Background: This study aimed to develop and validate an electronic frailty index (eFI) based on routine electronic health records (EHR) for older adult inpatients and to analyze the correlations between frailty and hospitalized events and costs.Methods: We created an eFI from routine EHR and validated the effectiveness by the consistency of the comprehensive geriatric assessment-frailty index (CGA-FI) with an independent prospective cohort. Then, we analyzed the correlations between frailty and hospitalized events and costs by regressions.Results: During the study period, 49,226 inpatients were included in the analysis, 42,821 (87.0%) of which had enough data to calculate an eFI. A strong correlation between the CGA-FI and eFI was shown in the validation cohort of 685 subjects . The sensitivity and specificity for an eFI≥0.15, the upper tertile, to identify frailty, defined as a CGA-FI≥0.25, were 64.8 and 88.7%, respectively. After adjusting for age, sex, and operation, an eFI≥0.15 showed an independent association with long hospital stay  and death in hospital . Moreover, eFI values (per 0.1) were positively associated with total costs , examination costs , treatment costs , nursing costs , pharmacy costs , and material costs  after adjusting aforementioned factors.Conclusions: We successfully developed an effective eFI from routine EHR from a general hospital in China. Frailty is an independent risk factor for long hospital stay and death in hospital. As the degree of frailty increases, the hospitalized costs increase accordingly. As the population of older adults rises globally, the condition of frailty is gaining prominent attention . In ChinTo reduce the burden of frailty assessment on clinicians, Clegg et al. developed an electronic FI (eFI) using routine electronic health records (EHR) in UK primary care . Based oPrevious studies on frailty in inpatients have mainly focused on the population in a certain department or with a particular disease type, which cannot fully reflect conditions across all departments. Our previous research has found that the prevalence of frailty between medical and surgical departments was significantly different , but theThis study aimed to develop and validate an eFI based on routine EHR for older adult inpatients and to analyze the correlations between frailty and hospitalized events and costs.We conducted a cohort study on frailty using 6-year EHR of older adult inpatients from a general hospital in Beijing, China. The hospital is public, owned by government, with great attention to geriatrics and gerontology. We used a two-step approach to develop and validate an eFI based on the routine EHR for older adult inpatients. First, we followed published guidance on creating a FI using the cumulative deficit model to create an eFI from routine EHR, which involved diagnosis, nursing assessment, and laboratory tests . Second,The patients included were at least 65 years of age in the routine EHR database from July 1, 2013 to September 30, 2019. There were 96,393 hospitalizations involving 51,824 older adult inpatients in the records. During the study period, 34,612 patients had only one hospitalization, while 17,212 patients had two or more hospitalizations in the records. To avoid duplication, we chose the last hospitalization for these patients with two or more hospitalizations in the records for analysis. Considering the meaninglessness of hospital stays that were extremely short (1 day or less) and special issues regarding hospital stays that were extremely long, we excluded 5% of the patients, which included 2,006  patients with hospital stay ≤ one day and 592  patients with hospital stay > 66 days. Therefore, 49,226 inpatients were eligible .For the validation exercise, we used a linked dataset on a prospective cohort of inpatients who had been assessed with the research standard CGA-FI. These participants were enrolled from September 2018 to February 2019 in the same hospital. The characteristics of the prospective cohort were described in detail in our previously published article . Among tAs shown in https://icd.who.int/browse10/2010/en#/)  codes for each diagnosis were determined by three clinicians after discussion and according to the global standard for diagnostic health information from the World Health Organization website (10/en#/) .These 20 items from the nursing assessments on the day of admission included the Barthel Index  , visual Given the importance of laboratory tests for frailty assessment , 29, we The eFI included a total of 45 factors and was calculated as the unweighted sum of the score for each factor, divided by the total number of non-missing items. There were no missing values regarding the diagnosis for all inpatients. Moreover, we did not want an absence of the Barthel Index, which contained 10 items and necessarily implied functional status. Therefore, these abovementioned 30 items in the eFI were guaranteed. We additionally excluded individuals who did not have at least 6 of the 10 items on the nursing assessments (except Barthel Index) or at least 3 of the 5 items on the laboratory measurements. Finally, we required ≥39 non-missing items, which were consistent with recommendations for constructing the FI . The misWe defined hospitalized events as long hospital stay (>14 days) and death in hospital. Hospitalized costs included total costs, examination costs, treatment costs, nursing costs, pharmacy costs, and material costs. The total cost was the sum of all payments to the hospital for this hospitalization. Examination costs meant the payment for all examinations, such as laboratory measurements and imaging examinations. Treatment costs referred to payments for treatments, mainly by doctors and therapists, such as operation fees and consultation fees. Nursing costs referred to payments for all nursing care. Pharmacy costs referred to payments for all medications used during this hospitalization. Material costs referred to the payment for all medical consumable materials, of which surgical and interventional consumables accounted for a large proportion. We calculated hospitalized costs in United States dollars using the average exchange rate from 2013 to 2019 (1 USD = 6.46 CNY) for international comparisons.t-tests or Mann-Whitney U for continuous variables. Pearson's correlation coefficient was used to describe the association between the continuous versions of the eFI and the CGA-FI. Additionally, a receiver operating characteristic (ROC) curve was calculated to estimate the area under the curve (AUC) for the eFI in relation to frailty, which was defined as CGA-FI≥0.25. We examined the association between frailty (eFI≥0.15) and hospitalized events using multivariate logistic regression, adjusting for age, sex, and operation  and assessed the prevalence of frailty in different departments. We conducted a separate analysis for each subgroup. The results of the logistic regression models are presented as odds ratios (ORs) and 95% confidence intervals (95% CIs).Descriptive statistics were compared between the groups using chi-square tests for categorical variables and P < 0.05 was considered statistically significant.To investigate the impact of the eFI on hospitalized costs, generalized linear regression models (gamma-distributed and log-linked) adjusted for age, sex, and operation were used for all patients and each subgroup. For patients with two or more hospitalizations in the records, we chose the last hospitalization for analysis. The eFI values were multiplied by 10 to give equal 0.1 increments in the generalized linear regression models. The use of a gamma-distributed generalized linear model with a log-transformed link function has been shown to be a good method to estimate health-care cost distributions that are generally right-skewed . The resWe performed sensitivity analyses to assess the robustness of the eFI as an independent associated factor of hospitalized events and costs. For patients with two or more hospitalizations , we used the first hospitalization records instead of the last hospitalization records. We also excluded 5% patients with extremely short hospital stay  or extremely long hospital stay . According to the method of constructing the eFI, 87.6%  of patients had sufficient data to calculate the eFI. For comparison with the original results, we still used 0.15 as the cut-off point of the eFI to identify frailty. Other statistical methods in the sensitivity analysis were the same as in the original analysis.All analyzes were performed using the R software program, version 3.5.3.P < 0.001) and death in hospital  were significantly different in these two groups. Moreover, the hospitalized costs in patients with insufficient data were significantly lower than those in the other group . Detailed information on the entire population is shown in P < 0.001).During the study period, 49,226 older adult inpatients from the EHR database were included . There wr = 0.716, P < 0.001)  . The AUC< 0.001) .P < 0.001) and had a lower percentage of males . The department distributions and operations were clearly different between these two groups (both P < 0.001). The frail group had significantly longer hospital days  and a higher mortality rate in the hospital  than the non-frail group. Meanwhile, the total hospitalized cost and each specific cost for the frail group were all significantly higher than those for the non-frail group  (We used 0.15 (tertile of eFI) as the cut-off value to indicate frailty in the 42,821 inpatients from the EHR database. Compared with the non-frail group, the frail group was significantly older  .P < 0.001) and death in hospital  for all older adult inpatients by multivariate logistic regression. In the subgroup analysis for different departments, the eFI was also independently associated with long hospital stay in all subgroups . The same results are presented for death in hospital   .P < 0.001), examination costs , treatment costs , nursing costs , pharmacy costs , and material costs  for all older adult inpatients. Subgroups of different departments showed similar correlations between the eFI and hospitalized costs   and US $4,790 , respectively . After a < 0.01) .When using the first hospitalization records instead of the last ones, the mortality in the hospital with sufficient data was 1.4%, which was lower than the 3.4% in the original data. There were 30.0%  of patients in the frail group, defined by an eFI≥0.15. All the trends between the frail group and the non-frail group were similar to those from the original analysis . After tTo our knowledge, this is the first study on detecting frailty in older adult inpatients by routine EHR in China. Our study demonstrates that an effective eFI by routine EHR for older adult inpatients can be developed from a general hospital in China, and the eFI was validated by agreement with the CGA-FI. Although the prevalence of frailty varied obviously among different departments, frailty was an independent risk factor for hospitalized events in each subgroup of different departments. As the degree of frailty increased, the hospitalized costs for older adult inpatients increased accordingly.In this study, we constructed an eFI to assess frailty using routine EHR. Our results showed a strong correlation between eFI and CGA-FI, which was consistent with the findings of Brundle et al. in the UK  and AbbaFirst, the cut-off value of the eFI for identifying frailty should be redefined according to the specific situation, instead of taking a generally