Using historical vital statistics to predict the distribution of under-five mortality by cause

Chalapati Rao, Timothy Adair, Yohannes KINFU

    Research output: Contribution to journalArticle

    5 Citations (Scopus)

    Abstract

    Cause-specific mortality data is essential for planning intervention programs to reduce mortality in the under age five years population (under-five). However, there is a critical paucity of such information for most of the developing world, particularly where progress towards the United Nations Millennium Development Goal 4 (MDG 4) has been slow.
    Original languageEnglish
    Pages (from-to)66-74
    Number of pages9
    JournalClinical Medicine and Research
    Volume9
    Issue number2
    DOIs
    Publication statusPublished - 2011

    Fingerprint

    Vital Statistics
    Mortality
    United Nations
    Population

    Cite this

    @article{df07dddf0e93480eb8e1d082e2042594,
    title = "Using historical vital statistics to predict the distribution of under-five mortality by cause",
    abstract = "Cause-specific mortality data is essential for planning intervention programs to reduce mortality in the under age five years population (under-five). However, there is a critical paucity of such information for most of the developing world, particularly where progress towards the United Nations Millennium Development Goal 4 (MDG 4) has been slow.",
    keywords = "Causes of death, Predictive model, Under-five mortality, Vital registration",
    author = "Chalapati Rao and Timothy Adair and Yohannes KINFU",
    year = "2011",
    doi = "10.3121/cmr.2010.959",
    language = "English",
    volume = "9",
    pages = "66--74",
    journal = "Clinical Medicine and Research",
    issn = "1539-4182",
    publisher = "Marshfield Clinic",
    number = "2",

    }

    Using historical vital statistics to predict the distribution of under-five mortality by cause. / Rao, Chalapati; Adair, Timothy; KINFU, Yohannes.

    In: Clinical Medicine and Research, Vol. 9, No. 2, 2011, p. 66-74.

    Research output: Contribution to journalArticle

    TY - JOUR

    T1 - Using historical vital statistics to predict the distribution of under-five mortality by cause

    AU - Rao, Chalapati

    AU - Adair, Timothy

    AU - KINFU, Yohannes

    PY - 2011

    Y1 - 2011

    N2 - Cause-specific mortality data is essential for planning intervention programs to reduce mortality in the under age five years population (under-five). However, there is a critical paucity of such information for most of the developing world, particularly where progress towards the United Nations Millennium Development Goal 4 (MDG 4) has been slow.

    AB - Cause-specific mortality data is essential for planning intervention programs to reduce mortality in the under age five years population (under-five). However, there is a critical paucity of such information for most of the developing world, particularly where progress towards the United Nations Millennium Development Goal 4 (MDG 4) has been slow.

    KW - Causes of death

    KW - Predictive model

    KW - Under-five mortality

    KW - Vital registration

    U2 - 10.3121/cmr.2010.959

    DO - 10.3121/cmr.2010.959

    M3 - Article

    VL - 9

    SP - 66

    EP - 74

    JO - Clinical Medicine and Research

    JF - Clinical Medicine and Research

    SN - 1539-4182

    IS - 2

    ER -