Administrative Health Care Research Articles

Assessment of coding-based frailty algorithms for long-term outcome prediction among older people in community settings: a cohort study from the Shizuoka Kokuho Database.

To assess the applicability of Electronic Frailty Index (eFI) and Hospital Frailty Risk Score (HFRS) algorithms to Japanese administrative claims data and to evaluate their association with long-term outcomes. A cohort study using a regional government administrative healthcare and long-term care (LTC) claims database in Japan 2014-18. Plan enrollees aged ≥50years. We applied the two algorithms to the cohort and assessed the scores' distributions alongside enrollees' 4-year mortality and initiation of government-supported LTC. Using Cox regression and Fine-Gray models, we evaluated the association between frailty scores and outcomes as well as the models' discriminatory ability. Among 827,744 enrollees, 42.8% were categorised by eFI as fit, 31.2% mild, 17.5% moderate and 8.5% severe. For HFRS, 73.0% were low, 24.3% intermediate and 2.7% high risk; 35 of 36 predictors for eFI, and 92 of 109 codes originally used for HFRS were available in the Japanese system. Relative to the lowest frailty group, the highest frailty group had hazard ratios [95% confidence interval (CI)] of 2.09 (1.98-2.21) for mortality and 2.45 (2.28-2.63) for LTC for eFI; those for HFRS were 3.79 (3.56-4.03) and 3.31 (2.87-3.82), respectively. The area under the receiver operating characteristics curves for the unadjusted model at 48months was 0.68 for death and 0.68 for LTC for eFI, and 0.73 and 0.70, respectively, for HFRS. The frailty algorithms were applicable to the Japanese system and could contribute to the identifications of enrollees at risk of long-term mortality or LTC use.

Association Between Changes in Postoperative Opioid Utilization and Long-Term Health Care Spending Among Surgical Patients With Chronic Opioid Utilization.

There is growing interest in identifying and developing interventions aimed at reducing the risk of increased, long-term opioid use among surgical patients. While understanding how these interventions impact health care spending has important policy implications and may facilitate the widespread adoption of these interventions, the extent to which they may impact health care spending among surgical patients who utilize opioids chronically is unknown. This study was a retrospective analysis of administrative health care claims data for privately insured patients. We identified 53,847 patients undergoing 1 of 10 procedures between January 1, 2004, and September 30, 2018 (total knee arthroplasty, total hip arthroplasty, laparoscopic cholecystectomy, open cholecystectomy, laparoscopic appendectomy, open appendectomy, cesarean delivery, functional endoscopic sinus surgery, transurethral resection of the prostate, or simple mastectomy) who had chronic opioid utilization (≥10 prescriptions or ≥120-day supply in the year before surgery). Patients were classified into 3 groups based on differences in opioid utilization, measured in average daily oral morphine milligram equivalents (MMEs), between the first postoperative year and the year before surgery: "stable" (<20% change), "increasing" (≥20% increase), or "decreasing" (≥20% decrease). We then examined the association between these 3 groups and health care spending during the first postoperative year, using a multivariable regression to adjust for observable confounders, such as patient demographics, medical comorbidities, and preoperative health care utilization. The average age of the sample was 62.0 (standard deviation [SD] 13.1) years, and there were 35,715 (66.3%) women. Based on the change in average daily MME between the first postoperative year and the year before surgery, 16,961 (31.5%) patients were classified as "stable," 15,463 (28.7%) were classified as "increasing," and 21,423 (39.8%) patients were classified as "decreasing." After adjusting for potential confounders, "increasing" patients had higher health care spending ($37,437) than "stable" patients ($31,061), a difference that was statistically significant ($6377; 95% confidence interval [CI], $5669-$7084; P < .001), while "decreasing" patients had lower health care spending ($29,990), a difference (-$1070) that was also statistically significant (95% CI, -$1679 to -$462; P = .001). These results were generally consistent across an array of subgroup and sensitivity analyses. Among patients with chronic opioid utilization before surgery, subsequent increases in opioid utilization during the first postoperative year were associated with increased health care spending during that timeframe, while subsequent decreases in opioid utilization were associated with decreased health care spending.

