on the health belief model. Two expert panels ensured the questionnaire’s content validity. The experts removed the overlapped items and items that did not represent the specific construct and developed a 51-item questionnaire. Next, we validated the questionnaire. We conducted a parallel analysis to determine the number of factors to be extracted. EFA constituted a five-factor model with 22 high loading items, which extracted 54% of the variance. We run four CFA models (single factor, five-factor, bifactor, and hierarchical) and tested the hypothesized five-factor model. It was found that the 21-item questionnaire (removed one efficacy item due to low loading) was satisfied with good psychometric properties and fitted with observed data in the bifactor model (RMSEA = 0.051, CFI = 0.954, TLI = 0.938, SRMR = 0.054). Hence, an individual’s perceived risk of getting NCDs was constituted with a general perceived risk construct and five specific constructs (perceived susceptibility, perceived barrier, perceived benefit, perceived self-efficacy, and perceived behavioral change intention). It can be measured using the developed questionnaire (NCD-PR5-21). Further research is warranted to assess the questionnaire’s utility in a mismatch between risk perception and current risk; and individualized counseling for behavioral change communication.
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Tan, Rhigel, and Thelma Alderite. "Validation of a Leadership Self-Efficacy Scale for Educational Leaders." TOFEDU: The Future of Education Journal 4, no. 5 (2025): 1015–25. https://doi.org/10.61445/tofedu.v4i5.524.

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This study aims to validate a scale that evaluates the leadership self-efficacy among educational leaders. The 92-item scale was developed by group of educational leadership practitioners at a Philippine university headed by Dr. Tan (2025) and was approved for use. To do so, the scale was administered to 200 educational leaders holding different positions. The data obtained were subjected to Exploratory Factor Analysis (EFA) and created a seven-factor model. The factors are set of leadership skills named as Continuous Learning and Professional Growth (Factor 1), Stress Management and Well-being (Factor 2), Communication and Collaboration (Factor 3), Inspirational and Visionary Leadership (Factor 4), Integrity and Ethical Leadership (Factor 5), Adaptability and Resilience (Factor 6), and Mentorship and Empowerment (Factor 7). The model was confirmed utilizing the resulting values of five goodness of fit indices (GFIs) generated by the Confirmatory Factor Analysis (CFA). All GFIs resulted to values falling within the thresholds. These GFIs are already adequate to confirm that the model is relatively good fit. The standardized factor loadings (SFLs) and composite reliability (CR) were also excellent, thus, establishing convergent validity. Also, the estimated average variance extracted of all factors provided evidence of discriminant validity and reliability, respectively. Overall, the study resulted in a valid and reliable 26-item scale that effectively measures leadership self-efficacy among educational leaders.
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Conference papers on the topic "Establishing the number of extracted factors in EFA"

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Perez-Blanco, H. "Optimization of Wind Energy Capture Using BET." In ASME 2011 Turbo Expo: Turbine Technical Conference and Exposition. ASMEDC, 2011. http://dx.doi.org/10.1115/gt2011-46302.

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The Blade Element Theory (BET) has been used to predict performance of wind turbines, and to optimize energy extraction from the wind. A literature search shows that the number of parameters that can be varied to attempt optimization within BET varies for different authors. However, a repeated assumption is that the BE should be operating at the incidence angle resulting in maximum lift to drag ratio. In the present work, the incidence angle is one of the parameters varied for optimization, along with five others: the two induction factors, the chord, and the flow and setting angles. The optimization satisfies five equality constraints and three inequality constraints. The optimizer uses Levenberg-Marquardt, Conjugate Gradient or Quasi-Newton methods to maximize the power extracted. The equations adopted employ the Prandtl tip loss and require specification of the airfoil for the section, the radius of the turbine, the wind speed and the radial distribution of solidity. Up to twenty five elements can be specified for each turbine. The influence of airfoils on power coefficients is shown, and deviations from the expected maximum lift to drag positions noted. Comparisons to the performance of small wind turbines from the commercial and open literature are attempted. Whereas such comparisons are difficult in that airfoils and solidities are not often specified, they yield a baseline for establishing the validity of the optimization procedure.
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Reports on the topic "Establishing the number of extracted factors in EFA"

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Rankin, Nicole, Deborah McGregor, Candice Donnelly, et al. Lung cancer screening using low-dose computed tomography for high risk populations: Investigating effectiveness and screening program implementation considerations: An Evidence Check rapid review brokered by the Sax Institute (www.saxinstitute.org.au) for the Cancer Institute NSW. The Sax Institute, 2019. http://dx.doi.org/10.57022/clzt5093.