accepted fixed value, such as 0.25 for the CGA-FI. Our study showed that the eFI was lower than the CGA-FI despite both having a strong correlation. The median CGA-FI was 0.198, while the median eFI was only 0.102 in the validation cohort. Therefore, applying cut-off value from CGA-FI to the eFI will underestimate the prevalence of the frail population. A similar study that simultaneously compared their own eFI and CGA-FI also showed that eFI was relatively lower than the CGA-FI for 85 participants in a Canadian primary care program . There wSecond, the timeliness of constructing the eFI should be considered in clinical practice. The routine EHR used to construct the eFI could be mainly collected within 24 h after admission in our study except for the diagnostic codes. Compared with the well-checked, ICD-10 coded discharge diagnosis, the uncoded admission diagnosis was relatively difficult to analyze. Therefore, we used the discharge diagnosis to calculate the eFI. If the admission diagnosis is improved by coding in the future, we would be able to obtain the eFI within 24 h after admission, which would be not only useful in follow-up of frail patients after discharge, but also in guiding to manage older adult inpatients in a targeted manner during hospitalization. In previous studies, the eFI was mainly developed in primary care settings , 23, 31.Third, the eFI should reflect the variability in frail conditions. Frailty is a dynamic condition, and individuals can fluctuate between states of severity of frailty . In prevThe primary advantage of EHR data is its large volume, but the biggest challenge of EHR-based studies is the presence of missing data . Our stuWe found that there were significant differences in the prevalence of frailty among the different departments. Generally, emergency departments and ICUs had the highest percentage of frailty (average: 82.8%), followed by internal medicine departments average: 34.1%), and surgical departments (average: 27.1%). This trend was consistent with the results of a recent multicenter survey on frailty in older adult inpatients in China  was significantly higher than that in other surgical departments and most internal medicine departments. This was mainly because the conditions for most orthopedic patients were braked by osteoarthritis or fractures, especially hip fractures. The activities of daily living assessments for these inpatients in this state could not reflect their daily conditions. We should keep this point in mind when we use the eFI to guide clinical practice for inpatients in department of orthopedics. In our study, the prevalence of frailty in department of cardiology was lower than that in most internal medicine departments. This result was consistent with results in our previous cohort study . Since nOur findings showed that the hospitalized cost of patients with frailty was significantly higher than that patients without frailty. The eFI was positively associated with costs after adjusting age, sex and operation. This trend exists in all kinds of hospitalized costs .Previous studies has also suggested that frailty was associated with healthcare expenditure, both in developed and developing countries , 15, 16.To our knowledge, previous studies did not simultaneously analyze the correlation between hospitalized costs and frailty in different departments. Our results showed that the increases in hospitalized costs in the department of orthopedics were smaller than those in other subgroups, especially in terms of material costs. There are several possible reasons to explain this phenomenon. Firstly, the cost of medical consumable materials is a large part of the total hospitalized cost in the department of orthopedics, whether for frail or non-frail inpatients. Secondly, the cost of medical consumable materials in China depends not only on the patient's condition but also on the patient's own choice. For example, patients with low income or without medical insurance may be more likely to choose the less expensive of similar products. Thirdly, the frailty assessment was influenced by patient braking, which may have led to an overestimation of the degree of frailty in many patients with fracture. However, even so, hospitalized costs are positively related to the degree of frailty for older adult inpatients in the department of orthopedics, after adjusting for age, sex, and operation.Overall, considering that in-hospital interventions on frailty have been effective for older adult inpatients to reverse the degree of frailty , timely There are three main limitations in this study. First, there was no follow-up data after discharge. We could not verify the long-term predictive value of the eFI for adverse events in the older adult inpatients because it is difficult to link our hospital EHR with national resident death records or EHR from other hospitals. Second, we used discharge but not admit diagnoses when building eFI. In our hospital, discharge diagnoses were inspected and coded in a variation of ICD-10 by specialists, which are the best source we can find representing the disease burden of patients at present. One of our targets in the future research is to build eFI based on the information gathered in first 24 h after admission, and to verify the value in prediction of outcomes. Third, despite its relatively large size, the EHR data in this study were from a single general hospital, which may not represent all hospitals in China. Multicenter studies with follow-up data are warranted in the future.In summary, we successfully developed an effective eFI using routine EHR for older adult inpatients from a general hospital in China. With good agreement with the CGA-FI, the eFI can be a useful and convenient tool to clinicians for detecting frailty in older adult inpatients. The prevalence of frailty varied obviously among different departments, but frailty was an independent risk factor for long hospital stay and death in hospital. As the degree of frailty increased, the hospitalized costs for older adult inpatients increased accordingly.The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.The studies involving human participants were reviewed and approved by the Ethics Committee of Beijing Hospital (No. 2019BJYYEC-131-01). The Ethics Committee waived the requirement of written informed consent for participation.Y-DL, JS, J-FY, and HW contributed conception and design of the study. Y-BX performed data collection, preparation, and cleansing. Y-DL and M-HD performed the statistical analysis and wrote the draft of the manuscript. All authors contributed to the article and approved the submitted version.This work was supported by the Non-profit Central Research Institute Fund of Chinese Academy of Medical Sciences, China (No. 2019PT320013); the Beijing Municipal Science and Technology Commission, China (D181100000218003); and the CAMS Innovation Fund for Medical Sciences (No. 2018-I2M-1-002).The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


House improvement (HI) to prevent mosquito house entry, and larval source management (LSM) targeting aquatic mosquito stages to prevent development into adult forms, are promising complementary interventions to current malaria vector control strategies. Lack of evidence on costs and cost-effectiveness of community-led implementation of HI and LSM has hindered wide-scale adoption. This study presents an incremental cost analysis of community-led implementation of HI and LSM, in a cluster-randomized, factorial design trial, in addition to standard national malaria control interventions in a rural area , in southern Malawi.Bacillus thuringiensis israelensis-based larviciding, while house improvement (henceforth HI) involved closing of eaves and gaps on walls, screening windows/ventilation spaces with wire mesh, and doorway modifications. Communities implemented all interventions. Costs were estimated retrospectively using the ‘ingredients approach’, combining ‘bottom-up’ and ‘top-down approaches’, from the societal perspective. To estimate the cost of independently implementing each intervention arm, resources shared between trial arms (e.g. overheads) were allocated to each consuming arm using proxies developed based on share of resource input quantities consumed. Incremental implementation costs (in 2017 US$) are presented for HI-only, LSM-only and HI + LSM arms. In sensitivity analyses, the effect of varying costs of important inputs on estimated costs was explored.In the trial, LSM comprised draining, filling, and The total economic programme costs of community-led HI and LSM implementation was $626,152. Incremental economic implementation costs of HI, LSM and HI + LSM were estimated as $27.04, $25.06 and $33.44, per person per year, respectively. Project staff, transport and labour costs, but not larvicide or screening material, were the major cost drivers across all interventions. Costs were sensitive to changes in staff costs and population covered.In the trial, the incremental economic costs of community-led HI and LSM implementation were high compared to previous house improvement and LSM studies. Several factors, including intervention design, year-round LSM implementation and low human population density could explain the high costs. The factorial trial design necessitated use of proxies to allocate costs shared between trial arms, which limits generalizability where different designs are used. Nevertheless, costs may inform planners of similar intervention packages where cost-effectiveness is known.Trial registration Not applicable. The original trial was registered with The Pan African Clinical Trials Registry on 3 March 2016, trial number PACTR201604001501493The online version contains supplementary material available at 10.1186/s12936-021-03800-4. Plasmodium falciparum malaria remains an important global health problem, especially in Africa. This reduction was largely attributed to vector control interventions: insecticide-treated nets (ITN) and indoor residual spraying (IRS) ) of standing water bodies. Following an initial training of trainers, health animators were trained on LSM rationale and methods and cascaded training to other community members. At the community level, village members first manually identified all water bodies on the ground, and produced hand-drawn maps, using their knowledge of their community topography. While satellite images were used in research sub-studies within the larger MMP project, no satellite images or other technology was used in identification and/or mapping of water bodies related to the community-led trial implementation. Using a predefined algorithm, community members targeted mapped water bodies for draining or filling, or larviciding if draining/filling were deemed inappropriate, e.g. for water bodies used for domestic purposes. Application of Bti was introduced later after communities had already started implementing draining and filling ). Source: McCann et al. unpublished data.Additional file 6. Map of the Majete Malaria Project larval source management and house improvement trial catchment area showing the relative locations of trial villages, organized in groups (red boundaries), called ‘Focal areas’ (FA) A, B and C) in which the interventions were implemented, project field offices (located in Chikwawa district) and the program manager’s office, located in Blantyre City. Base Maps adapted from Google Maps. The main road network connecting the project manager’s (COM) office and field sites is shown in yellow and white.


