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A maternal near miss (MNM) is an event in which a pregnant woman came close to maternal death, but did not die - hence the "near-miss". Traditionally, the analysis of maternal deaths has been the criteria of choice for evaluating women’s health and the quality of obstetric care. Due to the succes of modern medicine such deaths have become very rare events in many developed countries. This has led to an increased interest in analyzing so-called "near miss" events.

Background

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Maternal mortality is a sentinel event to assess the quality of a health care system. The standard indicator is the Maternal Mortality Ratio, defined as the ratio of the number of maternal deaths per 100,000 live births. Due to improved health care the ratio has been declining steadily in most countries. For example, in the UK 1952-1982 the ratio was halving every 10 years.[1] In the European Union the ratio has now stabilized at around 10 to 20.[2].

The small number of cases makes the evaluation of maternal mortality practically impossible[3] [4] Historically, the study of negative outcomes have been highly succesful in preventing their causes, this strategy of prevention therefore faces difficulties when if the number of negative outcome drop to low levels. In the UK, for example, the most dramatic decline in maternal death was achieved in Rochdale, an industrial town in the poorest area of England. In 1928 the town had a Maternal Mortality Ratio of over 900 per 100,000 live births, more than double the national average of the time. An enquiry into the causes of the deaths reduced the ratio to 280 per 100,000 pregnancies by 1934, only six years later, now the lowest in the country.[5]

The very low figures of maternal mortality have therefore stimulated an interest in investigating cases of life threatening obstetric morbidity or maternal near miss. There are several advantages of investigating near miss events over events with fatal outcome

  • near miss are more common than maternal deaths[6]
  • their review is likely to yield useful information on the same pathways that lead to severe morbidity and death,
  • investigating the care received may be less threatening to providers because the woman survived
  • one can learn from the women themselves since they can be interviewed about the care they received.
  • all near misses should be interpreted as free lessons and opportunities to improve the quality of service provision[7]
  • it is also clear that maternal deaths merely are the tip of the iceberg of maternal disability. For every woman who dies, many more will survive but often suffer from

life long disabilities. [8]

The growing interest is reflected in an increasing number of systematic reviews on the prevalence of near miss[9] [10]. The studies and reviews span

  • analytic attempts to define the concept more strictly,
  • descriptive efforts to measure and quantify new indicators (prevanlence) of near-miss for different geographical regions etc
  • explanatory efforts of the leading cause for morbidity

Definition

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Near miss are not easy to define. Definitions have relied on a variety of approaches, including criteria of organ dysfunction; criteria of clinical management such as admission to intensive care; signs and symptoms; or clinical entities such as eclampsia or uterine rupture. (to be expanded, use figure from Minkauskiene (2008))

Prevalence

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See Minkauskiene (2008) for indicators for a list of countries

See also

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Sources

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  • Adisasmita, Asri; Deviany, Poppy E.; Nandiaty, Fitri; Stanton, Cynthia; Ronsmans, Carine (2008). "Obstetric near miss and deaths in public and private hospitals in Indonesia". BMC Pregnancy and Childbirth. 8 (10): 10. doi:10.1186/1471-2393-8-10. PMC 2311270. PMID 18366625.{{cite journal}}: CS1 maint: date and year (link) CS1 maint: unflagged free DOI (link)

References

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  1. ^ Marsh 1998:176
  2. ^ Minkauskienė 2004:299
  3. ^ Minkauskienė 2004:299
  4. ^ See also the Poisson distribution for a discussion of statistical methodological difficulties when the number of cases is "small"
  5. ^ Lewis 2003:31
  6. ^ list is based on Adisasmita 2008 unless otherwise indicated
  7. ^ Tingle 2002:3
  8. ^ Lewis 2003:29
  9. ^ Adisasmita 2008
  10. ^ Dott 2005
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