Association between infectious diseases consultation and mortality in hospitalized patients with Gram-negative bloodstream infection: a retrospective population-wide cohort study.

OBJECTIVES: Data supporting routine infectious diseases (ID) consultation in Gram-negative bloodstream infection (GN-BSI) are limited. We evaluated the association between ID consultation and mortality in patients with GN-BSI in a retrospective population-wide cohort study in Ontario using linked health administrative databases. METHODS: Hospitalized adult patients with GN-BSI between April 2017 and December 2021 were included. The primary outcome was time to all-cause mortality censored at 30 days, analyzed using a mixed effects Cox proportional hazards model with hospital as a random effect. ID consultation 1-10 days after the first positive blood culture was treated as a time-varying exposure. RESULTS: Of 30,159 patients with GN-BSI across 53 hospitals, 11,013 (36.5%) received ID consultation. Median prevalence of ID consultation for patients with GN-BSI across hospitals was 35.0% with wide variability (range 2.7-76.1%, interquartile range 19.6-41.1%). 1041 (9.5%) patients who received ID consultation died within 30 days, compared to 1797 (9.4%) patients without ID consultation. In the fully-adjusted multivariable model, ID consultation was associated with mortality benefit (adjusted HR 0.82, 95% CI 0.77-0.88, p < 0.0001; translating to absolute risk reduction of -3.8% or NNT of 27). Exploratory subgroup analyses of the primary outcome showed that ID consultation could have greater benefit in patients with high-risk features (nosocomial infection, polymicrobial or non-Enterobacterales infection, antimicrobial resistance, or non-urinary tract source). CONCLUSIONS: Early ID consultation was associated with reduced mortality in patients with GN-BSI. If resources permit, routine ID consultation for this patient population should be considered to improve patient outcomes.

Real-world data are not always big data: the case for primary data collection on medication use in pregnancy in the context of birth defects research.

Many examples of the use of real-world data in the area of pharmacoepidemiology include "big data," such as insurance claims, medical records, or hospital discharge databases. However, "big" is not always better, particularly when studying outcomes with narrow windows of etiologic relevance. Birth defects are such an outcome, for which specificity of exposure timing is critical. Studies with primary data collection can be designed to query details about the timing of medication use, as well as type, dose, frequency, duration, and indication, that can better characterize the "real world." Because birth defects are rare, etiologic studies are typically case­control in design, like the National Birth Defects Prevention Study, Birth Defects Study to Evaluate Pregnancy Exposures, and Slone Birth Defects Study. Recall bias can be a concern, but the ability to collect detailed information about both prescription and over-the-counter medication use and other exposures such as diet, family history, and sociodemographic factors is a distinct advantage over claims and medical record data sources. Case­control studies with primary data collection are essential to advancing the pharmacoepidemiology of birth defects. This article is part of a Special Collection on Pharmacoepidemiology.

Using unsupervised clustering approaches to identify common mental health profiles and associated mental health-care service-use patterns in Ontario, Canada.

Mental health is a complex, multidimensional concept that goes beyond clinical diagnoses, including psychological distress, life stress, and well-being. In this study, we aimed to use unsupervised clustering approaches to identify multidimensional mental health profiles that exist in the population, and their associated service-use patterns. The data source was the 2012 Canadian Community Health Survey-Mental Health, linked to administrative health-care data; all Ontario, Canada, adult respondents were included. We used a partitioning around medoids clustering algorithm with Gower's proximity to identify groups with distinct combinations of mental health indicators and described them according to their sociodemographic and service-use characteristics. We identified 4 groups with distinct mental health profiles, including 1 group that met the clinical threshold for a depressive diagnosis, with the remaining 3 groups expressing differences in positive mental health, life stress, and self-rated mental health. The 4 groups had different age, employment, and income profiles and exhibited differential access to mental health-care services. This study represents the first step in identifying complex profiles of mental health at the population level in Ontario. Further research is required to better understand the potential causes and consequences of belonging to each of the mental health profiles identified. This article is part of a Special Collection on Mental Health.

