Malaria: GBD 2021

Disease Description

Malaria is a life-threatening disease spread to humans by some types of mosquitoes. It is mostly found in tropical countries. There are medications for prevention and treatment, but these are not always accessible.

An individual with uncomplicated malaria experiences one to two weeks of persistent fever, chills/shivering, sweating, joint pains, and headache. The individual will likely be lethargic and feverish, causing loss of daily function during the attack. Individuals with an untreated P. falciparum infection may develop severe malaria, which includes the symptoms of uncomplicated malaria but may also involve swelling, difficulty breathing, unconsciousness, and potentially death. Microscopy is considered the gold-standard diagnostic approach for the purposes of GBD. The relevant ICD-10 codes are B50-B54. [GBD-2019-Capstone-Appendix-Malaria-2021]

According to the latest world malaria report, there were 247 million cases of malaria in 2021 compared to 245 million cases in 2020. The estimated number of malaria deaths stood at 619,000 in 2021 compared to 625,000 in 2020.

Four African countries accounted for just over half of all malaria deaths worldwide: Nigeria (31.3%), the Democratic Republic of the Congo (12.6%), United Republic of Tanzania (4.1%) and Niger (3.9%). [WHO-Malaria-2021]

Modeling Malaria in GBD 2021

Malaria is modeled separately for inside versus outside sub-Saharan Africa in GBD 2021. However, Ethiopia is modeled as outside sub-Saharan Africa since it exhibits epidemiological trends and has data availability and quality more akin to non-African settings.

For countries in sub-Saharan Africa (Nigeria in the nutrition optimization model) the PfPR surveys by the Malaria Atlas Project (MAP) were used to predict malaria rates for both the non-fatal and fatal models.

For countries outside of sub-Saharan Africa (Ethiopia and Pakistan in this project) surveillance systems tend to be stronger and so national and subnational case reports are the primary data source for the nonfatal model.

Nonfatal modeling in all countries combined data sources to generate rasterized predictions of clinical incidence rates on a 5 km by 5 km grid. These predications were combined with high-resolution gridded population data to estimate total cases per pixel-year. These were then aggregated to GBD national/ subnational areas.

For the fatal model, MAP data was used to estimate the cause specific mortality rate. This was then combined with the incidence rate from the nonfatal model in order to find estimates for case fatality rate. This in turn was used to find annual mortality rates for each location. [GBD-2019-Capstone-Appendix-Malaria-2021]

Todo

GBD 2021 includes COVID shocks. We need to decide if and how we want to include this information.

A systematic review of malaria severity was conducted, from which simple severity splits were obtained and applied across all cases:

Malaria severity splits

Severity level	Lay description	Disability weight
Mild	Has a low fever and mild discomfort but no difficulty with daily activities.	0.006 (0.002–0.012)
Moderate	Has a fever and aches and feels weak, which causes some difficulty with daily activities.	0.051 (0.032–0.074)
Severe	Has a high fever and pain and feels very weak, which causes great difficulty with daily activities.	0.133 (0.088–0.19)

GBD Hierarchy

Cause Model Diagram

S: Susceptible to malaria

I: Infected and currently experiencing malaria

Model Assumptions and Limitations

Malaria has been modeled extensively by other teams. For this model, we will not be including any information on vectors and so it is not appropriate for interventions targetting malaria prevention or treatment directly.

There is evidence that people living in malaria endemic areas do gain immunity over their lifetimes. We assume this is represented in prevalence and incidence rates from GBD. We do not include in this model any gains in malaria resistance from prior exposure.

For the nutrition optimization work, our model is focused on child growth failure and its effects. Therefore, malaria is included to capture CGF’s effects on malaria, but does not include additional detail not relevant for this model.

Our rate based recovery method will approximate the duration seen in GBD, but might have simulants with improbably high or low durations due to random chance. In the future, we might consider a time based duration that would more accurately replicate the 14-28 day duration from GBD. We do not expect this limitation to have a significant impact on our results.

Because DisMod estimated an unrealistically high birth prevalence, the modelers set birth prevalence to zero. Consequently, the birth prevalence, incidence, and prevalence available from get_outputs are incongruous with one another.

