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Albrja/mic 5804/cpap artifact (#35)
Albrja/mic 5804/cpap artifact Add CPAP intervention data to artifact - *Category*: Data artifact - *JIRA issue*: https://jira.ihme.washington.edu/browse/MIC-5804 - *Research reference*: https://vivarium-research.readthedocs.io/en/latest/models/intervention_models/neonatal/cpap_intervention.html#calibration-strategy ### Changes and notes <!-- Change description – why, what, anything unexplained by the above. Include guidance to reviewers if changes are complex. --> ### Verification and Testing <!-- Details on how code was verified. Consider: plots, images, (small) csv files. -->
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‎src/vivarium_gates_mncnh/constants/data_keys.py‎

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@@ -203,6 +203,30 @@ def log_name(self):
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NEONATAL_ENCEPHALOPATHY = __NeonatalEncephalopath()
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class __NoCPAPIntervention(NamedTuple):
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# Keys that will be loaded into the artifact. must have a colon type declaration
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P_RDS: str = "intervention.no_cpap_intervention.p_rds"
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P_HOME: str = "intervention.no_cpap_intervention.probability_home_birth"
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P_BEmONC: str = "intervention.no_cpap_intervention.probability_BEMONC_birth"
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P_CEmONC: str = "intervention.no_cpap_intervention.probability_CEmONC_birth"
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P_CPAP_BEmONC: str = "intervention.no_cpap_intervention.probability_CPAP_BEMONC"
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P_CPAP_CEmONC: str = "intervention.no_cpap_intervention.probability_CPAP_CEmONC"
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P_CPAP_HOME: str = "intervention.no_cpap_intervention.probability_CPAP_home"
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RELATIVE_RISK: str = "intervention.no_cpap_intervention.relative_risk"
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PAF: str = "intervention.no_cpap_intervention.population_attributable_fraction"
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@property
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def name(self):
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return "no_CPAP_intervention"
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@property
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def log_name(self):
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return "no CPAP intervention"
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NO_CPAP_INTERVENTION = __NoCPAPIntervention()
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MAKE_ARTIFACT_KEY_GROUPS = [
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POPULATION,
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# TODO: list all key groups here
@@ -215,4 +239,5 @@ def log_name(self):
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PRETERM_BIRTH,
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NEONATAL_SEPSIS,
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NEONATAL_ENCEPHALOPATHY,
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NO_CPAP_INTERVENTION,
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]

‎src/vivarium_gates_mncnh/constants/data_values.py‎

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@@ -1,6 +1,8 @@
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from datetime import datetime
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from typing import NamedTuple
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from vivarium_gates_mncnh.constants.data_keys import NO_CPAP_INTERVENTION
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############################
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# Disease Model Parameters #
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############################
@@ -239,3 +241,50 @@ class __Pipelines(NamedTuple):
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PRETERM_DEATHS_DUE_TO_RDS_PROBABILITY = 0.85
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CHILD_INITIALIZATION_AGE = 0.1 / 365.0
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class __DeliveryFacilityTypes(NamedTuple):
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HOME = "home"
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CEmONC = "CEmONC"
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BEmONC = "BEmONC"
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NONE = "none"
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DELIVERY_FACILITY_TYPES = __DeliveryFacilityTypes()
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DELIVERY_FACILITY_TYPE_PROBABILITIES = {
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"Ethiopia": {
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NO_CPAP_INTERVENTION.P_HOME: 0.683,
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NO_CPAP_INTERVENTION.P_CEmONC: 0.266,
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NO_CPAP_INTERVENTION.P_BEmONC: 0.051,
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},
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"Nigeria": {
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NO_CPAP_INTERVENTION.P_HOME: 0.683,
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NO_CPAP_INTERVENTION.P_CEmONC: 0.266,
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NO_CPAP_INTERVENTION.P_BEmONC: 0.051,
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},
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"Pakistan": {
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NO_CPAP_INTERVENTION.P_HOME: 0.683,
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NO_CPAP_INTERVENTION.P_CEmONC: 0.266,
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NO_CPAP_INTERVENTION.P_BEmONC: 0.051,
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},
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}
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# Probability each of these facility types has access to CPAP
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CPAP_ACCESS_PROBABILITIES = {
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"Ethiopia": {
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NO_CPAP_INTERVENTION.P_CPAP_BEmONC: 0.075,
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NO_CPAP_INTERVENTION.P_CPAP_CEmONC: 0.393,
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NO_CPAP_INTERVENTION.P_CPAP_HOME: 0.0,
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},
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"Nigeria": {
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NO_CPAP_INTERVENTION.P_CPAP_BEmONC: 0.075,
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NO_CPAP_INTERVENTION.P_CPAP_CEmONC: 0.393,
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NO_CPAP_INTERVENTION.P_CPAP_HOME: 0.0,
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},
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"Pakistan": {
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NO_CPAP_INTERVENTION.P_CPAP_BEmONC: 0.075,
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NO_CPAP_INTERVENTION.P_CPAP_CEmONC: 0.393,
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NO_CPAP_INTERVENTION.P_CPAP_HOME: 0.0,
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},
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}

