Search

Background: Heart failure (HF), affecting 1–4% of adults in industrialized countries, is a major public health priority. Several algorithms based on administrative health data (HAD) have been developed to detect patients with HF in a timely and inexpensive manner, in order to perform real-world studies at the population level. However, their reported diagnostic accuracy is highly variable. Objective: To assess the diagnostic accuracy of validated HAD-based algorithms for detecting HF, compared to clinical diagnosis, and to investigate causes of heterogeneity. Methods: We included all diagnostic accuracy studies that utilized HAD for the diagnosis of congestive HF in the general adult population, using clinical examination or chart review as the reference standard. A systematic search of MEDLINE (1946–2023) and Embase (1947–2023) was conducted, without restrictions. The QUADAS-2 tool was employed to assess the risk of bias and concerns regarding applicability. Due to low-quality issues of the primary studies, associated with both the index test and the reference standard definition and conduct, and to the high level of clinical heterogeneity, a quantitative synthesis was not performed. Measures of diagnostic accuracy of the included algorithms were summarized narratively and presented graphically, by population subgroups. Results: We included 24 studies (161,524 patients) and extracted 36 algorithms. Algorithm selection was based on type of administrative data and DOR. Six studies (103,018 patients, 14 algorithms) were performed in the general outpatient population, with sensitivities ranging from 24.8 to 97.3% and specificities ranging from 35.6 to 99.5%. Eight studies (14,957 patients, 10 algorithms) included hospitalized patients with sensitivities ranging from 29.0 to 96.0% and specificities ranging from 65.8 to 99.2%. The remaining studies included subgroups of the general population or hospitalized patients with cardiologic conditions and were analyzed separately. Fourteen studies had one or more domains at high risk of bias, and there were concerns regarding applicability in 9 studies. Discussion: The considerable percentage of studies with a high risk of bias, together with the high clinical heterogeneity among different studies, did not allow to generate a pooled estimate of diagnostic accuracy for HAD-based algorithms to be used in an unselected general adult population. Systematic review registration: PROSPERO CRD42023487565 [ABSTRACT FROM AUTHOR]

Little is known about the impacts of fatigue after cancer treatment, including whether cancer-related fatigue impacts people’s use of healthcare. This study sought to examine how cancer-related fatigue impacts healthcare use after completing cancer treatment. A population-based survey was administered in Nova Scotia, Canada, to examine survivors’ experiences and needs after completing cancer treatment. Respondents included survivors of breast, melanoma, colorectal, prostate, hematologic, and young adult cancers who were 1–3 years post-treatment. Survey responses were linked to cancer registry, physicians’ claims, hospitalization, and ambulatory care data. Data were analyzed descriptively and using regression models. The final study cohort included 823 respondents. Younger respondents reported higher levels of cancer-related fatigue compared to older respondents. More females than males reported cancer-related fatigue. Upon adjusted analyses, those with cancer-related fatigue had lower odds of being discharged to primary care for their cancer-related follow-up (odds ratio = 0.71, p = 0.029). Moreover, those with cancer-related fatigue had 19% higher primary care use (incidence rate ratio = 1.19, p < 0.0001) and 37% higher oncology use (incidence rate ratio = 1.37, p < 0.016) during the follow-up period compared to those without cancer-related fatigue. Providers (oncology and primary care) may require additional support to identify clinically relevant fatigue and refer patients to appropriate resources and services.

Little is known about the impacts of fatigue after cancer treatment, including whether cancer-related fatigue impacts people's use of healthcare. This study sought to examine how cancer-related fatigue impacts healthcare use after completing cancer treatment. A population-based survey was administered in Nova Scotia, Canada, to examine survivors' experiences and needs after completing cancer treatment. Respondents included survivors of breast, melanoma, colorectal, prostate, hematologic, and young adult cancers who were 1–3 years post-treatment. Survey responses were linked to cancer registry, physicians' claims, hospitalization, and ambulatory care data. Data were analyzed descriptively and using regression models. The final study cohort included 823 respondents. Younger respondents reported higher levels of cancer-related fatigue compared to older respondents. More females than males reported cancer-related fatigue. Upon adjusted analyses, those with cancer-related fatigue had lower odds of being discharged to primary care for their cancer-related follow-up (odds ratio = 0.71, p = 0.029). Moreover, those with cancer-related fatigue had 19% higher primary care use (incidence rate ratio = 1.19, p < 0.0001) and 37% higher oncology use (incidence rate ratio = 1.37, p < 0.016) during the follow-up period compared to those without cancer-related fatigue. Providers (oncology and primary care) may require additional support to identify clinically relevant fatigue and refer patients to appropriate resources and services. [ABSTRACT FROM AUTHOR]

The use of routinely collected administrative health data for research can provide unique insights to inform decision-making and, ultimately, support better public health outcomes. Yet, since these data are primarily collected to administer healthcare service delivery, challenges exist when using such data for secondary purposes, namely epidemiologic research. Many of these challenges stem from the researcher's lack of control over the quality and consistency of data collection, and - furthermore - a lessened understanding of the data being analyzed. That said, we assert that these challenges can be partly mitigated through careful, systematic use of these data in epidemiologic research. This article presents considerations derived from experiences analyzing administrative health data (e.g., healthcare practitioner billings, hospitalizations, and prescription medication data) in the Canadian province of British Columbia (population of over 5 million in 2024), though we believe the underlying principles generalize beyond this region. Key considerations were organized around four themes: 1) Know the data and their primary use (understand their scope and limitations); 2) Understand classification and coding systems (appreciate the nuances regarding classification systems, versions, how they are employed in the primary uses of the data, and querying the values); 3) Transform data into meaningful forms (process data and apply identification algorithms, when necessary); 4) Recognize the importance of validity when defining analytic variables (make meaningful inferences based on data/algorithms). Although this article is not an exhaustive list of all considerations, we believe that it will provide pragmatic insights for those interested in leveraging administrative health data for epidemiologic research.

Community treatment orders (CTOs) have been introduced in many jurisdictions with evidence of increasing use over time as well as a disproportionate use in marginalised populations. Rates of CTOs also vary widely, both internationally and within the same country, for reasons that are poorly understood. This is despite evidence for effectiveness being mixed and, as a result, there have been calls for a reappraisal of this type of legislation. In the UK, a parliamentary committee on reforming the existing Mental Health Act recommended abolishing CTOs other than for people in the criminal justice system. Two recent Australian papers based on large state-wide administrative data-sets give conflicting results and came to markedly different conclusions regarding the desirability of reducing CTO rates. The debate about the effectiveness of CTOs therefore remains unresolved. This is of concern beyond Australia, as other jurisdictions such as England, Scotland and Canada have similar clinician-initiated orders.

Abstract Background Language barriers can impact health care and outcomes. Valid and reliable language data is central to studying health inequalities in linguistic minorities. In Canada, language variables are available in administrative health databases; however, the validity of these variables has not been studied. This study assessed concordance between language variables from administrative health databases and language variables from the Canadian Community Health Survey (CCHS) to identify Francophones in Ontario. Methods An Ontario combined sample of CCHS cycles from 2000 to 2012 (from participants who consented to link their data) was individually linked to three administrative databases (home care, long-term care [LTC], and mental health admissions). In total, 27,111 respondents had at least one encounter in one of the three databases. Language spoken at home (LOSH) and first official language spoken (FOLS) from CCHS were used as reference standards to assess their concordance with the language variables in administrative health databases, using the Cohen kappa, sensitivity, specificity, positive predictive value (PPV), and negative predictive values (NPV). Results Language variables from home care and LTC databases had the highest agreement with LOSH (kappa = 0.76 [95%CI, 0.735–0.793] and 0.75 [95%CI, 0.70–0.80], respectively) and FOLS (kappa = 0.66 for both). Sensitivity was higher with LOSH as the reference standard (75.5% [95%CI, 71.6–79.0] and 74.2% [95%CI, 67.3–80.1] for home care and LTC, respectively). With FOLS as the reference standard, the language variables in both data sources had modest sensitivity (53.1% [95%CI, 49.8–56.4] and 54.1% [95%CI, 48.3–59.7] in home care and LTC, respectively) but very high specificity (99.8% [95%CI, 99.7–99.9] and 99.6% [95%CI, 99.4–99.8]) and predictive values. The language variable from mental health admissions had poor agreement with all language variables in the CCHS. Conclusions Language variables in home care and LTC health databases were most consistent with the language often spoken at home. Studies using language variables from administrative data can use the sensitivity and specificity reported from this study to gauge the level of mis-ascertainment error and the resulting bias.

Abstract Background Side effects can occur within hours to days of starting antidepressant medications, whereas full therapeutic benefit for mood typically takes up to four weeks. This mismatch between time to harm and lag to benefit often leads to premature discontinuation of antidepressants, a phenomenon that can be partially reversed through early doctor-patient communication and follow-up. We investigated the relationship between relational continuity of care – the number of years family physicians have cared for older adult patients – and early follow-up care for patients prescribed antidepressants. Methods A retrospective cohort study was conducted on residents of Ontario, Canada aged 66 years or older who were dispensed their first antidepressant prescription through the provincial drug insurance program between April 1, 2016, and March 31, 2019. The study utilized multivariable regression to estimate the relationship between relational continuity and 30-day follow-up with the prescribing family physician. Separate estimates were generated for older adults living in urban, non-major urban, and rural communities. Results The study found a small positive relationship between relational continuity of care and follow-up care by the prescribing family physician for patients dispensed a first antidepressant prescription (RRR = 1.005; 95% CI = 1.004, 1.006). The relationship was moderated by the patients’ location of dwelling, where the effect was stronger for older adults residing in non-major urban (RRR = 1.009; 95% CI = 1.007, 1.012) and rural communities (RRR = 1.006; 95% CI = 1.002, 1.011). Conclusions Our findings do not provide strong evidence of a relationship between relational continuity of care and higher quality management of antidepressant prescriptions. However, the relationship is slightly more pronounced in rural communities where access to continuous primary care and specialized mental health services is more limited. This may support the ongoing need for the recruitment and retention of primary care providers in rural communities.

