About: Towards good practice for health statistics: lessons from the Millennium Development Goal health indicators

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An Entity of Type : schema:ScholarlyArticle, within Data Space : covidontheweb.inria.fr associated with source document(s)

Attributes	Values
type	Academic Article research paper schema:ScholarlyArticle
isDefinedBy	Covid-on-the-Web dataset
has title	Towards good practice for health statistics: lessons from the Millennium Development Goal health indicators
Creator	Christopher, Prof Murray, Christopher Murray, J
topic	covid:55a09d0431a0a229503564966c187411cc01025d#this
Source	Elsevier; Medline; PMC
abstract	Summary Health statistics are at the centre of an increasing number of worldwide health controversies. Several factors are sharpening the tension between the supply and demand for high quality health information, and the health-related Millennium Development Goals (MDGs) provide a high-profile example. With thousands of indicators recommended but few measured well, the worldwide health community needs to focus its efforts on improving measurement of a small set of priority areas. Priority indicators should be selected on the basis of public-health significance and several dimensions of measurability. Health statistics can be divided into three types: crude, corrected, and predicted. Health statistics are necessary inputs to planning and strategic decision making, programme implementation, monitoring progress towards targets, and assessment of what works and what does not. Crude statistics that are biased have no role in any of these steps; corrected statistics are preferred. For strategic decision making, when corrected statistics are unavailable, predicted statistics can play an important part. For monitoring progress towards agreed targets and assessment of what works and what does not, however, predicted statistics should not be used. Perhaps the most effective method to decrease controversy over health statistics and to encourage better primary data collection and the development of better analytical methods is a strong commitment to provision of an explicit data audit trail. This initiative would make available the primary data, all post-data collection adjustments, models including covariates used for farcasting and forecasting, and necessary documentation to the public.
has issue date	2007-03-16(xsd:dateTime)
bibo:doi	10.1016/s0140-6736(07)60415-2
bibo:pmid	17350457
has license	els-covid
sha1sum (hex)	55a09d0431a0a229503564966c187411cc01025d
schema:url	https://doi.org/10.1016/s0140-6736%2807%2960415-2
resource representing a document's title	Towards good practice for health statistics: lessons from the Millennium Development Goal health indicators
has PubMed Central identifier	PMC7137868
has PubMed identifier	17350457
schema:publication	The Lancet
resource representing a document's body	covid:55a09d0431a0a229503564966c187411cc01025d#body_text
is http://vocab.deri.ie/void#inDataset of	proxy:http/ns.inria.fr/covid19/55a09d0431a0a229503564966c187411cc01025d
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