Developing a prediction model of children asthma risk using population-based family history health records.

BACKGROUND: Identifying children at high risk of developing asthma can facilitate prevention and early management strategies. We developed a prediction model of children's asthma risk using objectively collected population-based children and parental histories of comorbidities. METHODS: We conducted a retrospective population-based cohort study using administrative data from Manitoba, Canada, and included children born from 1974 to 2000 with linkages to ≥1 parent. We identified asthma and prior comorbid condition diagnoses from hospital and outpatient records. We used two machine-learning models: least absolute shrinkage and selection operator (LASSO) logistic regression (LR) and random forest (RF) to identify important predictors. The predictors in the base model included children's demographics, allergic conditions, respiratory infections, and parental asthma. Subsequent models included additional multiple comorbidities for children and parents. RESULTS: The cohort included 195,666 children: 51.3% were males and 17.7% had asthma diagnosis. The base LR model achieved a low predictive performance with sensitivity of 0.47, 95% confidence interval (0.45-0.48), and specificity of 0.67 (0.66-0.67) using a predicted probability threshold of 0.20. Sensitivity significantly improved when children's comorbidities were included using LASSO LR: 0.71 (0.69-0.72). Predictive performance further improved by including parental comorbidities (sensitivity = 0.72 [0.70-0.73], specificity = 0.69 [0.69-0.70]). We observed similar results for the RF models. Children's menstrual disorders and mood and anxiety disorders, parental lipid metabolism disorders and asthma were among the most important variables that predicted asthma risk. CONCLUSION: Including children and parental comorbidities to children's asthma prediction models improves their accuracy.

Deprescribing research in nursing home residents using routinely collected healthcare data: a conceptual framework.

BACKGROUND: Efforts are needed to strengthen evidence and guidance for appropriate deprescribing for older nursing home (NH) residents, who are disproportionately affected by polypharmacy and inappropriate prescribing. Given the challenges of conducting randomized drug withdrawal studies in this population, data from observational studies of routinely collected healthcare data can be used to identify patients who are apparent candidates for deprescribing and evaluate subsequent health outcomes. To improve the design and interpretation of observational studies examining determinants, risks, and benefits of deprescribing specific medications in older NH residents, we sought to propose a conceptual framework of the determinants of deprescribing in older NH residents. METHODS: We conducted a scoping review of observational studies examining patterns and potential determinants of discontinuing or de-intensifying (i.e., reducing) medications for NH residents. We searched PubMed through September 2021 and included studies meeting the following criteria: conducted among adults aged 65 + in the NH setting; (2) observational study designs; (3) discontinuation or de-intensification as the primary outcome with key determinants as independent variables. We conceptualized deprescribing as a behavior through a social-ecological lens, potentially influenced by factors at the intrapersonal, interpersonal, organizational, community, and policy levels. RESULTS: Our search in PubMed identified 250 potentially relevant studies published through September 2021. A total of 14 studies were identified for inclusion and were subsequently synthesized to identify and group determinants of deprescribing into domains spanning the five core social-ecological levels. Our resulting framework acknowledges that deprescribing is strongly influenced by intrapersonal, patient-level clinical factors that modify the expected benefits and risks of deprescribing, including index condition attributes (e.g., disease severity), attributes of the medication being considered for deprescribing, co-prescribed medications, and prognostic factors. It also incorporates the hierarchical influences of interpersonal differences relating to healthcare providers and family caregivers, NH facility and health system organizational structures, community trends and norms, and finally healthcare policies. CONCLUSIONS: Our proposed framework will serve as a useful tool for future studies seeking to use routinely collected healthcare data sources and observational study designs to evaluate determinants, risks, and benefits of deprescribing for older NH residents.

A descriptive analysis of medical health services utilization of Veterans living in Ontario: a retrospective cohort study using administrative healthcare data.