Background: Side effects can occur within hours to days of starting antidepressant medications, whereas full therapeutic benefit for mood typically takes up to four weeks. This mismatch between time to harm and lag to benefit often leads to premature discontinuation of antidepressants, a phenomenon that can be partially reversed through early doctor-patient communication and follow-up. We investigated the relationship between relational continuity of care – the number of years family physicians have cared for older adult patients – and early follow-up care for patients prescribed antidepressants. Methods: A retrospective cohort study was conducted on residents of Ontario, Canada aged 66 years or older who were dispensed their first antidepressant prescription through the provincial drug insurance program between April 1, 2016, and March 31, 2019. The study utilized multivariable regression to estimate the relationship between relational continuity and 30-day follow-up with the prescribing family physician. Separate estimates were generated for older adults living in urban, non-major urban, and rural communities. Results: The study found a small positive relationship between relational continuity of care and follow-up care by the prescribing family physician for patients dispensed a first antidepressant prescription (RRR = 1.005; 95% CI = 1.004, 1.006). The relationship was moderated by the patients' location of dwelling, where the effect was stronger for older adults residing in non-major urban (RRR = 1.009; 95% CI = 1.007, 1.012) and rural communities (RRR = 1.006; 95% CI = 1.002, 1.011). Conclusions: Our findings do not provide strong evidence of a relationship between relational continuity of care and higher quality management of antidepressant prescriptions. However, the relationship is slightly more pronounced in rural communities where access to continuous primary care and specialized mental health services is more limited. This may support the ongoing need for the recruitment and retention of primary care providers in rural communities. [ABSTRACT FROM AUTHOR]

Violence is recognised as a cause of health harm, but it is not consistently or adequately captured in healthcare data systems. While administrative health records could be valuable sources of information for measuring violence, they remain underutilised in violence-related research. The present research aims to examine the suitability of violence indicators in emergency care, primary care, and linked healthcare datasets. Descriptive analyses were conducted with the 2015/16 Hospital Episode Statistics Accident and Emergency (HES A&E) and the 2021/22 Emergency Care Data Set (ECDS). The potential of the Clinical Practice Research Datalink (CPRD) and the South Wales Violence Surveillance dataset (a police and emergency department (ED) dataset linked by Public Health Wales) were shown using available evidence. Among the discussed datasets, the South Wales Violence Surveillance dataset has the most detail about violent acts and their contexts, while the CPRD includes a more extensive range of socioeconomic factors about patients and extensive linkage with other datasets. Currently, detailed safeguarding information is routinely removed from the ECDS extracts provided to researchers, limiting its utility for violence research. In the HES A&E, only physical violence was consistently recorded. Addressing these issues has the potential to improve the use of health administrative data in research on violence. [ABSTRACT FROM AUTHOR]

Shi-Tong Diao, Run Dong, Jin-Min Peng, Yan Chen, Shan Li, Shu-Hua He, Yi-Fan Wang, Bin Du, Li Weng On behalf of the China Critical Care Clinical Trials Group (CCCCTG)Medical Intensive Care Unit, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Peking Union Medical College & Chinese Academy of Medical Sciences, Beijing, People’s Republic of ChinaCorrespondence: Li Weng, Medical Intensive Care Unit, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Peking Union Medical College & Chinese Academy of Medical Sciences, 1 Shuai Fu Yuan, Beijing, 100730, People’s Republic of China, Email wengli@gmail.comBackground: Sepsis surveillance was important for resources allocation, prevention, and development of health policy.Objective: The aim of the study was to validate a modified International Classification of Diseases (ICD)-10 based algorithm for identifying hospitalized patients with sepsis.Methods: We retrospectively analyzed a prospective, single-center cohort of adult patients who were consecutively admitted to one medical ICU ward and ten non-ICU wards with suspected or confirmed infections during a 6-month period. A modified ICD-10 based algorithm was validated against a reference standard of Sequential Organ Failure Assessment (SOFA) score based on Sepsis-3. Sensitivity (SE), specificity (SP), positive predictive value (PPV), negative predictive value (NPV), and areas under the receiver operating characteristic curves (AUROCs) were calculated for modified ICD-10 criteria, eSOFA criteria, Martin’s criteria, and Angus’s criteria.Results: Of the 547 patients in the cohort, 332 (61%) patients met Sepsis-3 criteria and 274 (50%) met modified ICD-10 criteria. In the ICU setting, modified ICD-10 criteria had SE (84.47%), SP (88.57%), PPV (95.60), and NPV (65.96). In non-ICU settings, modified ICD-10 had SE (64.19%), SP (80.00%), PPV (80.33), and NPV (63.72). In the whole cohort, the AUROCs of modified ICD-10 criteria, eSOFA, Angus’s criteria, and Martin’s criteria were 0.76, 0.75, 0.62, and 0.62, respectively.Conclusion: This study demonstrated that modified ICD-10 criteria had higher validity compared with Angus’s criteria and Martin’s criteria. Validity of the modified ICD-10 criteria was similar to eSOFA criteria. Modified ICD-10 algorithm can be used to provide an accurate estimate of population-based sepsis burden of China.Keywords: sepsis, validation, International Classification of Diseases, administrative health data, identification

Abstract Background Studies have suggested that agreement between administrative health data and self-report for asthma status ranges from fair to good, but few studies benefited from administrative health data over a long period. We aimed to (1) evaluate agreement between asthma status ascertained in administrative health data covering a period of 30 years and from self-report, and (2) identify determinants of agreement between the two sources. Methods We used administrative health data (1983–2012) from the Quebec Birth Cohort on Immunity and Health, which included 81,496 individuals born in the province of Quebec, Canada, in 1974. Additional information, including self-reported asthma, was collected by telephone interview with 1643 participants in 2012. By design, half of them had childhood asthma based on health services utilization. Results were weighted according to the inverse of the sampling probabilities. Five algorithms were applied to administrative health data (having ≥ 2 physician claims over a 1-, 2-, 3-, 5-, or 30-year interval or ≥ 1 hospitalization), to enable comparisons with previous studies. We estimated the proportion of overall agreement and Kappa, between asthma status derived from algorithms and self-reports. We used logistic regression to identify factors associated with agreement. Results Applying the five algorithms, the prevalence of asthma ranged from 49 to 55% among the 1643 participants. At interview (mean age = 37 years), 49% and 47% of participants respectively reported ever having asthma and asthma diagnosed by a physician. Proportions of agreement between administrative health data and self-report ranged from 88 to 91%, with Kappas ranging from 0.57 (95% CI: 0.52–0.63) to 0.67 (95% CI: 0.62–0.72); the highest values were obtained with the [≥ 2 physician claims over a 30-year interval or ≥ 1 hospitalization] algorithm. Having sought health services for allergic diseases other than asthma was related to lower agreement (Odds ratio = 0.41; 95% CI: 0.25–0.65 comparing ≥ 1 health services to none). Conclusions These findings indicate good agreement between asthma status defined from administrative health data and self-report. Agreement was higher than previously observed, which may be due to the 30-year lookback window in administrative data. Our findings support using both administrative health data and self-report in population-based epidemiological studies.

An integrated disease management program otherwise called a clinical pathway was recently implemented in Saskatchewan, Canada for patients living with chronic obstructive pulmonary disease (COPD). This study compared the real-world costs and consequences of the COPD clinical pathway program with 2 control treatment programs. The study comprised adult COPD patients in Regina (clinical pathway group, N = 759) matched on propensity scores to 2 independent control groups of similar adults in (1) Regina (historical controls, N = 759) and (2) Saskatoon (contemporaneous controls, N = 759). The study measures included patient-level healthcare costs and acute COPD exacerbation outcomes, both tracked in population-based administrative health data over a one-year follow-up period. Analyses included Cox proportional hazards models and differences in means between groups. The bias-corrected and accelerated bootstrap method was used to calculate 95% confidence intervals (CI). The COPD pathway patients had lower risks of moderate (hazard ratio [HR] =0.57, 95% CI [0.40-0.83]) and severe (HR = 0.43, 95% CI [0.28-0.66]) exacerbations compared to the historical control group, but similar risks compared with the contemporaneous control group. The COPD pathway patients experienced fewer episodes of exacerbations compared with the historical control group (mean difference = −0.30, 95% CI [−0.40, −0.20]) and the contemporaneous control group (mean difference = −0.12, 95% CI [−0.20, −0.03]). Average annual healthcare costs in Canadian dollars were marginally higher among patients in the COPD clinical pathway (mean = $10 549, standard deviation [SD] =$18 149) than those in the contemporaneous control group ($8841, SD = $17 120), but comparable to the historical control group ($10 677, SD = $21 201). The COPD pathway provides better outcomes at about the same costs when compared to the historical controls, but only slightly better outcomes and at a marginally higher cost when compared to the contemporaneous controls. [ABSTRACT FROM AUTHOR]