A lack of cost information has been cited as a barrier to implementation and a limitation of implementation research. This paper explains how implementation researchers might optimize their measurement and inclusion of costs, building on traditional economic evaluations comparing costs and effectiveness of health interventions. The objective of all economic evaluation is to inform decision-making for resource allocation and to measure costs that reflect opportunity costs—the value of resource inputs in their next best alternative use, which generally vary by decision-maker perspective(s) and time horizon(s). Analyses that examine different perspectives or time horizons must consider cost estimation accuracy, because over longer time horizons, all costs are variable; however, with shorter time horizons and narrower perspectives, one must differentiate the fixed and variable costs, with fixed costs generally excluded from the evaluation. This paper defines relevant costs, identifies sources of cost data, and discusses cost relevance to potential decision-makers contemplating or implementing evidence-based interventions. Costs may come from the healthcare sector, informal healthcare sector, patient, participant or caregiver, and other sectors such as housing, criminal justice, social services, and education. Finally, we define and consider the relevance of costs by phase of implementation and time horizon, including pre-implementation and planning, implementation, intervention, downstream, and adaptation, and through replication, sustainment, de-implementation, or spread. Contributions to the literatureA lack of cost information has been cited as a barrier to implementation and a limitation of implementation research; therefore, this paper explains how implementation researchers might optimize their measurement and inclusion of costs, building on traditional economic evaluations of health interventions.The paper bridges health economics and implementation science to guide researchers in their approaches to costing in implementation studies.This paper defines and considers the relevance of costs by phase of implementation and time horizon, including pre-implementation and planning, implementation, intervention, downstream, and adaptation, and through replication, sustainment, de-implementation, or spread.Costs have been identified as a key outcome in implementation research , 2, yet Costs are incurred when employing an implementation strategy . If the Implementation costs are those related to the development and execution of the implementation strategy that targets one or more specific evidence-based interventions. The strategy will affect directly the subsequent intervention, possibly in its efficiency, utilization, or quality. For example, a recent US study examining the costs of implementing collaborative care to improve depression treatment tracked the costs of recruiting and employing case managers to coordinate services across organizations and staff; costs of training psychiatric consultants, social workers, and other care staff; costs of installing information technology; and costs accrued to study sites for planning local strategies to implement collaborative care . The ExpImplementation strategies may impose efficiency losses or gains when clinicians and other frontline workers participate in educational activities or training (instead of providing or “producing” direct care or services). There are two approaches to consider for these costs. One is to collect primary data to measure these activities. Another approach is to assume that the added time is minimal and can be ignored. The latter approach is appealing for its simplicity, but results can be misleading if these costs are greater than zero. For example, a program might assume that the implementation effort will be handled by primary care providers, who in a typical day spend 27% of their time face-to-face with patients and about 1–2 h nightly on documentation . ConsequThese are resource costs that result as a direct consequence of implementation strategies targeting evidence-based interventions. For example, implementation scientists may seek to increase the use of tissue plasminogen activator (tPA) for patients with ischemic stroke. In this case, the intervention costs are those associated with specific changes in the use of tPA. The intervention costs increase with uptake, whereas de-implementation studies typically seek to reduce the use of an intervention and its related costs. Intervention costs should also consider the costs borne by patients, participants, or caregivers, as a result of the intervention. In addition, the intervention may require other types of resources such as non-healthcare resources , informal care assistance and time, and patient resource costs (including time). Whenever possible, researchers should estimate the intervention costs separately from the implementation costs .We define downstream costs as those that are further subsequent to the implemented intervention and may have changed, intentionally or unintentionally, as a result of the implementation strategy and intervention. Potential downstream costs include healthcare utilization and costs, productivity costs of patients and caregivers, costs in sectors other than healthcare, and future related and unrelated health and non-health costs that accrue to the initial decision-maker or to other stakeholders. As shown in Fig. Our definition of downstream costs is different from traditional clinical cost-effectiveness analytic methods , 14. We Several additional factors should be considered when deciding whether or not to include downstream costs: (1) organizational complexity of the implementation strategy and intervention; (2) number of people affected by the intervention; (3) time horizon and ability to capture downstream costs and effects; (4) whether the intervention focuses on more acute issues versus preventive services with a late, long-term impact; and (5) stakeholder perspective. Unless there is a strong justification not to do so, we recommend tracking downstream costs, especially because these costs—intended and unintended—could affect sustainability and penetration, and may be of interest to various stakeholders.Healthcare costsThe costs of healthcare resources could come from hospitals , laboratory services and procedures, imaging tests, clinician services , medications, hospice care, home care, nursing homes, and other health service and education providers . Typically, healthcare utilization and costs are measured at the patient level ; time required for treatment, waiting, and travel; transportation costs ; and dependent-care costs  during treatment of the participant or patient. For instance, a nutrition intervention may require patients to incur cost of dietary changes. Another example is the time required for patients receiving oncology care . PreviouCaregiver costsSometimes caregivers are paid employees, but often caregivers are family members or friends. Caregiver costs include time and transportation to, from, and during the intervention, if the caregiver is accompanying the patient or participant. This can be non-trivial, for example, for lengthy hospital stays or on-going health education programs. Caregiver costs may also include the time of home-based care of a participant or patient at the cost of what it would have been if provided by paid staff, even if time were “donated” by a family member or friend. Caregiver costs are also termed “spillover effects.” A positive spillover effect would be reduced caregiving time resulting from improved patient health following an intervention , whereasProductivity costsProductivity costs refer to changes in earnings  of patients and caregivers due to changes in health or illness. Whether to include changes in earnings in economic evaluations has been debated, with guidance shifting over time , 23. PreOther sector costsOther sectors besides healthcare may be impacted by implementation activities, including criminal justice, housing, or education sectors, social services, or the environment . For exaResearch and development costsTraditional methods in economic evaluation suggest excluding research and development costs of the implementation strategies or interventions themselves. Research-specific activities that were essential to designing and developing the original implementation generally should be excluded, sometimes called pre-implementation or design components , 28. ForBesides the distinction between implementation, intervention, and downstream costs, there are different cost categories to consider that depend importantly on the study’s stakeholders, analytic perspective, and time horizon—evaluation aspects on which we expand later and that inform the decisions about which costs to capture. A brief discussion of the different cost categories includes healthcare costs, patient costs, caregiver costs, productivity costs, other sector costs, future costs, and the typically excluded research and development costs.Distinguishing fixed and variable costsImplementation analyses should differentiate between fixed and variable costs whenever possible. Accountants usually define fixed cost as costs for resources that do not change in a fiscal period, which might be a month or a year. Economists view fixed and variable costs based on whether they vary with scale of production . Seeing more patients requires more labor and supplies, which are variable costs that refer to relatively small changes in production. Another example is implementing additional “tweaks” to an information technology system as part of an implementation strategy , which would yield variable costs, but the information technology system itself would be considered a fixed cost. Equipment such as CT scanners, buildings, or costs for developing an educational program as an implementation strategy are fixed and do not change with scaling up, although there may be a future decision to purchase a new scanner or building when production is large enough.In the long term, when scale of production can see large changes in variation, all costs can be considered variable . In the AccuracyResearchers may ask, “how accurate do cost data need to be?” Accuracy describes the extent to which cost estimates reflect the “true” opportunity cost. In some controlled studies, the same costs may be incurred in the intervention and control groups. When computing the difference in costs between the intervention and control arm, these costs cancel each other out and may be excluded. However, including these costs has two advantages. First, it allows one to check whether the costs are the same in both groups. Second, including these costs may facilitate comparisons to other studies. Whether these costs are included or excluded needs to be stated explicitly in publications so the reader is not misled. Finally, more attention should be placed on accurately measuring large and highly variable costs. For example, implementation scientists should spend more effort measuring costs for hospitalizations, which are expensive, compared with brief phone calls, all else being equal.PrecisionCosting may also raise questions