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Background Lung cancer is the number one cause of cancer death worldwide.(1) It is the fifth most commonly diagnosed cancer in Australia (12,741 cases diagnosed in 2018) and the leading cause of cancer death.(2) The number of years of potential life lost to lung cancer in Australia is estimated to be 58,450, similar to that of colorectal and breast cancer combined.(3) While tobacco control strategies are most effective for disease prevention in the general population, early detection via low dose computed tomography (LDCT) screening in high-risk populations is a viable option for detecting asymptomatic disease in current (13%) and former (24%) Australian smokers.(4) The purpose of this Evidence Check review is to identify and analyse existing and emerging evidence for LDCT lung cancer screening in high-risk individuals to guide future program and policy planning. Evidence Check questions This review aimed to address the following questions: 1. What is the evidence for the effectiveness of lung cancer screening for higher-risk individuals? 2. What is the evidence of potential harms from lung cancer screening for higher-risk individuals? 3. What are the main components of recent major lung cancer screening programs or trials? 4. What is the cost-effectiveness of lung cancer screening programs (include studies of cost–utility)? Summary of methods The authors searched the peer-reviewed literature across three databases (MEDLINE, PsycINFO and Embase) for existing systematic reviews and original studies published between 1 January 2009 and 8 August 2019. Fifteen systematic reviews (of which 8 were contemporary) and 64 original publications met the inclusion criteria set across the four questions. Key findings Question 1: What is the evidence for the effectiveness of lung cancer screening for higher-risk individuals? There is sufficient evidence from systematic reviews and meta-analyses of combined (pooled) data from screening trials (of high-risk individuals) to indicate that LDCT examination is clinically effective in reducing lung cancer mortality. In 2011, the landmark National Lung Cancer Screening Trial (NLST, a large-scale randomised controlled trial [RCT] conducted in the US) reported a 20% (95% CI 6.8% – 26.7%; P=0.004) relative reduction in mortality among long-term heavy smokers over three rounds of annual screening. High-risk eligibility criteria was defined as people aged 55–74 years with a smoking history of ≥30 pack-years (years in which a smoker has consumed 20-plus cigarettes each day) and, for former smokers, ≥30 pack-years and have quit within the past 15 years.(5) All-cause mortality was reduced by 6.7% (95% CI, 1.2% – 13.6%; P=0.02). Initial data from the second landmark RCT, the NEderlands-Leuvens Longkanker Screenings ONderzoek (known as the NELSON trial), have found an even greater reduction of 26% (95% CI, 9% – 41%) in lung cancer mortality, with full trial results yet to be published.(6, 7) Pooled analyses, including several smaller-scale European LDCT screening trials insufficiently powered in their own right, collectively demonstrate a statistically significant reduction in lung cancer mortality (RR 0.82, 95% CI 0.73–0.91).(8) Despite the reduction in all-cause mortality found in the NLST, pooled analyses of seven trials found no statistically significant difference in all-cause mortality (RR 0.95, 95% CI 0.90–1.00).(8) However, cancer-specific mortality is currently the most relevant outcome in cancer screening trials. These seven trials demonstrated a significantly greater proportion of early stage cancers in LDCT groups compared with controls (RR 2.08, 95% CI 1.43–3.03). Thus, when considering results across mortality outcomes and early stage cancers diagnosed, LDCT screening is considered to be clinically effective. Question 2: What is the evidence of potential harms from lung cancer screening for higher-risk individuals? The harms of LDCT lung cancer screening include false positive tests and the consequences of unnecessary invasive follow-up procedures for conditions that are eventually diagnosed as benign. While LDCT screening leads to an increased frequency of invasive procedures, it does not result in greater mortality soon after an invasive procedure (in trial settings when compared with the control arm).(8) Overdiagnosis, exposure to radiation, psychological distress and an impact on quality of life are other known harms. Systematic review evidence indicates the benefits of LDCT screening are likely to outweigh the harms. The potential harms are likely to be reduced as refinements are made to LDCT screening protocols through: i) the application of risk predication models (e.g. the PLCOm2012), which enable a more accurate selection of the high-risk population through the use of specific criteria (beyond age and smoking history); ii) the use of nodule management algorithms (e.g. Lung-RADS, PanCan), which assist in the diagnostic evaluation of screen-detected nodules and cancers (e.g. more precise volumetric assessment of nodules); and, iii) more judicious selection of patients for invasive procedures. Recent evidence suggests a positive LDCT result may transiently increase psychological distress but does not have long-term adverse effects on psychological distress or health-related quality of life (HRQoL). With regards to smoking cessation, there is no evidence to suggest screening participation invokes a false sense of assurance in smokers, nor a reduction in motivation to quit. The NELSON and Danish trials found no difference in smoking cessation rates between LDCT screening and control groups. Higher net cessation rates, compared with general population, suggest those who participate in screening trials may already be motivated to quit. Question 3: What are the main components of recent major lung cancer screening programs or trials? There are no systematic reviews that capture the main components of recent major lung cancer