High-Dimensional Propensity Score-Adjusted Case-Crossover for Discovering Adverse Drug Reactions from Computerized Administrative Healthcare Databases.

Increasing availability of medico-administrative databases has prompted the development of automated pharmacovigilance signal detection methodologies. Self-controlled approaches have recently been proposed. They account for time-independent confounding factors that may not be recorded. So far, large numbers of drugs have been screened either univariately or with LASSO penalized regressions. We propose and assess a new method that combines the case-crossover self-controlled design with propensity scores (propensity score-adjusted case-crossover) built from high-dimensional data-driven variable selection, to account for co-medications or possibly other measured confounders. Comparison with the univariate and LASSO case-crossover was performed from simulations and a real-data study. Multiple regressions (LASSO, propensity score-adjusted case-crossover) accounted for co-medications and no other covariates. For the univariate and propensity score-adjusted case-crossover methods, the detection threshold was based on a false discovery rate procedure, while for LASSO, it relied on the Akaike Information Criterion. For the real-data study, two drug safety experts evaluated the signals generated from the analysis of 4099 patients with acute myocardial infarction from the French national health database. On simulations, our approach ranked the signals similarly to the LASSO and better than the univariate method while controlling the false discovery rate at the prespecified level, contrary to the univariate method. The LASSO provided the best sensitivity at the cost of larger false discovery rate estimates. On the application, our approach showed similar performances to the LASSO and better performances than the univariate method. It highlighted 43 signals out of 609 drug candidates: 22 (51%) were considered as potentially pharmacologically relevant, including seven (16%) regarded as highly relevant. Our findings show the interest of a propensity score combined with a case-crossover for pharmacovigilance. They also confirm that indication bias remains a challenge when mining medico-administrative databases.

Angiotensin blockade therapy and survival in pancreatic cancer: a population study

BackgroundPancreatic cancer (PC) is one of the most aggressive and challenging cancer types to effectively treat, ranking as the fourth-leading cause of cancer death in the United States. We investigated if exposures to angiotensin II receptor blockers (ARBs) or angiotensin I converting enzyme (ACE) inhibitors after PC diagnosis are associated with survival.MethodsPC patients were identified by ICD-9 diagnosis and procedure codes among the 3.7 million adults living in the Emilia-Romagna Region from their administrative health care database containing patient data on demographics, hospital discharges, all-cause mortality, and outpatient pharmacy prescriptions. Cox modeling estimated covariate-adjusted mortality hazard ratios for time-dependent ARB and ACE inhibitor exposures after PC diagnosis.Results8,158 incident PC patients were identified between 2003 and 2011, among whom 20% had pancreas resection surgery, 36% were diagnosed with metastatic disease, and 7,027 (86%) died by December 2012. Compared to otherwise similar patients, those exposed to ARBs after PC diagnosis experienced 20% lower mortality risk (HR=0.80; 95% CI: 0.72, 0.89). Those exposed to ACE inhibitors during the first three years of survival after PC diagnosis experienced 13% lower mortality risk (HR=0.87; 95% CI: 0.80, 0.94) which attenuated after surviving three years (HR=1.14; 95% CI: 0.90, 1.45).ConclusionsThe results of this large population study suggest that exposures to ARBs and ACE inhibitors after PC diagnosis are significantly associated with improved survival. ARBs and ACE inhibitors could be important considerations for treating PC patients, particularly those with the worst prognosis and most limited treatment options. Considering that these common FDA approved drugs are inexpensive to payers and present minimal increased risk of adverse events to patients, there is an urgent need for randomized clinical trials, large simple randomized trials, or pragmatic clinical trials to formally and broadly evaluate the effects of ARBs and ACE inhibitors on survival in PC patients.

Impact of the COVID-19 pandemic on primary care access for patients with gastrointestinal malignancies.

