An exploration of linkage fine-mapping on sequences from case-control studies

Linkage analysis maps genetic loci for a heritable trait by identifying genomic regions with excess relatedness among individuals with similar trait values. Analysis may be conducted on related individuals from families, or on samples of unrelated individuals from a population. For allelically heterogeneous traits, population-based linkage analysis can be more powerful than genotypic-association analysis. Here, we focus on linkage analysis in a population sample, but use sequences rather than individuals as our unit of observation. Earlier investigations of sequence-based linkage mapping relied on known sequence relatedness, whereas we infer relatedness from the sequence data. We propose two ways to associate similarity in relatedness of sequences with similarity in their trait values and compare the resulting linkage methods to two genotypic- association methods. We also introduce a procedure to label case sequences as potential carriers or non-carriers of causal variants after an association has been found. This post-hoc labeling of case sequences is based on inferred relatedness to other case sequences. Our simulation results indicate that methods based on sequence-relatedness improve localization and perform as well as genotypic-association methods for detecting rare causal variants. Sequence-based linkage analysis therefore has potential to fine-map allelically heterogeneous disease traits.

Linking Emergency Medical Services and Emergency Department Data to Improve Overdose Surveillance in North Carolina

Introduction Linking emergency medical services (EMS) data to emergency department (ED) data enables assessing the continuum of care and evaluating patient outcomes. We developed novel methods to enhance linkage performance and analysis of EMS and ED data for opioid overdose surveillance in North Carolina. Methods We identified data on all EMS encounters in North Carolina during January 1–November 30, 2017, with documented naloxone administration and transportation to the ED. We linked these data with ED visit data in the North Carolina Disease Event Tracking and Epidemiologic Collection Tool. We manually reviewed a subset of data from 12 counties to create a gold standard that informed developing iterative linkage methods using demographic, time, and destination variables. We calculated the proportion of suspected opioid overdose EMS cases that received International Classification of Diseases, Tenth Revision, Clinical Modification diagnosis codes for opioid overdose in the ED. Results We identified 12 088 EMS encounters of patients treated with naloxone and transported to the ED. The 12-county subset included 1781 linkage-eligible EMS encounters, with historical linkage of 65.4% (1165 of 1781) and 1.6% false linkages. Through iterative linkage methods, performance improved to 91.0% (1620 of 1781) with 0.1% false linkages. Among statewide EMS encounters with naloxone administration, the linkage improved from 47.1% to 91.1%. We found diagnosis codes for opioid overdose in the ED among 27.2% of statewide linked records. Practice Implications Through an iterative linkage approach, EMS–ED data linkage performance improved greatly while reducing the number of false linkages. Improved EMS–ED data linkage quality can enhance surveillance activities, inform emergency response practices, and improve quality of care through evaluating initial patient presentations, field interventions, and ultimate diagnoses.

Comparison of Single Linkage, Complete Linkage, and Average Linkage Methods on Community Welfare Analysis in Cities and Regencies in East Java

Community welfare is one of the important points for a region and is also the essence of national development. The welfare of the people in Indonesia is fairly unequal, especially in East Java. To be able to map an area to the welfare of its people in East Java, one way that can be used is to use clustering. The hierarchical clustering method is one of the clustering methods for grouping data. In hierarchical clustering, single linkage, complete linkage, and average linkage methods are suitable methods for grouping data, which will compare the best method to use. The results of the calculation show that the average linkage method with three clusters is the best calculation with a silhouette index value of 0.6054, with the 1st cluster there are 23 regions, namely the city/district with the highest community welfare, the 2nd cluster there are 11 regions, namely cities/districts with moderate social welfare, and in the third cluster there are 4 regions, namely cities/districts with the lowest community welfare.

1018Long-term risk of distant metastasis and post-metastasis survival for breast cancer: health record linkage study

Background After early breast cancer (BC) treatment, women need information about long-term prognosis. In this population-based health record linkage study, we assessed the cumulative incidence of distant metastasis (DM) conditional on the DM-free interval; and BC-specific survival post-metastasis. Methods We included all women diagnosed with non-metastatic BC in the NSW Cancer Registry, 2001-2002. We used linked records from hospitals, dispensed medicines, radiotherapy services and death registrations and applied stringent criteria to determine time to first DM and BC death. Results 6338 women were included (BC: localised 3885, regional 2453). The 5-year cumulative incidence of DM was 7.4% (95% confidence interval 6.6-8.3) for localised BC; 22.8% (21.2-24.5) for regional BC. For women DM-free at 5 years, it was 5.7% (5.0-6.6); and 11.4% (10.0-13.0) to year 10, respectively. The annual hazard for BC death following localised BC remained lower than that for non-BC causes; for regional BC, it was similar to that for non-BC causes at 8 years. Following DM (N = 1492), BC-specific survival varied widely (median 25 months, interquartile range 6-127). The probability of surviving BC for ≥5 years was 32.0% (29.4-34.7) overall; and 47.1% (42.6-51.5) for those with a DM-free interval >5 years. Conclusions Women’s risk of DM improves over time since diagnosis; and post-metastasis survival is longer for women with later DM. Key messages Health record linkage methods can be used for conditional risk estimates to inform women who remain DM-free after early BC about their risk of DM in subsequent years; and post-metastasis survival.