Purpose: Three generic claims‐based algorithms based on the Illness Classification of Diseases (10th revision‐ ICD‐10) codes, French Long‐Term Illness (LTI) data, and the Diagnosis Related Group program (DRG) were developed to identify retirees with cancer using data from the French national health insurance information system (Système national des données de santé or SNDS) which covers the entire French population. The present study aimed to calculate the algorithms' performances and to describe false positives and negatives in detail. Methods: Between 2011 and 2016, data from 7544 participants of the French retired self‐employed craftsperson cohort (ESPrI) were first matched to the SNDS data, and then toFrench population‐based cancer registries data, used as the gold standard. Performance indicators, such as sensitivity and positive predictive values, were estimated for the three algorithms in a subcohort of ESPrI. Results: The third algorithm, which combined the LTI and DRG program data, presented the best sensitivities (90.9%–100%) and positive predictive values (58.1%–95.2%) according to cancer sites. The majority of false positives were in fact nearby organ sites (e.g., stomach for esophagus) and carcinoma in situ. Most false negatives were probably due to under declaration of LTI. Conclusion: Validated algorithms using data from the SNDS can be used for passive epidemiological follow‐up for some cancer sites in the ESPrI cohort. [ABSTRACT FROM AUTHOR]

Objectives To determine the impact of the introduction of biologic DMARDs (bDMARDs) on severe infections among people newly diagnosed with RA compared with non-RA individuals. Methods In this population-based retrospective cohort study using administrative data (from 1990–2015) for British Columbia, Canada, all incident RA patients diagnosed between 1995 and 2007 were identified. General population controls with no inflammatory arthritis were matched to RA patients based on age and gender, and were assigned the diagnosis date (i.e. index date) of the RA patients they were matched with. RA/controls were then divided into quarterly cohorts according to their index dates. The outcome of interest was all severe infections necessitating hospitalization or occurring during hospitalization after the index date. We calculated 8-year severe infection rates for each cohort and conducted interrupted time-series analyses to compare severe infection trends in RA/controls with index date during pre-bDMARDs (1995–2001) and post-bDMARDs (2003–2007) periods. Results A total of 60 226 and 588 499 incident RA/controls were identified. We identified 14 245 severe infections in RA, and 79 819 severe infections in controls. The 8-year severe infection rates decreased among RA/controls with increasing calendar year of index date in the pre-bDMARDs period, but increased over time only among RA, not controls, with index date in the post-bDMARDs period. The adjusted difference between the pre- and post-bDMARDs secular trends in 8-year severe infection rates was 1.85 (P  = 0.001) in RA and 0.12 (P  = 0.29) in non-RA. Conclusion RA onset after bDMARDs introduction was associated with an elevated severe infection risk in RA patients compared with matched non-RA individuals. [ABSTRACT FROM AUTHOR]

Objective: To evaluate if access to team-based primary care is related to medication management outcomes for older adults. Methods: We completed two retrospective cohort studies using administrative health data for older adults (66+) in Ontario (n = 428,852) and Québec (n = 310,198) who were rostered with a family physician (FP) between the 2001/02 and 2017/18 fiscal years. We generated matched comparison groups of older adults rostered to an FP practicing in a team-based model, and older adults rostered to an FP in a non-team model. We compared the following outcomes between these groups: any adverse drug reactions (ADRs), any potentially inappropriate prescription (PIP), and polypharmacy. Average treatment effects of access to team-based care were estimated using a difference-in-differences estimator. Results: The risk of an ADR was 22 % higher (RR = 1.22, 95 % CI = 1.18, 1.26) for older adults rostered to a team-based FP in Québec and 6 % lower (RR = 0.943, 95 % CI = 0.907, 0.978) in Ontario. However, absolute risk differences were less than 0.5 %. Differences in the risk of polypharmacy were small in Québec (RR = 1.005, 95 % CI = 1.001, 1.009) and Ontario (RR = 1.004, 95 % CI = 1.001, 1.007) and had absolute risk differences of less than 1 % in both provinces. Effects on PIP were not statistically or clinically significant in adjusted models. Interpretation: We did not find evidence that access to team-based primary care in Ontario or Québec meaningfully improved medication management outcomes for older adults.

Background: Studies have suggested that agreement between administrative health data and self-report for asthma status ranges from fair to good, but few studies benefited from administrative health data over a long period. We aimed to (1) evaluate agreement between asthma status ascertained in administrative health data covering a period of 30 years and from self-report, and (2) identify determinants of agreement between the two sources. Methods: We used administrative health data (1983–2012) from the Quebec Birth Cohort on Immunity and Health, which included 81,496 individuals born in the province of Quebec, Canada, in 1974. Additional information, including self-reported asthma, was collected by telephone interview with 1643 participants in 2012. By design, half of them had childhood asthma based on health services utilization. Results were weighted according to the inverse of the sampling probabilities. Five algorithms were applied to administrative health data (having ≥ 2 physician claims over a 1-, 2-, 3-, 5-, or 30-year interval or ≥ 1 hospitalization), to enable comparisons with previous studies. We estimated the proportion of overall agreement and Kappa, between asthma status derived from algorithms and self-reports. We used logistic regression to identify factors associated with agreement. Results: Applying the five algorithms, the prevalence of asthma ranged from 49 to 55% among the 1643 participants. At interview (mean age = 37 years), 49% and 47% of participants respectively reported ever having asthma and asthma diagnosed by a physician. Proportions of agreement between administrative health data and self-report ranged from 88 to 91%, with Kappas ranging from 0.57 (95% CI: 0.52–0.63) to 0.67 (95% CI: 0.62–0.72); the highest values were obtained with the [≥ 2 physician claims over a 30-year interval or ≥ 1 hospitalization] algorithm. Having sought health services for allergic diseases other than asthma was related to lower agreement (Odds ratio = 0.41; 95% CI: 0.25–0.65 comparing ≥ 1 health services to none). Conclusions: These findings indicate good agreement between asthma status defined from administrative health data and self-report. Agreement was higher than previously observed, which may be due to the 30-year lookback window in administrative data. Our findings support using both administrative health data and self-report in population-based epidemiological studies. [ABSTRACT FROM AUTHOR]

Abstract Getting access to administrative health data for research purposes is a difficult and time-consuming process due to increasingly demanding privacy regulations. An alternative method for sharing administrative health data would be to share synthetic datasets where the records do not correspond to real individuals, but the patterns and relationships seen in the data are reproduced. This paper assesses the feasibility of generating synthetic administrative health data using a recurrent deep learning model. Our data comes from 120,000 individuals from Alberta Health’s administrative health database. We assess how similar our synthetic data is to the real data using utility assessments that assess the structure and general patterns in the data as well as by recreating a specific analysis in the real data commonly applied to this type of administrative health data. We also assess the privacy risks associated with the use of this synthetic dataset. Generic utility assessments that used Hellinger distance to quantify the difference in distributions between real and synthetic datasets for event types (0.027), attributes (mean 0.0417), Markov transition matrices (order 1 mean absolute difference: 0.0896, sd: 0.159; order 2: mean Hellinger distance 0.2195, sd: 0.2724), the Hellinger distance between the joint distributions was 0.352, and the similarity of random cohorts generated from real and synthetic data had a mean Hellinger distance of 0.3 and mean Euclidean distance of 0.064, indicating small differences between the distributions in the real data and the synthetic data. By applying a realistic analysis to both real and synthetic datasets, Cox regression hazard ratios achieved a mean confidence interval overlap of 68% for adjusted hazard ratios among 5 key outcomes of interest, indicating synthetic data produces similar analytic results to real data. The privacy assessment concluded that the attribution disclosure risk associated with this synthetic dataset was substantially less than the typical 0.09 acceptable risk threshold. Based on these metrics our results show that our synthetic data is suitably similar to the real data and could be shared for research purposes thereby alleviating concerns associated with the sharing of real data in some circumstances.

Abstract Background Linked electronic medical records and administrative data have the potential to support a learning health system and data-driven quality improvement. However, data completeness and accuracy must first be assessed before their application. We evaluated the processes, feasibility, and limitations of linking electronic medical records and administrative data for the purpose of quality improvement within five specialist diabetes clinics in Edmonton, Alberta, a province known for its robust health data infrastructure. Methods We conducted a retrospective cross-sectional analysis using electronic medical record and administrative data for individuals ≥ 18 years attending the clinics between March 2017 and December 2018. Descriptive statistics were produced for demographics, service use, diabetes type, and standard diabetes benchmarks. The systematic and iterative process of obtaining results is described. Results The process of integrating electronic medical record with administrative data for quality improvement was found to be non-linear and iterative and involved four phases: project planning, information generating, limitations analysis, and action. After limitations analysis, questions were grouped into those that were answerable with confidence, answerable with limitations, and not answerable with available data. Factors contributing to data limitations included inaccurate data entry, coding, collation, migration and synthesis, changes in laboratory reporting, and information not captured in existing databases. Conclusion Electronic medical records and administrative databases can be powerful tools to establish clinical practice patterns, inform data-driven quality improvement at a regional level, and support a learning health system. However, there are substantial data limitations that must be addressed before these sources can be reliably leveraged.

