Minimum Data Set Research Articles

Detecting Agitated Behaviors in Nursing Home Residents With Dementia Using Electronic Medical Records

ObjectivesAgitated behaviors (behaviors) are common in nursing home (NH) residents with Alzheimer's disease and related dementias (ADRD). Pragmatic trials of behavior management interventions rely on routinely collected Minimum Data Set (MDS) data to evaluate study outcomes, despite known underreporting. We describe a method to augment MDS-based behavioral measures with structured and unstructured data from NH electronic medical records (EMR). DesignRepeated cross-sectional analyses of EMR data from a single multistate NH corporation. Setting and ParticipantsLong-stay residents (at least 90 days in NH) with ADRD from January 2020 through August 2022. MethodsQuarterly and annual assessments of NH residents with ADRD during the study period were identified. For MDS, any behavior was defined as a score of 1 or higher on the Agitated and Reactive Behavior Scale. For structured EMR data, any behavior was defined as increased resident agitation, verbal aggression, or physical aggression on the Interventions to Reduce Acute Care Transfers, Change in Condition form (INTERACT). For unstructured EMR data, any behavior was defined using keyword searches of free-text orders. ResultsA total of 77,936 MDS assessments for 19,705 long-stay residents with ADRD in 322 NHs were identified; 14.8% (SD 35.6) of residents had behaviors per month using MDS alone, 16.2% (SD 36.9) using MDS and INTERACT, and 18.6% (SD 38.9) using MDS, INTERACT, and orders. Supplementing MDS with EMR data increased behavior identification by 3.8 percentage points (a 25.7% relative increase). Less than 0.5% had behaviors noted in all 3 sources consistently across study months. Conclusions and ImplicationsUsing EMR data increased detectable behaviors vs the MDS alone. The 3 sources captured different types of behaviors and using them together may be a more comprehensive identification strategy. These results are important for better targeting of interventions and allocation of resources to improve the quality of life for NH residents with ADRD-related behaviors.

National stakeholder consultation on how to measure care home residents' quality of life.

The Developing research resources And minimum data set for Care Homes' Adoption and use or DACHA study aims to create a prototype minimum data set combining residents' information recorded by care homes with their data held in health and social care data sets. The DACHA minimum data set will contain information on quality of life. Internationally and in the UK, there is no consensus on collecting information on quality of life in a standardised format equivalent to the consensus for health measures. This paper describes an online consultation with stakeholder representatives about how to measure the quality of life of residents in UK older-adult care homes, for inclusion in the DACHA minimum data set. We drew on principles of the Delphi technique, identifying participants knowledgeable about living, working in and visiting care homes, and preference scoring. We used a bespoke online research engagement platform (Thiscovery, www.thiscovery.org, Cambridge, UK) to engage the participants. Participants included care home staff and managers, old age specialists (clinical/research), commissioners/providers/regulators, primary care professionals, relatives/family carers of care home residents. The consultation is complementary to DACHA's research and patient and public involvement and engagement activities, which have involved people living in care homes; thus, care home residents were not included in this consultation. The first round asked 30 participants to rank the most important principles and domains to consider when measuring quality of life in care homes. Responses to round 1 informed the selection of quality of life measures that round 2 (September 2022) participants were asked to report their familiarity with and confidence in a range of outcome measures all of which met the criteria identified as important in round 1. Recruitment was extended in round 2, and 72 individuals participated. Based on the rankings and the qualitative feedback in round 2, we included four of the shortlisted quality of life outcome measures in DACHA's prototype minimum data set for care homes. The qualitative feedback suggested a shared understanding across the different representative groups about the strengths and limitations of the selected measures. This work makes an important contribution, understanding the opportunities that quality of life measures pose for different stakeholder groups as regular users of care home resident data. In future DACHA work, interviews and focus groups will collect further data about the perceptions of care home staff who completed measures during the pilot study and about the usefulness of the data collected via these measures. The quality-of-life section of the DACHA minimum data set can contribute to informing similar care home data sets internationally. This article presents independent research funded by the National Institute for Health and Care Research (NIHR) Health and Social Care Delivery Research programme as award number NIHR127234.