Implementation of Agglomerative Hierarchical Clustering Based on The Classification of Food Ingredients Content of Nutritional Substances

Health is something very precious. Maintaining health can be done in many ways, one of them by keeping your diet. The correct diet will keep your immune system so that it can avoid various diseases. The proper diet will also put the body in a balanced nutrition state, which all need to be nourished. Nutrient requirements include calories, protein, fat, carbohydrates, calcium, phosphorus, iron, vitamin A, vitamin B, and vitamin C with a mass of 100 grams each. To facilitate the search for nutrients needed, then build a system that can categorize food based on its nutritional status and calculate the average value of nutrients in agglomerative hierarchical clustering using average linkage. Calculation of intermediate linkage methods produces data that has some similarities to the data sought nutrients that can be seen from its index, so precise data are in each group.

Scalable Clustering with Supervised Linkage Methods

Data clustering plays a significant role in biomedical sciences, particularly in single-cell data analysis. Researchers use clustering algorithms to group individual cells into populations that can be evaluated across different levels of disease progression, drug response, and other clinical statuses. In many cases, multiple sets of clusters must be generated to assess varying levels of cluster specificity. For example, there are many subtypes of leukocytes (e.g. T cells), whose individual preponderance and phenotype must be assessed for statistical/functional significance. In this report, we introduce a novel hierarchical density clustering algorithm (HAL-x) that uses supervised linkage methods to build a cluster hierarchy on raw single-cell data. With this new approach, HAL-x can quickly predict multiple sets of labels for immense datasets, achieving a considerable improvement in computational efficiency on large datasets compared to existing methods. We also show that cell clusters generated by HAL-x yield near-perfect F1-scores when classifying different clinical statuses based on single-cell profiles. Our hierarchical density clustering algorithm achieves high accuracy in single cell classification in a scalable, tunable and rapid manner. We make HAL-x publicly available at: https://pypi.org/project/hal-x/

Data linkage methods for fathers and children: A scoping review.

Research has shown that paternal involvement positively impacts on child health and development. We aimed to develop a conceptual model of dimensions of fatherhood, identify and categorise methods used for linking fathers with their children in administrative health data, and map these methods onto the dimensions of fatherhood.We carried out a systematic scoping review to create a conceptual framework of paternal involvement and identify studies exploring the impact of paternal exposures on child health and development outcomes using administrative data.We identified four methods that have been used globally to link fathers and children in administrative data based on family or household identifiers using address data, identifiable information about the father on the child’s birth registration, health claims data, and Personal Identification Numbers (PINs). We did not identify direct measures of paternal involvement but mapping linkage methods to the framework highlighted possible proxies. The addition of paternal NHS numbers to birth notifications presents a way forward in the advancement of fatherhood research using administrative data sources.

Linking National Immigration Data to Provincial Repositories: The case of Canada

BackgroundCanadian health data repositories link datasets at the provincial level, based on their residents’ registrations to provincial health insurance plans. Linking national datasets with provincial health care registries poses several challenges that may result in misclassification and impact the estimation of linkage rates. A recent linkage of a federal immigration database in the province of Manitoba illustrates these challenges. Objectivesa) To describe the linkage of the federal Immigration, Refugees and Citizenship Canada Permanent Resident (IRCC-PR) database with the Manitoba healthcare registry and b) compare data linkage methods and rates between four Canadian provinces accounting for interprovincial mobility of immigrants. MethodsWe compared linkage rates by immigrant’s province of intended destination (province vs. rest of Canada). We used external nationwide immigrant tax filing records to approximate actual settlement and obtain linkage rates corrected for interprovincial mobility. ResultsThe immigrant linkage rates in Manitoba before and after accounting for interprovincial mobility were 84.8% and 96.1, respectively. Linkage rates did not substantially differ according to immigrants’ characteristics, with a few exceptions. Observed linkage rates across the four provinces ranged from 74.0% to 86.7%. After correction for interprovincial mobility, the estimated linkage rates increased >10 percentage points for the provinces that stratified by intended destination (British Columbia and Manitoba) and decreased up to 18 percentage points for provinces that could not use immigration records of those who did not intend to settle in the province (New Brunswick and Ontario). ConclusionsDespite variations in methodology, provincial linkage rates were relatively high. The use of a national immigration dataset for linkage to provincial repositories allows a more comprehensive linkage than that of province-specific subsets. Observed linkage rates can be biased downwards by interprovincial migration, and methods that use external data sources can contribute to assessing potential selection bias and misclassification.

Application of Data Linkage Techniques to Pacific Northwest Commercial Fishing Injury and Fatality Data

BackgroundCommercial fishing consistently has among the highest workforce injury and fatality rates in the United States. Data related to commercial fishing incidents are routinely collected by multiple organizations which do not currently coordinate or automatically link data. Each dataset has the potential to generate a more complete picture to inform prevention efforts. Our objective was to examine the utility of using statistical data linkage methods to link these datasets in support of incident surveillance and hazard assessment in the commercial fishing industry.MethodsIn this feasibility study, we identified true matches and discrepancies between de-identified datasets using the Python Record Linkage Toolkit. Four commercial fishing datasets from Oregon and Washington were linked: the Commercial Fishing Incident Database, the Vessel Casualty Database, the Nonfatal Injuries Database, and the Oregon Trauma Registry. The datasets each covered different date ranges within 2000 - 2017, containing 458, 524, 184, and 11 cases respectively. Several data linkage classifiers were evaluated.ResultsThe Naïve-Bayes classifier returned the highest number of true matches between these small datasets. A total of 41 true matches and 8 close matches were identified, of which 29 were determined to be duplicates. In addition, linkage highlighted 4 records that were not commercial fishing cases from Oregon and Washington. The optimum match parameters were the date, state, vessel official number, and number of people on board.ConclusionsStatistical data linkage enables accurate, routine matching for small de-identified injury and fatality datasets such as those in commercial fishing. It provides information needed to improve the accuracy of existing data records. It also enables expanding and sharpening details of individual incidents in support of occupational safety research.