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This study aimed at investigating the effectiveness of employing a computerized educational program, which according to Adobe Flash Professional CS5.5, in measuring the achievement of the sixth graders in the subject of "Technology" in the governmental schools in Nablus and their attitudes towards it. In order to answer the questions of the study and test its hypotheses, the researcher employed the Qusai- experimental Design. In other words, the researcher used two tools, namely: an immediate achievement test and the measurement of the attitudes towards learning the subject of 'technology'. The population of the study comprised (92) female students from the sixth grade at Hajja Rashda Al-Masri Girls Secondary School. Besides, they were divided into two groups, namely: experimental and control. The latter studied the unit of "Communication & Information Technology" by using the traditional method while the former studies the same unit by using a program designed by Adobe Flash program. After the analysis of the results of the two groups, the researcher concluded that the influence of using Adobe Flash on the development of the achievement of the sixth graders in the subject of 'technology' and their attitudes towards learning it is positive. Besides, there is a direct relation at the statistical level of (α=0.05) between the scholastic achievement and students' attitudes towards learning the subject of 'technology'. In light of the previous conclusions, Adobe Flash Professional CS5.5 in producing computerized educational programs. In addition, it is necessary to hold courses and workshops to the teachers and supervisors to raise their awareness to the importance of activating the educational computerized programs and employing them as a novel technology in the educational field.

BackgroundThe International Classification of Diseases (ICD) is revised over time and there are region-specific versions, including ICD-10-CA (Canada) and ICD-9-CM (USA). Studies spanning multiple ICD versions require crosswalks to translate diagnosis codes across versions, but manual crosswalk development is costly and requires clinical expertise.
ObjectiveTo evaluate the accuracy of a pre-trained large language model (LLM) to automatically translate chronic disease diagnosis codes from ICD-10-CA to ICD-9-CM.
ApproachEight prompts were developed to instruct the OpenAI Generative Pre-trained Transformer 4 (GPT-4) LLM to translate 1,272 ICD-10-CA codes for the Elixhauser Comorbidity Index to ICD-9-CM. Prompt accuracy (%) was measured against a crosswalk developed by the Canadian Institute of Health Information. Variability was assessed by replicating each prompt three times. Mean accuracy ± standard deviation was reported for each prompt across replications, for both five-digit and truncated three-digit codes.
ResultsThe highest prompt performance was observed when assigning a persona of a medical coding specialist (40.8% ± 0.9%), requesting justification for the selected code (41.4% ± 1.1%), and providing diagnosis code labels (47.5% ± 0.7%). For truncated three-digit codes, these prompts achieved accuracy of 82.0% ± 0.5%, 80.8% ± 0.9%, and 82.7% ± 0.1%, respectively. Combining these three prompting techniques marginally improved accuracy to 48.6% ± 0.7% for five-digit codes and 84.3% ± 0.2% for truncated three-digit codes.
ConclusionGeneral-purpose LLMs are currently not sufficiently accurate at automating ICD code translation for chronic diseases.
ImplicationsAdditional experiments with fine-tuning, task-specific training, and prompt engineering are needed to improve accuracy and reduce variability.

ObjectiveTo explore the prevalence of adverse drug events (ADE) leading to drug changes using Artificial Intelligence (AI) based free text analysis of primary care encounter notes .
ApproachWe used electronic medical record l encounter notes linked to population-based drug dispensation records. Dispensation records were used to identify drug switching to another agent in the same therapeutic class, which could suggest an undesirable side-effect requiring discontinuation of the drug. We annotated clinical notes by identifying ADEs after retrieving the last encounter note written for that patient on or before the identified date. We ran different AI models, including BERT and Large Language Models (LLMs), to identify ADEs.
ResultsWe annotated 1085 notes with 362 ADEs. These were used to train BERT models (precision or PPV: 0.584 ) and prompt-based LLM (0.563) but with improved sensitivity (0.725) and specificity (0.739) .
ConclusionsIdentifying ADEs requires deep medical knowledge and simple Natural Language Processing models trying to identify ADEs only on the basis of language of encounter notes was not successful based on the BERT model performance. Prompt based models have deep knowledge but were trained with the objective of producing grammatically correct, coherent sentences, not with the medical knowledge required. LLMs also require significant computational power.
ImplicationsWhile AI does have significant potential to advance our capacity for complex analytic tasks the current LLMs are not yet able to adequately identify ADEs in a corpus of electronic clinical notes.