32 Background: Primary care physicians (PCPs) provide essential support for cancer patients. Both primary and cancer care have been affected by the COVID-19 pandemic. In the US, cancer related encounters and screening decreased over 40% and 80% respectively in January to April 2020 compared to 2019 (London et al JCO Clin Cancer Inform 2020). However, the impact of the pandemic on primary care access for cancer patients remains unclear. Methods: This was a population-based, retrospective cohort study using administrative healthcare databases held at ICES in Ontario, Canada. Patients with a new gastrointestinal (GI) malignancy diagnosed within the year prior to the pandemic, between July 1 and Sept 30, 2019 (COVID-19 cohort), were compared to patients diagnosed in years unaffected by the pandemic, between July 1 – Sept 30, 2018 and July 1 – Sept 30, 2017 (pre-pandemic cohort). Both groups were followed for 12 months after initial cancer diagnosis. In the COVID-19 cohort, this allowed for at least 4 months of follow-up data occurring during the pandemic. The primary outcome was number of in-person and telemedicine visits with a PCP. Secondary outcomes were number of in-person and telemedicine visits with a medical oncologist, number of emergency department (ED) visits, and number of unplanned hospitalizations. Outcomes, reported as number of visits per person-year, were compared between the COVID-19 and pre-pandemic cohorts. Results: 2833 individuals diagnosed with a new GI malignancy in the COVID-19 cohort were compared to 5698 individuals in the pre-pandemic cohort. The number of in-person visits to PCPs per person-year significantly decreased from 7.13 [95% CI 7.05 – 7.20] in the pre-pandemic cohort to 4.75 [4.66 – 4.83] in the COVID-19 cohort. Telemedicine visits to PCPs increased from 0.06 [0.05 – 0.07] to 2.07 [2.01 – 2.12]. Combined in-person and telemedicine visits to PCPs decreased from 7.19 [7.11 – 7.26] to 6.82 [6.71 – 6.92]. In-person visits to medical oncologists decreased from 3.73 [3.68 – 3.79] to 2.87 [2.80 – 2.94], and telemedicine visits increased from 0.10 [0.10 – 0.11] to 0.95 [0.91 – 0.99]. Combined in-person and telemedicine visits to medical oncologists remained stable (3.84 [3.78 – 3.89] vs. 3.82 [3.74 – 3.90]). The number of ED visits per person-year decreased from 1.04 [1.01 – 1.07] in the pre-pandemic cohort to 0.93 [0.89 – 0.97] in the COVID-19 cohort. Unplanned hospitalizations did not show a significant change (0.56 [0.54 – 0.58] vs. 0.53 [0.50 – 0.56]). Conclusions: PCP visits for patients with newly diagnosed GI malignancies overall decreased during the pandemic, with a dramatic shift from in-person to telemedicine visits. Visits to medical oncologists also shifted from in-person to telemedicine, but the overall combined visits remained the same. While the number of ED visits decreased, the shift in ambulatory practices did not seem to impact the number of unplanned hospitalizations.

Factors predicting progression to AML and use of allogeneic hematopoietic stem cell transplant in patients with MDS

Background: Risk-benefit assessment data for myelodysplastic syndrome (MDS) treatment are scarce. We characterized baseline factors and clinical course data in patients with MDS who progressed to acute myeloid leukemia (AML) or underwent allogeneic hematopoietic stem cell transplantation (allo-HSCT) in a real-world observational study using demographic, disease, and treatment claims data from a large US administrative healthcare database.

Shift to a Younger Age and Regional Differences in Inflammatory Bowel Disease in Korea: Using Healthcare Administrative Data.

