BACKGROUND: The diagnosis of peripheral arterial disease (PAD) is commonly applied for symptoms related to atherosclerotic obstructions in the lower extremity, though its clinical manifestations range from an abnormal ankle-brachial index to critical limb ischemia. Subsequently, management and prognosis of PAD vary widely with the disease stage. A critical aspect is how this variation is addressed in administrative database-based studies that rely on diagnosis codes for case identification. The objective of this scoping review is to inventory the identification strategies used in studies on PAD that rely on administrative databases, to map the pros and cons of the International Classification of Diseases (ICD) codes applied, and to propose a first outline for a consensus framework for case identification in administrative databases.
METHODS: Registry-based reports published between 2010 and 2021 were identified through a systematic PubMed search. Studies were subcategorized on the basis of the expressed study focus: claudication, critical limb ischemia, or general peripheral arterial disease, and the ICD code(s) applied for case identification mapped.
RESULTS: Ninety studies were identified, of which 36 (40%) did not specify the grade of PAD studied. Forty-nine (54%) articles specified PAD grade studied. Five (6%) articles specified different PAD subgroups in methods and baseline demographics, but not in further analyses. Mapping of the ICD codes applied for case identification for studies that specified the PAD grade studied indicated a remarkable heterogeneity, overlap, and inconsistency.
CONCLUSIONS: A large proportion of registry-based studies on PAD fail to define the study focus. In addition, inconsistent strategies are used for PAD case identification in studies that report a focus. These findings challenge study validity and interfere with inter-study comparison. This scoping review provides a first initiative for a consensus framework for standardized case selection in administrative studies on PAD. It is anticipated that more uniform coding will improve study validity and facilitate inter-study comparisons.

Depression is one of the most common comorbidities of type 2 diabetes. The relationship between these two diseases seems to be bidirectional. Both conditions separately lead to significant morbidity and mortality, including hospitalization. Moreover, depression is associated with non-persistence with antidiabetic drugs.
To measure the effect of depression on morbidity and particularly on all-cause, diabetes-related, cardiovascular-related and major cardiovascular events-related hospitalization, adjusting for non-persistence to antidiabetic drugs and other confounders.
We performed a nested case-control study within a cohort of type 2 diabetic individuals initiating antidiabetic drugs. Using the health administrative data of the province of Quebec, Canada, we identified all-cause, diabetes-related, cardiovascular-related and major cardiovascular hospitalizations during a maximum follow-up of eight years after the initiation of antidiabetic drug treatment. A density sampling method matched all cases with up to 10 controls by age, sex, and the Elixhauser comorbidity index. The effect of depression on hospitalization was estimated using conditional logistic regressions adjusting for non-persistence to antidiabetic drug treatment and other variables.
We identified 41,550 all-cause hospitalized cases, of which 34,437 were related to cardiovascular (CV) diseases, 29,584 to diabetes, and 13,867 to major CV events. Depression was diagnosed in 2.51% of all-cause hospitalizations and 1.16% of matched controls. 69.11% of cases and 72.59% of controls were on metformin monotherapy. The majority (71.62% vs 75.02%, respectively) stayed on metformin monotherapy without adding or switching drugs during follow-up. Non-persistence was at similar rates (about 30%) in both groups. In the multivariable analyses, depression was associated with an increased risk for all-cause hospitalizations, with odds ratios (ORs) ranging from 2.21 (95% CI: 2.07-2.37) to 1.32 (95% CI: 1.22-1.44) according to the model adjustment (from the univariate to the fully adhjusted).
Depression increased the risk of all-cause hospitalizations among patients treated for diabetes, even after accounting for non-persistence and other potentially confounding factors. These results stress the impact of depression on diabetic patients\' use of health care resources.

OBJECTIVE: To measure the effect of depression on mortality of individuals newly treated with antidiabetic drugs, accounting for non-persistence to treatment.
METHODS: We conducted a nested case-control study within a cohort of newly treated individuals with diabetes. Using Quebec administrative data, we identified all-cause, diabetes-related, cardiovascular-related and major cardiovascular event deaths during a maximum follow-up of eight years. Each case was matched with up to 10 controls by age, sex, follow-up, and comorbidity index. We used conditional logistic regressions to estimate the effect of depression on mortality, adjusting for non-persistence to antidiabetic drug treatment, and other variables.
RESULTS: We retrieved 13,558 deaths, of which 3,652 were related to cardiovascular diseases, 2,112 to major cardiovascular events, and 311 to diabetes. Depression was associated with an increased risk of all-cause and cardiovascular-related deaths, with adjusted odds ratios (ORs) ranging from 1.32 (95% CI: 1.21-1.45) to 1.72 (95% CI: 1.57-1.88) depending on the model, but not with diabetes-related mortality.
CONCLUSIONS: Depression is independently associated with all-cause and cardiovascular-related mortality in individuals with type 2 diabetes, even when adjusting for non-persistence to antidiabetic drug treatment. Identifying risk factors for depression and implementing a screening and proper treatment for depression may help reducing mortality.

