2021 Protein energy malnutrition risk-attributable cause model¶

Overview
Protein Energy Malnutrition in GBD 2019/2021
Vivarium Modeling Strategy
Validation
References

Overview ¶

This page contains information pertaining to the 2021 protein energy malnutrition (PEM) risk-attributable cause model. The PEM cause model is 100% attributable to the child wasting risk factor. The child wasting risk exposure document for the nutrition optimization simulation can be found here.

Note

For information on the background of PEM and child wasting, see the 2020 joint risk-cause model for wasting and PEM.

List of abbreviations
AM	Acute malnutrition
MAM	Moderate acute malnutrtion
SAM	Severe acute malnutrition
TMREL	Theoretical minimum risk exposure level
CGF	Child growth failure composed of wasting stunging and underweight
PEM	Protein energy malnutrition

Protein Energy Malnutrition in GBD 2019/2021 ¶

PEM is responsible for both fatal and nonfatal outcomes within the GBD framework. GBD maintains a cause of death model called “Nutritional deficiencies” that is split into PEM and Other Nutritional Deficiencies that estimates PEM mortality. Nonfatal PEM cases are modelled independently, using the case definition moderate and severe acute malnutrition, defined in terms of weight-for-height Z-scores (WHZ). All PEM cases are attributed to the GBD Child Growth Failure risk factor. We include specifics on the PEM cause models below. [GBD-2019-Capstone-Appendix-Wasting], p789.

PEM Fatal Model ¶

GBD runs a parent CODEm model to estimate deaths attributable to nutritional deficiency, using vital registration and verbal autopsy data as inputs. The applicable ICD codes are as follows: [GBD-2019-Capstone-Appendix-Wasting]

PEM CoD ICD-10 Codes¶
GBD Cause	ICD-10 Code
Protein-energy malnutrition	E40-E46.9 (Kwashiorkor, marasmus, specified and unspecified proteincalorie malnutrition)
Other nutritional deficiencies	D51-D52.0 (vitamin B12 deficiency anaemia and folate deficiency anaemia)
Other nutritional deficiencies	D52.8-D53.9 (other nutritional anaemias)
Other nutritional deficiencies	D64.3 (other sideroblastic anaemias)
Other nutritional deficiencies	E51-E61.9 (thiamine, niacin, other B group vitamins, ascorbic acid, vitamin D, other vitamin, dietary calcium, dietary selenium, dietary zinc, and other nutrient element deficiencies)
Other nutritional deficiencies	E63-E64.0 (other nutritional deficiencies and sequelae of protein-calorie malnutrition)
Other nutritional deficiencies	E64.2-E64.9 (sequelae of vitamin C deficiency, rickets, other nutritional deficiencies, and unspecified nutritional deficiencies)
Other nutritional deficiencies	M12.1-M12.19 (Kashin-Beck disease)
Garbage code	D50, D50.0 and D50.9 (unspecified anaemia)

They then run (1) an under-5 PEM model, (2) a 5-and-over PEM model, and (3) an other nutritional deficiencies model. These models are scaled using CODCorrect to fit the parent nutritional deficiency model. [GBD-2019-Capstone-Appendix-Wasting]

Note that as PEM is defined as “a lack of dietary protein and/or energy”, it includes famines and severe droughts. These result in discontinuities in PEM estimation, which the GBD team accounts for. The appendix specifically mentions using the Tombstone report to estimate deaths due to the famine during the Great Leap Forward in the 1960s in China. [GBD-2019-Capstone-Appendix-Wasting]

PEM Nonfatal Model ¶

GBD’s nonfatal PEM model takes as its case definition “moderate and severe acute malnutrition”, defined in terms of distance from the mean WHZ score given by the WHO 2006 growth standard for children. The relevant ICD 10 codes are E40-E46.9, E64.0, and ICD 9 codes are 260-263.9. PEM is partitioned into the following four sequelae: [GBD-2019-Capstone-Appendix-Wasting]

Nonfatal PEM Sequelae 2019/2021¶
Sequela Name	WHZ range	Clinical description	Disability weights
Moderate wasting without oedema	{WHZ_i \| -3SD < WHZ_i < -2SD}	Asymptomatic	NA
Moderate wasting with oedema	{WHZ_i \| -3SD < WHZ_i < -2SD}	Is very tired and irritable and has diarrhoea	0.051 (0.031–0.079)
Severe wasting without oedema	{WHZ_i \| WHZ_i < -3SD}	Is extremely skinny and has no energy.	0.128 (0.082–0.183)
Severe wasting with oedema	{WHZ_i \| WHZ_i < -3SD}	Is very tired and irritable and has diarrhoea. Is extremely skinny and has no energy.	0.051 (0.031–0.079); 0.128 (0.082–0.183). Applied multiplicatively.