Development of a family-centred intervention for families and children with hearing loss transitioning into early intervention: application of the Behaviour Change Wheel

Objective To describe the application of the Behaviour Change Wheel (BCW) to the development of a family-centred intervention for families of children with hearing loss transitioning into early intervention. Design The BCW was used in a mixed methods design to understand the gaps in family-centred service provision and to identify appropriate intervention functions and implementation options to address these gaps. Study sample Families and health professionals participated in different steps of the BCW. Results The qualitative interviews revealed that families required individualised information and support. The quantitative and interview data suggested inconsistencies in the provision of information and support to some families. It was determined that administration of a needs assessment by the support professionals was the most appropriate target behaviour to address this issue. In the analysis of the professionals’ capabilities, opportunities, and motivations for administration of a needs assessment, a resource limitation was identified, and therefore, a Minimum Data Set for a Needs Assessment Tool was developed to facilitate the assessment of families’ needs during the transition period. Conclusions This study provided an example of how the BCW could be successfully applied to the design of a family-centred intervention for families of children with hearing loss.

The Effects of the Additional Roles Reimbursement Scheme (ARRS) staff in England on Prescription Patterns and Patient Satisfaction.

In 2019, the Additional Roles Reimbursement Scheme (ARRS) was introduced in England as a crucial component of the government's manifesto pledge to enhance access to general practice. The primary objective was to recruit 26,000 extra personnel through new roles into general practice. This study aimed to analyse the effects of ARRS staff on prescription rates and patient satisfaction. The study was a retrospective panel data analysis combining data from the General Workforce Minimum Dataset and National Health Service (NHS) Digital datasets about primary care practices and their activity from 2018 until 2022. The study included data from more than 6000 general practices. A linear regression analysis was conducted to determine the association between ARRS staff and prescription rates and patient satisfaction, controlling for patient and practice characteristics. The results showed that ARSS roles tend to be more frequent in larger general practices, with fewer full-time general practitioners per patient, and with more overseas trained general practitioners. The use of ARRS staff was significantly associated with lower prescription rates (β=-0.52, p<0.000) and higher patient satisfaction (β=3.2, p<0.000), after controlling for patient and practice characteristics. This study suggests that ARRS has the potential to have a positive role in primary care, notably through reduced prescription rates and improved patient satisfaction. Further research is needed to explore the long-term effects of ARRS on primary care, including patient outcomes and health care costs, and the potential barriers to its implementation.

Combined Application of Chemical Fertilizer and Organic Amendment Improved Soil Quality in a Wheat–Sweet Potato Rotation System

The long-term excessive use of chemical fertilizers may result in soil degradation, but manure and straw application is considered to be an effective approach for alleviating this problem. The aim of this study is to examine the long-term impacts of different fertilization patterns on soil quality variables in a wheat–sweet potato rotation system. Four treatments were conducted in a field trial for a duration of twelve years, including (1) no fertilizer (control, CK); (2) application of mineral fertilizers (NPK) alone; (3) NPK with crop straw return (NPKs); (4) combined use of NPK and farmyard manure (NPKm). Thirteen physical, chemical, and biological soil parameters were measured. The results showed that the NPKm and NPKs significantly improved the proportion of macroaggregates (&gt;0.25 mm) by 24.7% and 21.9% compared to the NPK alone, respectively. The proportion of microaggregates (0.053–0.25 mm) under the NPKm was 47.4% significantly higher than the NPKs. Additionally, the NPKm resulted in a 22.2% and 19.6% increase in the SOC content than the NPK and NPKs, respectively. In terms of soil-available K, the NPKs resulted in levels that were 42.1% and 49.6% higher than the NPKm and NPK alone, respectively. Long-term fertilization significantly decreased soil pH by 0.95–1.85 units compared to the control, whereas manure application could alleviate soil acidification, as shown when the pH increased by 10.6–18.7%. The NPKm and NPKs resulted in significantly increased soil pHs by 10.6% and 18.7% compared to the NPK alone, respectively. In addition, the NPKm and NPKs increased N-acetyl-β-D-glucosaminidase activity by 52.6% and 60.3% compared to the NPK alone. Determined by the minimum data set method, the NPKm treatment exhibited the highest soil quality index, followed by the NPKs and NPK. Our findings suggested that the combined use of chemical fertilizers with organic amendments proved beneficial for enhancing soil quality.