about precision. Precision depends on the unit of measurement and how finely the resources are measured. For example, imagine a study designed to use practice facilitation to implement telerehabilitation for patients who experienced a major cardiac event. It would be relatively easy to develop a one-time survey or interview to estimate accurately the average cost of this intervention . HoweverApproaches to costingMicro-costing has been used widely in the health economics literature. This involves tracking all of the inputs used to make a product or provide a service. Costs are estimated by multiplying the quantity of these inputs by their input costs. Many implementation studies will estimate costs using micro-costing methods, which can vary in their precision. The term micro-costing methods refers to a set of similar methods with different names, such as time and motion (TM) studies or time-driven activity-based costing (TD-ABC). Both TM and TD-ABC involve tracking the time it takes to conduct activities. Often process maps are linked to specific activities, resources, and events and their frequencies are calculated. The time for each activity is multiplied by appropriate wage rates and then summed to estimate total costs . Micro-cThe “gold standard” for measuring costs is to estimate opportunity costs, which are defined as the value of something that is given up to acquire the next best option, or what was foregone by purchasing what was purchased. Opportunity costs are specific to the decision-maker and they may change as the time horizon changes. Market prices sometimes provide a good estimate of opportunity costs. When estimating the cost of supplies, such as computers or bandages, it is easy to observe the unit costs by searching market prices on the Internet. In other sectors, such as prisons and healthcare, market prices for estimating the average cost of a 4-day stay are typically not readily available, in which case, production costs are used as proxies for opportunity costs.Micro-costing is prohibitively time consuming for estimating the cost of an inpatient stay. Fortunately, some organizations track labor and supply costs using activity-based cost (ABC) accounting databases to estimate their production costs. These systems track the variable costs provided to each patient. These databases also track fixed, capital costs, including equipment and space, and hospital overhead . ABC databases have become the gold standard for estimating healthcare costs in economic evaluation . These sAn increasing number of hospitals and healthcare systems are investing in ABC systems, because they enable the hospital managers to examine their production costs , 37, butBecause not all organizations use ABC systems, another estimation approach is to use macro-costing, also called gross costing, which is based on grouped or aggregated resources used , 38. OrgThere are national databases in the USA, such as the Healthcare Cost and Utilization Project (HCUP) , MedicarHow to generalize cost estimates from specific healthcare systems or settings is a question that remains. However, national-level estimates of costs and disaggregated resources required may be the most useful so that local organizations can adjust to their locality. At the very least, one can conduct sensitivity analysis to assess a range of costs that might be relevant to various localities . Importantly, using data from one hospital or organization for another makes implicit assumptions about the production process. Variation in hospital efficiency may be less consequential in economic evaluations that use a long-run time horizon, but they can be important when assessing variable costs in the short run , 45.Regardless of the potential difficulties, implementation scientists should focus on production costs when estimating costs, which are likely to lead to the best approximation of opportunity costs for implementation studies, with special attention to the contextual issues relevant to estimating production costs. Because there is no database reporting opportunity costs, implementation researchers might consider qualitative work prior to estimating production costs to ensure that the estimates reflect the variable cost within a decision-maker’s time horizon .Different types of costs may be incurred by stage in the implementation process  Table 11. Those Follow-up within implementation studies is often short, even as one seeks to estimate longer-term cost  outcomes for downstream impacts. For example, decision-making about an intervention that prevents injurious falls, a significant source of morbidity and mortality for older adults, is best informed by estimates of costs saved per fall avoided. However, the impact of such an intervention may not be observed for months or years afterwards . To overDepending on the stakeholders or decision-makers, there can be many analytic perspectives to consider for the cost and economic analysis of intervention implementation, for example, society , patient, caregiver, healthcare system, healthcare sector, criminal justice system, employer, or public health department. The costs that should be included in an analysis are those that accrue to or are paid by the decision-maker, and in some cases, stakeholders may care about broader costs than ones attributed to them . For exaA societal perspective considers every potential cost associated with the implementation effort, including healthcare sector resources, non-healthcare sector resources, informal caregiver or volunteer time, patient time and transportation, costs of changes in patient productivity, and future costs related and unrelated to the intervention  Fig. 1)1). ConduA recent study evaluated practice facilitation as an implementation strategy for improving small primary care practices’ adherence to clinical practice guidelines for cardiovascular risk reduction , 54. To Another example of the potential implications of the choice of analytic perspective relates to a study of substance abuse treatment. Societal cost-effectiveness analyses have shown that substance abuse treatment is cost-effective . Yet, thFurthermore, broad economic evaluations that maximize social welfare may conflict with narrower, focused evaluations that are frequent in implementation science. Decision-makers often want a stylized analysis, such as a budget impact analysis , 57, or As a rule, implementation scientists should sum the total costs of an implementation strategy and its impacts (and perhaps by stakeholder perspective) within an explicit time period Table . This maIn a study comparing group-based versus individual-based multidimensional-treatment foster care, the authors measured costs of feasibility, planning meetings, selection of service providers, staff training, fidelity monitoring and review, site visits, and feedback by stages of progress toward implementation . By captIdeally, implementation scientists should report both total intervention implementation costs and more granular cost measures. Because implementation strategies often target behavior change for organizations or professionals, the cost per practice or organization, per professional, or per end-user may be suitable metrics for the decision-maker. However, the major limitation of such denominators is the inability to attribute costs to a patient or participant and therefore to a subsequent health outcome; ultimately, the goal of implementing evidence-based interventions is to improve health outcomes and population health.Sometimes a decision-maker wants to know how comparative implementation strategies with their evidence-based intervention affect health outcomes Fig. . Such coCollecting information on patient outcomes may be burdensome and not central to an implementation study. Where there is robust evidence supporting an intervention being implemented, implementation studies may restrict themselves to measuring organizational and professional behavior change and report relevant outcomes, such as the additional cost for a percentage change in clinical guideline use. Other outcomes to consider for economic evaluation in implementation science could vary as widely as cost-per-patient or participant, cost-per-practice or per-health center, or cost-per-inappropriate MRI avoided. Implementation cost-effectiveness outcomes that incorporate patient-centered health outcomes can be added to decision analytic models by adapting the structure and evidence from intervention models .To address the potential bias in estimating costs in implementation studies, sensitivity analyses are essential. Substituting job classifications, varying program capacity , reflecting regional or national wage rates, and estimating best/worst case are approaches to testing robustness of results.Implementation scientists should consider a broad range of cost categories when assessing the economic implications of strategies for implementing evidence-based care—and measure the costs relevant to the choice of analytic perspective(s), time horizon(s), and outcomes. Implementation scientists should explicitly consider costs from all sectors, such as healthcare, justice, and education, with strong consideration of the magnitude of costs for each sector and the cost or time burden of collecting the data. Although many implementation studies in healthcare focus exclusively on healthcare costs that are relevant for a particular decision-maker, a broader analysis may be useful or applicable to more than that decision-maker alone, leading one to consider additional, disaggregated, or divisible cost categories.If researchers are using data that originate from accounting systems, where they can identify the variable and fixed costs, then they should determine how that system defines fixed costs and decide whether to treat those costs as fixed or variable given that the study time horizon may be different from the accounting system. Estimating costs by sector, phase, or even participant or patient subgroup can be useful to ensure a wide range of stakeholders understand the costs or savings that would accrue for their particular concern or perspective. Specific types of costs may affect adoption, implementation, fidelity, and sustainability.Studies need to be explicit about each included cost category, type of cost data and its source, and how costs were calculated, to enhance transparency and improve adoption decisions by others. Engaging an expert in economic evaluation and key stakeholders prior to implementation can help inform which costs to capture and how. Making all analytic assumptions clear, justifiable, and unambiguous, e.g., durability of the intervention’s effect, time horizon, analytic perspective(s), and reasons for including/excluding specific costs, allows readers and decision-makers to replicate or compare results and apply them to their own settings. As outlined here, conducting analyses by societal, stakeholder, and sectoral perspectives will allow for broader uptake and relevance of value assessments by various decision-makers. Such value assessments can also help decision-makers reallocate resources to address health equity, such as redirecting resources to areas of greatest need .