BACKGROUND: Health services utilization by Veterans following release may be different than the general population as the result of occupational conditions, requirements and injuries. This study provides the first longitudinal overview of Canadian Veteran healthcare utilization in the Ontario public health system. METHODS: This is a retrospective cohort study designed to use Ontario's provincial healthcare data to study the demographics and healthcare utilization of Canadian Armed Forces (CAF) & RCMP Veterans living in Ontario. Veterans were eligible for the study if they released between January 1, 1990 and March 31, 2013. Databases at the Institute for Clinical Evaluative Sciences were linked by a unique identifier to study non-mental health related hospitalizations, emergency department visits, and physician visits. Overall and age-stratified descriptive statistics were calculated in five-year intervals following the date of release. RESULTS: The cohort is comprised of 23, 818 CAF or RCMP Veterans. Following entry into the provincial healthcare system, 82.6 % (95 % CI 82.1-83.1) of Veterans saw their family physician at least once over the first five years following release, 60.7 % (95 % CI 60.0-61.3) saw a non-mental health specialist, 40.8 % (95 % CI 40.2-41.5) went to the emergency department in that same time period and 9.9 % (9.5-10.3) were hospitalized for non-mental health related complaints. Patterns of non-mental health services utilization appeared to be time and service dependant. Stratifying health services utilization by age of the Veteran at entry into the provincial healthcare system revealed significant differences in service use and intensity. CONCLUSION: This study provides the first description of health services utilization by Veterans, following release from the CAF or RCMP. This work will inform the planning and delivery of support to Veterans in Ontario.

Prevalence and incidence of Paget's disease of bone: Temporal trend over 20 years in the province of Quebec, Canada.

BACKGROUND: Paget's disease of bone (PDB) is a focal bone disorder characterized by an increased bone remodeling and an anarchic bone structure. A decline of prevalence and incidence of PDB has been observed in some countries. No epidemiological data are available on PDB in Canada. AIMS: We aimed at examining the evolution of the prevalence and incidence of PDB in Quebec (Canada) by analyzing health administrative databases. METHODS: PDB case definition relied on one or more hospitalizations, or one or more physician-billing claims with a diagnosis code of PDB. To identify incident cases, a 'run-in' period of four years (1996-1999) was used to exclude prevalent cases. For each fiscal year from 2000 to 2001 to 2019-2020 (population size 2,914,480), crude age and sex-specific prevalence and incidence rates of PDB among individuals aged ≥55 years were determined, and sex-specific rates were also standardized to the 2011 age structure of the Quebec population. Generalized linear regressions were used to test for linear changes in standardized prevalence and incidence rates. RESULTS: Over the study period, standardized prevalence of PDB has remained stable in Quebec, from 0.44 % in 2000/2001 to 0.43 % in 2019/2020 (mean change -0.002, p-value = 0.0935). For the 2019-2020 fiscal year, 13,165 men and women had been diagnosed with PDB and prevalence of PDB increased with age. Standardized incidence of PDB has decreased over time from 0.77/1000 in 2000/2001 to 0.28/1000 in 2019-2020 (mean change -0.228/year, p-value<0.0001), the incidence decreasing from 0.82/1000 to 0.37/1000 in men and from 0.76/1000 to 0.22/1000 in women, respectively. This decrease was observed in all age categories. CONCLUSION: With the exception of a slight increase in PDB prevalence up to 0.55 % in years 2005 to 2007, the prevalence of PDB has remained stable in Quebec over the past 20 years, 13,160 men and women being currently diagnosed with PDB. The incidence has decreased over time. Our results support the epidemiological changes of PDB reported in other countries.

Spatial distribution and determinants of childhood vaccination refusal in the United States.

Parental refusal and delay of childhood vaccination has increased in recent years in the United States. This phenomenon challenges maintenance of herd immunity and increases the risk of outbreaks of vaccine-preventable diseases. We examine US county-level vaccine refusal for patients under five years of age collected during the period 2012-2015 from an administrative healthcare dataset. We model these data with a Bayesian zero-inflated negative binomial regression model to capture social and political processes that are associated with vaccine refusal, as well as factors that affect our measurement of vaccine refusal. Our work highlights fine-scale socio-demographic characteristics associated with vaccine refusal nationally, finds that spatial clustering in refusal can be explained by such factors, and has the potential to aid in the development of targeted public health strategies for optimizing vaccine uptake.

Using administrative healthcare data to evaluate drug repurposing opportunities for cancer: the possibility of using beta-blockers to treat breast cancer.