Prescribing Trends and Associated Outcomes of Antiepileptic Drugs and Other Psychotropic Medications in US Nursing Homes: Proposal for a Mixed Methods Investigation.

Pilot data suggest that off-label, unmonitored antiepileptic drug prescribing for behavioral and psychological symptoms of dementia is increasing, replacing other psychotropic medications targeted by purposeful reduction efforts. This trend accelerated during the COVID-19 pandemic. Although adverse outcomes related to this trend remain unknown, preliminary results hint that harms may be increasing and concentrated in vulnerable populations. Using a mixed methods approach including both a retrospective secondary data analysis and a national clinician survey, this study aims to describe appropriate and potentially inappropriate antiepileptic and other psychoactive drug prescribing in US nursing homes (NHs), characteristics and patient-oriented outcomes associated with this prescribing, and how these phenomena may be changing under the combined stressors of the COVID-19 pandemic and the pressure of reduction initiatives. To accomplish the objective, resident-level, mixed-effects regression models and interrupted time-series analyses will draw on cohort elements linked at an individual level from the Centers for Medicare and Medicaid Services' (CMS) Minimum Data Set, Medicare Part D, Medicare Provider Analysis and Review, and Outpatient and Public Use Files. Quarterly cohorts of NH residents (2009-2021) will incorporate individual-level data, including demographics; health status; disease variables; psychotropic medication claims; comprehensive NH health outcomes; hospital and emergency department adverse events; and NH details, including staffing resources and COVID-19 statistics. To help explain and validate findings, we will conduct a national qualitative survey of NH prescribers regarding their knowledge and beliefs surrounding changing approaches to dementia care and associated outcomes. Funding was obtained in September 2022. Institutional review board exemption approval was obtained in January 2023. The CMS Data Use Agreement was submitted in May 2023 and signed in March 2024. Data access was obtained in June 2024. Cohort creation is anticipated by January 2025, with crosswalks finalized by July 2025. The first survey was fielded in October 2023 and published in July 2024. The second survey was fielded in March 2024. The results are in review as of July 2024. Iterative survey cycles will continue biannually until December 2026. Multidisciplinary dissemination of survey analysis results began in July 2023, and dissemination of secondary data findings is anticipated to begin January 2025. These processes are ongoing, with investigation to wrap up by June 2027. This study will detail appropriate and inappropriate antiepileptic drug use and related outcomes in NHs and describe disparities in long-stay subpopulations treated or not treated with psychotropics. It will delineate the impact of the pandemic in combination with national policies on dementia management and outcomes. We believe this mixed methods approach, including processes that link multiple CMS data sets at an individual level and survey-relevant stakeholders, can be replicated and applied to evaluate a variety of patient-oriented questions in diverse clinical populations. DERR1-10.2196/64446.

Outcomes of Medicaid Rebalancing May Differ Across Enrollee Populations.

Access to home- and community-based services (HCBS) may prevent or delay nursing home transitions among older adults. Medicaid's Balancing Incentive Program (BIP) (2011-2015) provided assistance for U.S. states to increase access to HCBS through infrastructure changes and spending benchmarks. We combined longitudinal data from the 2008-2019 Medicare Current Beneficiary Survey and Minimum Data Set and used survival modeling to examine the association between BIP exposure (living in a BIP-participant state vs. not) and time to long-term institutionalization (LTI, defined as a nursing home episode of 90+ days) among dual enrollees ages 65 and older. In the main effects model, BIP exposure was not associated with hazard of LTI. Interaction models showed that BIP exposure was associated with a lower hazard of LTI among Hispanic/Latinx enrollees, while the opposite was true among non-Hispanic White enrollees. Our findings suggest the outcomes of Medicaid rebalancing efforts may differ across enrollee subgroups.

