BMD - Health metric data from ISCD Official Positions 2019
About this page: This page combines data from authoritative sources with AI-assisted analysis to provide comprehensive metric benchmarks. The content has been written and reviewed by our team to ensure accuracy, relevance, and quality.
Our content creation process combines multiple approaches to ensure quality and accuracy:
Our commitment: We prioritize accuracy and transparency. If you find any errors or have concerns about content accuracy, please contact us.
How does your this metric compare to others in your demographic group? For All population of Female in the Adult age range, understanding normal variation is crucial for meaningful health assessment. Values typically range from 0.8 to 1.1, encompassing 90% of the population. This guide provides the data-driven insights you need to interpret your measurements accurately and make informed decisions about your health.
A measurement of this metric Healthcare providers and researchers utilize this metric (in appropriate units) as part of thorough health evaluation. This metric contributes to understanding overall health status and identifying potential areas of concern.
The procedure for measuring this metric follows evidence-based protocols designed to maximize accuracy and reproducibility. Key procedural elements include: appropriate subject positioning, correct equipment use, consistent timing, and accurate recording. When these elements are standardized, this metric measurements provide reliable data for health assessment and comparison.
The chart below shows how BMD is distributed across the population. The percentile values help you understand where you fall relative to others in your demographic group.
Insufficient data for visualization
This metric does not have enough statistical parameters for generating a visualization.
The distribution of this metric values across the population follows a characteristic pattern that reveals important health insights. The central 90% of values fall between 0.8 (5th percentile) and 1.1 (95th percentile), defining the typical range for healthy individuals. At the center, the median value of 0.9 indicates that half the population falls above and half below this point. The interquartile range—0.9 to 1.0—encompasses the middle 50% of values, representing the most common range. Understanding where your measurement falls within this distribution provides meaningful context for health assessment.
0.75
5% of the population falls below this value. This represents the lower range of typical variation.
0.87
25% of the population falls below this value. This represents the lower-middle range.
0.95
This is the middle value. 50% of the population falls below and 50% falls above this value.
1.03
75% of the population falls below this value. This represents the upper-middle range.
1.15
95% of the population falls below this value. This represents the upper range of typical variation.
0.95
The arithmetic average of all values. This may differ from the median if the distribution is skewed.
The intersection of demographic factors creates unique patterns in this metric that require matched reference data for accurate assessment. For Female All individuals aged Adult, the combination of ethnicity, age, and sex produces a specific profile that differs from other demographic combinations. Using precisely matched reference data provides the most relevant comparison for your individual measurement. This demographic specificity enhances the clinical utility and personal relevance of benchmark comparisons.
Multiple determinants shape this metric at individual and population levels. Inherited factors, developmental conditions, lifestyle choices, and current health status all contribute to observed values. Environmental and socioeconomic factors further influence patterns. Understanding these diverse influences supports nuanced interpretation of this metric measurements and realistic health goal setting.
this metric represents one component within the complex picture of overall health. No single metric defines health status; this metric values gain meaning when interpreted alongside other measurements, symptoms, lifestyle factors, and health history. A value that appears concerning in isolation may be less significant when viewed within broader context—and vice versa. Within Musculoskeletal assessment, this metric contributes specific information that clinicians synthesize with related indicators. Healthcare providers integrate this metric with other data to form comprehensive assessments and recommendations. Maintaining perspective on this metric as part of a larger health picture supports balanced interpretation and appropriate response to individual values.
Clinical utility of this metric extends beyond simple comparison to population norms. Healthcare providers consider: how values compare to demographic-matched benchmarks, whether significant changes have occurred, presence of associated symptoms, and relationship to other clinical findings. individual clinical significance depends on broader context. this metric contributes specific information to musculoskeletal evaluation. This nuanced approach enables meaningful clinical decision-making.
Research on this metric has established robust population benchmarks that inform clinical practice and public health policy. Population research on this metric combines rigorous measurement protocols with representative sampling to establish reliable benchmarks. These data support clinical practice, public health surveillance, and ongoing research.
this metric data serves practical purposes across multiple contexts. For individuals: understanding your values relative to benchmarks, tracking changes over time, and informing health discussions with providers. For healthcare: screening, diagnosis, treatment monitoring, and outcome assessment. For researchers: studying population health trends, evaluating interventions, and identifying health disparities. For public health: surveillance, policy development, and health promotion. This multi-level utility makes this metric benchmarks valuable across the health ecosystem.
Данные о здоровье населения России собираются Росстатом в рамках выборочных наблюдений состояния здоровья населения.
Система здравоохранения России обеспечивает бесплатную медицинскую помощь через систему обязательного медицинского страхования (ОМС) и включает регулярную диспансеризацию.
Официальные данные Росстат / Минздрав ↗
Примечание: Основные данные из CDC NHANES (США). Местная статистика из официальных национальных обследований здоровья. (2024-01)
Normal this metric encompasses a range of values that varies by demographic group. For individuals aged Adult, Female, All population, the median value is 0.9. Values between the 5th and 95th percentiles (0.8 to 1.1) represent normal variation. Using demographic-matched benchmarks ensures appropriate comparison.
Percentiles show where your this metric falls relative to others in your demographic group. At the 50th percentile (0.9), half the population is above and half below. Between the 25th (0.9) and 75th (1.0) percentiles represents the middle half of the distribution—where most healthy values fall. Percentiles at extreme ends (below 5th or above 95th) are less common but not necessarily abnormal. Context matters for interpretation.
this metric can change over time due to age-related processes, lifestyle modifications, health conditions, and interventions. Some factors are relatively fixed (like genetics), while others respond to deliberate changes (like exercise or diet). In the Adult age range, age-related changes may be occurring. Tracking your this metric over time reveals personal trends that provide valuable health information. Consistent measurement conditions enable meaningful comparison of values over time.
Consider discussing your this metric with a healthcare provider if: values fall significantly outside normal range (below 5th or above 95th percentile), you've noticed substantial changes over time, values are associated with symptoms, or you have questions about health implications. Being at a percentile extreme doesn't automatically indicate problems—many healthy individuals naturally fall at distribution tails. Clinical significance depends on context, symptoms, and other health factors. Healthcare providers can offer personalized interpretation.
this metric values differ across ethnic groups due to genetic, environmental, and lifestyle factors. All populations show characteristic patterns that reflect population-specific genetics, dietary traditions, activity patterns, and environmental influences. These differences are normal and expected—not indicators of better or worse health. Using All-specific reference data ensures your comparison reflects meaningful variation rather than expected population differences. This demographic specificity improves the accuracy and relevance of health assessment.