Abstract Background Prognostic information for patients with hypertension is largely based on population averages. The purpose of this study was to compare the performance of four machine learning approaches for personalized prediction of incident hospitalization for cardiovascular disease among newly diagnosed hypertensive patients. Methods Using province-wide linked administrative health data in Alberta, we analyzed a cohort of 259,873 newly-diagnosed hypertensive patients from 2009 to 2015 who collectively had 11,863 incident hospitalizations for heart failure, myocardial infarction, and stroke. Linear multi-task logistic regression, neural multi-task logistic regression, random survival forest and Cox proportional hazard models were used to determine the number of event-free survivors at each time-point and to construct individual event-free survival probability curves. The predictive performance was evaluated by root mean squared error, mean absolute error, concordance index, and the Brier score. Results The random survival forest model has the lowest root mean squared error value at 33.94 and lowest mean absolute error value at 28.37. Machine learning methods provide similar discrimination and calibration in the personalized survival prediction of hospitalizations for cardiovascular events in patients with hypertension. Neural multi-task logistic regression model has the highest concordance index at 0.8149 and lowest Brier score at 0.0242 for the personalized survival prediction. Conclusions This is the first personalized survival prediction for cardiovascular diseases among hypertensive patients using administrative data. The four models tested in this analysis exhibited a similar discrimination and calibration ability in predicting personalized survival prediction of hypertension patients.

Accidental overdoses are now the leading cause of death among people with HIV (PWH) in British Columbia (BC). We examined the utilization and retention of opioid agonist therapy (OAT).Adult PWH (≥19 years) with ≥ 1 OAT dispensation in BC between 2008 and 2020 were included (n = 1,515). OAT treatment episodes were formed based on specific criteria for slow-release oral morphine (SROM), methadone, injectable OAT (iOAT), and buprenorphine/naloxone. Retention in treatment was defined as any episode lasting ≥ 12 months. Logistic regression with generalized estimating equations modeled retention-associated factors.There was a 56.6% decline in OAT retention over time. Buprenorphine treatment exhibited significantly lower odds of retention (OR: 0.58; 95% CI: 0.36–0.92) compared to methadone. Conversely, no significant change in retention odds was observed for SROM (0.72; 0.33–1.54) and iOAT (0.81; 0.31–2.12). Factors associated with increased odds of retention included a 10-year increase in age (1.69; 1.46–1.95), previous retention history (1.96; 1.40–2.73), achieving OAT therapeutic dose (8.22; 6.67–10.14), and suppressed HIV viral load (1.35; 1.10–1.67). Individuals with a lifetime HCV diagnosis receiving iOAT were more likely to retain (3.61; 1.20–10.83). Each additional year on OAT during the study period was associated with a 4% increase in the odds of retention.A significant proportion of PWH had a history of OAT prescribing but experienced low retention rates. Retention outcomes were more positive for SROM and iOAT. The association between OAT medication type and retention odds may be particularly influenced by HCV diagnosis. Optimal management of opioid use disorder among PWH, with an emphasis on attaining the therapeutic dose is crucial.

Background: Linked electronic medical records and administrative data have the potential to support a learning health system and data-driven quality improvement. However, data completeness and accuracy must first be assessed before their application. We evaluated the processes, feasibility, and limitations of linking electronic medical records and administrative data for the purpose of quality improvement within five specialist diabetes clinics in Edmonton, Alberta, a province known for its robust health data infrastructure. Methods: We conducted a retrospective cross-sectional analysis using electronic medical record and administrative data for individuals ≥ 18 years attending the clinics between March 2017 and December 2018. Descriptive statistics were produced for demographics, service use, diabetes type, and standard diabetes benchmarks. The systematic and iterative process of obtaining results is described. Results: The process of integrating electronic medical record with administrative data for quality improvement was found to be non-linear and iterative and involved four phases: project planning, information generating, limitations analysis, and action. After limitations analysis, questions were grouped into those that were answerable with confidence, answerable with limitations, and not answerable with available data. Factors contributing to data limitations included inaccurate data entry, coding, collation, migration and synthesis, changes in laboratory reporting, and information not captured in existing databases. Conclusion: Electronic medical records and administrative databases can be powerful tools to establish clinical practice patterns, inform data-driven quality improvement at a regional level, and support a learning health system. However, there are substantial data limitations that must be addressed before these sources can be reliably leveraged. [ABSTRACT FROM AUTHOR]

Getting access to administrative health data for research purposes is a difficult and time-consuming process due to increasingly demanding privacy regulations. An alternative method for sharing administrative health data would be to share synthetic datasets where the records do not correspond to real individuals, but the patterns and relationships seen in the data are reproduced. This paper assesses the feasibility of generating synthetic administrative health data using a recurrent deep learning model. Our data comes from 120,000 individuals from Alberta Health's administrative health database. We assess how similar our synthetic data is to the real data using utility assessments that assess the structure and general patterns in the data as well as by recreating a specific analysis in the real data commonly applied to this type of administrative health data. We also assess the privacy risks associated with the use of this synthetic dataset. Generic utility assessments that used Hellinger distance to quantify the difference in distributions between real and synthetic datasets for event types (0.027), attributes (mean 0.0417), Markov transition matrices (order 1 mean absolute difference: 0.0896, sd: 0.159; order 2: mean Hellinger distance 0.2195, sd: 0.2724), the Hellinger distance between the joint distributions was 0.352, and the similarity of random cohorts generated from real and synthetic data had a mean Hellinger distance of 0.3 and mean Euclidean distance of 0.064, indicating small differences between the distributions in the real data and the synthetic data. By applying a realistic analysis to both real and synthetic datasets, Cox regression hazard ratios achieved a mean confidence interval overlap of 68% for adjusted hazard ratios among 5 key outcomes of interest, indicating synthetic data produces similar analytic results to real data. The privacy assessment concluded that the attribution disclosure risk associated with this synthetic dataset was substantially less than the typical 0.09 acceptable risk threshold. Based on these metrics our results show that our synthetic data is suitably similar to the real data and could be shared for research purposes thereby alleviating concerns associated with the sharing of real data in some circumstances. [ABSTRACT FROM AUTHOR]

Abstract Background Network analysis, a technique for describing relationships, can provide insights into patterns of co-occurring chronic health conditions. The effect that co-occurrence measurement has on disease network structure and resulting inferences has not been well studied. The purpose of the study was to compare structural differences among multimorbidity networks constructed using different co-occurrence measures. Methods A retrospective cohort study was conducted using four fiscal years of administrative health data (2015/16 – 2018/19) from the province of Manitoba, Canada (population 1.5 million). Chronic conditions were identified using diagnosis codes from electronic records of physician visits, surgeries, and inpatient hospitalizations, and grouped into categories using the Johns Hopkins Adjusted Clinical Group (ACG) System. Pairwise disease networks were separately constructed using each of seven co-occurrence measures: lift, relative risk, phi, Jaccard, cosine, Kulczynski, and joint prevalence. Centrality analysis was limited to the top 20 central nodes, with degree centrality used to identify potentially influential chronic conditions. Community detection was used to identify disease clusters. Similarities in community structure between networks was measured using the adjusted Rand index (ARI). Network edges were described using disease prevalence categorized as low (

Objective: Opioid use disorder (OUD) is a chronic relapsing disorder with a problematic pattern of opioid use, affecting nearly 27 million people worldwide. Machine learning (ML)-based prediction of OUD may lead to early detection and intervention. However, most ML prediction studies were not based on representative data sources and prospective validations, limiting their potential to predict future new cases. In the current study, we aimed to develop and prospectively validate an ML model that could predict individual OUD cases based on representative large-scale health data. Method: We present an ensemble machine-learning model trained on a cross-linked Canadian administrative health data set from 2014 to 2018 (n = 699,164), with validation of model-predicted OUD cases on a hold-out sample from 2014 to 2018 (n = 174,791) and prospective prediction of OUD cases on a non-overlapping sample from 2019 (n = 316,039). We used administrative records of OUD diagnosis for each subject based on International Classification of Diseases (ICD) codes. Results: With 6409 OUD cases in 2019 (mean [SD], 45.34 [14.28], 3400 males), our model prospectively predicted OUD cases at a high accuracy (balanced accuracy, 86%, sensitivity, 93%; specificity 79%). In accord with prior findings, the top risk factors for OUD in this model were opioid use indicators and a history of other substance use disorders. Conclusion: Our study presents an individualized prospective prediction of OUD cases by applying ML to large administrative health datasets. Such prospective predictions based on ML would be essential for potential future clinical applications in the early detection of OUD. [ABSTRACT FROM AUTHOR]

Background: Prognostic information for patients with hypertension is largely based on population averages. The purpose of this study was to compare the performance of four machine learning approaches for personalized prediction of incident hospitalization for cardiovascular disease among newly diagnosed hypertensive patients. Methods: Using province-wide linked administrative health data in Alberta, we analyzed a cohort of 259,873 newly-diagnosed hypertensive patients from 2009 to 2015 who collectively had 11,863 incident hospitalizations for heart failure, myocardial infarction, and stroke. Linear multi-task logistic regression, neural multi-task logistic regression, random survival forest and Cox proportional hazard models were used to determine the number of event-free survivors at each time-point and to construct individual event-free survival probability curves. The predictive performance was evaluated by root mean squared error, mean absolute error, concordance index, and the Brier score. Results: The random survival forest model has the lowest root mean squared error value at 33.94 and lowest mean absolute error value at 28.37. Machine learning methods provide similar discrimination and calibration in the personalized survival prediction of hospitalizations for cardiovascular events in patients with hypertension. Neural multi-task logistic regression model has the highest concordance index at 0.8149 and lowest Brier score at 0.0242 for the personalized survival prediction. Conclusions: This is the first personalized survival prediction for cardiovascular diseases among hypertensive patients using administrative data. The four models tested in this analysis exhibited a similar discrimination and calibration ability in predicting personalized survival prediction of hypertension patients. [ABSTRACT FROM AUTHOR]