Incident reporting and data monitoring of sexual violence and harassment on public transport

IntroductionSexual violence and harassment occur in different settings, including public transport, and cause significant harm to victim-survivors' mental and physical health. While sexual violence and harassment on public transport is a documented problem, the public transport sector uptake has been slow and the true prevalence of sexual violence and harassment on public transport is yet to be determined. We aimed to investigate existing public transport and key stakeholder data systems, and explore the views of service providers, to ultimately improve sexual violence and harassment incident reporting and data monitoring. MethodsUsing a mixed methods design, researchers worked with public transport providers and other interested stakeholders in one Australian state. Twelve organizations were involved in the project and six provided current data on public transport safety for analysis. An audit of public transport and stakeholder websites was also undertaken to assess sexual violence and harassment incident reporting pathways. In addition, seven managers from five public transport organizations were interviewed to capture views on gender sensitive data collection and monitoring, and the challenges and enabling factors facing the sector. ResultsThe results uncovered varied sexual violence and harassment reporting formats and subsequent limited reporting of sexual violence and harassment incidents on public transport. In addition, poor-quality data sets lacked gender sensitivity, comprehensiveness, comparability, reliability and accessibility. Three key themes from public transport providers, on sexual violence and harassment data collection included increasing incident reporting, barriers and enablers of collecting and analysing quality data and governance frameworks. A minimum data set collection tool has been developed and could be tested in future. ConclusionTo improve women's safety on public transport, enhanced gender-sensitive data collection and monitoring is required. Long term government investment in sexual violence and harassment primary prevention strategies on public transport are urgently needed.

A novel coal quality index and its estimation using diffuse reflectance spectroscopy

ABSTRACT An essential component of coal’s efficient use and large-scale environmental impact management is evaluating its quality. This study introduces a novel quality indexing technique that incorporates multiple coal properties, providing a comprehensive measure of coal quality. Conventional methods for assessing coal quality, such as proximate and ultimate analyses, are often time-consuming, costly, and require extensive sample preparation, prompting the need for a more efficient approach. In this study, we used the minimum dataset (MDS) approach to estimate a comprehensive coal quality index (CQI) and show that the diffuse reflectance spectroscopy (DRS) may be used as a rapid, nondestructive technique for estimating CQI as a single quality indicator for coal. A total of 212 coal samples of different ranks and grades are collected from the Tertiary and Gondwana coal basins of India. Ten chemical properties of coal were measured following the conventional laboratory-based methods. The spectral responses of the coal samples were measured across the visible-NIR-SWIR (350–2500 nm) range. The MDS approach was adopted while estimating the CQI from the lab-based coal quality parameters. We also examined different chemometric models for estimating CQI values from spectroscopic data. Results showed that the feature selection-based partial-least-square regression (PLSRFS) model may be used to estimate CQI with coefficient of determination (R2) values as high as 0.7 with root-mean-squared error (RMSE) of 0.06. While CQI serves as a single coal quality parameter, the capability to use the DRS approach as a rapid and noninvasive sensing approach for coal quality assessment provides new opportunity to characterize coal quality in an instant and cost-effective manner, especially, when a large number of coal samples have to be tested. The integration of the CQI with DRS could facilitate better decision-making in coal utilization and management, leading to optimized performance and reduced environmental impact.

Discovery of alkaline laccases from basidiomycete fungi through machine learning-based approach

BackgroundLaccases can oxidize a broad spectrum of substrates, offering promising applications in various sectors, such as bioremediation, biomass fractionation in future biorefineries, and synthesis of biochemicals and biopolymers. However, laccase discovery and optimization with a desirable pH optimum remains a challenge due to the labor-intensive and time-consuming nature of the traditional laboratory methods.ResultsThis study presents a machine learning (ML)-integrated approach for predicting pH optima of basidiomycete fungal laccases, utilizing a small, curated dataset against a vast metagenomic data. Comparative computational analyses unveiled the structural and pH-dependent solubility differences between acidic and neutral-alkaline laccases, helping us understand the molecular bases of enzyme pH optimum. The pH profiling of the two ML-predicted alkaline laccase candidates from the basidiomycete fungus Lepista nuda further validated our computational approach, showing the accuracy of this comprehensive method.ConclusionsThis study uncovers the efficacy of ML in the prediction of enzyme pH optimum from minimal datasets, marking a significant step towards harnessing computational tools for systematic screening of enzymes for biotechnology applications.Graphical

A proof-of-concept minimum data set for older care home residents: what value does this add?