Fishing fleets and targeted stocks are the basis for the design of multiannual management plans at European or Mediterranean levels. Management Strategy Evaluation and bioeconomic modeling need data at a specific level of resolution in terms of time, area and type of fishing activity for analyzing measures for management procedures using simulations. Within the Data Collection Framework, data are to be aggregated at different levels, e.g.: fleet segment and métier, the former linked to the predominant gear and the size of the vessel and the latter to the activity itself. Fishing costs are collected by fleet segment, effort and landings by fleet segment and métier. Bioeconomic modeling for management purposes requires data at the same resolution. The aim of this paper is to describe a methodology, implemented in SECFISH R package, to disaggregate variable cost data from the fleet segment to the métier level. The presented tool allows to determine the correlation between the variable costs of a vessel and its activities to estimate costs at the activity level (e.g. métiers). The tool is applied to selected Italian fleet segments characterized by a variety of métiers and high dynamicity. In mixed fisheries, the interaction between fishing fleets and targeted fish stocks is the cornerstone for the design of multiannual management plans (MAP) for fisheries management at the European and Mediterranean levels –4. On thData availability is often a limiting factor for the design of a bioeconomic model, which adds complexity to the implementation . Having In 2000, the European Union (EU) designed a specific framework to guarantee that each Member State carries out a regular collection and management of a wide range of fishery data needed for scientific advice in support of the Common Fisheries Policy (CFP). Within the Data Collection Framework  2019/910), aimed at collecting information for fisheries assessment, bioeconomic modelling, scientific advice, different levels of aggregation for data provision are defined. In our context we focus on:fleet segment: a group of vessels with the same length class  and a predominant fishing gear during the year;métier: a characterisation of fishing operations targeting a similar (assemblage of) species, using similar gear, during the same period of the year and/or within the same area.The economic information gathered by fleet segment includes operating costs that are linked to the activity of the vessel and fixed costs that incur independent of effort. The variable costs, together with other more stable components , are included in the relevant economic indicators aimed at identifying the status of a fishery in terms of profitability , Current Revenues/BER, Return on Fixed Tangible Assets (RoFTA), and Return on Investment (ROI))  and are Most bioeconomic mixed fisheries models present in the literature also require data on the cost of fishing at métier level . SpecifiA vessel can fish in different areas with different gears on different stocks over the year; hence, the overall sum of annual cost data only reflect an average across different fishing activities, not allowing to extrapolate the specificities of the different fishing activities that may relate not only to the impact on the marine resources   but alsoWithin the DCF, the fleet segment and the métier have been historically recognized as separate entities. Indeed, the so-called transversal data  are available by fleet segment and by métier, being strictly linked to the fishing activity, while the data on the fixed and variable costs are collected at the fleet segment level. While the fixed costs, e.g. insurance, are mainly related to vessel characteristics, the variable costs, i.e. mainly fuel and labor costs are more closely linked to the fishing activity.As observed by the Planning Group for ECONomics  and STECThe uses of cost data for fisheries’ management strategies based on fishing effort regimes and spatially oriented multi-annual plans are manifold: a recent example is the application of BEMTOOL, IAM and SMART models for simulating the impact of different fishing effort regimes on Western Mediterranean demersal fisheries . In simihttps://CRAN.R-project.org/package=SECFISH). To illustrate the working mechanism of the method, the approach has been applied to selected Italian fleet segments, characterized by activities in different métiers.The main aim of this paper is to describe a cost-efficient methodology allowing to derive annual cost data at métier resolution through a linear disaggregation algorithm. The algorithm, based on the current available resolution of variable costs and transversal data in the DCF (i.e. at fleet segment level) is implemented in an R package and published on R Cran  and transversal variables ; second, the costs by fleet segment and the transversal variables by fleet segment and métier are used to assign the annual costs by fleet segment to specific métiers. Only the first phase requires access to individual vessel data, while the second phase was designed to be run by any end-user by access and use of publicly available information.In In the second phase the coefficients derived in the first phase are, then, utilized to disaggregate the variable costs data, available at fleet segment resolution, at the métier level. Consistency checks are finally carried out between original data and disaggregated data.https://stecf.jrc.ec.europa.eu/data-dissemination).Both costs by fleet segment and transversal data by métier are accessible through the EC data dissemination tool  or vs. revenues minus total variable costs or vs. revenues minus fuel costs or vs. effort.The effort can be expressed in terms of hours at sea or fishing days per KW.This approach allows also to explore maintenance or other costs versus the effort relationships. However, we focused on “labor” and “fuel,” which are usually more closely linked to the transversal data  and are the most relevant cost items for fishing vessels. The relationship between energy costs and effort have been already investigated during the Workshop on Allocation of Economic Data at disaggregated level as related to the DCF held in Hamburg in 2011 and the Workshops on European economic database and on disaggregation of economic data as related to the DCF held in Malta in 2012. –21 proviFor each combination “fleet segment-métier,” the significant dependence of the variable costs on the selected transversal variable  was tested. Evident difference among the slopes of the different métiers within a fleet segment is interpreted as a signal of possible different cost structure. Especially in this case, it could be relevant to statistically explore the métier effect on the costs for the purpose of disaggregation.x1 , x2 , and their combination x1x2, while the error is assumed to be distributed as a standard normal distribution. Furthermore, α, β and γ are the coefficients that are utilized to disaggregate the costs, if the métier is statistically significant.Proceeding within phase 1, we applied the generalized linear model (GLM) approach  with a GIn each fleet segment, the métiers representing a defined threshold (e.g. 80%) of the vessels’ activities observed have to be considered to avoid spurious relationships and convergence problems when fitting the GLM. The threshold can be set as an input in the corresponding function in the SECFISH R package.According to the results of the GLM, three cases are then identified:Dependency of variable costs on the transversal variables exclusively;Dependency of variable costs on the métier and on the transversal variables separately;Dependency of variable costs on the métier and on the transversal variables, separately and combined.In case 1, the variable costs are assumed to vary linearly with the transversal variables equally for all the métiers:In cases 2 and 3, the disaggregation of the variable costs was carried out following the decision support flow in α multiplied by the transversal variable by métier, being α the same for all métiers. The variable costs by fleet segment were finally disaggregated according to the selected model, and a comparison between the official costs by fleet segment and the estimated costs by métier (summed up) was carried out as a consistency check.When https://cran.r-project.org/web/packages/SECFISH/index.html) through ad hoc functions that allow to apply all the steps reported in The above described methodology was implemented in SECFISH R package published on R Cran  and plots (e. g. linear regressions plots).https://datacollection.jrc.ec.europa.eu/wordef/fishing-activity-metier, https://datacollection.jrc.ec.europa.eu/wordef/fleet-segment-dcf). On one hand, fuel costs, labor costs and fuel consumption are linked to individual vessels; on the other hand, a prevalent métier is associated with each vessel according to the métier most frequently applied in terms of fishing activity, thus excluding those observations attributable to less relevant métiers. The variable costs associated with each observed vessel were then adjusted proportionally to the percentage of activity of the observations: if the prevalent métier represents the 75% for the total activity monitored for a vessel, all the type of variable costs associated to that métier for that vessel were estimated as the 75% of the variable costs collected for the vessel. By doing this, a new dataset of individual vessel data was generated, where each vessel is associated with a unique prevalent métier and its variable costs are approximately solely the ones due to that métier. Within each fleet segment a number of métier are, thus, present.Individual vessel data on variable costs (fuel costs/consumption and labor costs) and fishing activity  were used to test the significance of the influence of the métier on the cost structure. 