Suicides, drug poisonings, and alcohol-related deaths cluster with health and social disadvantage in 4.1 million citizens from two nations.

BACKGROUND: Deaths from suicides, drug poisonings, and alcohol-related diseases ('deaths of despair') are well-documented among working-age Americans, and have been hypothesized to be largely specific to the U.S. However, support for this assertion-and associated policies to reduce premature mortality-requires tests concerning these deaths in other industrialized countries, with different institutional contexts. We tested whether the concentration and accumulation of health and social disadvantage forecasts deaths of despair, in New Zealand and Denmark. METHODS: We used nationwide administrative data. Our observation period was 10 years (NZ = July 2006-June 2016, Denmark = January 2007-December 2016). We identified all NZ-born and Danish-born individuals aged 25-64 in the last observation year (NZ = 1 555 902, Denmark = 2 541 758). We ascertained measures of disadvantage (public-hospital stays for physical- and mental-health difficulties, social-welfare benefit-use, and criminal convictions) across the first nine years. We ascertained deaths from suicide, drugs, alcohol, and all other causes in the last year. RESULTS: Deaths of despair clustered within a population segment that disproportionately experienced multiple disadvantages. In both countries, individuals in the top 5% of the population in multiple health- and social-service sectors were at elevated risk for deaths from suicide, drugs, and alcohol, and deaths from other causes. Associations were evident across sex and age. CONCLUSIONS: Deaths of despair are a marker of inequalities in countries beyond the U.S. with robust social-safety nets, nationwide healthcare, and strong pharmaceutical regulations. These deaths cluster within a highly disadvantaged population segment identifiable within health- and social-service systems.

Seasonal Variations in Stroke Occurrence.

BACKGROUND: Understanding seasonal variations in stroke can help stakeholders identify underlying causes in seasonal trends, and tailor resources appropriately to times of highest needs. We sought to evaluate the seasonal occurrence of stroke and its subtypes. METHODS: We conducted a retrospective cohort study using administrative data from January 1st, 2003, to December 31st, 2017, in Ontario, Canada's most populous province. We evaluated seasonal variations in stroke occurrence by subtype, via age/sex standardized rates and adjusted rate ratios using Poisson regressions. In those with stroke, we evaluated 30-day case fatality risks by season, adjusted for age, sex, stroke type, and comorbid conditions, and then used Cox proportional hazard models to estimate the effect of season on the fatality. The administrative data used in this study were from the Canadian Institute for Health Information's Discharge Abstract Database, the National Ambulatory Care Reporting System Database, the Ontario Registered Persons Database, and the 2006 and 2011 Canada Census and linked administrative databases. RESULTS: During our study period, we observed 394,145 strokes or TIA events, with a decrease in monthly hospitalization/emergency department visits per 100,000 people between January 2003 and December 2017 from 24.22 to 17.43. Compared to the summer, overall stroke occurrence was similar in the spring but slightly lower in the fall (adjusted rate ratio [aRR] 0.97, 95% confidence interval [CI] 0.96-0.98) and winter (aRR 0.94, 95% CI: 0.94-0.95). There were minor variations by stroke subtype. Winter was associated with the highest risk of stroke case fatality compared to the summer (12.4% vs. 11.4%, adjusted hazard ratio 1.10, 95% CI: 1.07-1.13). CONCLUSIONS: We found seasonal variations in stroke occurrence and case fatality, although the absolute differences were small. Further work is needed to better understand how environmental or meteorological factors might affect stroke risk.

Risk Factors Associated with Major Adverse Cardiovascular Events after Ischemic Stroke: A Linked Registry Study.

