COMSA-Mozambique

For the purpose of illustration, comsamoz_public_openVAout and comsamoz_public_broad contain publicly available (deidentified) individual-level cause of death (COD) data for neonates aged 0–27 days from the Countrywide Mortality Surveillance for Action project in Mozambique (COMSA-Mozambique). The cause in this example data are obtained using the InSilicoVA algorithm.

data(comsamoz_public_openVAout)  # load data in R environment

class(comsamoz_public_openVAout)  # list
names(comsamoz_public_openVAout)  # different components

comsamoz_public_openVAout$age_group  # age group
comsamoz_public_openVAout$va_algo  # algorithm
head(comsamoz_public_openVAout$data)  # head of the specific COD data
# for these 6 individuals, the causes of deaths are "Other and unspecified neonatal CoD",
# "Birth asphyxia", "Neonatal sepsis", "Birth asphyxia", "Birth asphyxia", "Neonatal sepsis"

data(comsamoz_public_broad)  # load data in R environment
head(comsamoz_public_broad$data)  # head of the stored broad COD data
# for these 6 individuals, the causes of deaths are "other", "ipre", "sepsis_meningitis_inf",
# "ipre", "ipre", "sepsis_meningitis_inf"

Misclassification Matrix Estimates Based on CHAMPS

This stores estimates of misclassification matrices for different computer-coded verbal autopsy (CCVA) algorithms, age groups, and countries based on the COD data from the CHAMPS project.

CHAMPS Data: The Child Health and Mortality Prevention Surveillance (CHAMPS) Network gathers premortem clinical and laboratory data, along with postmortem verbal autopsy (VA) and minimally invasive tissue sampling (MITS), from sites in Bangladesh, Ethiopia, Kenya, Mali, Mozambique, Sierra Leone, and South Africa. A panel of physicians and scientists uses the diagnostic test results and clinical records to ascertain a causal chain. This creates a limited paired COD data from two diagnoses: a gold standard (CHAMPS cause from here on) and one based on VA.

Estimates: We model this data using the efficient country-specific misclassification matrix modeling framework proposed in Pramanik et al. (2025). Mmat_champs stores estimates from this modeling for

• two age groups: "neonate" for 0-27 days, "child" for 1-59 months.

• three CCVA algorithms: "eava" for EAVA, "insilicova" for InSilicoVA, and "interva" for InterVA.

• eight countries: "Bangladesh", "Ethiopia", "Kenya", "Mali", "Mozambique", "Sierra Leone", and "South Africa". It also has an estimate for "other" for all countries outside CHAMPS.

Mmat_champs is a nested list. For example, Mmat_champs$neonate$eava$postsumm$Mozambique contains posterior summaries of misclassification estimates for neonates based on EAVA algorithm in Mozambique. Similarly, Mmat_champs$neonate$eava$postmean$Mozambique and Mmat_champs$neonate$eava$asDirich$Mozambique contain posterior mean and Diichlet approximation of the posterior.

For any age group, algorithm, and country, the posterior estimates are stored in three formats:

• "postsumm": Array of posterior summary X CHAMPS broad cause X VA broad cause.

  • Posterior summaries are mean (posterior mean), min (minimum), 2.5% (2.5% percentile), 25% (25% percentile), 50% (50% percentile), 75% (75% percentile), 97.5% (97.5% percentile), and max (maximum).

  • For example, Mmat_champs$neonate$eava$postsumm$Mozambique[,"pneumonia",] are posterior summaries for CHAMPS cause "pneumonia". Rows are posterior summaries. Columns are VA predicted broad causes.

• "postmean": Matrix of CHAMPS broad cause X VA broad cause. Contains posterior mean of misclassification matrices.

  • For example, Mmat_champs$neonate$eava$postmean$Mozambique["pneumonia",] are posterior means for CHAMPS cause "pneumonia".

• "asDirich": Matrix of CHAMPS broad cause X VA broad cause. Stores concentration (or scale) parameters of Dirichlet distribution that best approximates the posterior distibution of misclassification matrices based on the CHAMPS data.

  • For example, Dirichlet distribution with parameters Mmat_champs$neonate$eava$asDirich$Mozambique["pneumonia",] best approximate the misclassification posterior for CHAMPS cause "pneumonia".