Study Question: How do endometriosis diagnoses and subtypes reported in administrative health data compare with surgically confirmed disease?, Summary Answer: For endometriosis diagnosis, we observed substantial agreement and high sensitivity and specificity between administrative health data-International Classification of Diseases (ICD) 9 codes-and surgically confirmed diagnoses among participants who underwent gynecologic laparoscopy or laparotomy., What Is Known Already: Several studies have assessed the validity of self-reported endometriosis in comparison to medical record reporting, finding strong confirmation. We previously reported high inter- and intra-surgeon agreement for endometriosis diagnosis in the Endometriosis, Natural History, Diagnosis, and Outcomes (ENDO) Study., Study Design, Size, Duration: In this validation study, participants (n = 412) of the Utah operative cohort of the ENDO Study (2007-2009) were linked to medical records from the Utah Population Database (UPDB) to compare endometriosis diagnoses from each source. The UPDB is a unique database containing linked data on over 11 million individuals, including statewide ambulatory and inpatient records, state vital records, and University of Utah Health and Intermountain Healthcare electronic healthcare records, capturing most Utah residents., Participants/materials, Setting, Methods: The ENDO operative cohort consisted of individuals aged 18-44 years with no prior endometriosis diagnosis who underwent gynecologic laparoscopy or laparotomy for a variety of surgical indications. In total, 173 women were diagnosed with endometriosis based on surgical visualization of disease, 35% with superficial endometriosis, 9% with ovarian endometriomas, and 14% with deep infiltrating endometriosis. Contemporary administrative health data from the UPDB included ICD diagnostic codes from Utah Department of Health in-patient and ambulatory surgery records and University of Utah and Intermountain Health electronic health records., Main Results and the Role of Chance: For endometriosis diagnosis, we found relatively high sensitivity (0.88) and specificity (0.87) and substantial agreement (Kappa [Κ] = 0.74). We found similarly high sensitivity, specificity, and agreement for superficial endometriosis (n = 143, 0.86, 0.83, Κ  = 0.65) and ovarian endometriomas (n = 38, 0.82, 0.92, Κ  = 0.58). However, deep infiltrating endometriosis (n = 58) had lower sensitivity (0.12) and agreement (Κ  = 0.17), with high specificity (0.99)., Limitations, Reasons for Caution: Medication prescription data and unstructured data, such as clinical notes, were not included in the UPDB data used for this study. These additional data types could aid in detection of endometriosis. Most participants were white or Asian with Hispanic ethnicity reported 11% of the time, which may limit generalizability to some US states. Additionally, given that participants whose administrative health records we utilized were also part of the ENDO Study, the surgeons may have been more vigilant in diagnostic coding due to the operative forms they completed for the ENDO Study, which may have led to increased validity. However, the codes compared in the UPDB would have been entered by medical coders as part of standard clinical practice., Wider Implications of the Findings: We observed substantial agreement between administrative health data and surgically confirmed endometriosis diagnoses overall, and for superficial and ovarian endometrioma subtypes. These findings may provide reassurance to researchers using administrative healthcare records to assess risk factors and long-term health outcomes of endometriosis. Our findings corroborate prior research that demonstrates high specificity but low sensitivity for deep infiltrating endometriosis, indicating deep infiltrating endometriosis is not reliably annotated in administrative healthcare data. This suggests that medical record-based deep infiltrating endometriosis diagnoses may be suitable for etiologic studies but not for surveillance or detection studies., Study Funding/competing Interest(s): The original ENDO Study was funded by the Intramural Research Program, Eunice Kennedy Shriver National Institute of Child Health and Human Development, National Institutes of Health (contracts NO1-DK-6-3428; NO1-DK-6-3427; 10001406-02). We acknowledge partial support for the UPDB through grant P30 CA2014 from the National Cancer Institute, University of Utah and from the University of Utah's program in Personalized Health and Center for Clinical and Translational Science. This research was also supported by the NCRR grant, 'Sharing Statewide Health Data for Genetic Research' (R01 RR021746, G. Mineau, PI) with additional support from the Utah Department of Health and Human Services, University of Utah. Additionally, this research was supported by the Utah Cancer Registry, which is funded by the National Cancer Institute's SEER Program, Contract No. HHSN261201800016I, the US Centers for Disease Control and Prevention's National Program of Cancer Registries, Cooperative Agreement No. NU58DP007131, with additional support from the University of Utah and Huntsman Cancer Foundation. Research reported in this publication was also supported by the National Institutes of Health (Award Numbers R01HL164715 [to L.V.F., K.C.S., and A.Z.P.] and K01AG058781 [to K.C.S.]), by the Huntsman Cancer Institute's Breast and Gynecologic Cancers Center, and by the Doris Duke Foundation's COVID-19 Fund to Retain Clinical Scientists funded by the American Heart Association. A.C.K. was supported by Training Grant Number 5T15LM007124 from the National Library of Medicine to K.E. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health or other sponsors. There are no competing interests among any of the authors., Trial Registration Number: N/A., (© The Author(s) 2024. Published by Oxford University Press on behalf of European Society of Human Reproduction and Embryology.)

Abstract Background Algorithms used to identify disease cases in administrative health data may be sensitive to changes in the data over time. Control charts can be used to assess how variations in administrative health data impact the stability of estimated trends in incidence and prevalence for administrative data algorithms. We compared the stability of incidence and prevalence trends for multiple juvenile diabetes algorithms using observed-expected control charts. Methods Eighteen validated algorithms for juvenile diabetes were applied to administrative health data from Manitoba, Canada between 1975 and 2018. Trends in disease incidence and prevalence for each algorithm were modelled using negative binomial regression and generalized estimating equations; model-predicted case counts were plotted against observed counts. Control limits were set as predicted case count ±0.8*standard deviation. Differences in the frequency of out-of-control observations for each algorithm were assessed using McNemar’s test with Holm-Bonferroni adjustment. Results The proportion of out-of-control observations for incidence and prevalence ranged from 0.57 to 0.76 and 0.45 to 0.83, respectively. McNemar’s test revealed no difference in the frequency of out-of-control observations across algorithms. A sensitivity analysis with relaxed control limits (2*standard deviation) detected fewer out-of-control years (incidence 0.19 to 0.33; prevalence 0.07 to 0.52), but differences in stability across some algorithms for prevalence. Conclusions Our study using control charts to compare stability of trends in incidence and prevalence for juvenile diabetes algorithms found no differences for disease incidence. Differences were observed between select algorithms for disease prevalence when using wider control limits.

Abstract Background We described the impact of different lengths of lookback window (LW), a retrospective time period to observe diagnoses in administrative data, on the prevalence and incidence of eight chronic diseases. Methods Our study populations included people living with HIV (N = 5151) and 1:5 age-sex-matched HIV-negative individuals (N = 25,755) in British Columbia, Canada, with complete follow-up between 1996 and 2012. We measured period prevalence and incidence of diseases in 2012 using LWs ranging from 1 to 16 years. Cases were deemed prevalent if identified in 2012 or within a defined LW, and incident if newly identified in 2012 with no previous cases detected within a defined LW. Chronic disease cases were ascertained using published case-finding algorithms applied to population-based provincial administrative health datasets. Results Overall, using cases identified by the full 16-year LW as the reference, LWs ≥8 years and ≥ 4 years reduced the proportion of misclassified prevalent and incidence cases of most diseases to

BackgroundAllowing patients to remain at home and decreasing the number of unnecessary emergency room visits have become important policy goals in modern healthcare systems. However, the lack of available literature makes it critical to identify determinants that could be associated with increased emergency department (ED) visits in patients receiving integrated home care (IHC).MethodsA retrospective observational study was carried out in a large Italian region among patients with at least one IHC event between January 1, 2012 and December 31, 2017. IHC is administered from 8 am to 8 pm by a team of physicians, nurses, and other professionals as needed based on the patient’s health conditions. A clinical record is opened at the time a patient is enrolled in IHC and closed after the last service is provided. Every such clinical record was defined as an IHC event, and only ED visits that occurred during IHC events were considered. Sociodemographic, clinical and IHC variables were collected. A multivariate, stepwise logistic analysis was then performed, using likelihood of ED visit as a dependent variable.ResultsA total of 29 209 ED visits were recorded during the 66 433 IHC events that took place during the observation period. There was an increased risk of ED visits in males (odds ratio [OR] = 1.29), younger patients, those with a family caregiver (OR = 1.13), and those with a higher number of cohabitant family members. Long travel distance from patients’ residence to the ED reduced the risk of ED visits. The risk of ED visits was higher when patients were referred to IHC by hospitals or residential facilities, compared to referrals by general practitioners. IHC events involving patients with neoplasms (OR = 1.91) showed the highest risk of ED visits.ConclusionEvidence of sociodemographic and clinical determinants of ED visits may offer IHC service providers a useful perspective to implement intervention programmes based on appropriate individual care plans and broad-based client assessment.