INTRODUCTION: Survivors of stroke are at risk of experiencing subsequent major adverse cardiovascular events (MACE). We aimed to determine the incidence of, and risk factors for, MACE after first-ever ischemic stroke, by age group (18-64 years vs. ≥65 years). METHODS: Observational cohort study using patient-level data from the Australian Stroke Clinical Registry (2009-2013), linked with hospital administrative data. We included adults with first-ever ischemic stroke who had no previous acute cardiovascular admissions and followed these patients for 2 years post-discharge, or until the first post-stroke MACE event. A Fine-Gray sub-distribution hazard model, accounting for the competing risk of non-cardiovascular death, was used to determine factors for incident post-stroke MACE. RESULTS: Among 5,994 patients with a first-ever ischemic stroke (median age 73 years, 45% female), 17% were admitted for MACE within 2 years (129 events per 1,000 person-years). The median time to first post-stroke MACE was 117 days (89 days if aged <65 years vs. 126 days if aged ≥65 years; p = 0.025). Among patients aged 18-64 years, receiving intravenous thrombolysis (sub-distribution hazard ratio [SHR] 0.51 [95% CI, 0.28-0.92]) or being discharged to inpatient rehabilitation (SHR 0.65 [95% CI, 0.46-0.92]) were associated with a reduced incidence of post-stroke MACE. In those aged ≥65 years, being unable to walk on admission (SHR 1.33 [95% CI 1.15-1.54]), and history of smoking (SHR 1.40 [95% CI 1.14-1.71]) or atrial fibrillation (SHR 1.31 [95% CI 1.14-1.51]) were associated with an increased incidence of post-stroke MACE. Acute management in a large hospital (>300 beds) for the initial stroke event was associated with reduced incidence of post-stroke MACE, irrespective of age group. CONCLUSIONS: MACE is common within 2 years of stroke, with most events occurring within the first year. We have identified important factors to consider when designing interventions to prevent MACE after stroke, particularly among those aged <65 years.

Exploring the relationships between pathogen-specific prenatal infections requiring inpatient admission and domains of offspring behaviour at age 5.

BACKGROUND: Research exploring the relationship between prenatal infection and child behavioural outcomes would benefit from further studies utilising full-population samples with the scale to investigate specific infections and to employ robust designs. We tested the association among several common infections requiring inpatient admission during and after pregnancy with a range of childhood behavioural outcomes, to determine whether any negative impact was specific to the period of foetal development. METHODS: The sample included all mother-offspring pairs from the Australian state of New South Wales (NSW) for whom the child commenced their first year of full-time schooling in 2009 (~age 5 years; n = 77,302 offspring), with records linked across four health administrative data sets including the NSW perinatal data collection (PDC), the NSW admitted patient data collection (APDC) and the NSW component of the 2009 Australian Early Development Census (AEDC). Multivariable linear regression was used to test associations between a number of infections requiring inpatient admission during and after pregnancy with a range of teacher assessed behavioural outcomes. RESULTS: Associations specific to the prenatal period were only found for streptococcus A although this would need to be reproduced in external samples given the low prevalence. Otherwise, 12 out of 15 selected infections either showed no association prenatally or also demonstrated associations in the 12 months after pregnancy. For example, prenatal hepatitis C, influenza and urinary E. coli infections were associated with lower scores of several domains of childhood behaviour, but even stronger associations were found when these same maternal infections occurred after pregnancy. CONCLUSIONS: The prenatal infections we tested appeared not to impact childhood behaviour by altering foetal neurodevelopment. Rather, the strong associations we found among infections occurring during and after pregnancy point to either residual socioeconomic/lifestyle factors or a shared familial/genetic liability between infections and behavioural problems.

Validating use of diagnostic codes in Canadian administrative data for identification of adverse drug events.