These are mapped onto clinically-defined wasting states as follows:

Clinical definitions 2019/2021¶
Condition	Estimated by GBD sequelae
Kwashiorkor	{Moderate wasting with oedema} + {Severe wasting with oedema}
Marasmus	{Severe wasting without oedema} + {Severe wasting with oedema}

The above table represents GBD definitions. In the literature these definitions are highly debated, often defining marasmus as strictly “severe wasting without oedema”.

The nonfatal estimation pipeline comprises five models:

Nonfatal PEM sub-models 2019/2021¶
Modeled entity	Age	Modeling software
Prevalence of WHZ <-2SD	under-5	STGPR
Prevalence of WHZ <-3SD	under-5	STGPR
Proportion of WHZ <-2SD with oedema	under-5	DisMod
Proportion of WHZ <-3SD with oedema	under-5	DisMod
All WHZ <-2SD (PEM)	All ages	DisMod

For the all-age model, they set the duration of PEM to 9 months after consulting with nutrition experts. The current modelers (as of June 2021 no longer have documentation of these conversations, which took place sometime before 2015). They used a remission rate of 0.25 - 1.25 remitted cases of PEM per person-year of illness. Note this is a rather wide interval that allowed DisMod to choose a remission rate within the given bounds based on other input data. [GBD-2019-Capstone-Appendix-Wasting]

From the all-age model, they then derived (1) a prevalence:incidence ratio that was applied across all categories of non-fatal PEM, and (2) a moderate:severe wasting ratio for both under and over 5. [GBD-2019-Capstone-Appendix-Wasting]

The modelers then assumed that there is zero prevalence of oedema in anyone over 5. [GBD-2019-Capstone-Appendix-Wasting]

Additionally, they calculated the fraction of wasting attributable to severe worm infestation and subtracted this out of all wasting, attributing the remainder to PEM. They assumed no oedema due to worms, and the prevalence:incidence ratio derived from the all-age PEM model. [GBD-2019-Capstone-Appendix-Wasting]

The modelers used child anthropometry data from health surveys, literature, and national reports, from which they estimate the WHZ SDs that correspond with the case definitions. They additionally used SMART datasets to estimate the proportion under 5 with oedema. In the GBD 2019 Appendix, they note, “Future work in systematically evaluating longitudinal datasets on nutrition and growth failure will allow us to improve the empirical basis for PEM incidence estimates, including improved resolution for the component categories.” [GBD-2019-Capstone-Appendix-Wasting]

Cause Hierarchy ¶

Vivarium Modeling Strategy ¶

PEM parameters¶
State	Measure	Value	Notes
Wasting exposure cat2 (MAM)	disability weight	\(\frac{{\sum_{sequelae\in \text{MAM}}} \scriptstyle{\text{disability_weight}_s \times\ \text{prevalence}_s}}{{\sum_{sequelae\in xt{MAM}} \scriptstyle{\text{prevalence}_s}}}\)	disability weight for MAM
Wasting exposure cat1 (SAM)	disability weight	\(\frac{{\sum_{sequelae\in \text{SAM}}} \scriptstyle{\text{disability_weight}_s \times\ \text{prevalence}_s}}{{\sum_{sequelae\in \text{SAM}} \scriptstyle{\text{prevalence}_s}}}\)	disability weight for SAM
Wasting exposure cat3 and cat4	disability weight	0	No disability in wasting cat3 or cat4
Wasting exposure cat1 and cat2 (SAM and MAM)	excess mortality	\(\frac{\text{deaths_c387}}{\text{population} \times \text{prevalence_c387}}\)	death counts come from codcorrect
wasting exposure cat3 and cat4	excess mortality rate	0	No PEM deaths in wasting cat3 or cat4
All	cause specific mortality	\(\frac{\text{deaths_c387}}{\text{population}}\)	death counts come from codcorrect

Todo

Determine the status of GBD 2021 PEM model and decide how to proceed. Remember that CIFF implementation used 2019 version.