• DCNeT, a comorbidity network-based temporal deep learning framework is presented. • Two attention mechanisms are proposed to represent patients' hospitalizations. • Time intervals between hospitalizations are modeled to fully use time information. • DCNeT outperforms the state-of-the-art methods on real-world datasets. • Illustrate an important use of administrative health data. Patients with mental disorders (MDs) are at higher subsequent risk of developing cardiovascular diseases (CVDs) than the general population. Early identification of cardiovascular risk in patients with MDs is beneficial for timely intervention and reducing disease burden. Recently, deep learning approaches have been increasingly applied in CVDs risk prediction. However, these methods have three major issues: 1) mostly relying on multiple types of clinical data, 2) not sufficiently mining and utilizing comorbidity patterns hidden in complex correlations among various diseases, and 3) not fully leveraging the time information, including the irregular intervals. To address these issues, we propose a disease comorbidity network-based temporal deep learning framework (DCNeT) to predict the subsequent CVDs risk for patients with MDs based on routinely collected administrative health data. Firstly, to identify the comorbidity patterns of MDs, we construct a disease comorbidity network (DCN) for MDs and apply graph embedding method to generate disease embeddings for each disease in the DCN. Then, a code attention mechanism is proposed to obtain the weight of each disease which is embedded into dense vectors based on the structure of DCN. We present a view attention mechanism to compute the attention weights of different types of features including disease embeddings, basic features, and disease indicators for generating the final representations of patients' hospitalizations. Furthermore, to fully utilize the information on the irregular time intervals between hospitalizations, a time encoding module is designed, and the time-aware LSTM is adopted to model the irregular time intervals and capture the temporal patterns of patients' hospitalizations. The experimental results show that DCNeT outperforms the state-of-the-art methods, with the area under the receiver operating characteristic curve of 0.7658, 0.8143, 0.8110, and 0.7839 on four datasets, respectively. The ablation experiments further demonstrate that each module of DCNeT, including the code attention, view attention, and time encoding module, contributes to its superior performance, with average improvements of 1.20 %, 1.65 %, and 1.13 % in accuracy, respectively. Our DCNeT could be utilized as an efficient framework for identifying high-risk groups of CVDs among patients with MDs that may benefit from screening and preventive strategies. [ABSTRACT FROM AUTHOR]

Purpose: Infertility is a topic of growing interest, and female infertility is often treated with gonadotropins. Evidence regarding comparative safety and efficacy of different gonadotropin formulations is available from clinical studies, while real-world data are missing. The present study aims to investigate effectiveness and safety of treatment with different gonadotropin formulations in women undergoing medically assisted procreation treatments in Latium, a region in central Italy, through a real-world data approach. Methods: A retrospective population-based cohort study in women between the ages of 18 and 45 years who were prescribed with at least one gonadotropin between 2007 and 2019 was conducted. Women were enrolled from the regional drug dispense registry, and data on their clinical history, exposure to therapeutic cycles (based on recombinant "REC" or extractives "EXT" gonadotropin, or combined protocol "CMD" (REC + EXT)), and maternal/infantile outcomes were linked from the regional healthcare administrative databases. Multivariate logistic regression models were applied to estimate the association between exposure and outcomes. Results: Overall, 90,292 therapeutic cycles prescribed to 35,899 women were linked to pregnancies. Overall, 15.8% of cycles successfully led to pregnancy. Compared to extractives, recombinant and combined treatments showed a stronger association with conception rate (RRREC adj = 1.06, 95% CI: 1.01–1.12; RRCBD adj = 1.17, 95% CI: 1.11–1.24). Maternal outcomes occurred in less than 5% of deliveries, and no significant differences between treatments were observed (REC vs EXT, pre-eclampsia: RR adj = 1.24, 95% CI: 0.86–1.79, ovarian hyperstimulation syndrome: RR adj = 1.25, 95% CI: 0.59–2.65, gestational diabetes: RR adj = 1.06, 95% CI: 0.84–1.35). Regarding infantile outcomes, similar results were obtained for different gonadotropin formulations (REC vs EXT: low birth weight: RR adj = 0.98, 95% CI: 0.83–1.26, multiple births: RR adj = 1.06, 95% CI: 0.92–1.23, preterm birth: RR adj = 1.03, 95% CI: 0.92–1.26). Conclusions: Efficacy and safety profiles of REC proved to be similar to those of EXT. Regarding the efficacy in terms of conception rate and birth rate, protocols using the combined approach performed slightly better. Outcomes related to maternal and infantile safety were generally very rare, and safety features were overlapping between gonadotropin formulations. [ABSTRACT FROM AUTHOR]

Background: Network analysis, a technique for describing relationships, can provide insights into patterns of co-occurring chronic health conditions. The effect that co-occurrence measurement has on disease network structure and resulting inferences has not been well studied. The purpose of the study was to compare structural differences among multimorbidity networks constructed using different co-occurrence measures.Methods: A retrospective cohort study was conducted using four fiscal years of administrative health data (2015/16 - 2018/19) from the province of Manitoba, Canada (population 1.5 million). Chronic conditions were identified using diagnosis codes from electronic records of physician visits, surgeries, and inpatient hospitalizations, and grouped into categories using the Johns Hopkins Adjusted Clinical Group (ACG) System. Pairwise disease networks were separately constructed using each of seven co-occurrence measures: lift, relative risk, phi, Jaccard, cosine, Kulczynski, and joint prevalence. Centrality analysis was limited to the top 20 central nodes, with degree centrality used to identify potentially influential chronic conditions. Community detection was used to identify disease clusters. Similarities in community structure between networks was measured using the adjusted Rand index (ARI). Network edges were described using disease prevalence categorized as low (< 1%), moderate (1 to < 7%), and high (≥7%). Network complexity was measured using network density and frequencies of nodes and edges.Results: Relative risk and lift highlighted co-occurrences between pairs of low prevalence health conditions. Kulczynski emphasized relationships between high and low prevalence conditions. Joint prevalence focused on highly-prevalent conditions. Phi, Jaccard, and cosine emphasized associations involving moderately prevalent conditions. Co-occurrence measurement differences significantly affected the number and structure of identified disease clusters. When limiting the number of edges to produce visually interpretable graphs, networks had significant dissimilarity in the percentage of co-occurrence relationships in common, and in their selection of the highest-degree nodes.Conclusions: Multimorbidity network analyses are sensitive to disease co-occurrence measurement. Co-occurrence measures should be selected considering their intrinsic properties, research objectives, and the health condition prevalence relationships of greatest interest. Researchers should consider conducting sensitivity analyses using different co-occurrence measures. [ABSTRACT FROM AUTHOR]

Background: Hospitalizations fell precipitously among the general population during the COVID-19 pandemic. It remains unclear whether individuals experiencing homelessness experienced similar reductions. Objective: To examine how overall and cause-specific hospitalizations changed among individuals with a recent history of homelessness (IRHH) and their housed counterparts during the first wave of the COVID-19 pandemic, using corresponding weeks in 2019 as a historical control. Design: Population-based cohort study conducted in Ontario, Canada, between September 30, 2018, and September 26, 2020. Participants: In total, 38,617 IRHH, 15,022,368 housed individuals, and 186,858 low-income housed individuals matched on age, sex, rurality, and comorbidity burden. Main Measures: Primary outcomes included medical-surgical, non-elective (overall and cause-specific), elective surgical, and psychiatric hospital admissions. Key Results: Average rates of medical-surgical (rate ratio: 3.8, 95% CI: 3.7–3.8), non-elective (10.3, 95% CI: 10.1–10.4), and psychiatric admissions (128.1, 95% CI: 126.1–130.1) between January and September 2020 were substantially higher among IRHH compared to housed individuals. During the peak period (March 17 to June 16, 2020), rates of medical-surgical (0.47, 95% CI: 0.47–0.47), non-elective (0.80, 95% CI: 0.79–0.80), and psychiatric admissions (0.86, 95% CI: 0.84–0.88) were significantly lower among housed individuals relative to equivalent weeks in 2019. No significant changes were observed among IRHH. During the re-opening period (June 17–September 26, 2020), rates of non-elective hospitalizations for liver disease (1.41, 95% CI: 1.23–1.69), kidney disease (1.29, 95% CI: 1.14–1.47), and trauma (1.19, 95% CI: 1.07–1.32) increased substantially among IRHH but not housed individuals. Distinct hospitalization patterns were observed among IRHH even in comparison with more medically and socially vulnerable matched housed individuals. Conclusions: Persistence in overall hospital admissions and increases in non-elective hospitalizations for liver disease, kidney disease, and trauma indicate that the COVID-19 pandemic presented unique challenges for recently homeless individuals. Health systems must better address the needs of this population during public health crises. [ABSTRACT FROM AUTHOR]

Abstract Background Administrative health data are increasingly used to detect adverse drug events (ADEs). However, the few studies evaluating diagnostic codes for ADE detection demonstrated low sensitivity, likely due to narrow code sets, physician under-recognition of ADEs, and underreporting in administrative data. The objective of this study was to determine if combining an expanded ICD code set in administrative data with e-prescribing data improves ADE detection. Methods We conducted a prospective cohort study among patients newly prescribed antidepressant or antihypertensive medication in primary care and followed for 2 months. Gold standard ADEs were defined as patient-reported symptoms adjudicated as medication-related by a clinical expert. Potential ADEs in administrative data were defined as physician, ED, or hospital visits during follow-up for known adverse effects of the study medication, as identified by ICD codes. Potential ADEs in e-prescribing data were defined as study drug discontinuations or dose changes made during follow-up for safety or effectiveness reasons. Results Of 688 study participants, 445 (64.7%) were female and mean age was 64.2 (SD 13.9). The study drug for 386 (56.1%) patients was an antihypertensive, and for 302 (43.9%) an antidepressant. Using the gold standard definition, 114 (16.6%) patients experienced an ADE, with 40 (10.4%) among antihypertensive users and 74 (24.5%) among antidepressant users. The sensitivity of the expanded ICD code set was 7.0%, of e-prescribing data 9.7%, and of the two combined 14.0%. Specificities were high (86.0–95.0%). The sensitivity of the combined approach increased to 25.8% when analysis was restricted to the 27% of patients who indicated having reported symptoms to a physician. Conclusion Combining an expanded diagnostic code set with e-prescribing data improves ADE detection. As few patients report symptoms to their physician, higher detection rates may be achieved by collecting patient-reported outcomes via emerging digital technologies such as patient portals and mHealth applications.