AIMS: While diagnostic codes from administrative health data might be a valuable source to identify adverse drug events (ADEs), their ability to identify unintended harms remains unclear. We validated claims-based diagnosis codes for ADEs based on events identified in a prospective cohort study and assessed whether key attributes predicted their documentation in administrative data. METHODS: This was a retrospective analysis of 3 prospective cohorts in British Columbia, from 2008 to 2015 (n = 13 969). We linked prospectively identified ADEs to administrative insurance data to examine the sensitivity and specificity of different diagnostic code schemes. We used logistic regression to assess which key attributes (e.g., type of event, symptoms and culprit medications) were associated with better documentation of ADEs in administrative data. RESULTS: Among 1178 diagnosed events, the sensitivity of the diagnostic codes in administrative data ranged from 3.4 to 52.6%, depending on the database and codes used. We found that documentation was worse for certain types of ADEs (dose-related: odds ratio [OR]: 0.32, 95% confidence interval [CI]: 0.15, 0.69; nonadherence events (OR: 0.35, 95% CI: 0.20, 0.62), and better for those experiencing arrhythmias (OR: 4.19, 95% CI: 0.96, 18.28). CONCLUSION: ADEs were not well documented in administrative data. Alternative methods should be explored to capture ADEs for health research.

Validating methods used to identify non-adherence adverse drug events in Canadian administrative health data.

AIMS: Medication non-adherence is a type of adverse drug event that can lead to untreated and exacerbated chronic illness, and that drives healthcare utilization. Research using medication claims data has attempted to identify instances of medication non-adherence using the proportion of days covered or by examining gaps between medication refills. We sought to validate these measures compared to a gold standard diagnosis of non-adherence made in hospital. METHODS: This was a retrospective analysis of adverse drug events diagnosed during three prospective cohorts in British Columbia between 2008 and 2015 (n = 976). We linked prospectively identified adverse drug events to medication claims data to examine the sensitivity and specificity of typical non-adherence measures. RESULTS: The sensitivity of the non-adherence measures ranged from 22.4% to 37.5%, with a proportion of days covered threshold of 95% performing the best; the non-persistence measures had sensitivities ranging from 10.4% to 58.3%. While a 7-day gap was most sensitive, it classified 61.2% of the sample as non-adherent, whereas only 19.6% were diagnosed as such in hospital. CONCLUSIONS: The methods used to identify non-adherence in administrative databases are not accurate when compared to a gold standard diagnosis by healthcare providers. Research that has relied on administrative data to identify non-adherent patients both underestimates the magnitude of the problem and may label patients as non-adherent who were in fact adherent.

Improving Accuracy of Administrative Data for Perforated Appendicitis Classification.

INTRODUCTION: Reliance on International Classification of Diseases, Tenth Revision, Clinical Modification (ICD-10-CM) diagnosis codes may misclassify perforated appendicitis with resultant research, fiscal, and public health implications. We aimed to improve the accuracy of administrative data for perforated appendicitis classification relying on ICD-10-CM codes from 2015 to 2018. METHODS: We conducted a retrospective study of randomly sampled patients aged ≤18 years diagnosed with acute appendicitis from eight children's hospitals. Patients were identified using the Pediatric Health Information System, and true perforation status was determined by medical record review. We developed two algorithms by leveraging Pediatric Health Information System data elements and data mining (DM) approaches. The two developed algorithm performance was compared against algorithms that exclusively relied on ICD-10-CM codes using area under the curve and other measures. RESULTS: Of 1051 clinically validated encounters that were included, 383 (36.4%) patients were identified to have perforated appendicitis. The two algorithms developed using DM approaches primarily leveraged ICD-10-CM codes and length of stay. DM-developed algorithms had a significantly higher accuracy than algorithms relying exclusively on ICD-10-CM (P value < 0.01): sensitivity and specificity for DM-developed algorithms were 0.86-0.88 and 0.95-0.97, respectively, which were overall higher than algorithms that relied on only ICD-10-CM. CONCLUSIONS: This study provides an algorithm that can improve the accuracy of perforated appendicitis classification using commonly available elements in administrative data. We recommend that this algorithm is used in future appendicitis classification to ensure valid reporting, hospital-level benchmarking, and fiscal or public health assessments.

The incidences and clinical outcomes of cryptococcosis in Taiwan: A nationwide, population-based study, 2002-2015.