ObjectiveAdministrative data often lacks complete family linkages, particularly to fathers, hindering familial health research. We compared methods to address missing paternal linkages in research investigating the familial transfer of mental disorders.
ApproachA population-based cohort study of Manitoba (Canada) born adults, +18 years old between 1977 and 2017 with maternal linkages. Three methods were used to address missing paternal linkages: indicator category, complete case, and multiple imputation, which identified three candidate fathers for each individual within -4 to +7 years of mother's age with same postal code. For each method, the association of maternal and paternal history with mental disorder risk during follow-up was tested using multivariable logistic regression models adjusted for demographics and comorbidities.
ResultsThe cohort included 142,549 individuals; 22.6% lacked paternal linkages. Using indicator category, maternal and paternal histories were associated with mental disorder risk, with odds ratio (OR) 1.49, 95% confidence interval (CI): 1.45-1.52 and OR 1.34, 95% CI: 1.30-1.37, respectively. Similar results were obtained in the complete case analysis (OR 1.49, 95% CI: 1.45-1.53 and OR 1.33, 95% CI: 1.30-1.37, for maternal and paternal history, respectively). With multiple imputation, maternal history's association with mental disorder risk remained consistent with the other methods (pooled OR 1.52, 95% CI: 1.49-1.56); paternal history was associated with a smaller risk (pooled OR 1.25 95% CI: 1.22-1.29).
Conclusions and ImplicationsThe three methods for addressing missing paternal linkages produced similar findings about the familial transfer of mental disorders. Future familial studies could incorporate multiple methods to demonstrate robustness of findings.

ObjectivesMental disorders are a major public health concern. Genetic and environmental factors, both reflected in family health histories, jointly contribute to the onset of mental disorders. We examined the intergenerational transmission of mental disorders using objectively-measured family health histories from three generations.
ApproachA population-based cohort study was conducted using administrative healthcare databases from Manitoba, Canada. The cohort included offspring who were 18 years or older between 1977 and 2020 with linkage to 1+ parent and 1+ grandparent. Mental disorders were identified using diagnosis codes from hospitalization and outpatient physician visit records and included mood and anxiety, psychotic, and substance use disorders. Logistic regression models were mutually adjusted for mental disorder history in grandparents, parents and/or siblings in addition to offspring demographics: sex, region, decade of birth and income quintile, and comorbidity. Odds ratios (ORs) and 95% confidence intervals (95% CIs) were estimated.
ResultsOut of 125,070 individuals, 59.1% were females and 57.8% were urban residents. 41,552 (33.2%) had a mental disorder during study period and 108,682 (86.9%) had a family member with a mental disorder history. Individuals were more likely to have a mental disorder if they had a family history: mother (OR 1.52, 95% CI 1.48-1.56), father (OR 1.21, 95% CI 1.17-1.25), sibling (OR 1.33, 95% CI 1.28-1.39), grandparent (OR 1.06, 95% CI 1.03-1.09). Compared with other mental disorders, psychotic disorders had the strongest association with family history: mother (OR 2.37, 95% CI 2.00-2.82), father (OR 3.00, 95% CI 2.40-3.76), sibling (OR 2.34, 95% CI 1.79-3.05). However, there was no association between psychotic disorders and grandparent history (OR 1.00, 95% CI 0.90-1.11).
ConclusionsWe observed a strong association between mental disorders family history across three generations and the risk of the mental disorders in offspring. This association was observed for all the investigated mental disorders. This work highlights the value of multigenerational data linkage in understanding the intergenerational transfer of mental disorders.