‎src/vivarium_gates_mncnh/data/loader.py‎

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@@ -83,6 +83,15 @@ def get_data(
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data_keys.PRETERM_BIRTH.CSMR: load_standard_data,
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data_keys.NEONATAL_SEPSIS.CSMR: load_standard_data,
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data_keys.NEONATAL_ENCEPHALOPATHY.CSMR: load_standard_data,
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data_keys.NO_CPAP_INTERVENTION.P_RDS: load_p_rds,
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data_keys.NO_CPAP_INTERVENTION.P_HOME: load_probability_birth_facility_type,
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data_keys.NO_CPAP_INTERVENTION.P_BEmONC: load_probability_birth_facility_type,
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data_keys.NO_CPAP_INTERVENTION.P_CEmONC: load_probability_birth_facility_type,
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data_keys.NO_CPAP_INTERVENTION.P_CPAP_HOME: load_cpap_facility_access_probability,
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data_keys.NO_CPAP_INTERVENTION.P_CPAP_BEmONC: load_cpap_facility_access_probability,
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data_keys.NO_CPAP_INTERVENTION.P_CPAP_CEmONC: load_cpap_facility_access_probability,
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data_keys.NO_CPAP_INTERVENTION.RELATIVE_RISK: load_no_cpap_relative_risk,
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data_keys.NO_CPAP_INTERVENTION.PAF: load_no_cpap_paf,
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}
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return mapping[lookup_key](lookup_key, location, years)
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@@ -413,6 +422,90 @@ def load_lbwsg_paf(
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return df.sort_index()
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def load_p_rds(
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lookup_key: str, location: str, years: Optional[Union[int, str, List[int]]] = None
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) -> float:
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csmr = get_data(data_keys.PRETERM_BIRTH.CSMR, location, years)
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p_rds = csmr * data_values.PRETERM_DEATHS_DUE_TO_RDS_PROBABILITY
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return p_rds
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def load_probability_birth_facility_type(
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lookup_key: str, location: str, years: Optional[Union[int, str, List[int]]] = None
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) -> float:
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return data_values.DELIVERY_FACILITY_TYPE_PROBABILITIES[location][lookup_key]
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def load_cpap_facility_access_probability(
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lookup_key: str, location: str, years: Optional[Union[int, str, List[int]]] = None
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) -> float:
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return data_values.CPAP_ACCESS_PROBABILITIES[location][lookup_key]
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def load_no_cpap_relative_risk(
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lookup_key: str, location: str, years: Optional[Union[int, str, List[int]]] = None
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) -> float:
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return 1 / 0.53
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def load_no_cpap_paf(
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lookup_key: str, location: str, years: Optional[Union[int, str, List[int]]] = None
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) -> float:
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# Get all no_cpap data for calculations
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p_rds = get_data(data_keys.NO_CPAP_INTERVENTION.P_RDS, location, years)
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p_home = get_data(data_keys.NO_CPAP_INTERVENTION.P_HOME, location, years)
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p_BEmONC = get_data(data_keys.NO_CPAP_INTERVENTION.P_BEmONC, location, years)