6,747 associated trips carried out by 519 Italian vessels, classified as belonging to the demersal trawlers (DTS) and hooks (HOK) fleet segments  with difhttps://stecf.jrc.ec.europa.eu/dd/fleet), while the transversal data by fleet segment/metier have been extracted from the Fishery Dependent Information (FDI) data repository (https://stecf.jrc.ec.europa.eu/dd/fdi) for the years 2015–2016.Following the second phase of the methodology, the DCF variable cost data of the selected Italian fleet segments were extracted from the EU Commission Fleet economic data repository , to avoid spurious relationships , OTB_DWS (bottom otter trawl for deep water species) and OTB_MDD , while for HOK VL1218, it was LLD_LPF.Figs Similarly, a significant dependency of fuel consumption on hours at sea was observed for all segments, except for DTS VL2440.The labor costs are closely correlated with the revenues due to the crew share remuneration method applied by most Italian fisheries, –21 with In all the fleet segments, labor costs were not influenced by the métier, but only by the revenues, except for DTS VL2440, for which a significant impact of the métier on the costs was found. For DTS VL1218, DTS VL1824 and HOK VL1218, the revenues appear to be the main explanatory variable, with an average explained deviance of 94%.Following the workflow in For fuel costs and fuel consumption, Only for the DTS VL2440 fleet segment was the métier found to also have an influence on labor costs, while for the other fleet segments, these costs were driven uniquely by the revenues.The variable costs were disaggregated according to the relationships (and corresponding coefficients) reported in For DTS VL1218 and DTS VL2440, larger differences between cost by fleet segment and the sum of estimated costs by métier were obtained for the fuel costs, while the sum of the disaggregated labor costs seems to be globally more in line with the figures by fleet segment. For DTS VL1824, the 2015 data are less consistent than the 2016 data for both labor and fuel costs.Several bioeconomic models require variable costs as input at the resolution of metiers or comparable levels. However, these cost data are usually only available at higher aggregation, namely at fleet segment level as defined under EU data collection legislation, thus hampering the application of the models. As a consequence, the structure of variable costs has to be assumed homogeneous for all métiers throughout a fleet segment when applying these models.In order to overcome this issue, individual vessel data on activities by métier need to be explored. This investigation can also be useful to obtain a more accurate characterization of the fleet, not only based on the technical characteristics of the vessels but also on the habits of the fishermen in terms of visited fishing areas and métiers.Within bioeconomic models, the variable costs are generally assumed to be proportional to the effort. However, no standardized procedure to estimate the coefficients, describing this linear dependency, is currently published.The methodology described here allows both to carry out this investigation on individual vessel data in order to facilitate the identification of the main metiers exerted by the fleet segments and to explore whether and how the structure of their variable costs depends on factors other than vessel length and main gear. This deeper insight in cost structures should improve the quality of bioeconomic projections.In order to apply the methodology presented here, annual cost and fishing activity data by métier are needed for individual vessels. In addition to aggregated cost data, information needed includes the type of fishing activity at trip level and the cost data for the sampled vessels. Hence, access to individual data is crucial in order to apply the method. Because this type of data, although possibly anonymized, are expected to potentially contain sensitive information, they could be obtained through a formal request to Member States, specifying the purpose of their use. This drawback could be overcome, utilizing specific assumptions on the crew share (for labor costs) and on the fuel cost/consumption per unit of effort; these assumptions can be based on literature or expert knowledge, rather than on individual vessel data.The method is not limited to fleet segments and métiers. It can be adapted to any level of fishing activity and vessel grouping for which cost data are available at higher level, as long as activity data are accessible at the required resolution.The focus of the present work was on energy (fuel) and labor costs; these are indeed the major components of the operating costs, the former being the major ones for some active gears, like demersal trawls, and strictly linked to the fishing time, while the latter depend mostly on the revenues. Nonetheless, the methodology is applicable for any type of variable cost.An application to the Italian trawlers and longliners fleet segments, which are characterized by a marked dynamism and mobility in terms of fishing activity , is now It should be noticed that, on one hand, the methodology facilitates the disaggregation of the variable costs in the presence of dependency both on the métier and on the transversal variables; on the other hand, it helps identifying the relationship between costs and transversal variables in the case of a unique prevalent métier or non-dependence on the métier. Concerning these two macro-groups, the results showed a different structure in the energy costs analysis, while a similar dependency of the labor costs on the revenues was detected for both. This is consistent with the crew share system widely used in most Italian fisheries.Regarding the trawlers fleet segments, the relevance of the métier on the energy costs seems consistent with the characteristics of the explored métiers; indeed, for smaller and medium sized trawlers (vessel length between 12 and 24 meters), the métier targeting demersal resources and the one targeting demersal and deep-water resources represent quite different types of activity. While the demersal fishing activity is generally carried out on the continental shelf, producing lower energy costs, the mixed one tends to be distributed offshore with longer steaming times, causing higher fuel consumption and thus energy costs. The difference between the métiers is more explicit for large trawlers (length>24 meters), where even the deep-water métier, characterized by a more specific deep-water species target, is well represented.For these fleet segments, the selected models explain a satisfactory amount of the variability in the data and allowed to disaggregate the fuel costs by metier, with a percentage of difference from the aggregated official values of around 19%; this is still consistent with the choice to consider only the metiers representing 80% of activity in the individual vessel data to avoid convergence problems.For the fleet segment composed by vessels mainly using passive gears such as longlines (coded under HOK VL1218), it was not possible to properly test the significance of the métier on fuel and labor costs because, according to the individual vessel data, this fleet segment exerts only one prevalent métier in the individual vessel data.The results for labour costs generally highlight the strong dependency on revenues, as expected for the crew share remuneration system applied in Italy. Indeed, only for large trawlers (length>24 meters) the métier was found to have an impact on the crew share. This is due to the higher variability in fishing activity within this particular fleet segment . Indeed, for example, a trip of a trawler targeting deep-water species (OTB_DWS) generally can last more than one day, as it is carried out in more distant fishing grounds; this is different from the trip of trawler just targeting demersal species (OTB_DES), which is performed closer to the port of origin. It is not surprising that the salary needs to fit the duration of the fishing trip along with revenues.Moreover, this methodology allows not only to highlight the dependency of the variable costs on the type of métier utilized, but also on the fishing grounds most commonly visited. Indeed, the application of the model to the Belgian fleet , of determining the biological and socio-economic effects of specific spatial closures  .The possibility of obtaining variable costs at a higher resolution would, in combination with a raster of effort or revenues data by fishing ground or grid cell, allow to also predict the fleet adaptation/effort displacement starting from historical data . This ouFrom this perspective, the development of this methodology could represent a step forward to the possibility of properly testing spatial fishing restrictions in fishery by accounting, in a meaningful way, for fleet adaptation/effort displacement and drivers in individual fisher decision-making in a meaningful way .The focus of this paper is on active gears (trawls), for which the fishing effort can be quite precisely expressed in terms of hours at sea and kW. More exploration is necessary for analyses on passive gears other than hooks, such as gillnet and trammel net, for which the effort needs to be defined according to more specific variables, e.g., for number of trips.A necessary disclaimer for the proper use of the method presented here is related to the completeness of the input data: in order to allow a reliable analysis, the individual vessel dataset used for deriving the regression coefficients needs to be large enough and properly represent the distribution of the effort by métier within the whole fleet segment. The more individual vessel data represent the fleet segments for which a disaggregation is desired (in terms of proportion of fleet segments and métier), the more, the estimated costs by métier are expected to be consistent with the data available at the aggregated level.Another limit of the analysis is addressed by pooling individual vessel data from different years. This has the advantage of increasing the sample size. However, especially if more than three years are grouped, the independence hypothesis of the observations could be violated. To account for this dependency structure, it may be advisable to include a vessel random effect or a time series model , when modeling the GLM on individual vessel data, for counting a “year effect.” Moreover, other distributions for the GLM could be explored in the future for a positive continuous response variable  in order to relax assumptions of normality in the residuals. Based on these considerations, the developed methodology can be extended to accommodate the modeling of the costs for vessels applying passive gears and to account for random effects.The variable costs of a fishery represent a component of the expenses that can be controlled by vessels’ owners to increase the cash flow and that cannot be generally disregarded by any modeling approach of a fishery.In order to properly utilize the available data collected under the European Data Collection Framework within bioeconomic models for the evaluation of fishery management measures, it is crucial to reconcile the economic and biological fishery data resolution.The methodology described here has been implemented in the SECFISH R package and allows to derive annual cost data to the métier level and/or eventually to the fishing area level through a linear disaggregation algorithm based on variable costs and transversal data. It is a cost-efficient technique as it is intended to produce more disaggregated cost data without changing the resolution scales currently implemented in the DCF, hence not increasing the financial budget supporting the data collection at MS level.