Todo

Continue to add to this section as needed

Data Description

State Definitions

State	State name	Definition
S	Susceptible	Simulant does not currently have malaria disease
I	Infected	Simulant currently has malaria

State Data

State	Measure	Value	Notes
S	prevalence	1-prevalence_calculated
S	birth prevalence	1 - prevalence_calculated for the post neonatal/1-5 month age group
S	emr	0
S	disability weight	0
I	prevalence_calculated	incidence_rate_c345 * duration_c345
I	birth prevalence	prevalence_calculated for the post neonatal/1-5 month age group
I	excess mortality rate	\(\frac{\text{deaths_c345}}{\text{population} \,\times\, \text{prevalence_calculated}}\)
I	disability weight	0 for early neonatal (ID 2) and late neonatal (ID 3) age groups, \(\displaystyle{\sum_{s\in \text{sequelae_malaria}}} \scriptstyle{\text{disability_weight}_s \,\times\, \text{prevalence}_s}\) for all others	Malaria sequelae are: 121, 122, 123
All	cause-specific mortality rate	0 for early neonatal (ID 2) and late neonatal (ID 3) age groups, \(\frac{\text{deaths_c345}}{\text{population}}\) for all other age groups	See note below for justification

Note

A note on the the neonatal age groups

This Vivarium modeling strategy is an indirect attempt to sets the cause model age start to the 1 month of age (post neonatal age group for GBD 2019 and 1-5 month age group for GBD 2021) despite the GBD age start parameter being the early neonatal age group (0 to 6 days). The exclusion of the the early and late neonatal age groups from the cause model as a strategy that allows us to increase the timestep of our cause models.

However, setting the age start parameter to 1 month in vivarium is not especially straight forward, so we took a compromise strategy of:

• Setting birth prevalence equal to the prevalence among the 1 month old age group, and

• Setting CSMR, DW, and incidence/remission rates to zero for the neonatal age groups

The rationale behind excluding the neonatal age groups from this cause model is related to the Relationship between timesteps and modeled rates in Vivarium as described on the Choosing an Appropriate Time Step page. Essentially, high EMR in the neonatal age groups may require a smaller time step to meet validation criteria, which we did not meet for the neonatal age groups in initial versions of the model.

Notably, there are no risk factors that affect malaria during the neonatal age groups in the nutrition optimization model, so not modeling malaria among these age groups will not affect our model. However, mortality due to malaria should be included in mortality due to other causes for the early and late neonatal age groups (which will be achieved with CSMR=0 in these age groups).

We calculate prevalence using the equation prevalence = incidence * duration. (See assumptions and limitations for the need to replace GBD’s prevalence). This is appropriate because malaria has a short and relatively uniform duration of 14-28 days [GBD-2019-Capstone-Appendix-Malaria-2021]. This assumption is valid under steady state conditions.

Transition Data

Transition	Source State	Sink State	Value	Notes
i	S	I	0 for neonatal age groups, \(\frac{\text{incidence_rate_c345}}{1-\text{prevalence_calculated}}\) for all other ages	Equivalent to “load standard data” Vivarium public health function for incidence rates (“susceptible-population” incidence rate). Incidence in GBD are estimated for the total population. Here we transform incidence to be a rate within the susceptible population.
r	I	S	0 for neonatal age groups, \(\frac{1}{\text{duration_c345}}\) for all other ages

Data Sources and Definitions

Value	Source	Description	Notes
prevalence_calculated	Calculated from incidence (como) and duration (literature/gbd)	Duration-based calculation of malaria prevalence
deaths_c345	codcorrect	Deaths from malaria
duration_c345	Uniform distribution between 14 and 28 days	Obtained from [GBD-2019-Capstone-Appendix-Malaria-2021]	This value should not vary by age group
incidence_rate_c345	como	Incidence of malaria within the entire population
population	demography	Mid-year population for given age/sex/year/location
prevalence_s{sid}	como	Prevalence of sequela with id sid	Sequela used here are 121, 122, and 123
disability_weight_s{sid}	YLD appendix	Disability weight of sequela with id sid	Sequela used here are 121, 122, and 123

Restrictions

Restriction type	Value	Notes
Male only	False
Female only	False
YLL only	False
YLD only	False
YLL age group start	early neonatal, ID = 2 (0-6 days)
YLL age group end	95 plus	age_group_id = 235; 95 years +
YLD age group start	early neonatal, ID = 2 (0-6 days)
YLD age group end	95 plus	age_group_id = 235; 95 years +

Validation Criteria

Simulation results should replicate the GBD 2021 cause-specific mortality rate, excess mortality rate, incidence rate, and prevalence for all age/sex/location groups. Notably, these measures should be tracked over time in the simulation to ensure that simulation rates do not deviate from GBD rates as the simulation progresses.

References

GBD-2019-Capstone-Appendix-Malaria-2021(1,2,3,4)

Appendix to: GBD 2019 Diseases and Injuries Collaborators. Global burden of 369 diseases and injuries in 204 countries and territories, 1990–2019: a systematic analysis for the Global Burden of Disease Study 2019. The Lancet. 17 Oct 2020;396:1204-1222

WHO-Malaria-2021

Malaria Fact Sheet. World Health Organization. Retrieved 14 July 2023. https://www.who.int/news-room/fact-sheets/detail/malaria