Research using healthcare administrative data with a validated algorithm can reveal the real-world data of rare diseases. We investigated an accurate algorithm for detecting incident cases of inflammatory bowel disease (IBD) from healthcare data and analyzed the nationwide population-basedepidemiological features in Korea. Healthcare data from Songpa-Kangdong districts in Seoul were extracted from the National Health Insurance Service and analyzed to identify the best algorithm reflecting the cohort data. The most accurate criterion was applied to the entire database for further analysis. With the selected working criteria, 37,555 incident cases of IBD (Crohn's Disease [CD], 13,130; ulcerative colitis [UC], 24,425) were identified from 2005 to 2016. The male-to-female ratio was 2.5:1 for CD and 1.4:1 for UC. Over 12years, the annual standardized incidence rate (SIR) per 100,000 people increased from 1.6 to 2.7 and 3.8 to 4.3 for CD and UC, respectively. The peak age at diagnosis of UC shifted from 55-59years to 20-24years, whereas that of CD shifted from 19 to 17 years. The SIR of CD was higher in metropolitan areas than in non-metropolitan areas. This nationwide population-based epidemiologic study of Korean IBDrevealeda gradual increase in the incidence rates and a notable shift toward younger age at diagnosis. Males were predominant in both CD and UC. There was an urban-rural difference in the SIR of CD.

Impact of locus of care on outcomes in adolescents and young adults with osteosarcoma and Ewing sarcoma treated at pediatric versus adult cancer centers: An IMPACT cohort study.

Location of cancer care (LOC; pediatric vs. adult center) impacts outcomes in adolescents and young adults (AYA) with some cancer types. Data on the impact of LOC on survival in AYA with osteosarcoma (OSS) and Ewing sarcoma (EWS) are limited OBJECTIVES: To compare differences in demographics, disease/treatment characteristics, and survival in a population-based cohort of AYA with OSS or EWS treated at pediatric versus adult centers METHODS: The Initiative to Maximize Progress in Adolescent Cancer Therapy (IMPACT) cohort captured demographic, disease, and treatment data for all AYA (15-21years old) diagnosed with OSS and EWS in Ontario, Canada between 1992 and 2012. Patients were linked to provincial administrative health care databases. Outcomes were compared between patients treated in pediatric versus adult centers. One hundred thirty-seven AYA were diagnosed with OSS (LOC: 47 pediatric, 90 adult) and 84 with EWS (38 pediatric, 46 adult). AYA treated at pediatric centers were more likely to be enrolled in a clinical trial (OSS 55% vs. 1%, p<.001; EWS 53% vs. 2%, p<.001) and received higher cumulative chemotherapy doses. Five-year event-free survival (EFS ± standard error) in OSS and EWS were 47% ± 4% and 43% ± 5%, respectively. In multivariable analysis, the impact of LOC (pediatric vs. adult center) on EFS in OSS (adjusted hazard ratio [HR] 1.15, 95% confidence interval [CI]: 0.58-2.27, p=.69) and EWS (adjusted HR 1.82, 95% CI: 0.97-3.43, p=.06) was not statistically significant. Despite disparities in trial participation and chemotherapy doses, outcomes did not differ by LOC suggesting that AYA with bone tumors can be treated at either pediatric or adult centers.

Incidence and Antiseizure Medications of Post-stroke Epilepsy in Umbria: A Population-Based Study Using Healthcare Administrative Databases.

Introduction: Post-stroke epilepsy (PSE) requires long-term treatment with antiseizure medications (ASMs). However, epidemiology of PSE and long-term compliance with ASM in this population are still unclear. Here we report, through population-level healthcare administrative data, incidence, risk factors, ASM choice, and ASM switch over long-term follow-up.Materials and Methods: This is a population-based retrospective study using Umbria healthcare administrative database. Population consisted of all patients with acute stroke, either ischaemic or hemorrhagic, between 2013 and 2018. ICD-9-CM codes were implemented to identify people with stroke, while PSE was adjudicated according to previously validated algorithm, such as EEG and ≥1 ASM 7 days after stroke.Results: Overall, among 11,093 incident cases of acute stroke (75.9% ischemic), 275 subjects presented PSE, for a cumulative incidence of 2.5%. Patients with PSE were younger (64 vs. 76 years), more frequently presented with hemorrhagic stroke, and had longer hospital stay (15.5 vs. 11.2 days) compared with patients without PSE. Multivariable Cox proportional hazards models confirmed that PSE associated with hemorrhagic stroke, younger age, and longer duration of hospital stay. Levetiracetam was the most prescribed ASM (55.3%), followed by valproate and oxcarbazepine. Almost 30% of patients prescribed with these ASMs switched treatment during follow-up, mostly toward non-enzyme-inducing ASMs. About 12% of patients was prescribed ASM polytherapy over follow-up.Conclusions: Post-stroke epilepsy is associated with hemorrhagic stroke, younger age, and longer hospital stay. First ASM is switched every one in three patients, suggesting the need for treatment tailoring in line with secondary prevention.