Large-scale epidemiological data on cryptococcosis other than cryptococcal meningitis (CM), human immunodeficiency virus (HIV)- or solid organ transplantation (SOT)-associated cryptococcosis are limited. This study investigated the disease burden of cryptococcosis in Taiwan over 14 years. Incident episodes of cryptococcosis, comorbidities, treatment, and outcomes were captured from Taiwan's National Health Insurance Research Database and National Death Registry between 2002 and 2015. Of 6647 episodes analyzed, the crude incidence rate per 100 000 population increased from 1.48 in 2002 to 2.76 in 2015, which was driven by the growing trend in the non-CM group (0.86-2.12) but not in the CM group (0.62-0.64). The leading three comorbidities were diabetes mellitus (23.62%), malignancy (22.81%), and liver disease (17.42%). HIV accounted for 6.14% of all episodes and was associated with the highest disease-specific incidence rate (269/100 000 population), but the value dropped 16.20% biennially. Within 90 days prior to cohort entry, 30.22% of episodes had systemic corticosteroid use. The in-hospital mortality of all episodes was 10.80%, which varied from 32.64% for cirrhosis and 13.22% for HIV to 6.90% for SOT. CM was associated with a higher in-hospital mortality rate than non-CM (19.15% vs. 6.33%). At diagnosis, only 48.53% of CM episodes were prescribed an amphotericin-based regimen. The incidence rate of cryptococcosis was increasing, especially that other than meningitis and in the non-HIV population. A high index of clinical suspicion is paramount to promptly diagnose, treat, and improve cryptococcosis-related mortality in populations other than those with HIV infection or SOT.

This nationwide study showed that the incidence rate of cryptococcosis doubled from 2002 to 2015. Non-meningeal cryptococcosis and non-HIV/nontransplant (NHNT)-associated cryptococcosis contributed to this increase. Our study highlighted the underestimated burden of cryptococcosis in the NHNT hosts.

Multimorbidity prevalence and health outcome prediction: assessing the impact of lookback periods, disease count, and definition criteria in health administrative data at the population-based level.

BACKGROUND: Health administrative databases play a crucial role in population-level multimorbidity surveillance. Determining the appropriate retrospective or lookback period (LP) for observing prevalent and newly diagnosed diseases in administrative data presents challenge in estimating multimorbidity prevalence and predicting health outcome. The aim of this population-based study was to assess the impact of LP on multimorbidity prevalence and health outcomes prediction across three multimorbidity definitions, three lists of diseases used for multimorbidity assessment, and six health outcomes. METHODS: We conducted a population-based study including all individuals ages > 65 years on April 1st, 2019, in Québec, Canada. We considered three lists of diseases labeled according to the number of chronic conditions it considered: (1) L60 included 60 chronic conditions from the International Classification of Diseases (ICD); (2) L20 included a core of 20 chronic conditions; and (3) L31 included 31 chronic conditions from the Charlson and Elixhauser indices. For each list, we: (1) measured multimorbidity prevalence for three multimorbidity definitions (at least two [MM2+], three [MM3+] or four (MM4+) chronic conditions); and (2) evaluated capacity (c-statistic) to predict 1-year outcomes (mortality, hospitalisation, polypharmacy, and general practitioner, specialist, or emergency department visits) using LPs ranging from 1 to 20 years. RESULTS: Increase in multimorbidity prevalence decelerated after 5-10 years (e.g., MM2+, L31: LP = 1y: 14%, LP = 10y: 58%, LP = 20y: 69%). Within the 5-10 years LP range, predictive performance was better for L20 than L60 (e.g., LP = 7y, mortality, MM3+: L20 [0.798;95%CI:0.797-0.800] vs. L60 [0.779; 95%CI:0.777-0.781]) and typically better for MM3 + and MM4 + definitions (e.g., LP = 7y, mortality, L60: MM4+ [0.788;95%CI:0.786-0.790] vs. MM2+ [0.768;95%CI:0.766-0.770]). CONCLUSIONS: In our databases, ten years of data was required for stable estimation of multimorbidity prevalence. Within that range, the L20 and multimorbidity definitions MM3 + or MM4 + reached maximal predictive performance.

Development of the multivariate administrative data cystectomy model and its impact on misclassification bias.