ObjectiveDeveloping resources And minimum data set for Care Homes’ Adoption (DACHA) is a four-year NIHR-funded project to establish what data need to be in place to support research, service development and innovation uptake in care homes for older people. An aspirational minimum data set (MDS) specification was identified, which included data from several sources and pilot variables. Approach767 care home residents (with a valid NHS number recorded) across three integrated care systems consented to linkage of their care home record with administrative health and social care records from NHS England. Data were linked using pseudonymised NHS number. Variables were derived to summarise healthcare utilisation. Where variables are sourced from more than one data set, expert opinion on quality across these data sets informed hierarchical selection. We reported descriptive statistics including missingness across each of the &gt;300 variables in the MDS, with information on how these were accessed and derived, where appropriate. Descriptive summaries were informed by stakeholder engagement and cover ambulance and emergency department utilisation. An ongoing DACHA PPIE panel contributed to MDS development through sharing opinions on data access and linkage, and best sources of information across different domains. ResultsAnalysis is in progress. All results will be in pre-print publication by 31 April 2024. ConclusionsThe MDS constructed gives rich insights on healthcare utilisation and resident needs, with opportunity to understand how these interrelate. ImplicationsAs a proof-of-concept study, the findings of this research will inform national decision making on future linked care home data sets.

A game of snakes and ladders: the world of complex health and social care data linkage

Objective and ApproachResearch on social care services requires large, comprehensive, routine datasets. Launched in November 2019, Developing resources And minimum data set for Care Homes’ Adoption (DACHA) study aims to develop a prototype minimum dataset as proof of concept and to propose implementation. We describe our experience since 2021 of identifying, applying for, and linking care home, local, integrated care system (ICS) and national datasets, including direct-care software-provider data, GP and NHS England (NHSE) datasets. ResultsOur key challenges: Risk aversion and fragmented information governance within and between organisations. Complex information governance structures must be understood before participants are recruited. Shifts in the data landscape: data ownership moved from clinical commissioning groups to ICSs; NHSE merged with NHS Digital. Research funding: research is required of ICSs, but it is not a system priority, so processes and funding aren’t in place, and research burdens an already strained system. Our key lessons: Formalising agreements early on and obtaining, and maintaining, senior, committed buy-in. Studies should value the data sharing process through the inclusion of data controllers and information governance staff in the system in research project budgets. The facilitation of GP and social care data collection nationally must be balanced with its administrative burden. ImplicationsThe DACHA study overlapped with the COVID-19 pandemic and could only engage a limited number (n=3) of ICSs within project resources. Nevertheless, the complexity of the system and the plurality of actors delay or even block legitimate research interests.

Improving surveillance of emergency department activity through natural language processing

Objective and ApproachManagers, clinicians and policymakers have limited information on near-real time patterns of attendance at emergency departments (ED). Our aim was to develop a surveillance system through extracting information from the presenting complaint to develop and feed activity dashboards, improve injury surveillance and support research. We developed a Natural Language Processing (NLP) pipeline using the General Architecture for Text Engineering (GATE) system. The pipeline produced Unified Medical Language System (UMLS) codes from ED presenting complaint records and also implemented the Joint Action on Monitoring Injuries in Europe (JAMIE) Minimum Dataset. We classified complaints into 31 common problem presentations (Coughs, Bleeds, Mental Health, Dizziness...), along with categories from JAMIE (Mechanism of Injury, Location, Activity, Intent…). ResultsPreliminary results are from a pilot study of 86,416 presenting complaints from Morriston Hospital in 2014. The most common terms used for grouped symptoms/categories were “Injury” (38.6%, n=28691), “Pain” (22.8%, n=19420), “Unwell” (7.2%, n=6117), “Bleeding” (6.7%, n=5760), and “Shortness of Breath” (3.8%, n=3255). 32.3% of records contained Mechanism of Injury codes, including, falls (15.2%, n=13174), struck by/against (6.9%, n=5960), cuts (5.6%, n=4833), road traffic collisions (2.5%, n=2124) and poisoning (1%, n=900). We are continuing to refine and validate the categories using precision and recall and with comparison with coded diagnoses. Conclusions and Implications NLP can be used to extract valuable additional detailed information from ED data. We will further develop the algorithms for all hospitals in Wales for data from 2023 and design dashboards to produce a near-real time surveillance system.