Describing the linkage between administrative social assistance and health care databases in Ontario, Canada.

BackgroundThe linkage of records across administrative databases has become a powerful tool to increase information available to undertake research and analytics in a privacy protective manner.ObjectiveThe objective of this paper was to describe the data integration strategy used to link the Ontario Ministry of Children, Community and Social Services (MCCSS)-Social Assistance (SA) database with administrative health care data.MethodsDeterministic and probabilistic linkage methods were used to link the MCCSS-SA database (2003-2016) to the Registered Persons Database, a population registry containing data on all individuals issued a health card number in Ontario, Canada. Linkage rates were estimated, and the degree of record linkage and representativeness of the dataset were evaluated by comparing socio-demographic characteristics of linked and unlinked records.ResultsThere were a total of 2,736,353 unique member IDs in the MCCSS-SA database from the 1st January 2003 to 31st December 2016; 331,238 (12.1%) were unlinked (linkage rate = 87.9%). Despite 16 passes, most record linkages were obtained after 2 deterministic (76.2%) and 14 probabilistic passes (11.7%). Linked and unlinked samples were similar for most socio-demographic characteristics (i.e., sex, age, rural dwelling), except migrant status (non-migrant versus migrant) (standardized difference of 0.52). Linked and unlinked records were also different for SA program-specific characteristics, such as social assistance program, Ontario Works and Ontario Disability Support Program (standardized difference of 0.20 for each), data entry system, Service Delivery Model Technology only and both Service Delivery Model Technology and Social Assistance Management System (standardized difference of 0.53 and 0.52, respectively), and months on social assistance (standardized difference of 0.43).ConclusionsAdditional techniques to account for sub-optimal linkage rates may be required to address potential biases resulting from this data linkage. Nonetheless, the linkage between administrative social assistance and health care data will provide important findings on the social determinants of health.

Understanding Health Care Administrators' Data and Information Needs for Decision Making during the COVID-19 Pandemic: A Qualitative Study at an Academic Health System.

Objective. The COVID-19 pandemic created an unprecedented strain on the health care system, and administrators had to make many critical decisions to respond appropriately. This study sought to understand how health care administrators used data and information for decision making during the first 6 mo of the COVID-19 pandemic. Materials and Methods. We conducted semistructured interviews with administrators across University of Florida (UF) Health. We performed an inductive thematic analysis of the transcripts. Results. Four themes emerged from the interviews: 1) common types of health systems or hospital operations data; 2) public health and other external data sources; 3) data interaction, integration, and exchange; and 4) novelty and evolution in data, information, or tools used over time. Participants illustrated the organizational, public health, and regional information they considered essential (e.g., hospital census, community positivity rate, etc.). Participants named specific challenges they faced due to data quality and timeliness. Participants elaborated on the necessity of data integration, validation, and coordination across different boundaries (e.g., different hospital systems in the same metro areas, public health agencies at the local, state, and federal level, etc.). Participants indicated that even within the first 6 mo of the COVID-19 pandemic, the data and tools used for making critical decisions changed. Discussion. While existing medical informatics infrastructure can facilitate decision making in pandemic response, data may not always be readily available in a usable format. Interoperable infrastructure and data standardization across multiple health systems would help provide more reliable and timely information for decision making. Conclusion. Our findings contribute to future discussions of improving data infrastructure and developing harmonized data standards needed to facilitate critical decisions at multiple health care system levels.HighlightsThe study revealed common health systems or hospital operations data and information used in decision making during the first 6 mo of the COVID-19 pandemic.Participants described commonly used internal data sources, such as resource and financial reports and dashboards, and external data sources, such as federal, state, and local public health data.Participants described challenges including poor timeliness and limited local relevance of external data as well as poor integration of data sources within and across organizational boundaries.Results suggest the need for continued integration and standardization of health data to support health care administrative decision making during pandemics or other emergencies.

