Population-based administrative data have been used to study osteoporosis-related fracture risk factors and outcomes, but there has been limited research about the validity of these data for ascertaining fracture cases. The objectives of this study were to: (a) compare fracture incidence estimates from administrative data with estimates from population-based clinically-validated data, and (b) test for differences in incidence estimates from multiple administrative data case definitions.
Methods
Thirty-five case definitions for incident fractures of the hip, wrist, humerus, and clinical vertebrae were constructed using diagnosis codes in hospital data and diagnosis and service codes in physician billing data from Manitoba, Canada. Clinically-validated fractures were identified from the Canadian Multicentre Osteoporosis Study (CaMos). Generalized linear models were used to test for differences in incidence estimates.
Results
For hip fracture, sex-specific differences were observed in the magnitude of underand over-ascertainment of administrative data case definitions when compared with CaMos data. The length of the fracture-free period to ascertain incident cases had a variable effect on over-ascertainment across fracture sites, as did the use of imaging, fixation, or repair service codes. Case definitions based on hospital data resulted in under-ascertainment of incident clinical vertebral fractures. There were no significant differences in trend estimates for wrist, humerus, and clinical vertebral case definitions.
Conclusions
The validity of administrative data for estimating fracture incidence depends on the site and features of the case definition.
osteoporosis fracture case definitions, administrative data fracture identification, population-based fracture surveillance, ICD coding osteoporotic fracture, fracture case validation, health administrative data bone, low-trauma fracture definition, claims data fracture ascertainment, fracture epidemiology methods, osteoporosis surveillance population
PMID 22537071 22537071 DOI 10.1186/1471-2458-12-301 10.1186/1471-2458-12-301 Lisa Lix et al. 2012, Lisa Lix 2012
Cite this article
Lisa M Lix, Mahmoud Azimaee, Beliz Acan Osman, Patricia Caetano, Suzanne Morin, Colleen Metge, David Goltzman, Nancy Kreiger, Jerilynn Prior, & William D Leslie (2012). Osteoporosis-related fracture case definitions for population-based administrative data. BMC public health, 12(1), 301. https://doi.org/10.1186/1471-2458-12-301
Lisa M Lix, Mahmoud Azimaee, Beliz Acan Osman, Patricia Caetano, Suzanne Morin, Colleen Metge, et al. Osteoporosis-related fracture case definitions for population-based administrative data. BMC Public Health. 2012;12(1):301. doi:10.1186/1471-2458-12-301
Lisa M Lix, et al. "Osteoporosis-related fracture case definitions for population-based administrative data." BMC public health, vol. 12, no. 1, 2012, pp. 301.
Minjeur M et al., 2026·Journal of Restorative Reproductive Medicine
Infertility is a clinical condition that is recognized by the symptom of an inability to conceive through sexual intercourse or to sustain a pregnancy, with that symptom indicating underlying male and/or female pathology.
This definition of infertility was developed through a structured, consensus-informed process involving broad stakeholder engagement. Initially, multiple definitions currently used by various medical professional organizations were reviewed, and a definition document was drafted and submitted to the Board of Directors of the International Institute for Restorative Reproductive Medicine (IIRRM). All IIRRM members were invited to provide feedback on the draft. Approximately 2,500 individuals and 44 organizations from 92 countries were then invited to review the proposed document, representing clinical, scientific, patient, policy, and advocacy perspectives. Submitted comments were reviewed thematically, with suggested revisions evaluated for clarity, clinical relevance, inclusiveness, and consistency with contemporary restorative reproductive medicine. Following this review, 3 substantive changes, 18 minor changes, and 15 citation corrections were incorporated into the final draft which resulted in a revised definition intended to better reflect the medical, social, and practical realities of modern infertility evaluation and care. Final approval by the IIRRM Board of Directors was unanimous.
