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    LTIR Calculator — Lost-Time Incident Rate

    POR Equipo editorial RiskoPilot · REVISADO EL 2026-07-11

    En resumen

    LTIR (Lost-Time Incident Rate) is calculated as (lost-time incidents × 200,000) ÷ total hours worked. A lost-time incident is a recordable injury or illness that caused at least one day away from work beyond the day of injury. LTIR is the strictest of the three OSHA metrics — narrower than DART, and narrower than TRIR. US private-industry average is around 0.9 (BLS 2022).

    Calculadora LTIR

    Lost-Time Incident Rate — recordable injuries that caused at least one day away from work, per 100 FTEs per year.

    LTIR
    1.20
    Ver fórmula

    (Lost-time incidents × 200,000) ÷ Total hours worked

    Ejemplo: 3 × 200,000 ÷ 500,000 = 1.20

    Esta calculadora es una herramienta general. OSHA y otros reguladores usan definiciones estandarizadas — verifique la jurisdicción antes de reportar.

    LTIR vs TRIR vs DART — the OSHA hierarchy

    TRIR is the broadest count (all recordables). DART adds the severity filter of days-away OR restricted OR transferred. LTIR narrows further to only days-away cases. Any lost-time case is a DART case, and any DART case is a TRIR case — but not vice versa. In numbers: LTIR ≤ DART ≤ TRIR, always.

    International conventions: per million hours worked

    Outside the US, LTIR is typically reported as lost-time incidents per million hours worked (LTIFR). To convert: LTIFR = LTIR × 5. UK HSE, IOGP Report 456, ILO conventions, and most European reporting frameworks use the per-million-hours denominator. Report both if your operation spans jurisdictions.

    Why LTIR is a poor leading indicator

    LTIR only counts events that already caused a person to be away from work. It is deeply retrospective. Programmes serious about safety pair LTIR reporting with leading indicators: near-miss reporting rate, GFT drift on the organisational profile, CAPA verification rate, safety observations per FTE. Tripod Beta and BowTie analysis are the methodologies that turn each lost-time event into learning that reduces the next.

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