ObjectivesPrediction of asthma risk can potentially be improved by including family history of asthma and related diagnoses, which reflect both genetics and shared environments. We tested the improvements in offspring asthma risk prediction using objectively-measured maternal, paternal, and offspring histories of comorbid conditions from administrative healthcare databases.
ApproachA population-based cohort study was conducted using data from Manitoba, Canada. Children born from 1974 to 2000 with linkages to at least one parent using family identification numbers were included. Asthma diagnosis and comorbidities were identified from hospital and outpatient physician visit records. Lasso regression models were used to assess performance and identify important predictors. The base model included offspring demographics, diagnosed allergic conditions and respiratory infections, and diagnosed parental asthma. Subsequent models included multiple comorbid chronic health conditions for offspring and parents.
ResultsThe cohort included 195,666 offspring; 51% were males, 13.6% had a parental asthma diagnosis, and 17.7% had an asthma diagnosis (median age at diagnosis: 6.0 years; interquartile range 3.0-11.0 years). The base model achieved a modest prediction performance with an area under the receiver operating characteristic curve of 0.60, sensitivity of 0.46 and a specificity of 0.67 using a threshold of 0.20. Sensitivity significantly improved when we included offspring chronic health conditions (sensitivity= 0.69; specificity = 0.66); both measures further improved when we additionally included parents' chronic health conditions (sensitivity= 0.72; specificity = 0.70). Chronic obstructive pulmonary disease, noninfectious gastroenteritis and otitis media were among the variables that added incremental predictive value of asthma risk with odd ratios of 1.36, 1.25 and 1.18, respectively.
ConclusionsIncluding offspring and parents' chronic health conditions, identified objectively from administrative healthcare databases, improved the performance of asthma risk prediction models in children. Health histories of comorbid conditions provide important factors to improve risk prediction models of chronic health conditions, which will facilitate disease prevention and treatment strategies.

Family health history is a well-established risk factor for many health conditions but the systematic collection of health histories, particularly for multiple generations and multiple family members, can be challenging. Routinely-collected electronic databases in a select number of sites worldwide offer a powerful tool to conduct multigenerational health research for entire populations. At these sites, administrative and healthcare records are used to construct familial relationships and objectively-measured health histories. We review and synthesize published literature to compare the attributes of routinely-collected, linked databases for three European sites (Denmark, Norway, Sweden) and three non-European sites (Canadian province of Manitoba, Taiwan, Australian state of Western Australia) with the capability to conduct population-based multigenerational health research. Our review found that European sites primarily identified family structures using population registries, whereas non-European sites used health insurance registries (Manitoba and Taiwan) or linked data from multiple sources (Western Australia). Information on familial status was reported to be available as early as 1947 (Sweden); Taiwan had the fewest years of data available (1995 onwards). All centres reported near complete coverage of familial relationships for their population catchment regions. Challenges in working with these data include differentiating biological and legal relationships, establishing accurate familial linkages over time, and accurately identifying health conditions. This review provides important insights about the benefits and challenges of using routinely-collected, population-based linked databases for conducting population-based multigenerational health research, and identifies opportunities for future research within and across the data-intensive environments at these six sites.

ObjectiveA family history of a chronic disease often predicts disease risk, with predictive value determined by heritability, the proportion of variation in risk explained by inherited genetic factors. Our objective was to assess the validity of disease heritability estimates from electronic healthcare records (EHRs) that capture family relationships and disease diagnoses.
ApproachA population-based investigation was conducted using healthcare records from Manitoba, Canada for 1970 to 2021. We constructed family relationships for up to four generations using health insurance registration information containing unique family and individual identifiers. Health histories for family members were created using diagnosis codes in hospital and physician visit records. Linear mixed-effects models were used to estimate heritability (h) for 130 chronic health conditions using open-source Clinical Classifications Software that defines clinically-meaningful disease categories. Comparisons between EHR-derived estimates and genetically-derived estimates from published studies were used to assess validity of the methodology.
ResultsHealth insurance registration data were used to construct 10,000 families that included 116,879 individuals. Median family size was 9 (interquartile range: 8). Median observation time was 39.6 years (interquartile range: 25.7). Males comprised half (51.0%) of family members. A total of 272,114 familial relationships were identified; slightly more than half (53%) were first degree (i.e., child and parent) relationships. One-third (33.2%) of families were comprised of four generations; only 15.3% were comprised of two generations. Heritability estimates were consistent with published genetically-derived estimates for several conditions, including diabetes (EHR h = 0.29 vs. 0.22), anemia (EHR h = 0.21 vs. 0.20), and asthma (EHR h = 0.34 vs. 0.33). However, inconsistencies were identified for pancreatic disorders, gastrointestinal conditions, some mental health conditions, and heart disease.
ConclusionEHRs provide a promising approach to explore heritability of selected health conditions in large, diverse populations. Inconsistencies between EHR-derived and genetically-derived estimates are indicative of the limitations of diagnoses recorded for administrative purposes. Future research will explore sex-specific heritability estimates and effects of change in disease diagnosis coding over time.