Note

The 2020 Codcorrect model for PEM is not yet completed. Check here on central machinary to see latest codcorrect modeling. https://hub.ihme.washington.edu/pages/viewpage.action?spaceKey=GBD2020&title=GBD+2020+CodCorrect+Tracking

and here for scheduled finishing time (currently scheduled to complete on july 30th- 12July2021) https://hub.ihme.washington.edu/pages/viewpage.action?spaceKey=GBD2020&title=GBD+2020+Release+1+Computation

The following code can be used to access draw-level deaths for PEM

# GBD 2019 (this is the version we will use for PEM for now)

 get_draws(gbd_id_type = 'cause_id',
        gbd_id = [387], #pem
        source = "codcorrect",
        metric_id = 1, #counts
        measure_id = 1, #deaths
        location_id = [179],
        sex_id = [1,2],
        age_group_id = [4,5],
        gbd_round_id = 6,
        year_id  =2019,
        decomp_step = 'step5')


# GBD 2020 (not fully formed)

get_draws(gbd_id_type = 'cause_id',
        gbd_id = [387], #pem
        source = "codcorrect",
        metric_id = 1, #counts
        measure_id = 1, #deaths
        location_id = [179],
        sex_id = [1,2],
        age_group_id = [388,389,238,34],
        gbd_round_id = 7,
        year_id  = 2020,
        decomp_step = 'step3', #this is the latest decomp step,  will get updated
        version_id = 260) #this is the latest version, will get updated

PEM Data Sources and Definitions¶
Variable	Source	Description	Notes
MAM sequelae		{s198, s2033}	Moderate wasting with eodema, moderate wasting without oedema
SAM sequelae		{s2036, s199}	Severe wasting with eodema, severe wasting without oedema

Note

The 2020 Como model for PEM is not yet completed, with only 100 draw. Check here on central machinary to see latest como modeling. https://hub.ihme.washington.edu/display/GBD2020/COMO+tracking

To pull PEM sequelae prevalence, use the following code

#GBD 2019

get_draws(gbd_id_type = 'sequela_id',
         gbd_id = [198,2033,2036,199],
         source = "como",
         location_id = [179],
         sex_id = [1,2],
         age_group_id = [2,3,4,5],
         gbd_round_id = 6,
         decomp_step = 'step5')


#GBD 2020 (currently only 100 draws)

 get_draws(gbd_id_type = 'sequela_id',
         gbd_id = [198,2033,2036,199],
         source = "como",
         location_id = [179],
         sex_id = [1,2],
         age_group_id = [2,3,388,389,238,34],
         gbd_round_id = 7,
         decomp_step = 'iterative')


 #as well as from db_queries

 from db_queries import get_sequela_metadata

 hierarchy_2019 = get_sequela_metadata(sequela_set_id=2, gbd_round_id=6, decomp_step="step4")
 hierarchy_2019.loc[(hierarchy_2019.cause_id==387)] #2019

PEM Restrictions 2019¶
Restriction type	Value	Notes
Male only	False
Female only	False
YLL only	False
YLD only	False
YLL age group start	Post Neonatal	age_group_id = 4
YLL age group end	95 plus	age_group_id = 235
YLD age group start	Early Neonatal	age_group_id = 2
YLD age group end	95 Plus	age_group_id = 235

PEM Restrictions 2021¶
Restriction type	Value	Notes
Male only	False
Female only	False
YLL only	False
YLD only	False
YLL age group start	1-5 months	age_group_id = 388
YLL age group end	95 plus	age_group_id = 235
YLD age group start	Early Neonatal	age_group_id = 2
YLD age group end	95 Plus	age_group_id = 235

#age group id differences between 2019 and 2021

#2021 age ids
early nn = 2
late nn = 3
1m-5m = 388   #2019 it was 4 = postneonatal
6m-11m = 389  #2019 it was 4 = postneonatal
12m-23m = 238 #2019 it was 5 = 1-5
2y-4y = 34    #2019 it was 5 = 1-5

Validation ¶

All of the following should match expected values for the PEM model:

prevalence

ylds

csmr

emr