BACKGROUND: Misclassification bias (MB) is the deviation of measured from true values due to incorrect case assignment. This study compared MB when cystectomy status was determined using administrative database codes vs. predicted cystectomy probability. METHODS: We identified every primary cystectomy-diversion type at a single hospital 2009-2019. We linked to claims data to measure true association of cystectomy with 30 patient and hospitalization factors. Associations were also measured when cystectomy status was assigned using billing codes and by cystectomy probability from multivariate logistic regression model with covariates from administrative data. MB was the difference between measured and true associations. RESULTS: 500 people underwent cystectomy (0.12% of 428 677 hospitalizations). Sensitivity and positive predictive values for cystectomy codes were 97.1% and 58.6% for incontinent diversions and 100.0% and 48.4% for continent diversions, respectively. The model accurately predicted cystectomy-incontinent diversion (c-statistic [C] 0.999, Integrated Calibration Index [ICI] 0.000) and cystectomy-continent diversion (C:1.000, ICI 0.000) probabilities. MB was significantly lower when model-based predictions was used to impute cystectomy-diversion type status using for both incontinent cystectomy (F = 12.75; p < .0001) and continent cystectomy (F = 11.25; p < .0001). CONCLUSIONS: A model using administrative data accurately returned the probability that cystectomy by diversion type occurred during a hospitalization. Using this model to impute cystectomy status minimized MB. Accuracy of administrative database research can be increased by using probabilistic imputation to determine case status instead of individual codes.

Applying machine learning to identify pediatric patients with newly diagnosed acute lymphoblastic leukemia using administrative data.

Case identification in administrative databases is challenging as diagnosis codes alone are not adequate for case ascertainment. We utilized machine learning (ML) to efficiently identify pediatric patients with newly diagnosed acute lymphoblastic leukemia. We tested nine ML models and validated the best model internally and externally. The optimal model had 97% positive predictive value (PPV) and 99% sensitivity in internal validation; 94% PPV and 82% sensitivity in external validation. Our ML model identified a large cohort of 21,044 patients, demonstrating an efficient approach for cohort assembly and enhancing the usability of administrative data.

A comparison of cohorts of children with cerebral palsy from a population register and hospital admission data: A data linkage study.

BACKGROUND: Administrative health data, such as hospital admission data, are often used in research to identify children/young people with cerebral palsy (CP). OBJECTIVES: To compare sociodemographic, clinical details and mortality of children/young people identified as having CP in either a CP population registry or hospital admission data. METHODS: We identified two cohorts of children/young people (birth years 2001-2010, age at study end or death 2 months to 19 years 6 months) with a diagnosis of CP from either (i) the New South Wales (NSW)/Australian Capital Territory (ACT) CP Register or (ii) NSW hospital admission data (2001-2020). Using record linkage, these data sources were linked to each other and NSW Death, Perinatal, and Disability datasets. We determined the sensitivity and positive predictive value (PPV) of CP diagnosis in hospital admission data compared with the NSW/ACT CP Register (gold standard). We then compared the sociodemographic and clinical characteristics and mortality of the two cohorts available through record linkage using standardised mean difference (SMD). RESULTS: There were 1598 children/young people with CP in the NSW/ACT CP Register and 732-2439 children/young people with CP in hospital admission data, depending on the case definition used. The sensitivity of hospital admission data for diagnosis of CP ranged from 0.40-0.74 and PPV 0.47-0.73. Compared with children/young people with CP identified in the NSW/ACT CP Register, a greater proportion of those identified in hospital admission data (one or more admissions with G80 case definition) were older, lived in major cities, had comorbidities including epilepsy, gastrostomy use, intellectual disability and autism, and died during the study period (SMD > 0.1). CONCLUSIONS: Sociodemographic and clinical characteristics differ between cohorts of children/young people with CP identified using a CP register or hospital admission data. Those identified in hospital admission data have higher rates of comorbidities and death, suggesting some may have progressive conditions and not CP. These differences should be considered when planning and interpreting research using various data sources.