IntroductionMajor osteoporotic fractures (MOF) are associated with significant morbidity and healthcare system burden.
Objectives and ApproachWe aimed to determine whether sibling fracture history is associated with MOF risk amongst individuals from a population-based cohort using objectively-ascertained measures of fracture history. This retrospective cohort study used administrative databases from the province of Manitoba, Canada, which has a universal healthcare system. The cohort included individuals aged 40 years and older between 1997 and 2015 with linkage to at least one sibling. The exposure was MOF diagnosis occurring at age 40 years or older in a randomly selected sibling. The outcome was incident clinically-diagnosed MOF (hip, wrist, humerus or spine) identified in hospital and physician records using established case definitions. A multivariable Cox proportional hazards regression was used to test the association of sibling fracture history with the risk of MOF in individuals after adjustment for known fracture risk factors.
ResultsThe cohort included 217,519 individuals; 92% were linked to full siblings (i.e., same mother/father) and 49% were females. During a median follow-up of 11 years (IQR 5 -15), 7274 (3.3%) incident MOF cases were identified. Sibling MOF history was associated with increased risk of MOF (HR 1.71, 95% CI 1.48–1.97). The risk was elevated in both men (HR 1.63, 95% CI 1.29-2.06) and women (HR 1.78, 95% CI 1.48-2.13) but was higher among sisters (HR 2.08, 95% CI 1.65-2.61) compared to brothers (HR 1.67, 95% CI 1.20-2.32). In a secondary analysis of sibling fracture site, the highest risk was observed with diagnosis of wrist followed by spine fractures (HR 1.86, 95% CI 1.57-2.21 and HR 1.46, 95% CI 1.08-1.98, respectively).
ConclusionSibling fracture history is associated with increased MOF risk in individuals and should be considered as a candidate risk factor for improving fracture risk prediction.

IntroductionAdministrative health data capture diagnoses using the International Classification of Diseases (ICD), which has multiple versions over time. To facilitate longitudinal investigations using these data, we aimed to map diagnoses identified in three ICD versions – ICD-8 with adaptations (ICDA-8), ICD-9 with clinical modifications (ICD-9-CM), and ICD-10 with Canadian adaptations (ICD-10-CA) – to mutually exclusive chronic health condition categories adapted from the open source Clinical Classifications Software (CCS).
MethodsWe adapted the CCS crosswalk to 3-digit ICD-9-CM codes for chronic conditions and resolved the one-to-many mappings in ICD-9-CM codes. Using this adapted CCS crosswalk as the reference and referring to existing crosswalks between ICD versions, we extended the mapping to ICDA-8 and ICD-10-CA. Each mapping step was conducted independently by two reviewers and discrepancies were resolved by consensus through deliberation and reference to prior research. We report the frequencies, agreement percentages and 95% confidence intervals (CI) from each step.
ResultsWe identified 354 3-digit ICD-9-CM codes for chronic conditions. Of those, 77 (22%) codes had one-to-many mappings; 36 (10%) codes were mapped to a single CCS category and 41 (12%) codes were mapped to combined CCS categories. In total, the codes were mapped to 130 adapted CCS categories with an agreement percentage of 92% (95% CI: 86%–98%). Then, 321 3-digit ICDA-8 codes were mapped to CCS categories with an agreement percentage of 92% (95% CI: 89%–95%). Finally, 3583 ICD-10-CA codes were mapped to CCS categories; 111 (3%) had a fair or poor mapping quality; these were reviewed to keep or move to another category (agreement percentage=77% [95% CI: 69%–85%]).
ConclusionsWe developed crosswalks for three ICD versions (ICDA-8, ICD-9-CM, and ICD-10-CA) to 130 clinically meaningful categories of chronic health conditions by adapting the CCS classification. These crosswalks will benefit chronic disease studies spanning multiple decades of administrative health data.