Factors associated with COVID-19 vaccine receipt at two integrated healthcare systems in New York City: a cross-sectional study of healthcare workers

ObjectivesTo examine the factors associated with COVID-19 vaccine receipt among healthcare workers and the role of vaccine confidence in decisions to vaccinate, and to better understand concerns related to COVID-19...

Enhance Access to Kidney Transplantation and Living Kidney Donation (EnAKT LKD): Statistical Analysis Plan of a Registry-Based, Cluster-Randomized Clinical Trial.

Enhance Access to Kidney Transplantation and Living Kidney Donation (EnAKT LKD) is a quality improvement intervention designed to enhance access to kidney transplantation and living kidney donation. We conducted a cluster-randomized clinical trial to evaluate the effect of the intervention versus usual care on completing key steps toward receiving a kidney transplant. To prespecify the statistical analysis plan for the EnAKT LKD trial. The EnAKT LKD trial is a pragmatic, 2-arm, parallel-group, registry-based, open-label, cluster-randomized, superiority, clinical trial. Randomization was performed at the level of the chronic kidney disease (CKD) programs (the "clusters"). Twenty-six CKD programs in Ontario, Canada. More than 10 000 patients with advanced CKD (ie, patients approaching the need for dialysis or receiving maintenance dialysis) with no recorded contraindication to receiving a kidney transplant. The trial data (including patient characteristics and outcomes) will be obtained from linked administrative health care databases (the "registry"). Stratified covariate-constrained randomization was used to allocate the 26 CKD programs (1:1) to provide the intervention or usual care from November 1, 2017, to December 31, 2021 (4.17 years). CKD programs in the intervention arm received the following: (1) support for local quality improvement teams and administrative needs; (2) tailored education and resources for staff, patients, and living kidney donor candidates; (3) support from kidney transplant recipients and living kidney donors; and (4) program-level performance reports and oversight by program leaders. The primary outcome is completing key steps toward receiving a kidney transplant, where up to 4 unique steps per patient will be considered: (1) patient referred to a transplant center for evaluation, (2) a potential living kidney donor begins their evaluation at a transplant center to donate a kidney to the patient, (3) patient added to the deceased donor transplant waitlist, and (4) patient receives a kidney transplant from a living or deceased donor. Using an intent-to-treat approach, the primary outcome will be analyzed using a patient-level constrained multistate model adjusting for the clustering in CKD programs. The EnAKT LKD trial period is November 1, 2017, to December 31, 2021. We expect to analyze and report the results once the data for the trial period is available in linked administrative health care databases. The EnAKT LKD trial is registered with the U.S. National Institute of Health at clincaltrials.gov (NCT03329521 available at https://clinicaltrials.gov/ct2/show/NCT03329521). Version 1.0 August 26, 2022.

Effectiveness and safety of direct oral anticoagulants with antiplatelet agents in patients with venous thromboembolism: A multi‐database cohort study

BackgroundPatients with venous thromboembolism (VTE) often have comorbidities that require use of antiplatelets. However, evidence on the effects of concomitant use of direct oral anticoagulants (DOACs) and antiplatelets in this high‐risk population is scarce. Our international, multi‐database cohort study assessed the real‐world effectiveness and safety of concomitant use of DOACs and antiplatelets among patients with VTE. MethodsWe assembled two population‐based cohorts using administrative health care databases from Québec and Germany. We included patients with incident VTE who initiated treatment with a DOAC or a vitamin K antagonist (VKA), while being exposed to antiplatelets (acetylsalicylic acid, clopidogrel, ticagrelor, prasugrel, dipyridamole). The study period spanned from 2012 to 2016 (Québec) or 2019 (Germany). Concomitant use of DOACs and antiplatelets was compared with concomitant use of VKAs and antiplatelets, using inverse probability of treatment weighting to balance exposure groups. Cox proportional hazards models estimated site‐specific hazard ratios (HRs) and 95% confidence intervals (CIs) of major bleeding, all‐cause mortality (primary outcomes), and recurrent VTE (secondary outcome). Site‐specific estimates were meta‐analyzed using random‐effects models. ResultsOverall, 4971 patients with VTE initiated concomitant use of a DOAC (n = 2289) or a VKA (n = 2682) and antiplatelets. Compared with concomitant use of VKAs and antiplatelets, concomitant use of DOACs and antiplatelets was associated with similar risks of major bleeding (HR, 0.81; 95% CI, 0.46‐1.45), all‐cause mortality (HR, 1.25; 95% CI, 0.87‐1.79), and recurrent VTE (HR, 0.96; 95% CI, 0.40‐2.27). ConclusionsAmong patients with VTE using antiplatelets, there were no major differences in effectiveness and safety between DOACs and VKAs.