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Single Algorithm

calib_out_specific = vacalibration(va_data = setNames(list(comsamoz_public_openVAout$data),
                                                      list(comsamoz_public_openVAout$va_algo)),
                                   age_group = comsamoz_public_openVAout$age_group,
                                   country = "Mozambique")

round(calib_out_specific$p_uncalib, 3) # uncalibrated (rounded upto 3 significant digits)
round(calib_out_specific$pcalib_postsumm["insilicova",,], 3) # calibrated (rounded upto 3 significant digits)

calib_out_broad = vacalibration(va_data = setNames(list(comsamoz_public_broad$data),
                                                   list(comsamoz_public_broad$va_algo)),
                                age_group = comsamoz_public_broad$age_group,
                                country = "Mozambique")

calib_out_deathcount = vacalibration(va_data = setNames(list(colSums(comsamoz_public_broad$data)),
                                                        list(comsamoz_public_broad$va_algo)),
                                     age_group = comsamoz_public_broad$age_group,
                                     country = "Mozambique")

#################################### uncalibrated ####################################
round(calib_out_specific$p_uncalib, 3)  # specific cause
round(calib_out_broad$p_uncalib, 3)  # broad cause
round(calib_out_deathcount$p_uncalib, 3)  # broad-cause-specific death count


#################################### calibrated ####################################
round(calib_out_specific$pcalib_postsumm["insilicova",,], 3)  # specific cause
round(calib_out_broad$pcalib_postsumm["insilicova",,], 3)  # broad cause
round(calib_out_deathcount$pcalib_postsumm["insilicova",,], 3)  # broad-cause-specific death count

Fetching stored misclassification estimates by default

# default
calib_out_specific = vacalibration(va_data = setNames(list(comsamoz_public_openVAout$data),
                                                      list(comsamoz_public_openVAout$va_algo)),
                                   age_group = comsamoz_public_openVAout$age_group,
                                   country = "Mozambique")

# misclassification estimates provided by user
calib_out_specific_mmat = vacalibration(va_data = setNames(list(comsamoz_public_openVAout$data),
                                                           list(comsamoz_public_openVAout$va_algo)),
                                        Mmat.asDirich = setNames(list(Mmat_champs[[comsamoz_public_openVAout$age_group]][[comsamoz_public_openVAout$va_algo]]$asDirich[["Mozambique"]]),
                                                           list(comsamoz_public_openVAout$va_algo)),
                                   age_group = comsamoz_public_openVAout$age_group,
                                   country = "Mozambique")

Below is a comparison of uncalibrated and calibrated CSMF estimates

#################################### uncalibrated ####################################
round(calib_out_specific$p_uncalib, 3)  # default
round(calib_out_specific_mmat$p_uncalib, 3)  # user provided misclassification estimate


#################################### calibrated ####################################
round(calib_out_specific$pcalib_postsumm["insilicova",,], 3)  # default
round(calib_out_specific_mmat$pcalib_postsumm["insilicova",,], 3)  # user provided misclassification estimate

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Multiple Algorithms

For example, let below are broad-cause-specific death counts based on EAVA and InSilicoVA among neonate in Mozambique:

va_data_example = list("eava" = c("congenital_malformation" = 40, "pneumonia" = 175,
                                  "sepsis_meningitis_inf" = 265, "ipre" = 220,
                                  "other" = 30, "prematurity" = 170),
                       "insilicova" = c("congenital_malformation" = 5, "pneumonia" = 145,
                                        "sepsis_meningitis_inf" = 370, "ipre" = 330,
                                        "other" = 60, "prematurity" = 290))

The data can be similarly input as above. When multiple algorithms are provided, vacalibration() by default performs algorithm-specific calibration and an ensemble calibration that combines all algorithms to provide a calibrated CSMF estimate for the population.

calib_out_ensemble = vacalibration(va_data = va_data_example,
                                   age_group = "neonate", country = "Mozambique")

Here is a comparison of uncalibrated, and algorithm-specific and ensemble calibration:

round(calib_out_ensemble$p_uncalib, 3) # uncalibrated
round(calib_out_ensemble$pcalib_postsumm["eava",,], 3) # EAVA-specific calibration
round(calib_out_ensemble$pcalib_postsumm["insilicova",,], 3) # InSilicoVA-specific calibration
round(calib_out_ensemble$pcalib_postsumm["ensemble",,], 3) # Ensemble calibration

Set ensemble = F to turn off ensemble calibration in vacalibration().

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