Case identification of non-traumatic brain injury in youth using linked population data.

BACKGROUND: Population-level administrative data provides a cost-effective means of monitoring health outcomes and service needs of clinical populations. This study aimed to present a method for case identification of non-traumatic brain injury in population-level data and to examine the association with sociodemographic factors. METHODS: An estimated resident population of youth aged 0-24 years was constructed using population-level datasets within the New Zealand Integrated Data Infrastructure. A clinical consensus committee reviewed the International Classification of Diseases Ninth and Tenth Editions codes and Read codes for inclusion in a case definition. Cases were those with at least one non-traumatic brain injury code present in the five years up until 30 June 2018 in one of four databases in the Integrated Data Infrastructure. Rates of non-traumatic brain injury were examined, both including and excluding birth injury codes and across age, sex, ethnicity, and socioeconomic deprivation groups. RESULTS: Of the 1 579 089 youth aged 0-24 years on 30 June 2018, 8154 (0.52%) were identified as having one of the brain injury codes in the five-years to 30 June 2018. Rates of non-traumatic brain injury were higher in males, children aged 0-4 years, Maori and Pacific young people, and youth living with high levels of social deprivation. CONCLUSION: This study presents a comprehensive method for case identification of non-traumatic brain injury using national population-level administrative data.

Estimating the Prevalence of Dementia in New Zealand Using Capture-Recapture Analysis on Routinely Collected Health Data.

OBJECTIVES: Community based dementia prevalence studies are expensive and resource intensive. Aotearoa New Zealand (NZ) has never had a community based dementia prevalence study representing all major ethnic groups. In recent years, dementia prevalence estimates have been derived from routinely collected health data but issues of underdiagnosis and undercoding limit their utility. Capture-recapture techniques can estimate the number of dementia cases missing from health datasets by modelling the ascertained overlaps between linked data sources. METHODS: Three routinely collected national health data sets-interRAI, Public hospital discharges, and Pharmaceuticals-were linked and all prevalent cases of dementia in NZ for the year 1 January 2021-31 December 2021 were identified. Capture-recapture analysis fitted eight loglinear models to the data, with the best fitting model used to estimate the number of prevalent cases missing from all three datasets. RESULTS: We estimated that almost half (47.8%) of dementia cases are not present in any of the three datasets. Dementia prevalence increased from 3.7% to 7.1% (95% CI 6.9%-7.4%) in the NZ 60+ population and from 4.9% to 9.2% (95% CI 8.9%-9.6%) in the NZ 65+ population when missing cases were included. Estimates of missing cases were significantly higher (p < 0.001) in Maori (49.2%), Pacific peoples (50.6%) and Asian (59.6%) compared to Europeans (46.4%). CONCLUSIONS: This study provides updated estimates of dementia prevalence in NZ and the proportion of undiagnosed dementia in NZ, highlighting the need for better access to dementia assessment and diagnosis.

Costs of care trajectories of people with dementia compared with matched controls. Longitudinal analysis of linked health and administrative data.

OBJECTIVES: To provide insight into the health and social care costs during the disease trajectory in persons with dementia and the impact of institutionalization and death on healthcare costs compared with matched persons without dementia. METHODS: Electronic health record data from family physicians were linked with national administrative databases to estimate costs of primary care, medication, secondary care, mental care, home care and institutional care for people with dementia and matched persons from the year before the recorded dementia diagnosis until death or a maximum of 4 years after the diagnosis. RESULTS: Total mean health and social care costs among persons with dementia increased substantially during the disease trajectory, mainly due to institutional care costs. For people who remained living in the community, mean health and social care costs are higher for people with dementia than for those without dementia, while for those who are admitted to a long-term care facility, mean health and social care costs are higher for people without dementia than for those with dementia. CONCLUSIONS: The steep rise in health and social care costs across the dementia care trajectory is mainly due to increasing costs for institutional care. For those remaining in the community, home care costs and hospital care costs were the main cost drivers. Future research should adopt a societal perspective to investigate the influence of including social costs.