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p_CEmONC = get_data(data_keys.NO_CPAP_INTERVENTION.P_CEmONC, location, years)
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p_CPAP_home = get_data(data_keys.NO_CPAP_INTERVENTION.P_CPAP_HOME, location, years)
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p_CPAP_BEmONC = get_data(data_keys.NO_CPAP_INTERVENTION.P_CPAP_BEmONC, location, years)
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p_CPAP_CEmONC = get_data(data_keys.NO_CPAP_INTERVENTION.P_CPAP_CEmONC, location, years)
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relative_risk = get_data(data_keys.NO_CPAP_INTERVENTION.RELATIVE_RISK, location, years)
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# rr_cpap = 1 / relative_risk)
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# p_rds_cpap = (1 / relative_risk) * p_rds_no_cpap
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# p_rds_no_cpap = p_rds_cpap * relative_risk
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# Get death probability of each path
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# p_home_no_cpap = p_home * p_rds_no_cpap
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# p_BEmONC_no_cpap = p_BEmONC * 1 - p_CPAP_BEmONC * p_rds_no_cpap
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# p_CEmONC_no_cpap = p_CEmONC * 1 - p_CPAP_CEmONC * p_rds_no_cpap
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# p_BEmONC_cpap = p_BEmONC * p_CPAP_BEmONC * p_rds_cpap
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# p_CEmONC_cpap = p_CEmONC * p_CPAP_CEmONC * p_rds_cpap
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# p_rds = (
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# p_home * p_rds_cpap * relative_risk
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# + p_BEmONC * (1 - p_CPAP_CEmONC) * p_rds_cpap * relative_risk
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# + p_CEmONC * (1 - p_CPAP_CEmONC) * p_rds_cpap * relative_risk
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# + p_BEmONC * p_CPAP_BEmONC * p_rds_cpap
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# + p_CEmONC * p_CPAP_CEmONC * p_rds_cpap
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# )
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# p_rds = (
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# 0.5 * 1.0 * p_rds_cpap * (1 / 0.53)
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# + 0.1 * (1 - 0.075) * p_rds_cpap * (1 / 0.53)
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# + 0.4 * (1 - 0.393) * p_rds_cpap * (1 / 0.53)
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# + 0.1 * 0.075 * p_rds_cpap
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# + 0.4 * 0.393 * p_rds_cpap
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# )
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# p_rds_cpap(
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# (0.5 * 1 * (1 / 0.53))
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# + (0.1 * (1 - 0.075) * (1 / 0.53))
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# + (0.4 * (1 / 0.393) * (1 / 0.53))
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# + (0.1 * 0.075)
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# + (0.4 * 0.393)
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# ) = p_rds
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p_rds_cpap = p_rds / (
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(p_home * relative_risk)
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+ (p_BEmONC * (1 - p_CPAP_BEmONC) * relative_risk)
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+ (p_CEmONC * (1 - p_CPAP_CEmONC) * relative_risk)
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+ (p_BEmONC * p_CPAP_BEmONC)
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+ (p_CEmONC * p_CPAP_CEmONC)
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)
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paf_no_cpap = 1 - (p_rds_cpap / p_rds)
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paf_no_cpap = paf_no_cpap.fillna(0.0)
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return paf_no_cpap
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def load_lbwsg_exposure(
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key: str, location: str, years: Optional[Union[int, str, List[int]]] = None
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) -> pd.DataFrame:

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