Using Administrative Health Care Databases to Identify Patients With End-Stage Kidney Disease With No Recorded Contraindication to Receiving a Kidney Transplant.

Background:Administrative health care databases can be efficiently analyzed to describe the degree to which patients with end-stage kidney disease (ESKD) have access to kidney transplantation. Measures of access to transplantation are better represented when restricting to only those patients eligible to receive a kidney transplant. The way administrative data can be used to assess kidney transplant eligibility in the absence of clinical data has not been well described.Objective:To demonstrate a method that uses administrative health care databases to identify patients with ESKD who have no recorded contraindication to receiving a kidney transplant.Design and setting:Population-based cohort study using linked administrative health care databases in Ontario, Canada.Patients:Adult patients with ESKD approaching the need for dialysis (predialysis) or receiving maintenance dialysis between January 1, 2013 and March 31, 2015 in Ontario, Canada.Measurements:Recipient of a kidney-only or kidney-pancreas transplant.Methods:We assessed more than 80 baseline characteristics, including demographic information, comorbidities, kidney-specific characteristics, and referral and listing criteria for kidney transplantation. We compared these characteristics between patients who did and did not receive a kidney transplant.Results:We included 23 642 patients with ESKD (11 195 who were predialysis and 12 447 receiving maintenance dialysis). Over a median follow-up of 3.2 years (25th, 75th percentile: 1.3, 5.6), 3215 (13.6%) received a kidney-only or kidney-pancreas transplant. Of the studied characteristics available in administrative databases, >97% of patients with one or more of these characteristics did not receive a kidney transplant during follow-up: ESKD-modified Charlson Comorbidity Index score ≥7 (a higher score represents greater comorbidity), home oxygen use, age above 75 years, dementia, living in a long-term care facility, receiving at least one physician house call in the past year, and a combination of select malignancies (ie, lung, lymphoma, cervical, colorectal, liver, active multiple myeloma, and bladder cancer). Using these combined criteria reduced the total number of patients from 23 642 to 12 539 with no recorded contraindications to transplant (a 47% reduction), while the proportion who received a kidney transplant changed from 13.6% (denominator of 23 642) to 24.9% (denominator of 12 539).Limitations:Administrative databases are unable to capture all the complexities of determining transplant eligibility.Conclusion:We identified several criteria available within administrative health care databases that can be used to identify patients with ESKD who have no recorded contraindications to kidney transplant. These criteria could be applied when reporting measures of access to kidney transplantation that require knowledge of transplant eligibility.

An Empirical Study of Procrastination Practices at Workplace and their Associated Factors across Institutions: Case of Healthcare Administrative Professionals

An Empirical Study of Procrastination Practices at Workplace and their Associated Factors across Institutions: Case of Healthcare Administrative Professionals

Deep Learning for Predicting Urgent Hospitalizations in Elderly Population Using Healthcare Administrative Databases

Deep Learning for Predicting Urgent Hospitalizations in Elderly Population Using Healthcare Administrative Databases

Determinants of Hospital Use and Physician Services Among Adults With a History of Homelessness.