Utilization of broad- versus narrow-spectrum antibiotics for the treatment of outpatient community-acquired pneumonia among adults in the United States.

PURPOSE: To characterize antibiotic utilization for outpatient community-acquired pneumonia (CAP) in the United States. METHODS: We conducted a cohort study among adults 18-64 years diagnosed with outpatient CAP and a same-day guideline-recommended oral antibiotic fill in the MarketScan® Commercial Database (2008-2019). We excluded patients coded for chronic lung disease or immunosuppressive disease; recent hospitalization or frequent healthcare exposure (e.g., home wound care, patients with cancer); recent antibiotics; or recent infection. We characterized utilization of broad-spectrum antibiotics (respiratory fluoroquinolone, ß-lactam + macrolide, ß-lactam + doxycycline) versus narrow-spectrum antibiotics (macrolide, doxycycline) overall and by patient- and provider-level characteristics. Per 2007 IDSA/ATS guidelines, we stratified analyses by otherwise healthy patients and patients with comorbidities (coded for diabetes; chronic heart, liver, or renal disease; etc.). RESULTS: Among 263 914 otherwise healthy CAP patients, 35% received broad-spectrum antibiotics (not recommended); among 37 161 CAP patients with comorbidities, 44% received broad-spectrum antibiotics (recommended). Ten-day antibiotic treatment durations were the most common for all antibiotic classes except macrolides. From 2008 to 2019, broad-spectrum antibiotic use substantially decreased from 45% to 19% in otherwise healthy patients (average annual percentage change [AAPC], -7.5% [95% CI -9.2%, -5.9%]), and from 55% to 29% in patients with comorbidities (AAPC, -5.8% [95% CI -8.8%, -2.6%]). In subgroup analyses, broad-spectrum antibiotic use varied by age, geographic region, provider specialty, and provider location. CONCLUSIONS: Real-world use of broad-spectrum antibiotics for outpatient CAP declined over time but remained common, irrespective of comorbidity status. Prolonged duration of therapy was common. Antimicrobial stewardship is needed to aid selection according to comorbidity status and to promote shorter courses.

Validity of Using Japanese Administrative Data to Identify Inpatients With Acute Pulmonary Embolism: Referencing the COMMAND VTE Registry.

BACKGROUND: Acute pulmonary embolism (PE) is a life-threatening in-hospital complication. Recently, several studies have reported the clinical characteristics of PE among Japanese patients using the diagnostic procedure combination (DPC)/per diem payment system database. However, the validity of PE identification algorithms for Japanese administrative data is not yet clear. The purpose of this study was to evaluate the validity of using DPC data to identify acute PE inpatients. METHODS: The reference standard was symptomatic/asymptomatic PE patients included in the COntemporary ManageMent AND outcomes in patients with Venous ThromboEmbolism (COMMAND VTE) registry, which is a cohort study of acute symptomatic venous thromboembolism (VTE) patients in Japan. The validation cohort included all patients discharged from the six hospitals included in both the registry and DPC database. The identification algorithms comprised diagnosis, anticoagulation therapy, thrombolysis therapy, and inferior vena cava filter placement. Each algorithm's sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) were estimated. RESULTS: A total of 43.4% of the validation cohort was female, with a mean age of 67.3 years. The diagnosis-based algorithm showed a sensitivity of 90.2% (222/246; 95% confidence interval [CI], 85.8-93.6%), a specificity of 99.8% (228,485/229,027; 95% CI, 99.7-99.8%), a PPV of 29.1% (222/764; 95% CI, 25.9-32.4%) and an NPV of 99.9% (228,485/229,509; 95% CI, 99.9-99.9%) for identifying symptomatic/asymptomatic PE. Additionally, 94.6% (159/168; 95% CI, 90.1-97.5%) of symptomatic PE patients were identified using the diagnosis-based algorithm. CONCLUSION: The diagnosis-based algorithm may be a relatively sensitive method for identifying acute PE inpatients in the Japanese DPC database.