Determining the minimum data set of geriatric assessment at the Iran primary health care referral system: shifting from fragmentation to integration care for older people

BackgroundGeriatric assessment (GA) is a multidimensional process that disrupts the primary health care (PHC) referral system. Accessing consistent data is central to the provision of integrated geriatric care across multiple healthcare settings. However, due to poor-quality data and documentation of GA, developing an agreed minimum data set (MDS) is required. Therefore, this study aimed to develop a GA-MDS in the PHC referral system to improve data quality, data exchange, and continuum of care to address the multifaceted necessities of older people.MethodsIn our study, the items to be included within GA-MDS were determined in a three-stepwise process. First, an exploratory literature search was done to determine the related items. Then, we used a two-round Delphi survey to obtain an agreement view on items to be contained within GA-MDS. Finally, the validity of the GA-MDS content was evaluated.ResultsSixty specialists from different health geriatric care disciplines scored data items. After, the Delphi phase from the 230 selected items, 35 items were removed by calculating the content validity index (CVI), content validity ratio (CVR), and other statistical measures. Finally, GA-MDS was prepared with 195 items and four sections including administrative data, clinical, physiological, and psychological assessments.ConclusionsThe development of GA-MDS can serve as a platform to inform the geriatric referral system, standardize the GA process, and streamline their referral to specialized levels of care. We hope GA-MDS supports clinicians, researchers, and policymakers by providing aggregated data to inform medical practice and enhance patient-centered outcomes.

Effects of Urbanization on Soil Quality in the Rural‒Urban Gradient of Bengaluru, India

Understanding the impact of urbanization on soil quality is crucial for sustainable land management practices. This study was conducted in Bengaluru, India, to estimate the soil quality index (SQI) under different rural‒urban gradient (RUG) zones. Twenty-four sampling sites were identified along the RUG, and soil samples were collected monthly over five months during the October to February of 2020-2021. The soil quality assessment involved selecting the minimum data set (MDS) via principal component analysis (PCA) and correlation, scoring soil indicators, and combining these scores to create the soil quality index (SQI). PCA was used to identify key soil properties, which included microbial biomass carbon (MBC), SOC, N, manganese (Mn), and urease for different RUG zones derived from the MDS. The rural zones had the highest SQI (0.57), followed by the peri-urban (0.47 and 0.48) and urban (0.45 and 0.47) zones. These findings emphasize the importance of sustainable land management practices to preserve and boost soil quality across diverse regions, particularly in the face of rapid urbanization and industrialization.

Prevalence of respiratory signs in dogs with chronic gastroenteropathy in a referral population in the United Kingdom: 42 cases (2020).

To determine the prevalence of respiratory signs in a referral population of dogs with chronic gastroenteropathy in the United Kingdom, and to describe the epidemiology and clinical presentation of the affected dogs. Retrospective review of canine patients diagnosed with chronic gastroenteropathy in a referral centre in the United Kingdom. Data included signalment, gastrointestinal and respiratory signs, blood analysis results and, where present, diagnostic imaging techniques, endoscopy, videofluoroscopic swallow study, bronchoalveolar lavage results and treatment response. To be included, a follow-up of a minimum of 3 months was required. Cases were excluded if there was no minimum diagnostic dataset, the aetiology of the respiratory signs was other than gastrointestinal disease or a non-gastrointestinal systemic disease was the cause of the gastrointestinal signs. One hundred and forty-eight dogs were included and 42 (28.4%) had concurrent respiratory signs. Gagging (69%) and vomiting (52.4%) were the most common gastrointestinal signs, while coughing (50%), tachypnoea (33.3%) and reverse sneezing (30.9%) were the most common respiratory signs. Abdominal ultrasound was performed in 31 (73.8%) while CT scan was the most common modality for thoracic (62.5%) and head (21.9%) imaging. Endoscopic procedures included gastrointestinal (54.8%), bronchoscopy (19%) and rhinoscopy (23.8%). Dietary modification was the most common treatment (66.67%). Eleven dogs (26.2%) had a relapse of the respiratory signs during the follow-up period, of which, nine (81.8%) had a concurrent relapse of the gastrointestinal signs. Concurrent respiratory signs in dogs with chronic gastroenteropathy are frequent. A clinical improvement of these respiratory signs might be achieved during the therapeutic management of gastrointestinal disorders.

Soil quality indicators related to the deterioration of Kikuyu grass Cenchrus clandestinus (Hochst. ex Chiov.) Morrone

Inadequate grassland management has resulted in the degradation of extensive areas, loss of productivity and sustainability of many of them, reflecting a common reality among livestock farmers in Colombia. Therefore, at the Paysandú Agricultural Station of the Universidad Nacional de Colombia, located in the Santa Elena township of the city of Medellín, a study was carried out to determine a soil quality index (SQI) by evaluating the physical and chemical indicators that were most related to the deterioration and dry matter production of kikuyu grass (Cenchrus clandestinus). A minimum data set (MDS) was established for the most sensitive indicators, selected by principal component analysis (PCA), and a nonlinear scoring function was used to obtain the SQI. Statistical differences were found between all the treatments in relation to dry matter production (P&lt;0.05: 1.91x10-32). The most sensitive indicators were bulk density BD &gt; total porosity TP &gt; macropores MAC &gt; micropores MIC &gt; penetration resistance PR &gt; effective cation exchange capacity ECEC &gt; pH. As a result, the following formula was obtained: SQI = (0.225×BD) + (0.224×TP) + (0.220×MAC) + (0.218×MIC) + (0.113×PR) + (0.0879×ECEC) + (0.0877×pH). This index should be tested in kikuyu grass-dominated pastures located in the Colombian high tropics. The baseline is critical at values &gt; 0.58 Mg m-3 for BD and &gt; 2.25 MPa for PR. In addition, the optimum pH range for kikuyu grass development was between 5.4 and 6.4.

Physical therapy registries worldwide: A systematic review.

Nowadays, the demand for physical therapy services has grown significantly over the last few decades due to an aging population, a rise in chronic conditions, and increased awareness of the benefits of physical therapy for injury recovery and managing various health issues. Collecting and managing data from physical therapy services is highly significant and beneficial. One of the information management systems that facilitates data collection related to physical therapy services is a physical therapy registry. In this systematic review, we aimed to identify physical therapy registries worldwide and examine the characteristics and data elements of each registry. PubMed, Scopus, Web of Science, and IEEE databases were searched from inception until March 19, 2023 by using keywords and Medical Subject Headings (MeSH) terms regarding "registries" and "physical therapy." The criteria for inclusion in the study were: (1) studies with the English language; (2) original studies, and online access to the physical therapy registry is available; (3) full-text available; (4) studies related to the aims of the study, and (5) studies that have sufficient available information regarding the minimum datasets and other characteristics physical therapy registry. The methodological quality of the included studies was independently assessed by two reviewers using the Effective Public Health Practice Project's (EPHPP) quality assessment tool. Sixteen studies were eligible to be included. The findings of this review indicated that the oldest physical therapy registry was established in 1992, while the newest one was established in 2017. The USA has the highest number of physical therapy registries (n = 7). Ten registries were funded by the government, and the data source for most registries was collected in clinics (n = 11). The geographical coverage of 10 registries was national. All registries collected administrative data (such as sociodemographic data, healthcare provider's data, and others) and clinical data (such as diagnosis, type of physical therapy intervention, pain location, comorbidities, and others) through web-based systems. The data collection method in half of the registries was retrospective (n = 8 out of 16). According to the EPHPP quality assessment tool, 11 studies (73%) were rated as moderate, 3 (20%) as weak, and 1 (7%) as strong. This systematic review found that most developed countries have implemented web-based physical therapy registries to collect administrative and clinical data at the national level. It is recommended that developing countries design and implement similar registries based on these characteristics and extracted data elements. Additionally, these registries should be designed to enable data sharing and interoperability with other international health information systems.

Soil quality assessment and mapping in basaltic terrain of Central India for sustainable soil and crop management using integrated PCA and GIS

The cereal-based cropping system plays a vital role in ensuring food security in the Indian subcontinent. However, the productivity of these systems has seen a continuous decline due to the degradation of soil quality. This study aims to develop a Soil Quality Index (SQI) for such cropping systems. A detailed survey was conducted in the Bareli watershed of Seoni district, Madhya Pradesh, at a 1:10000 scale using high-resolution satellite data and Geographic Information System (GIS) technology. The survey identified and mapped 5 soil series: Diwartola, Diwara, Bareli-1, Bareli-2 and Bareli-3. Soil quality was evaluated based on morphological, physical and chemical properties as well as fertility parameters. Key indicators for soil quality assessment included sand, silt, clay content, bulk density, hydraulic conductivity, available water capacity and coefficient of linear extensibility (COLE). Additionally, pH, electrical conductivity, organic carbon, cation exchange capacity and nutrients like N, P, K, Fe, Mn, Cu and Zn were considered. The SQI was calculated using integrated principal component analysis, which involved selecting a minimum data set (MDS), assigning weights and scoring indicators. The results revealed that Diwartola soils had high quality (242.7 ha, 13.5 % TGA), Bareli-1 and Bareli-3 soils were of medium quality (462.8 ha, 25.7 % TGA), while Diwara and Bareli-2 soils were of low quality (966.1 ha, 53.8 % TGA). Agro-interventions such as agri-horticulture, agro-forestry, silvi-pasture, intensive cultivation and soil and water conservation measures were recommended based on the different mapping units.

Impacts of land use and management methods on soil quality dynamics in central highlands of Ethiopia.

Land degradation often results in poor soil quality in many parts of Ethiopia, including the study area. To address this issue and promote sustainable land management practices, various land use and management methods (LUMMs) have been implemented. However, little information is available regarding how these management practices influence overall soil quality dynamics of the study area. This study aimed at evaluating soil quality dynamics in the Urago micro-watershed, central highlands of Ethiopia, under major LUMMs: barren land (BL), grassland (GL), established farm boundary (EFB), restored degraded land (RDL), and stone-supported soil bund (SSB). Forty-five disturbed and fifteenundisturbed soil samples were collected from the ploughed soil layer (0-20cm) of each LUMM and analysed for selected physicochemical properties to be used as indicators of soil quality. Principal component analysis and multiple correlation were used to select the minimum data set (MDS) to evaluate the overall soil quality index (SQI). The MDS included SOC, clay content, exchangeable Mg2+, and available P, which could replace other indicators for assessing the overall soil quality dynamics of the study watershed. The result showed notable variations in particle-size fractions, soil organic carbon (SOC), total nitrogen (TN), available P (av. P), and exchangeable Na+, K+, and Mg2+ levels among the LUMMs. RDL had higher sand and silt contents than SSB, whereas SSB had higher clay content compared to RDL, GL, and BL. GL, RDL, and EFB showed significantly higher levels of SOC, TN, and av. P, respectively, compared to other LUMMs. The obtained SQI showed that GL had the highest score (0.847), followed by SSB (0.703), RDL (0.701), EFB (0.644), and BL (0.628). This underscores the significance of stone-supported soil bund and restored degraded land as an efficient management method to enhance soil quality and agro-ecosystem through conserving soil and encouraging sustainable farming practices.