People experiencing homelessness have diverse patterns of healthcare use. This study examined the distribution and determinants of healthcare encounters among adults with a history of homelessness. Administrative healthcare records were linked with survey data for a general cohort of adults with a history of homelessness and a cohort of homeless adults with mental illness. Binary and count models were used to identify factors associated with hospital admissions, emergency department visits and physician visits for comparison across the 2 cohorts. During the 1-year follow-up period, a higher proportion of people in the cohort with a mental illness used any inpatient (27% vs 14%), emergency (63% vs 53%), or physician services (90% vs 76%) compared to the general homeless cohort. People from racialized groups were less likely use nearly all health services, most notably physician services. Other factors, such as reporting of a regular source of care, poor perceived general health, and diagnosed chronic conditions were associated with higher use of all health services except psychiatric inpatient care. When implementing interventions for patients with the greatest health needs, we must consider the unique factors that contribute to higher healthcare use, as well as the barriers to healthcare access.

Validation Study of Algorithms to Identify Malignant Tumors and Serious Infections in a Japanese Administrative Healthcare Database.

This retrospective observational study validated case-finding algorithms for malignant tumors and serious infections in a Japanese administrative healthcare database. Random samples of possible cases of each disease (January 2015-January 2018) from two hospitals participating in the Medical Data Vision Co., Ltd. (MDV) database were identified using combinations of ICD-10 diagnostic codes and other procedural/billing codes. For each disease, two physicians identified true cases among the random samples of possible cases by medical record review; a third physician made the final decision in cases where the two physicians disagreed. The accuracy of case-finding algorithms was assessed using positive predictive value (PPV) and sensitivity. There were 2,940 possible cases of malignant tumor; 180 were randomly selected and 108 were identified as true cases after medical record review. One case-finding algorithm gave a high PPV (64.1%) without substantial loss in sensitivity (90.7%) and included ICD-10 codes for malignancy and photographing/imaging. There were 3,559 possible cases of serious infection; 200 were randomly selected and 167 were identified as true cases after medical record review. Two case-finding algorithms gave a high PPV (85.6%) with no loss in sensitivity (100%). Both case-finding algorithms included the relevant diagnostic code and immunological infection test/other related test and, of these, one also included pathological diagnosis within 1 month of hospitalization. The case-finding algorithms in this study showed good PPV and sensitivity for identification of cases of malignant tumors and serious infections from an administrative healthcare database in Japan.

Hospitalization for COVID-19 is associated with a higher risk of subsequent hospitalization for psychiatric disorders: A French nationwide longitudinal study comparing hospitalizations for COVID-19 and for other reasons.

Although COVID-19 has been associated with psychiatric symptoms in patients, no study to date has examined the risk of hospitalization for psychiatric disorders after hospitalization for this disease. We aimed to compare the proportions of hospitalizations for psychiatric disorders in the 12months following either hospitalization for COVID-19 or hospitalization for another reason in the adult general population in France during the first wave of the current pandemic. We conducted a retrospective longitudinal nationwide study based on the national French administrative healthcare database. Among the 2,894,088 adults hospitalized, 96,313 (3.32%) were admitted for COVID-19. The proportion of patients subsequently hospitalized for a psychiatric disorder was higher for COVID-19 patients (11.09 vs. 9.24%, OR=1.20 95%CI 1.18-1.23). Multivariable analyses provided similar results for a psychiatric disorder of any type and for psychotic and anxiety disorders (respectively, aOR=1.06 95%CI 1.04-1.09, aOR=1.09 95%CI 1.02-1.17, and aOR=1.11 95%CI 1.08-1.14). Initial hospitalization for COVID-19 in intensive care units and psychiatric history were associated with a greater risk of subsequent hospitalization for any psychiatric disorder than initial hospitalization for another reason. Compared with hospitalizations for other reasons, hospitalizations for COVID-19 during the first wave of the pandemic in France were associated with a higher risk of hospitalization for a psychiatric disorder during the 12months following initial discharge. This finding should encourage clinicians to increase the monitoring and assessment of psychiatric symptoms after hospital discharge for COVID-19, and to propose post-hospital care, especially for those treated in intensive care.