Alcea rosea) plants. In this study, we show that alphasatellites CLCuMuA and GDarSLA attenuate and delay symptom development in Nicotiana benthamiana. The presence of either alphasatellites reduce the accumulation of the helper virus CLCuMuV-IN. However, the levels of the associated betasatellite, LuLDB, remains unchanged. These results suggest that the alphasatellites could contribute to the host defence and understanding their role in disease development is important for developing resistance strategies.Alphasatellites are small coding DNA satellites frequently associated with a begomovirus/betasatellite complex, where they are known to modulate virulence and symptom development. Two distinct alphasatellites, namely, Cotton leaf curl Multan alphasatellite (CLCuMuA), and Gossypium darwinii symptomless alphasatellite (GDarSLA) associated with Cotton leaf curl Multan virus-India (CLCuMuV-IN) and Ludwigia leaf distortion betasatellite (LuLDB) were found to be associated with yellow mosaic disease of hollyhock . The viral DNA molecules were quantified in N. benthamiana plants by qPCR. The sequences were analysed using the MEGA-X tool, and a phylogenetic tree was generated. Genetic diversity among the CLCuMuA and GDarSLA was analysed using the DnaSP tool.Tandem repeat constructs of two distinct alphasatellites, namely, CLCuMuA and GDarSLA associated with CLCuMuV-IN and LuLDB were generated. We observed a reduction in symptom severity and accumulation of helper virus in the presence of two alphasatellites isolated from naturally infected hollyhock plants. However, no reduction in the accumulation of betasatellite was observed. The phylogenetic and genetic variability study revealed the evolutionary dynamics of these distinct alphasatellites , which could explain the role of hollyhock-associated alphasatellites in plants.This study provides evidence that alphasatellites have a role in symptom modulation and suppress helper virus replication without any discernible effect on the replication of the associated betasatellite.The online version contains supplementary material available at 10.1186/s12985-021-01718-6. Begomovirus, family Geminiviridae have a small circular ssDNA genome and are transmitted by whiteflies  with CLCuMuA-PK [Pakistan: Cotton: 99], while GDarSLA [India:New Delhi: Hollyhock: 16] with GDarSLA [Pakistan: tor Fig. . Alphasabatatas) –32. Wher77 bp sha) plants .Fig. 2PhTo further understand the genetic diversity of CLCuMuA and GDarSLA alphasatellites, gene diversity (Hd) and nucleotide diversity (π) was calculated. The gene diversity (Hd) and nucleotide diversity (π) have been highlighted in bold Table . CLCuMuAN. benthamiana plants inoculated with an agroinfectious clone of CLCuMuV-IN alone did not develop symptoms. Plants inoculated with CLCuMuV-IN and the dimeric betasatellite LuLDB developed typical symptoms of downward leaf curling, yellowing, vein thickening and stunted growth as early as 7 dpi  of plants nts Fig. a–d. The nts Fig. a. Howevents Fig. a, e. Butnts Fig. a, e. Thints Fig. a, e. Intnts Fig. b, f. LuLnts Fig. b, f.Fig.Cotton leaf curl Bangalore betasatellite (CLCuBaB) [Cotton leaf curl Multan betasatellite (CLCuMuB), LuLDB [, and Malvastrum yellow mosaic alphasatellite (MaYA). In recent years, the association of CLCuMuV with Malvaceae , Solanaceae , Cucurbitaceae (luffa species) family of crop plants have been extensively studied in India and Pakistan  , Cotton ), LuLDB , and Malan Figs. , 3 [39–4N. benthamiana. However, symptoms appear only when CLCuMuV-IN and its associated betasatellite, LuLDB are co-inoculated. When either CLCuMuA or GDarSLA was co-inoculated with LuLDB and/or CLCuMuV, symptoms were delayed and attenuated and the helper virus accumulated to relatively lower levels at early stages of infection  reduces symptoms severity and the relative accumulation of its associated betasatellite, Tomato leaf curl betasatellite (ToLCB), without affecting the accumulation of the helper virus [Tomato yellow leaf curl China alphasatellite (TYLCCNA) reduces the accumulation of both the helper virus Tomato yellow leaf curl China virus (TYLCCNV) and its associated betasatellite Tomato yellow leaf curl China betasatellite  was co-inoculated with different cassava mosaic geminiviruses (CMGs) [Euphorbia yellow mosaic alphasatellite (EuYMA), the accumulation of Euphorbia yellow mosaic virus (EuYMV) DNA A increases in two plant host E. heterophylla and N. benthamiana, whereas EuYMA reduces EuYMV DNA-A levels in Arabidopsis thaliana [. On the contrary EuYMV DNA-B accumulation increases in the presence of EuYMA, in E. heterophylla and its levels in N. benthamiana and A. thaliana remains unchanged [Furthermore, variation in the begomovirus genome allows the virus population to be dynamic and might help broaden their host range . A higheer virus . In anotTYLCCNB, ). Delay s (CMGs) . Interess (CMGs) . The conthaliana . On the nchanged .Suppression of silencing by α-Rep, its interaction and interference with βC1 activity, blocking the function of the helper virus Rep are some of the hypotheses put forward to explain the mechanism underlying the interaction between alphasatellites and its helper virus/betasatellite complex , 45, 46.In summary, our results demonstrate a new and differential interaction between alphasatellites CLCuMuA or GDarSLA and the associated helper virus and betasatellite complex. However, the specific molecular mechanism underlying this interaction remains to be determined. The evolutionary dynamics of α-Rep suggest further, it might have evolved from a common ancestor and then undergone selective differentiation and speciation. The higher genetic variability in CLCuMuA alphasatellite suggests that it might have acquired selective advantage during the course of evolution enabling it  to infect several families of crop plants. It has been also hypothesized that alphasatellites associated with begomoviruses might have been acquired by the helper viruses to modulate the virulence to achieved enhance virus fitness. As these interactions could be host-specific, the interaction between these viral DNAs in the natural host hollyhock remains to be determined. Based on this study, transcriptome analysis of the differentially regulated genes in plants co-inoculated with alphasatellites, the nature of the interaction of α-Rep with other viral proteins such as helper viral Rep and Transcriptional activation protein (TrAP) and cellular replisome can be examined to get molecular details of these interactions. Alpha-Rep is known to suppress TGS which could reduce viral DNA methylation allowing replication. It is tempting to speculate that α-Rep could modulate other epigenetic modifications by interacting with various cellular and viral proteins, which could, in turn, affect replication and accumulation of the helper virus. These studies could help us understand why satellite DNA molecules are acquired by the helper viruses and the biological significance of these interactions.Additional file 1. Details of the sequences, primers and standard curves of viral DNAs used for the phylogenetic analysis and quantitative real-time PCR.


The regeneration time and the amount of sol have been determined. Regularities of change in the radius of particles of silicic acid sol depending on age are determined. It is established that at an early age (up to 5 days), the radius of sol particles can be determined in accordance with the Rayleigh equation, and at a later age, in accordance with the Heller equation. The results of the calculation show that at the age of 1–5 days, the radius of the sol particles is 17.1–17.9 nm, and then the particles become coarser and the particle radius is 131.2–143 nm at the age of 19 days. The work of adhesion of silicic acid sol to lime and the heat of wetting are estimated. It is shown that the work of adhesion of water to lime is 28.9 erg/cm2, and that of the sol is 32.8 erg/cm2. The amount of heat Q released when lime is wetted with SiO2 sol is 15.0 kJ/kg, and when lime is wetted with water, it is 10.6 kJ/kg.Lime materials are in great demand for the restoration of the walls of historical buildings. However, lime coatings have insufficient resistance during operation. The purpose of this work was the modification of lime mortars with silicic acid sol in order to obtain more durable crystalline materials for construction purposes. A technology has been developed for obtaining a silica-containing additive, which consists in passing a liquid glass solution with a density of 1.053 kg/m For the restoration of the walls of buildings, lime compositions are widely used. Lime finishing coatings have high vapor permeability, and have good compatibility with previously finished surfaces ,2,3. HowThe lime compositions Holvi, Kalcemur, Silakra-lime, as well as Antik 1 and Antik 2 and others containing targeted additives, produced by various commercial firms, are widely used for the restoration of cultural heritage sites ,8,9. To In ,17,18, tIn ,23,24, i2, vapor permeability μ ≤ 100, coefficient of thermal expansion—±50% of limestone.In China, during the restoration of historical buildings in the cities of Shanghai and Hangzhou, a system consisting of adhesives and injection mortars based on hydraulic lime (HL) was used to repair and restore exfoliated surfaces made of natural stone, gypsum and brick ,26. The The book  proposesIn recent literature on lime and carbonates used in cement materials, the papers of Songhui Liu and Yuli Wang stands out ,33,34. P2, passes into calcium silicate hydrates, and contributes to a significant (up to 30%) reduction in the number of pores from a size of 1 nm and above (filling occurs in pores by sol particles and products of its interaction) 4− tetrahedra. On the surface, it is hydrated by the addition of H+ cation exchangers to unsaturated oxygen in the Si–O– ion to form OH. In a neutral medium, these particles are almost uncharged [+ cation is replaced by the Na+ cation. Due to the small charge, large ionic radius, and tendency to hydration, the Na+ cation passes into the diffuse layer of counterions, and the sol becomes negatively charged. The physical meaning of the electrokinetic, or zeta-(ζ-) potential is defined as the work that must be expended to transfer a unit charge from a point in the solution volume with a potential equal to zero to a slip