Objectives People with intellectual and developmental disabilities (IDD) have been disproportionately impacted by COVID-19, a population more likely to experience poor health outcomes. An Applied Health Research Question (AHRQ) request through ICES led to an investigation that monitored COVID-19 related infections and outcomes in adults with and without IDD. Approach The ICES-AHRQ team approved a request from the Ministry of Children, Community and Social Services (MCCSS), to determine the proportion of adults with IDD tested and confirmed positive for COVID-19. The scientists also explored if positive IDD cases experienced similar health outcomes to the general population. The open cohort was derived by linking those with a COVID-19 test in Ontario Laboratories Information System, from January 2020 to December 2021, to supplemented positive case data and administrative health databases. An algorithm was used to define IDD by a series of inpatient hospitalizations, emergency department and/or physician visits, with IDD diagnostic codes. Results Similar rates of testing and positivity were observed for those with (46%; 6%) and without IDD (43%; 5%). However, adults with IDD confirmed positive for COVID-19 were mostly male (62% vs 49%), aged 18-29 (40% vs 27%), had medical conditions associated with frailty (15% vs 4%), and from the lowest neighborhood income quintile (27% vs 23%), compared to the non-IDD population, respectively. Cumulatively, deaths and hospitalizations following a COVID-19 diagnosis were two-times more likely in the IDD population, while comparable rates of intensive care unit (ICU) admissions were reported. Particularly, adults with IDD aged 18-54 experienced higher rates of hospitalizations (48% vs 30%), ICU admissions (56% vs 36%), and deaths (26% vs 6%), compared to the same age group in those without IDD. Conclusion/Implications Real-time administrative health data and analytics were used to support Ontario’s COVID-19 response in those with IDD. To inform decision making and policy actions related to immediate testing strategies, MCCSS used such findings to increase the availability of testing for adults with IDD, who may face multiple barriers.

Objectives Equitable vaccine distribution was essential in supporting Ontario’s COVID-19 response, particularly for persons with intellectual and developmental disabilities (IDD), who have been disproportionately impacted by the pandemic. An Applied Health Research Question (AHRQ) request through ICES, sought to report on COVID-19 vaccination uptake in adults with and without IDD. Approach To examine the proportion of adults with IDD vaccinated for COVID-19, a request from the Ministry of Children, Community and Social Services (MCCSS) was approved by the ICES-AHRQ team. A secondary goal was to explore if vaccinated individuals with IDD experienced similar levels of protection from COVID-19 infection, to the general population. The open cohort was derived by linking vaccination events reported in Ontario COVID-19 Vaccine Data, between December 2020 to December 2021, to several administrative health databases. An algorithm was used to define IDD by a series of inpatient hospitalizations, emergency department and/or physician visits, with IDD diagnostic codes. Results The proportion who received at least two doses was similar between both groups (82% (IDD); 83% (non-IDD)). However, despite emerging evidence suggesting that high-risk populations should receive a third dose, a lower proportion of people with IDD (25%), compared to the general population without IDD (30%), had done so. The IDD subpopulation is generally younger, and partially explained the third dose findings when examined by age. Withal, adults with IDD vaccinated with at least two doses were mostly male (62% vs 48%), 18-29 years of age (44% vs 17%), and from the lowest neighborhood income quintile (26% vs 18%), compared to the non-IDD population, respectively. Cumulatively, adults with IDD had comparable rates of confirmed breakthrough case infections following vaccination (17%), to those without IDD (19%). Conclusion/Implications Administrative health data and analytics through the ICES-AHRQ program were used in responding to COVID-19, and providing public health guidance, for those with IDD. To inform decision making and policy actions related to immediate vaccination strategies, MCCSS used such findings to target vaccination efforts in specific IDD subpopulations, across Ontario.

Objectives The COVID-19 pandemic has necessitated access to large health system datasets to inform the public health response. To meet this need, the Provincial Health Services Authority and the British Columbia (BC) Ministry of Health collaborated to create a population-based platform that integrates COVID-19 datasets with sociodemographic and administrative health data. Approach A BC COVID Data Library proof-of-concept was created as a cloud-based, dynamic platform composed of de-identified datasets. The BC COVID-19 Cohort (BCC19C) represents a subset composed of people accessing COVID-19 health services (e.g., testing, vaccination) and linked health histories. Provincial COVID-19 datasets are updated daily and include COVID-19 lab tests, case surveillance, vaccinations and hospitalizations/deaths. These can be linked to administrative data holdings for the BC population, which are updated weekly/monthly and include vital statistics, medications, hospital admissions, medical visits, among others. A patient matching algorithm creates unique patient keys that allows the same individual to be linked across datasets. Results The BCC19C has been used provincially to 1) support ongoing surveillance, reporting, and modelling of COVID-19; 2) describe and characterize the epidemiology of COVID-19; and 3) inform acute care planning, public health interventions and health care services in BC. Ongoing and completed BCC19C analyses include assessment of vaccine safety, vaccine effectiveness, and characteristics associated with infection and severe outcomes; use of medical visit data for syndromic surveillance and monitoring of unintended outcomes of the pandemic (e.g., mental health visits); and characterization of long-COVID. Availability of linked administrative data holdings has been crucial for identifying non-COVID control groups, measuring sociodemographics and co-morbidities, and complementing COVID-19 datasets for more complete capture of health outcomes (e.g., deaths, hospitalizations). Conclusions The large scope/breadth and timeliness of the linkable datasets integrated within the COVID Data Library and the BCC19C has supported the public health response in BC. Additional linkage to other data sources will further strengthen this data platform.

Objectives We will enrich the cancer research ecosystem in Canada through linking cancer registry and administrative health data to the Canadian Partnership for Tomorrow’s Health (CanPath) cohort and biobank. CanPath is Canada’s largest population health study, including 1% of the Canadian population, which seeks to investigate cancer development. Approach We are achieving record-level linkage of the CanPath harmonized dataset to provincial cancer registry data, and hospitalization and ambulatory care data from the Canadian Institutes of Health Information (CIHI). The CanPATH harmonized dataset includes comprehensive genetics, environment, lifestyle, and behaviour data. Our linkage activities will result in interprovincial data sharing, with centrally-held linked data, a first in Canadian history. We will demonstrate the CanPath-cancer registry-CIHI linkage potential by investigating the impact of the COVID-19 pandemic on healthcare utilization and outcomes among those with cancer. Results The linkage is ongoing and anticipated to be completed by September 2022. Linked data will be made available through the CanPath Data Safe Haven, a cloud-based solution that meets the legal requirements of the data sharing agreements and provincial privacy policies, and is accessible to researchers through secure access. The CanPath Data Safe Haven will be a federated data platform for Canadian researchers to access, analyze, and contribute research in a collaborative environment. By linking these datasets, this project will: address concerns related to accessibility of cancer data in Canada; bring more value to existing data; support an enhanced understanding of the impacts of cancer on marginalized populations; and create a more integrated approach to cancer data access and management. Conclusion CanPath will be the first program in Canadian history to combine the wealth of cohort resources with cancer registry and administrative health data in a central location at a national scale. We will provide a single point of access for researchers to conduct novel investigations into cancer development and outcomes.

Objectives To link population-based survey data to routinely collected administrative health data to enable investigation of how cancer survivors' ongoing physical, emotional, and practical needs and experiences after completing cancer treatment impact their healthcare utilization, including discharge from oncology to primary care. Approach The "Cancer Transitions Survey" is a population-based survey examining survivors' experiences and needs after completing cancer treatment. The survey was administered by the Nova Scotia Cancer Registry (NSCR) as part of a national study, the largest of its kind in Canada. Respondents included Nova Scotian survivors of breast, melanoma, colorectal, prostate, hematologic, and young adult cancers who were 1-3 years after treatment. Survey responses were linked to cancer registry, physicians' claims, hospitalization, and ambulatory care data. The data linkage provided a full four years of healthcare utilization data for each cancer survivor, beginning one year after their cancer diagnosis. Results 1557 survivors responded to the survey and subsequently had their data linked. Collectively, breast, colorectal, and prostate cancer survivors represented 78.5% of survey respondents. Most respondents (65.3%) were 65 years of age or older and 69.8% had an existing co-morbid condition. Regression analyses are now being conducted to investigate whether the type and magnitude of post-treatment care needs, and the interventions (services and supports) received, impact health care utilization in the survivorship period, including discharge to primary care. Conclusion This study represents a unique opportunity to link data unavailable in administrative health data: namely, self-reported needs and use of non-physician services and supports (e.g., support groups, counselling). As such, this dataset permits investigation of healthcare utilization and patterns of care that cannot be accomplished using administrative health data alone.