screening trials and programs. We extracted evidence from original studies and clinical guidance documents and organised this into key groups to form a concise set of components for potential implementation of a national lung cancer screening program in Australia: 1. Identifying the high-risk population: recruitment, eligibility, selection and referral 2. Educating the public, people at high risk and healthcare providers; this includes creating awareness of lung cancer, the benefits and harms of LDCT screening, and shared decision-making 3. Components necessary for health services to deliver a screening program: a. Planning phase: e.g. human resources to coordinate the program, electronic data systems that integrate medical records information and link to an established national registry b. Implementation phase: e.g. human and technological resources required to conduct LDCT examinations, interpretation of reports and communication of results to participants c. Monitoring and evaluation phase: e.g. monitoring outcomes across patients, radiological reporting, compliance with established standards and a quality assurance program 4. Data reporting and research, e.g. audit and feedback to multidisciplinary teams, reporting outcomes to enhance international research into LDCT screening 5. Incorporation of smoking cessation interventions, e.g. specific programs designed for LDCT screening or referral to existing community or hospital-based services that deliver cessation interventions. Most original studies are single-institution evaluations that contain descriptive data about the processes required to establish and implement a high-risk population-based screening program. Across all studies there is a consistent message as to the challenges and complexities of establishing LDCT screening programs to attract people at high risk who will receive the greatest benefits from participation. With regards to smoking cessation, evidence from one systematic review indicates the optimal strategy for incorporating smoking cessation interventions into a LDCT screening program is unclear. There is widespread agreement that LDCT screening attendance presents a ‘teachable moment’ for cessation advice, especially among those people who receive a positive scan result. Smoking cessation is an area of significant research investment; for instance, eight US-based clinical trials are now underway that aim to address how best to design and deliver cessation programs within large-scale LDCT screening programs.(9) Question 4: What is the cost-effectiveness of lung cancer screening programs (include studies of cost–utility)? Assessing the value or cost-effectiveness of LDCT screening involves a complex interplay of factors including data on effectiveness and costs, and institutional context. A key input is data about the effectiveness of potential and current screening programs with respect to case detection, and the likely outcomes of treating those cases sooner (in the presence of LDCT screening) as opposed to later (in the absence of LDCT screening). Evidence about the cost-effectiveness of LDCT screening programs has been summarised in two systematic reviews. We identified a further 13 studies—five modelling studies, one discrete choice experiment and seven articles—that used a variety of methods to assess cost-effectiveness. Three modelling studies indicated LDCT screening was cost-effective in the settings of the US and Europe. Two studies—one from Australia and one from New Zealand—reported LDCT screening would not be cost-effective using NLST-like protocols. We anticipate that, following the full publication of the NELSON trial, cost-effectiveness studies will likely be updated with new data that reduce uncertainty about factors that influence modelling outcomes, including the findings of indeterminate nodules. Gaps in the evidence There is a large and accessible body of evidence as to the effectiveness (Q1) and harms (Q2) of LDCT screening for lung cancer. Nevertheless, there are significant gaps in the evidence about the program components that are required to implement an effective LDCT screening program (Q3). Questions about LDCT screening acceptability and feasibility were not explicitly included in the scope. However, as the evidence is based primarily on US programs and UK pilot studies, the relevance to the local setting requires careful consideration. The Queensland Lung Cancer Screening Study provides feasibility data about clinical aspects of LDCT screening but little about program design. The International Lung Screening Trial is still in the recruitment phase and findings are not yet available for inclusion in this Evidence Check. The Australian Population Based Screening Framework was developed to “inform decision-makers on the key issues to be considered when assessing potential screening programs in Australia”.(10) As the Framework is specific to population-based, rather than high-risk, screening programs, there is a lack of clarity about transferability of criteria. However, the Framework criteria do stipulate that a screening program must be acceptable to “important subgroups such as target participants who are from culturally and linguistically diverse backgrounds, Aboriginal and Torres Strait Islander people, people from disadvantaged groups and people with a disability”.(10) An extensive search of the literature highlighted that there is very little information about the acceptability of LDCT screening to these population groups in Australia. Yet they are part of the high-risk population.(10) There are also considerable gaps in the evidence about the cost-effectiveness of LDCT screening in different settings, including Australia. The evidence base in this area is rapidly evolving and is likely to include new data from the NELSON trial and incorporate data about the costs of targeted- and immuno-therapies as these treatments become more widely available in Australia.
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