Introduction: Cancer registries and hospital electronic medical records are commonly used to investigate drug repurposing candidates for cancer. However, administrative data are often more accessible than data from cancer registries and medical records. Therefore, we evaluated if administrative data could be used to evaluate drug repurposing for cancer by conducting an example study on the association between beta-blocker use and breast cancer mortality. Methods: A retrospective cohort study of women aged ≥50 years with incident breast cancer was conducted using a linked dataset with statewide hospital admission data and nationwide medication claims data. Women receiving beta blockers and first-line anti-hypertensives prior to and at diagnosis were compared. Breast cancer molecular subtypes and metastasis status were inferred by algorithms from commonly prescribed breast cancer antineoplastics and hospitalization diagnosis codes, respectively. Subdistribution hazard ratios (sHR) and corresponding 95% confidence intervals (CIs) for breast cancer mortality were estimated using Fine and Gray's competing risk models adjusted for age, Charlson comorbidity index, congestive heart failure, myocardial infraction, molecular subtype, presence of metastasis at diagnosis, and breast cancer surgery. Results: 2,758 women were hospitalized for incident breast cancer. 604 received beta-blockers and 1,387 received first-line antihypertensives. In total, 154 breast cancer deaths were identified over a median follow-up time of 2.7 years. We found no significant association between use of any beta-blocker and breast-cancer mortality (sHR 0.86, 95%CI 0.58-1.28), or when stratified by beta-blocker type (non-selective, sHR 0.42, 95%CI 0.14-1.25; selective, sHR 0.95, 95%CI 0.63-1.43). Results were not significant when stratified by molecular subtypes (e.g., triple negative breast cancer (TNBC), any beta blocker, sHR 0.16, 95%CI 0.02-1.51). Discussion: It is possible to use administrative data to explore drug repurposing opportunities. Although non-significant, an indication of an association was found for the TNBC subtype, which aligns with previous studies using registry data. Future studies with larger sample size, longer follow-up are required to confirm the association, and linkage to clinical data sources are required to validate our methodologies.

More than two-dozen prescription drug-based risk scores are available for risk adjustment: A systematic review.

OBJECTIVE: While several prescription drug-based risk indices have been developed, their design, performance, and application has not previously been synthesized. STUDY DESIGN AND SETTING: We searched Ovid MEDLINE, CINAHL and Embase from inception through March 3, 2020 and included studies that developed or updated a prescription drug-based risk index. Two reviewers independently performed screening and extracted information on data source, study population, cohort sizes, outcomes, study methodology and performance. Predictive performance was evaluated using C statistics for binary outcomes and R2 for continuous outcomes. The PROSPERO ID for this review is CRD42020165498. RESULTS: Of 19,112 articles that were retrieved, 124 were full-text screened and 25 were included, each of which represented a de novo or updated drug-based index. The indices were customized to varied age groups and clinical populations and most commonly evaluated outcomes including mortality (36%), hospitalization (24%) and healthcare costs (24%). C statistics ranged from 0.62 to 0.92 for mortality and 0.59 to 0.72 for hospitalization, while adjusted R2 for healthcare costs ranged from 0.06 to 0.62. Seven of the 25 risk indices included used global drug classification algorithms. CONCLUSIONS: More than two-dozen prescription drug-based risk indices have been developed and they differ significantly in design, performance and application.

Depiction of Trends in Administrative Healthcare Data from Hospital Information System.

BACKGROUND: administrative healthcare data are among main components of hospital information system. Such data can be analyzed and deployed for a variety of purposes. The principal aim of this research was to depict trends of administrative healthcare data from HIS in a general hospital from March 2011 to March 2014. METHODS: data set used for this research was extracted from the SQL database of the hospital information system in Razi general hospital located in Marand. The data were saved as CSV (Comma Separated Values) in order to facilitate data cleaning and analysis. The variables of data set included patient's age, gender, final diagnosis, final diagnosis code based on ICD-10 classification system, date of hospitalization, date of discharge, LOS(Length of Stay), ward, and survival status of the patient. Data were analyzed and visualized after applying appropriate cleansing and preparing techniques. RESULTS: morbidity showed a constant trend over three years. Pregnancy, childbirth and the puerperium were the leading category of final diagnosis (about 32.8 %). The diseases of the circulatory system were the second class accounting for 13 percent of the hospitalization cases. The diseases of the digestive system had the third rank (10%). Patients aged between 14 and 44 constituted a higher proportion of total cases. Diseases of the circulatory system was the most common class of diseases among elderly patients (age≥65). The highest rate of mortality was observed among patients with final diagnosis of the circulatory system diseases followed by those with diseases of the respiratory system, and neoplasms. Mortality rate for the ICU and the CCU patients were 62% and 33% respectively. The longest average of LOS (7.3 days) was observed among patients hospitalized in the ICU while patients in the Obstetrics and Gynecology ward had the shortest average of LOS (2.4 days). Multiple regression analysis revealed that LOS was correlated with variables of surgery, gender, and type of payment, ward, the class of final diagnosis and age. CONCLUSION: this study presents trends in administrative health care data residing in hospital information system of a general public hospital. Patterns in morbidity, mortality and length of stay can inform decision making in health care management. Mining trends in administrative healthcare data can add value to the health care management.