maternal mortality sri lanka estimating maternal mortality i_lozano_110110_ihme

Estimating maternal mortality I

November 1, 2010

Rafael Lozano

Professor of Global Health

Outline

• Dependent variable

o PMDF to maternal mortality rates

• Model form and covariates

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Challenges in modeling causes of death

• Data are missing for many years

• Non-sampling error can be large in some settings

• There is marked variation in temporal trends across countries

• The available covariates explain only a moderate component of the variance

What we have

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What we want

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How can we get there?

• Statistical models can be used to help explore relationships:

o Identify factors (from the literature) that are likely related to maternal mortality

o Estimate the empirical relationship between those factors and the outcome of interest using a regression model

o Use those empirical relationships to inform our estimates of maternal mortality

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Outline

• Dependent variable

o PMDF to maternal mortality rates

• Model form and covariates

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Dependent variable selection

• Dependent variable choices:

o Maternal mortality ratio (MMR)

o Proportion of all deaths due to maternal causes (PMDF)

o Maternal mortality rates

• Dependent variable can either be a summary measure or an age-specific measure

• We model the log of the age-specific maternal mortality rates

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Choice of dependent variable

• Why model rates rather than the PMDF?

o The PMDF is particularly sensitive to other causes of death

─ For example, in the event of an earthquake, epidemic (such as HIV), or increase in RTIs, the PMDF will be influenced

─ This requires not only modeling maternal mortality, but modeling everything else that explains variation in the PMDF

o At the extremes of the PMDF (close to zero, close to one), models can behave unpredictably

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Why Model Age-Specific Rates?

• Maternal death rate varies by age, we choose to model age-specific rates to allow for different countries to have different levels and time trends in the maternal death rate.

• Shifts in fertility for example to older ages in some countries influences the age-pattern of maternal mortality.

• Modeling all ages combined forces all countries to have identical patterns over time which is undesirable.

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Processing input data

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Why use the PMDF to get to rates?

• We do not calculate the MMR or maternal mortality rate directly from the raw data, but calculate the age-specific (i.e., 15-19, 20-24…45-49) fraction of all deaths in women due to maternal causes (PMDF)

• We then multiply these PMDFs by the all-cause adult mortality estimates discussed earlier, to arrive at the number of maternal deaths

• This allows for the correction of underreporting in the level of maternal mortality as the all-causes mortality “envelopes”

• It also may reduce the effect of recall bias in survey data

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PMDF to population maternal rates

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From input data source

PMDF to population maternal deaths, by age

Input data source

Age group # of maternal deaths

# of all-cause deaths

PMDF National mortality

envelopes

Population maternal deaths

15-19 80 1,000 8.0% 1,355 108

20-24 132 1,100 12.0% 1,990 239

25-29 132 1,100 12.0% 3,775 453

30-34 143 1,300 11.0% 4,935 543

35-39 126 1,400 9.0% 5,700 513

40-44 90 1,500 6.0% 6,575 395

45-49 61 1,900 3.2% 7,725 247

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Outline

• Dependent variable

o PMDF to maternal mortality rates

• Model form and covariates

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Form of the regression model

• Rates can be modeled directly, as with OLS or robust linear approaches (such as Huber-White, Tukey, or median regression)

o Robust approaches are important because of the presence of outliers, zeros, and other extreme observations

• Rates can also be modeled using count models such as Poisson or negative binomial models

o The data does not meet the Poisson assumption that the mean equals the variance, in other words, the data is over-dispersed

o The degree of over-dispersion is related to age, which can be allowed for using the generalized negative binomial model

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So what is related to maternal mortality?

• Fertility

• GDP

• Education

• Neonatal mortality

• HIV prevalence

• Coverage of skilled birth attendance or in-facility birth

• Others?

• And what do we have in a complete time series for all countries?

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Transforming the covariates

• Examine the relationship between each of these covariates and the log of the maternal mortality rate (dependent variable)

• Transformations for model:

o log of the total fertility rate

o log of the distributed lag of GDP per capita

o HIV-squared as well as HIV sero-prevalence

• Look for co-linearity

o SBA highly co-linear with neonatal mortality and GDP per capita, and was also only available for 1986-2008

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TFR vs. ln(maternal mortality rate)

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-4-2

02

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ln(m

ater

nal m

orta

lity

rate

)

0 2 4 6 8TFR

15-19 20-2425-29 30-34

35-39 40-4445-49

First stage regression model

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  Robust Regression  Coefficient Std. Error

Intercept 4.715 0.100ln(TFR) 1.903 0.022

ln(GDP per capita) -0.511 0.010Neonatal mortality 13.662 0.721

Education -0.086 0.003HIV 0.108 0.005HIV² -0.001 0.000

Age 15-19 -1.176 0.021Age 20-24 -0.374 0.020Age 25-29 -0.077 0.020Age 35-39 -0.165 0.020Age 40-44 -0.633 0.021Age 45-49 -1.390 0.025

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HIV counterfactual

• HIV is believed to be a major contributor to maternal deaths

• What would happen to maternal mortality if we “turned off” the effect of HIV at the population level?

o Develop a counterfactual scenario: what would have happened with maternal mortality if there had been no HIV

• In the linear model, switch HIV prevalence to zero, rather than its observed value

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