Objectives To determine whether the introduction of biological DMARDs (bDMARDs) was associated with reduced incidences of total hip and knee arthroplasty (THA/TKA) among patients with RA compared with OA. Methods Using a population-based cohort in British Columbia, Canada, RA and OA patients diagnosed between 1995 and 2007 were divided into semi-annual cohorts according to diagnosis date. For each cohort, we calculated 8-year incidence rates of THA and TKA. We compared levels and trends of THA/TKA incidence in RA/OA patients diagnosed during pre-bDMARDs (1995–2001) and post-bDMARDs (2003–2007) periods using interrupted time-series analysis, adjusting for baseline characteristics. Adjusted 8-year total joint arthroplasty incidence estimated for RA/OA cohorts diagnosed five years after bDMARDs introduction were compared with expected rates assuming no bDMARDs introduction, based on extrapolation of pre-bDMARDs trends. Results We identified 60 227 RA and 288 260 OA incident cases. For cohorts diagnosed pre-bDMARDs, 8-year THA/TKA incidence rates increased over time in both RA and OA. For cohorts diagnosed post-bDMARDs, these rates decreased over time in RA but continued to increase for OA. For RA, differences between the post- and pre-bDMARDs secular trends in incidence rates were −0.49 (P  = 0.002) for THA and −0.36 (P  = 0.003) for TKA, compared with +0.40 (P  = 0.006) and +0.54 (P  < 0.001), respectively, for OA. For RA cohorts diagnosed five years after bDMARDs introduction, 8-year incidences were 26.9% and 12.6% lower for THA and TKA, respectively, than expected rates. In contrast, corresponding rates in OA were higher by 11.7% and 16.6%, respectively. Conclusion Arthritis onset after bDMARDs introduction is associated with a significant reduction in THA/TKA incidence in RA, but not in OA. The reduction reflects a significant improvement in RA treatment during the biological era. [ABSTRACT FROM AUTHOR]

Afsaneh Roshanghalb,1 Cristina Mazzali,2 Emanuele Lettieri1 1Department of Management, Economics & Industrial Engineering, Politecnico di Milano, Milan, Italy; 2Quality and Clinical Risk Unit, Niguarda Hospital, Milan, ItalyCorrespondence: Afsaneh Roshanghalb Email afsaneh.roshanghalb@polimi.itBackground: Controlling the quality of care through readmissions and mortality for patients with heart failure (HF) is a national priority for healthcare regulators in developed countries. In this longitudinal cohort study, using administrative data such as hospital discharge forms (HDFs), emergency departments (EDs) accesses, and vital statistics, we test new covariates for predicting mortality and readmissions of patients hospitalized for HF and discuss the use of combined outcome as an alternative.Methods: Logistic models, with a stepwise selection method, were estimated on 70% of the sample and validated on the remaining 30% to evaluate 30-day mortality, 30-day readmissions, and the combined outcome. We followed an extraction method for any-cause mortality and unplanned readmission within 30 days after incident HF hospitalization. Data on patient admission and previous history were extracted by HDFs and ED dataset.Results: Our principal findings demonstrate that the model’s discriminant ability is consistent with literature both for mortality (AUC=0.738, CI (0.729– 0.748)) and readmissions (AUC=0.578, CI (0.562– 0.594)). Additionally, the discriminant ability of the composite outcome model is satisfactory (AUC=0.675, CI (0.666– 0.684)).Conclusion: Hospitalization characteristics and patient history introduced in the logistic models do not improve their discriminant ability. The composite outcome prediction is led more by mortality than readmission, without improvements for the comprehension of the readmission phenomenon.Keywords: administrative health data, heart failure, mortality, patient readmission

Summary: Background: Immigrants face multiple barriers in accessing healthcare; however, empirical assessment of access presents serious methodological issues, and evidence on undocumented immigrants is scant and based mainly on non-representative samples. We examine avoidable hospitalization (AH) as an indicator of poor access to primary care (PC) in Italy, where a universal healthcare system guarantees access but fails to assign general practitioners to undocumented immigrants. Methods: Using anonymized national hospital discharge records in 2019, undocumented immigrants were identified through an administrative financing code. Potential effects of poor access to PC were measured by focusing on the incidence of AH, differentiated among chronic, acute and vaccine-preventable conditions, comparing Italian citizens, documented (foreign nationals with residence permits) and undocumented immigrants. We estimated odd ratios (ORs) through logistic regression models, controlling for individual and contextual confounders. Findings: Compared with Italians, undocumented and documented immigrants adjusted odd ratios (OR) for the risk of AH were 1·422 (95% CI 1·322-1·528) and 1·243 (95% CI 1·201-1·287), respectively. Documented immigrants showed ORs not significantly greater than 1 for AH due to chronic diseases compared with Italians, while undocumented immigrants registered higher adjusted OR for all AH categories – chronic (OR 1·187; 95% CI 1·064-1·325), acute (OR 1·645; 95% CI 1·500-1·803) and vaccine-preventable (OR 2·170; 95% CI 1·285-3·664). Interpretation: Documented and undocumented immigrants face considerably higher risk of AH compared to Italians. Considering the burden of AHs, access to PC (including preventive and ambulatory care) should be provided to undocumented immigrants, and additional barriers to care for all immigrants should be further explored. Funding: None.

Background: Algorithms used to identify disease cases in administrative health data may be sensitive to changes in the data over time. Control charts can be used to assess how variations in administrative health data impact the stability of estimated trends in incidence and prevalence for administrative data algorithms. We compared the stability of incidence and prevalence trends for multiple juvenile diabetes algorithms using observed-expected control charts.Methods: Eighteen validated algorithms for juvenile diabetes were applied to administrative health data from Manitoba, Canada between 1975 and 2018. Trends in disease incidence and prevalence for each algorithm were modelled using negative binomial regression and generalized estimating equations; model-predicted case counts were plotted against observed counts. Control limits were set as predicted case count ±0.8*standard deviation. Differences in the frequency of out-of-control observations for each algorithm were assessed using McNemar's test with Holm-Bonferroni adjustment.Results: The proportion of out-of-control observations for incidence and prevalence ranged from 0.57 to 0.76 and 0.45 to 0.83, respectively. McNemar's test revealed no difference in the frequency of out-of-control observations across algorithms. A sensitivity analysis with relaxed control limits (2*standard deviation) detected fewer out-of-control years (incidence 0.19 to 0.33; prevalence 0.07 to 0.52), but differences in stability across some algorithms for prevalence.Conclusions: Our study using control charts to compare stability of trends in incidence and prevalence for juvenile diabetes algorithms found no differences for disease incidence. Differences were observed between select algorithms for disease prevalence when using wider control limits. [ABSTRACT FROM AUTHOR]

Background: We described the impact of different lengths of lookback window (LW), a retrospective time period to observe diagnoses in administrative data, on the prevalence and incidence of eight chronic diseases.Methods: Our study populations included people living with HIV (N = 5151) and 1:5 age-sex-matched HIV-negative individuals (N = 25,755) in British Columbia, Canada, with complete follow-up between 1996 and 2012. We measured period prevalence and incidence of diseases in 2012 using LWs ranging from 1 to 16 years. Cases were deemed prevalent if identified in 2012 or within a defined LW, and incident if newly identified in 2012 with no previous cases detected within a defined LW. Chronic disease cases were ascertained using published case-finding algorithms applied to population-based provincial administrative health datasets.Results: Overall, using cases identified by the full 16-year LW as the reference, LWs ≥8 years and ≥ 4 years reduced the proportion of misclassified prevalent and incidence cases of most diseases to < 20%, respectively. The impact of LWs varied across diseases and populations.Conclusions: This study underscored the importance of carefully choosing LWs and demonstrated data-driven approaches that may inform these choices. To improve comparability of prevalence and incidence estimates across different settings, we recommend transparent reporting of the rationale and limitations of chosen LWs. [ABSTRACT FROM AUTHOR]

Introduction: Electronic health records (EHR) and administrative healthcare data (AHD) are frequently used in geriatric mental health research to answer various health research questions. However, there is an increasing amount and complexity of data available that may lend itself to alternative analytic approaches using machine learning (ML) or artificial intelligence (AI) methods. We performed a systematic review of the current application of ML or AI approaches to the analysis of EHR and AHD in geriatric mental health.Methods: We searched MEDLINE, Embase, and PsycINFO to identify potential studies. We included all articles that used ML or AI methods on topics related to geriatric mental health utilizing EHR or AHD data. We assessed study quality either by Prediction model Risk OF Bias ASsessment Tool (PROBAST) or Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2) checklist.Results: We initially identified 391 articles through an electronic database and reference search, and 21 articles met inclusion criteria. Among the selected studies, EHR was the most used data type, and the datasets were mainly structured. A variety of ML and AI methods were used, with prediction or classification being the main application of ML or AI with the random forest as the most common ML technique. Dementia was the most common mental health condition observed. The relative advantages of ML or AI techniques compared to biostatistical methods were generally not assessed. Only in three studies, low risk of bias (ROB) was observed according to all the PROBAST domains but in none according to QUADAS-2 domains. The quality of study reporting could be further improved.Conclusion: There are currently relatively few studies using ML and AI in geriatric mental health research using EHR and AHD methods, although this field is expanding. Aside from dementia, there are few studies of other geriatric mental health conditions. The lack of consistent information in the selected studies precludes precise comparisons between them. Improving the quality of reporting of ML and AI work in the future would help improve research in the field. Other courses of improvement include using common data models to collect/organize data, and common datasets for ML model validation.