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A probabilistic method to estimate the burden of maternal morbidity in resource-poor settings: preliminary development and evaluation.

Fottrell E, Högberg U, Ronsmans C, Osrin D, Azad K, Nair N, Meda N, Ganaba R, Goufodji S, Byass P, Filippi V - Emerg Themes Epidemiol (2014)

Bottom Line: Preliminary evaluation of the method compared the burden of maternal morbidity and specific causes from the probabilistic tool with clinical classifications of 489 recently-delivered women from Benin, Bangladesh and India.Using training datasets, it was possible to create a probabilistic tool that handled uncertainty of women's self reports of pregnancy and delivery experiences in a unique way to estimate population-level burdens of maternal morbidity and specific causes that compared well with clinical classifications of the same data.When applied to test datasets, the method overestimated the burden of morbidity compared with clinical review, although possible conceptual and methodological reasons for this were identified.

View Article: PubMed Central - HTML - PubMed

Affiliation: UCL Institute for Global Health, University College London, 30 Guilford Street, London WC1N 1EH, United Kingdom. e.fottrell@ucl.ac.uk.

ABSTRACT

Background: Maternal morbidity is more common than maternal death, and population-based estimates of the burden of maternal morbidity could provide important indicators for monitoring trends, priority setting and evaluating the health impact of interventions. Methods based on lay reporting of obstetric events have been shown to lack specificity and there is a need for new approaches to measure the population burden of maternal morbidity. A computer-based probabilistic tool was developed to estimate the likelihood of maternal morbidity and its causes based on self-reported symptoms and pregnancy/delivery experiences. Development involved the use of training datasets of signs, symptoms and causes of morbidity from 1734 facility-based deliveries in Benin and Burkina Faso, as well as expert review. Preliminary evaluation of the method compared the burden of maternal morbidity and specific causes from the probabilistic tool with clinical classifications of 489 recently-delivered women from Benin, Bangladesh and India.

Results: Using training datasets, it was possible to create a probabilistic tool that handled uncertainty of women's self reports of pregnancy and delivery experiences in a unique way to estimate population-level burdens of maternal morbidity and specific causes that compared well with clinical classifications of the same data. When applied to test datasets, the method overestimated the burden of morbidity compared with clinical review, although possible conceptual and methodological reasons for this were identified.

Conclusion: The probabilistic method shows promise and may offer opportunities for standardised measurement of maternal morbidity that allows for the uncertainty of women's self-reported symptoms in retrospective interviews. However, important discrepancies with clinical classifications were observed and the method requires further development, refinement and evaluation in a range of settings.

No MeSH data available.


Distribution of aggregateda broad cause categories for severe acute maternal morbidity (SAMM) cases according to clinician diagnoses and the InterSAMM probabilistic method for 1734 deliveries in Benin and Burkina Faso. Indeterminate cause CSMFs of 0.1% (InterSAMM) and 0% (clinicians) omitted from the graph.
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Figure 3: Distribution of aggregateda broad cause categories for severe acute maternal morbidity (SAMM) cases according to clinician diagnoses and the InterSAMM probabilistic method for 1734 deliveries in Benin and Burkina Faso. Indeterminate cause CSMFs of 0.1% (InterSAMM) and 0% (clinicians) omitted from the graph.

Mentions: Given that clinicians only assigned causes to those identified as SAMM cases, comparison of cause distributions are presented with and without the InterSAMM-derived causes of non-SAMM morbid cases (Table 3). With few exceptions, overall cause-specific morbidity fractions were similar in every cause category and population distributions of causes compared well. When aggregated into broad cause-categories of similar aetiologiesa, the rank order of causes for SAMM cases (>90% likelihood) was identical (Figure 3).


A probabilistic method to estimate the burden of maternal morbidity in resource-poor settings: preliminary development and evaluation.

Fottrell E, Högberg U, Ronsmans C, Osrin D, Azad K, Nair N, Meda N, Ganaba R, Goufodji S, Byass P, Filippi V - Emerg Themes Epidemiol (2014)

Distribution of aggregateda broad cause categories for severe acute maternal morbidity (SAMM) cases according to clinician diagnoses and the InterSAMM probabilistic method for 1734 deliveries in Benin and Burkina Faso. Indeterminate cause CSMFs of 0.1% (InterSAMM) and 0% (clinicians) omitted from the graph.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3975153&req=5

Figure 3: Distribution of aggregateda broad cause categories for severe acute maternal morbidity (SAMM) cases according to clinician diagnoses and the InterSAMM probabilistic method for 1734 deliveries in Benin and Burkina Faso. Indeterminate cause CSMFs of 0.1% (InterSAMM) and 0% (clinicians) omitted from the graph.
Mentions: Given that clinicians only assigned causes to those identified as SAMM cases, comparison of cause distributions are presented with and without the InterSAMM-derived causes of non-SAMM morbid cases (Table 3). With few exceptions, overall cause-specific morbidity fractions were similar in every cause category and population distributions of causes compared well. When aggregated into broad cause-categories of similar aetiologiesa, the rank order of causes for SAMM cases (>90% likelihood) was identical (Figure 3).

Bottom Line: Preliminary evaluation of the method compared the burden of maternal morbidity and specific causes from the probabilistic tool with clinical classifications of 489 recently-delivered women from Benin, Bangladesh and India.Using training datasets, it was possible to create a probabilistic tool that handled uncertainty of women's self reports of pregnancy and delivery experiences in a unique way to estimate population-level burdens of maternal morbidity and specific causes that compared well with clinical classifications of the same data.When applied to test datasets, the method overestimated the burden of morbidity compared with clinical review, although possible conceptual and methodological reasons for this were identified.

View Article: PubMed Central - HTML - PubMed

Affiliation: UCL Institute for Global Health, University College London, 30 Guilford Street, London WC1N 1EH, United Kingdom. e.fottrell@ucl.ac.uk.

ABSTRACT

Background: Maternal morbidity is more common than maternal death, and population-based estimates of the burden of maternal morbidity could provide important indicators for monitoring trends, priority setting and evaluating the health impact of interventions. Methods based on lay reporting of obstetric events have been shown to lack specificity and there is a need for new approaches to measure the population burden of maternal morbidity. A computer-based probabilistic tool was developed to estimate the likelihood of maternal morbidity and its causes based on self-reported symptoms and pregnancy/delivery experiences. Development involved the use of training datasets of signs, symptoms and causes of morbidity from 1734 facility-based deliveries in Benin and Burkina Faso, as well as expert review. Preliminary evaluation of the method compared the burden of maternal morbidity and specific causes from the probabilistic tool with clinical classifications of 489 recently-delivered women from Benin, Bangladesh and India.

Results: Using training datasets, it was possible to create a probabilistic tool that handled uncertainty of women's self reports of pregnancy and delivery experiences in a unique way to estimate population-level burdens of maternal morbidity and specific causes that compared well with clinical classifications of the same data. When applied to test datasets, the method overestimated the burden of morbidity compared with clinical review, although possible conceptual and methodological reasons for this were identified.

Conclusion: The probabilistic method shows promise and may offer opportunities for standardised measurement of maternal morbidity that allows for the uncertainty of women's self-reported symptoms in retrospective interviews. However, important discrepancies with clinical classifications were observed and the method requires further development, refinement and evaluation in a range of settings.

No MeSH data available.