plane with a potential equal to ζ. It has been established that the electrokinetic potential is (−) 0.053 V.Colloidal (SiOncharged . In an a3 through a cation exchange column and obtaining a silica acid sol with a pH of 3–4 and a charge of (−) 0.053 V. The regeneration time and the amount of sol were determined.In this way, a technology has been developed for obtaining a silica-containing additive, which consists in passing a liquid glass solution with a density of 1.053 kg/mThe results of determining the coagulation threshold are shown in 2(SO4)3, which causes coagulation of the sol, is kc = 1.168 × 10−6 mol/L.It has been established that the amount of electrolyte AlThe assessment of the effect of stabilizers was carried out by the turbodimetric method. Effective stabilizers of silicic acid sol—gelatin and PVA—have been identified, which make it possible to prevent coagulation of silicic sol. The threshold of coagulation and the protective number were determined.S that is the amount of substance required to stabilize a unit volume of the sol. The protective number S (g/L sol) was calculated according to the equation [stc is a concentration of the stabilizer solution, g/L; and protV is a volume of the stabilizer solution required to prevent coagulation of the sol, mL.It has been established that gelatin and polyvinyl alcohol (PVA) are effective stabilizers for silica sol . The proequation :(6)S=cstThe results of calculation and experiment showed that the protective number of gelatin and PVA, S = 3.81 and 0.41 g/L of the sol, respectively.Regularities of change in the radius of particles of silicic acid sol depending on age are determined. It has been established that at an early age (up to 5 days) the radius of sol particles can be determined in accordance with the Rayleigh equation, and at a later age, in accordance with the Heller equation. The results of the calculation show that at the age of 1–5 days, the radius of the sol particles is 17.1–17.9 nm, and then the particles become coarser and the particle radius is 131.2–143 nm at the age of 19 days.A method for assessing the activity of the sol have been proposed, which consists in the potentiometric determination of the pH of the “sol-lime” system and the calculation of the amount of sol chemically bound to lime.2 was placed into a flask containing a certain volume of sol (sol mass is 40 g) with a known concentration and pH, and the pH of the system was measured using a pH meter. The amount of sol m was determined in accordance with the stoichiometric equilibrium according to the equation of interaction between lime and sol. Determination of the activity of the sol was carried out according to the following method, which consisted in determining its amount that went into interaction with lime. To assess the amount of silicic acid sol that went into interaction with lime, the change in the pH of the “lime—sol” system was determined. For this purpose, a sample of Ca(OH)2, the position of the first equivalence point can be determined by the formula:K1 is the dissociation constant of H2SiO3 for the first stage; K2 is the dissociation constant for the second stage.When polybasic weak acids interact with strong bases, which include Ca (OH)K1 = 2.2·10−10 and K2 = 1.6·10−12, then the pH of the first equivalence point will be 10.73. The amount of sol used for interaction with lime was calculated at pH corresponding to the first equivalence point, i.e., equal to pH1 = 10.73. Then, two multiplied this value, because in the position of the first equivalence point, the amount of sol is ½ of its total amount that interacted with lime [Since Additionally, the activity of the silicic acid sol was evaluated by the change in the compressive strength of the samples of lime compositions with the addition of the sol, depending on its aging period. Samples were prepared with a ratio of components lime:sand = 1:3 and water:lime = 2 with the addition of 2% sol at a ratio of lime:sol = 1:0.25, lime:sol = 1:0.5 and lime:sol = 1:1. The samples were cured under air-dry conditions. The test results are given in It has been established that the use of an “older” sol leads to a decrease in compressive strength, so with a ratio of lime:sol = 1:1, the compressive strength at the age of 28 days when using the sol with an aging period of 1 day is 1.7 MPa, and when using a sol with an aging period of 15 days—1.1 MPa.D is the diffusion coefficient; τ is time, s.An additional confirmation of the dependence of the high activity of the sol is provided by the calculation data of the root-mean-square shift of the particle D of a dispersed particle was calculated using the Einstein equation:k is the Boltzmann constant, equal to 1.38 × 10−23 J/K; T is temperature, K; η is the viscosity of the medium, Pa s; and r is the particle radius, m.The diffusion coefficient r = 72 nm,At −6 m. The root-mean-square shift of a particle with a radius of 17 nm was 1.89 × 10 aging 1 day  shift of a particle with a radius of 72 nm was 7.72 × 10ng 1 day .When developing the formulation of lime compositions, the ratio of lime:sand components varied from 2 to 4. To study the patterns of formation of the structure and properties of finishing compositions and coatings based on them, compositions with different lime:sol (l:s) ratios were studied. For comparison, compositions with the addition of Aerosil were also investigated.It has been established that according to the complex of rheological, technological and physical–mechanical properties, the ratio lime:sol = 1:1 is optimal. The introduction of a sol additive (the age of the sol is 1 h) helps to increase the compressive strength of lime mortars. The compressive strength of the composition with a ratio of components lime:sand = 1:3, water/lime = 2 with the addition of sol at a ratio of l:s = 1:0.5 at the age of 7 days was R = 0.68 MPa and at the age of 56 days R = 0.95 MPa, while in the control, respectively, R = 0.25 MPa and R = 0.61 MPa.An increase in the age of the sol to 11 days reduces the effectiveness of the additive and causes a smaller increase in compressive strength at the age of 28 days of air-dry hardening, amounting to 39% at a ratio of l:s =1:1 . The int2 in the hardened lime composition position . For thiRegularities of hardening of lime compositions with the addition of sol have been established. The calculation results show that the content of free lime in the control compositions is 49.8% of the mass of lime used for kneading, and with the addition of sol is 39.6%, which confirms the chemical interaction of lime with sol.In continuation of further studies to assess the physicochemical interaction of lime with silicic acid sol, the work of adhesion of the sol to lime and the heat of wetting were evaluated.The work of the liquid to lime was calculated by the formula:2. The calculation results show that the work of adhesion of water to lime is 28.9 erg/cm2, and that of the sol is 32.8 erg/cm2 (1 erg = 10−7 J), which leads to the best conditions for the interaction of the sol with lime.It has been established that the contact angle of wetting the lime substrate with sol is 58°, and with water 53°, the surface tension of the sol practically does not differ from the value of the surface tension of water and is 69.8 erg/cm2 sol was 15.0 kJ/kg, and when lime was wetted with water, it was 10.6 kJ/kg. Higher values of the heat of wetting, in our opinion, are due to the additional heat released due to the interaction of lime with sol.The calculation results showed that the amount of specific heat Q released when lime was wetted with SiOHigher values of the heat of wetting, in our opinion, are due to the additional heat released due to the interaction of lime with the sol.When evaluating the structure of decorative plaster coatings, it was found that the control composition is characterized by greater porosity, and the pores are larger (0.25–0.43 mm). The composition with silicic acid sol has a more monolithic structure with pores 0.10–0.21 mm in size .When evaluating the structure of coatings  based on2 crystals are visible, and the crystals are larger in When analyzing the structure of coatings based on the control composition (a) and on the basis of the composition with the addition of silicic acid sol (b), Ca(OH)3 through a cation exchange column and obtaining a silica acid sol with a pH of 3–4 and a charge of (−) 0.053 V. The regeneration time and the amount of sol were determined.A technology has been developed for obtaining a silica-containing additive, which consists in passing a liquid glass solution with a density of 1.053 kg/mRegularities of change in the radius of particles of silicic acid sol depending on age are determined. It has been established that at an early age (up to 5 days), the radius of sol particles can be determined in accordance with the Rayleigh equation, and at a later age, in accordance with the Heller equation. The results of the calculation show that at the age of 1–5 days, the radius of the sol particles is 17.1–17.9 nm, and then the particles become coarser and the particle radius is 131.2–143 nm at the age of 19 days.A method for assessing the activity of the sol has been proposed, which consists in the potentiometric determination of the pH of the “sol-lime” system and the calculation of the amount of sol chemically bound to lime.Effective stabilizers of silicic acid sol—gelatin and PVA—have been identified, which make it possible to prevent coagulation of silicic sol. The threshold of coagulation and the protective number were determined.Regularities of hardening of lime compositions with the addition of sol have been established. It has been revealed that the content of free lime in the control compositions is 49.8% of the mass of lime used for kneading, and with the sol addition of 39.6%, which confirms the chemical interaction of lime with the sol.2, and that of the sol is 32.8 erg/cm2. The amount of heat Q released when lime is wetted with SiO2 sol is 15.0 kJ/kg, and when lime is wetted with water, it is 10.6 kJ/kg.The work of adhesion of silicic acid sol to lime and the heat of wetting have been estimated. It has shown that the work of adhesion of water to lime is 28.9 erg/cmModification of lime finishing compositions with a complex additive based on silicic acid sol was carried out. The following main results were obtained: