Assessing Quality of Care of Elderly Patients Using the ACOVE Quality Indicator Set: A Systematic Review

Background: Care of the elderly is recognized as an increasingly important segment of health care. The Assessing Care Of Vulnerable Elderly (ACOVE) quality indicators (QIs) were developed to assess and improve the care of elderly patients. Objectives: The purpose of this review is to summarize studies that assess the quality of care using QIs from or based on ACOVE, in order to evaluate the state of quality of care for the reported conditions. Methods: We systematically searched MEDLINE, EMBASE and CINAHL for English-language studies indexed by February 2010. Articles were included if they used any ACOVE QIs, or adaptations thereof, for assessing the quality of care. Included studies were analyzed and relevant information was extracted. We summarized the results of these studies, and when possible generated an overall conclusion about the quality of care as measured by ACOVE for each condition, in various settings, and for each QI. Results: Seventeen studies were included with 278 QIs (original, adapted or newly developed). The quality scores showed large variation between and within conditions. Only a few conditions showed a stable pass rate range over multiple studies. Overall, pass rates for dementia (interquartile range (IQR): 11%-35%), depression (IQR: 27%-41%), osteoporosis (IQR: 34%-43%) and osteoarthritis (IQR: 29-41%) were notably low. Medication management and use (range: 81%-90%), hearing loss (77%-79%) and continuity of care (76%-80%) scored higher than other conditions. Out of the 278 QIs, 141 (50%) had mean pass rates below 50% and 121 QIs (44%) had pass rates above 50%. Twenty-three percent of the QIs scored above 75%, and 16% scored below 25%. Conclusions: Quality of care per condition varies markedly across studies. Although there has been much effort in improving the care for elderly patients in the last years, the reported quality of care according to the ACOVE indicators is still relatively lo

Quality and complexity measures for data linkage and deduplication

Summary. Deduplicating one data set or linking several data sets are increasingly important tasks in the data preparation steps of many data mining projects. The aim of such linkages is to match all records relating to the same entity. Research interest in this area has increased in recent years, with techniques originating from statistics, machine learning, information retrieval, and database research being combined and applied to improve the linkage quality, as well as to increase performance and efficiency when linking or deduplicating very large data sets. Different measures have been used to characterise the quality and complexity of data linkage algorithms, and several new metrics have been proposed. An overview of the issues involved in measuring data linkage and deduplication quality and complexity is presented in this chapter. It is shown that measures in the space of record pair comparisons can produce deceptive quality results. Various measures are discussed and recommendations are given on how to assess data linkage and deduplication quality and complexity. Key words: data or record linkage, data integration and matching, deduplication, data mining pre-processing, quality and complexity measures

Sociodemographic differences in linkage error: An examination of four large-scale datasets

© 2018 The Author(s). Background: Record linkage is an important tool for epidemiologists and health planners. Record linkage studies will generally contain some level of residual record linkage error, where individual records are either incorrectly marked as belonging to the same individual, or incorrectly marked as belonging to separate individuals. A key question is whether errors in linkage quality are distributed evenly throughout the population, or whether certain subgroups will exhibit higher rates of error. Previous investigations of this issue have typically compared linked and un-linked records, which can conflate bias caused by record linkage error, with bias caused by missing records (data capture errors). Methods: Four large administrative datasets were individually de-duplicated, with results compared to an available 'gold-standard' benchmark, allowing us to avoid methodological issues with comparing linked and un-linked records. Results were compared by gender, age, geographic remoteness (major cities, regional or remote) and socioeconomic status. Results: Results varied between datasets, and by sociodemographic characteristic. The most consistent findings were worse linkage quality for younger individuals (seen in all four datasets) and worse linkage quality for those living in remote areas (seen in three of four datasets). The linkage quality within sociodemographic categories varied between datasets, with the associations with linkage error reversed across different datasets due to quirks of the specific data collection mechanisms and data sharing practices. Conclusions: These results suggest caution should be taken both when linking younger individuals and those in remote areas, and when analysing linked data from these subgroups. Further research is required to determine the ramifications of worse linkage quality in these subpopulations on research outcomes

Updated fracture incidence rates for the US version of FRAX®

# The Author(s) 2009. This article is published with open access at Springerlink.com Summary On the basis of updated fracture and mortality data, we recommend that the base population values used in the US version of FRAX ® be revised. The impact of suggested changes is likely to be a lowering of 10-year fracture probabilities. Introduction Evaluation of results produced by the US version of FRAX ® indicates that this tool overestimates the likelihood of major osteoporotic fracture. In an attempt to correct this, we updated underlying fracture and mortality rates for the model. Methods We used US hospital discharge data from 2006 t

Prevalence of osteoporosis and incidence of hip fracture in women - secular trends over 30 years

<p>Abstract</p> <p>Background</p> <p>The number of hip fractures during recent decades has been reported to be increasing, partly because of an increasing proportion of elderly women in the society. However, whether changes in hip fracture annual incidence in women are attributable to secular changes in the prevalence of osteoporosis is unclear.</p> <p>Methods</p> <p>Bone mineral density was evaluated by single-photon absorptiometry at the distal radius in 456 women aged 50 years or above and living in the same city. The measurements were obtained by the same densitometer during three separate time periods: 1970-74 (n = 106), 1987-93 (n = 175) and 1998-1999 (n = 178), and the age-adjusted prevalence of osteoporosis in these three cohorts was calculated. Additionally, all hip fractures sustained in the target population of women aged 50 years or above between 1967 and 2001 were registered, whereupon the crude and the age-adjusted annual incidence of hip fractures were calculated.</p> <p>Results</p> <p>There was no significant difference in the age-adjusted prevalence of osteoporosis when the three cohorts were compared (P = 1.00). The crude annual incidence (per 10,000 women) of hip fracture in the target population increased by 110% from 40 in 1967 to 84 in 2001. The overall trend in the crude incidence between 1967 and 2001 was increasing (1.58 per 10,000 women per year; 95 percent confidence interval, 1.17 to 1.99), whereas the age-adjusted incidence was stable over the same period (0.22 per 10,000 women per year; 95 percent confidence interval, -0.16 to 0.60).</p> <p>Conclusions</p> <p>The increased number of hip fracture in elderly women is more likely to be attributable to demographic changes in the population than to secular increase in the prevalence of osteoporosis.</p

Data Linkage: A powerful research tool with potential problems

Background: Policy makers, clinicians and researchers are demonstrating increasing interest in using data linked from multiple sources to support measurement of clinical performance and patient health outcomes. However, the utility of data linkage may be compromised by sub-optimal or incomplete linkage, leading to systematic bias. In this study, we synthesize the evidence identifying participant or population characteristics that can influence the validity and completeness of data linkage and may be associated with systematic bias in reported outcomes

Automation of a problem list using natural language processing

BACKGROUND: The medical problem list is an important part of the electronic medical record in development in our institution. To serve the functions it is designed for, the problem list has to be as accurate and timely as possible. However, the current problem list is usually incomplete and inaccurate, and is often totally unused. To alleviate this issue, we are building an environment where the problem list can be easily and effectively maintained. METHODS: For this project, 80 medical problems were selected for their frequency of use in our future clinical field of evaluation (cardiovascular). We have developed an Automated Problem List system composed of two main components: a background and a foreground application. The background application uses Natural Language Processing (NLP) to harvest potential problem list entries from the list of 80 targeted problems detected in the multiple free-text electronic documents available in our electronic medical record. These proposed medical problems drive the foreground application designed for management of the problem list. Within this application, the extracted problems are proposed to the physicians for addition to the official problem list. RESULTS: The set of 80 targeted medical problems selected for this project covered about 5% of all possible diagnoses coded in ICD-9-CM in our study population (cardiovascular adult inpatients), but about 64% of all instances of these coded diagnoses. The system contains algorithms to detect first document sections, then sentences within these sections, and finally potential problems within the sentences. The initial evaluation of the section and sentence detection algorithms demonstrated a sensitivity and positive predictive value of 100% when detecting sections, and a sensitivity of 89% and a positive predictive value of 94% when detecting sentences. CONCLUSION: The global aim of our project is to automate the process of creating and maintaining a problem list for hospitalized patients and thereby help to guarantee the timeliness, accuracy and completeness of this information

Geographic Clustering Of Diabetic Lower-Extremity Amputations In Low-Income Regions Of California

For patients suffering from diabetes and other chronic conditions, a large body of work demonstrates income-related disparities in access to coordinated preventive care. Much less is known about associations between poverty and consequential negative health outcomes. Few studies have assessed geographic patterns that link household incomes to major preventable complications of chronic diseases. Using statewide facility discharge data for California in 2009, we identified 7,973 lower-extremity amputations in 6,828 adults with diabetes. We mapped amputations based on residential ZIP codes and used data from the Census Bureau to produce corresponding maps of poverty rates. Comparisons of the maps show amputation "hot spots" in lower-income urban and rural regions of California. Prevalence-adjusted amputation rates varied tenfold between high-income and low-income regions. Our analysis does not support detailed causal inferences. However, our method for mapping complication hot spots using public data sources may help target interventions to the communities most in need