Victims of sexual abuse have more co-morbidities than other persons in the same age and the most affected group are adolescent girls. Little is known about how this is reflected in health care consumption patterns prior to the registered diagnosis. The aim of this investigation was to study health care consumption patterns among girls, 12-17 years old, 1 and 2 years prior to their diagnoses of sexual abuse. Through the Stockholm Region administrative database (VAL), data of co-morbidities, number of health care visits, and prescribed drugs were collected for cases (girls age 12-17 with diagnoses of sexual abuse, n = 519) and controls matched for age and socio-economic status (n = 4920) between 2011-2018. Health care consumption and co-morbidities were significantly higher for the cases compared to controls, with a rise 1 year before the diagnoses: the total number of health care visits (including no shows) 1 year prior to the first recording of the diagnosis was 20.4 (18.1-22.7) for the cases and 6.2 (5.8-6.6) for the controls. The most frequent visits 1 year prior to the diagnosis were to outdoor clinics, with a mean value of 19.1 (16.9-21.3) visits for the cases and 5.7 (5.3-6.1) for the controls, followed by psychiatric clinics with a mean value of 12.7 (10.6-14.8) visits for the cases and 2.0 (1.7-2.3) visits for the controls. The least visited health care clinic 1 year prior to the diagnosis was the emergency ward with a mean value of 1.3 (1.1-1.5) visits for the cases and 0.5 (0.4-0.5) visits for the controls. The most common psychiatric co-morbidities registered among the cases during the first year before the diagnosis of sexual abuse were stress, suicide attempt, and psychosis. Neuroleptics, sleeping pills, antidepressants, and tranquilizers were more frequently dispensed in cases than in controls. Similar patterns were found 2 years prior to the diagnosis. We encourage clinicians to actively ask for exposure of sexual abuse in girls with high health care consumption, making early detection and treatment of sexual abuse available as soon as possible.

UNASSIGNED: The aim of this study is to derive and to validate a cohort of rectal cancer surgical patients within administrative datasets using text-search analysis of pathology reports.
UNASSIGNED: A text-search algorithm was developed and validated on pathology reports from 694 known rectal cancers, 1000 known colon cancers, and 1000 noncolorectal specimens. The algorithm was applied to all pathology reports available within the Ottawa Hospital Data Warehouse from 1996 to 2010. Identified pathology reports were validated as rectal cancer specimens through manual chart review. Sensitivity, specificity, and positive predictive value (PPV) of the text-search methodology were calculated.
UNASSIGNED: In the derivation cohort of pathology reports (n = 2694), the text-search algorithm had a sensitivity and specificity of 100% and 98.6%, respectively. When this algorithm was applied to all pathology reports from 1996 to 2010 (n = 284,032), 5588 pathology reports were identified as consistent with rectal cancer. Medical record review determined that 4550 patients did not have rectal cancer, leaving a final cohort of 1038 rectal cancer patients. Sensitivity and specificity of the text-search algorithm were 100% and 98.4%, respectively. PPV of the algorithm was 18.6%.
UNASSIGNED: Text-search methodology is a feasible way to identify all rectal cancer surgery patients through administrative datasets with high sensitivity and specificity. However, in the presence of a low pretest probability, text-search methods must be combined with a validation method, such as manual chart review, to be a viable approach.

Objectives To compare the prevalence of major malformations using different case ascertainment definitions and to evaluate their impact on maternal asthma-major malformations association. Methods A cohort of pregnancies with and without asthma between 1990 and 2010 was formed. We used two classification methods: the Two step Congenital Malformation Classification (TCMC) and the Canadian Congenital Anomalies Surveillance System (CCASS). Within each method, three case definitions were compared: (1) ≥1 diagnosis in the hospital database; (2) ≥1 diagnosis in the hospital database or ≥2 in the medical claims; and (3) ≥1 diagnosis in the hospital database or ≥1 in the medical claims. We calculated the prevalence of major malformations and adjusted odds ratios (aORs) for maternal asthma association. Results Of 467,946 pregnancies, 12.3 % were with active asthma. The prevalence estimates were: TCMC 5.10-7.08 % and CCASS 7.03-10.57 %. Asthma-major malformations association was weaker with the CCASS (aOR 1.14-1.20) versus TCMC (aOR 1.22-1.26). Discussion The case ascertainment definitions with ≥1 hospitalization are likely to be the most reliable in similar administrative databases. The case ascertainment definition had a considerable impact on the prevalence of major malformations, but hardly influenced the aORs. Future studies should formally assess the validity of the case ascertainment definitions and allow generalizability to other maternal exposures.

Administrative data are increasingly used in healthcare research. However, in order to avoid biases, their use requires careful study planning. This paper describes the methodological principles and criteria used in a study on epidemiology, outcomes and process of care of patients hospitalized for heart failure (HF) in the largest Italian Region, from 2000 to 2012.
Data were extracted from the administrative data warehouse of the healthcare system of Lombardy, Italy. Hospital discharge forms with HF-related diagnosis codes were the basis for identifying HF hospitalizations as clinical events, or episodes. In patients experiencing at least one HF event, hospitalizations for any cause, outpatient services utilization, and drug prescriptions were also analyzed.
Seven hundred one thousand, seven hundred one heart failure events involving 371,766 patients were recorded from 2000 to 2012. Once all the healthcare services provided to these patients after the first HF event had been joined together, the study database totalled about 91 million records. Principles, criteria and tips utilized in order to minimize errors and characterize some relevant subgroups are described.
The methodology of this study could represent the basis for future research and could be applied in similar studies concerning epidemiology, trend analysis, and healthcare resources utilization.