clinical-guidelines/diagnostic-criteria-and-classification/terminology-and-definitionsethics-and-policy/advocacy-and-public-understanding/public-awarenessresearch-methods/framing-and-discourse-analysis/framing-devices
Open Access
Polyendocrine metabolic ovarian syndrome (PMOS), previously named polycystic ovary syndrome (PCOS), affects one in eight women. However, the term PCOS is inaccurate, implying pathological ovarian cysts, obscuring diverse endocrine and metabolic features, and contributing to delayed diagnosis, fragmented care, and stigma, while curtailing research and policy framing. Building on an international mandate for change, we outline an unprecedented, rigorous, multistep global consensus process for the name change. Funding and governance were established with engagement of 56 leading academic, clinical, and patient organisations. Using iterative global surveys (with responses from 14 360 people with PCOS and multidisciplinary health professionals from all world regions), modified Delphi methods, nominal group technique workshops, and marketing and implementation analyses, we identified principles prioritising scientific accuracy, clarity, stigma avoidance, cultural appropriateness, and implementation feasibility. An accurate new name was prioritised over retaining the PCOS acronym or a generic name. Implementation approaches prioritised evolution rather than transformation. Preferred terms were polyendocrine, metabolic, and ovarian, reflecting the condition's multisystem pathophysiology, and polyendocrine metabolic ovarian syndrome was the consensus new name. Accuracy was improved by omitting cysts and by capturing endocrine, metabolic, and ovarian dysfunction. A co-designed global implementation strategy, including a transition period, education, and alignment with health systems and disease classification, is under way.
fertility-awareness/effectiveness/typical-and-perfect-useresearch-methods/measurement-and-statistics/outcome-definitions
Open Access
Fertility awareness-based methods (FABMs), also known as natural family planning (NFP), enable couples to identify the days of the menstrual cycle when intercourse may result in pregnancy ("fertile days"), and to avoid intercourse on fertile days if they wish to avoid pregnancy. Thus, these methods are fully dependent on user behavior for effectiveness to avoid pregnancy. For couples and clinicians considering the use of an FABM, one important metric to consider is the highest expected effectiveness (lowest possible pregnancy rate) during the correct use of the method to avoid pregnancy. To assess this, most studies of FABMs have reported a method-related pregnancy rate (a cumulative proportion), which is calculated based on all cycles (or months) in the study. In contrast, the correct use to avoid pregnancy rate (also a cumulative proportion) has the denominator of cycles with the correct use of the FABM to avoid pregnancy. The relationship between these measures has not been evaluated quantitatively. We conducted a series of simulations demonstrating that the method-related pregnancy rate is artificially decreased in direct proportion to the proportion of cycles with intermediate use (any use other than correct use to avoid or targeted use to conceive), which also increases the total pregnancy rate. Thus, as the total pregnancy rate rises (related to intermediate use), the method-related pregnancy rate falls artificially while the correct use pregnancy rate remains constant. For practical application, we propose the core elements needed to assess correct use cycles in FABM studies.
Fertility awareness-based methods (FABMs) can be used by couples to avoid pregnancy, by avoiding intercourse on fertile days. Users want to know what the highest effectiveness (lowest pregnancy rate) would be if they use an FABM correctly and consistently to avoid pregnancy. In this simulation study, we compare two different measures: (1) the method-related pregnancy rate; and (2) the correct use pregnancy rate. We show that the method-related pregnancy rate is biased too low if some users in the study are not using the method consistently to avoid pregnancy, while the correct use pregnancy rate obtains an accurate estimate. SHORT In FABM studies, the method-related pregnancy rate is biased too low, but the correct use pregnancy rate is unbiased.
Existing fracture risk assessment tools are not designed to predict fracture-associated consequences, possibly contributing to the current undermanagement of fragility fractures worldwide. We aimed to develop a risk assessment tool for predicting the conceptual risk of fragility fractures and its consequences. The study involved 8965 people aged ≥60 years from the Dubbo Osteoporosis Epidemiology Study and the Canadian Multicentre Osteoporosis Study. Incident fracture was identified from X-ray reports and questionnaires, and death was ascertained though contact with a family member or obituary review. We used a multistate model to quantify the effects of the predictors on the transition risks to an initial and subsequent incident fracture and mortality, accounting for their complex interrelationships, confounding effects, and death as a competing risk. There were 2364 initial fractures, 755 subsequent fractures, and 3300 deaths during a median follow-up of 13 years (interquartile range [IQR] 7-15). The prediction model included sex, age, bone mineral density, history of falls within 12 previous months, prior fracture after the age of 50 years, cardiovascular diseases, diabetes mellitus, chronic pulmonary diseases, hypertension, and cancer. The model accurately predicted fragility fractures up to 11 years of follow-up and post-fracture mortality up to 9 years, ranging from 7 years after hip fractures to 15 years after non-hip fractures. For example, a 70-year-old woman with a T-score of -1.5 and without other risk factors would have 10% chance of sustaining a fracture and an 8% risk of dying in 5 years. However, after an initial fracture, her risk of sustaining another fracture or dying doubles to 33%, ranging from 26% after a distal to 42% post hip fracture. A robust statistical technique was used to develop a prediction model for individualization of progression to fracture and its consequences, facilitating informed decision making about risk and thus treatment for individuals with different risk profiles.