Source code for tlo.methods.newborn_outcomes

from pathlib import Path

import numpy as np
import pandas as pd

from tlo import DateOffset, Module, Parameter, Property, Types, logging
from tlo.events import Event, IndividualScopeEventMixin
from tlo.lm import LinearModel
from tlo.methods import Metadata, demography, newborn_outcomes_lm, pregnancy_helper_functions
from tlo.methods.causes import Cause
from tlo.methods.healthsystem import HSI_Event
from tlo.methods.hiv import HSI_Hiv_TestAndRefer
from tlo.methods.postnatal_supervisor import PostnatalWeekOneNeonatalEvent
from tlo.util import BitsetHandler

logger = logging.getLogger(__name__)
logger.setLevel(logging.INFO)


[docs]class NewbornOutcomes(Module): """ This module is responsible for simulating the key conditions/complications experienced by a neonate immediately following birth (either at home or in a health facility). Important predictors of newborn health also generated by this model include low birth weight and small for gestational age. Complications modelled include early-onset neonatal sepsis, neonatal encephalopathy, congenital birth anomalies, failure to breathe at birth and complications of prematurity (respiratory distress syndrome and retinopathy) This module also manages any interventions that are delivered by skilled birth attendants to newborns following a facility delivery via HSI_NewbornOutcomes_CareOfTheNewbornBySkilledAttendantAtBirth and interventions delivered as part of postnatal care via HSI_NewbornOutcomes_ReceivesPostnatalCheck. """
[docs] def __init__(self, name=None, resourcefilepath=None): super().__init__(name) self.resourcefilepath = resourcefilepath # First we define dictionaries which will store the current parameters of interest (to allow parameters to # change between 2010 and 2020) and the linear models self.current_parameters = dict() self.nb_linear_models = dict() # This dictionary will store information related to the neonates delivery that does not need to be stored in # the main data frame self.newborn_care_info = dict() # and then define a dictionary which will hold the required consumables for each intervention self.item_codes_nb_consumables = dict()
INIT_DEPENDENCIES = {'Demography', 'HealthSystem'} OPTIONAL_INIT_DEPENDENCIES = {'HealthBurden'} ADDITIONAL_DEPENDENCIES = { 'CareOfWomenDuringPregnancy', 'Labour', 'PostnatalSupervisor', 'PregnancySupervisor' } METADATA = { Metadata.DISEASE_MODULE, Metadata.USES_HEALTHSYSTEM, Metadata.USES_HEALTHBURDEN, } # Declare Causes of Death CAUSES_OF_DEATH = { 'early_onset_sepsis': Cause(gbd_causes='Neonatal disorders', label='Neonatal Disorders'), 'late_onset_sepsis': Cause(gbd_causes='Neonatal disorders', label='Neonatal Disorders'), 'encephalopathy': Cause(gbd_causes='Neonatal disorders', label='Neonatal Disorders'), 'neonatal_respiratory_depression': Cause(gbd_causes='Neonatal disorders', label='Neonatal Disorders'), 'preterm_other': Cause(gbd_causes='Neonatal disorders', label='Neonatal Disorders'), 'respiratory_distress_syndrome': Cause(gbd_causes='Neonatal disorders', label='Neonatal Disorders'), 'congenital_heart_anomaly': Cause(gbd_causes='Congenital birth defects', label='Congenital birth defects'), 'limb_or_musculoskeletal_anomaly': Cause(gbd_causes='Congenital birth defects', label='Congenital birth ' 'defects'), 'urogenital_anomaly': Cause(gbd_causes='Congenital birth defects', label='Congenital birth defects'), 'digestive_anomaly': Cause(gbd_causes='Congenital birth defects', label='Congenital birth defects'), 'other_anomaly': Cause(gbd_causes='Congenital birth defects', label='Congenital birth defects'), } # Declare Causes of Disability CAUSES_OF_DISABILITY = { 'Retinopathy of Prematurity': Cause(gbd_causes='Neonatal disorders', label='Neonatal Disorders'), 'Neonatal Encephalopathy': Cause(gbd_causes='Neonatal disorders', label='Neonatal Disorders'), 'Neonatal Sepsis Long term Disability': Cause(gbd_causes='Neonatal disorders', label='Neonatal Disorders'), 'Preterm Birth Disability': Cause(gbd_causes='Neonatal disorders', label='Neonatal Disorders') } PARAMETERS = { # n.b. Parameters are stored as LIST variables due to containing values to match both 2010 and 2015 data. # CARE SEEKING 'prob_care_seeking_for_complication': Parameter( Types.LIST, 'baseline probability that a mother will seek care for an unwell neonate following delivery'), 'prob_day_reaches_week_one_event': Parameter( Types.LIST, 'Probabilities for day of attendance at postnatal week one event'), # EARLY ONSET SEPSIS... 'prob_early_onset_neonatal_sepsis_day_0': Parameter( Types.LIST, 'baseline probability of a neonate developing early onset sepsis between birth and 24hrs'), 'rr_eons_maternal_chorio': Parameter( Types.LIST, 'relative risk of EONS in newborns whose mothers have chorioamnionitis'), 'rr_eons_maternal_prom': Parameter( Types.LIST, 'relative risk of EONS in newborns whose mothers have PROM'), 'rr_eons_preterm_neonate': Parameter( Types.LIST, 'relative risk of EONS in preterm newborns'), 'cfr_early_onset_sepsis': Parameter( Types.LIST, 'case fatality rate for a neonate due to neonatal sepsis'), 'cfr_late_onset_sepsis': Parameter( Types.LIST, 'case fatality rate for a neonate due to neonatal sepsis'), # NOT BREATHING AT BIRTH.... 'prob_failure_to_transition': Parameter( Types.LIST, 'baseline probability of a neonate developing intrapartum related complications ' '(previously birth asphyxia) following delivery '), 'prob_enceph_no_resus': Parameter( Types.LIST, 'Probability of a newborn who was not breathing at birth, and didnt receive resuscitation, ' 'developing encephalopathy '), 'cfr_failed_to_transition': Parameter( Types.LIST, 'case fatality rate for a neonate following failure to transition'), # ENCEPHALOPATHY AND RESPIRATORY DEPRESSION... 'prob_encephalopathy': Parameter( Types.LIST, 'baseline odds of a neonate developing encephalopathy of any severity following birth'), 'rr_enceph_neonatal_sepsis': Parameter( Types.LIST, 'relative risk of neonatal encephalopathy if the neonate is also septic'), 'rr_enceph_obstructed_labour': Parameter( Types.LIST, 'relative risk for encephalopathy if the mothers labour was obstructed'), 'rr_enceph_acute_hypoxic_event': Parameter( Types.LIST, 'relative risk for encephalopathy if the mothers experience an acute event in labour'), 'prob_enceph_severity': Parameter( Types.LIST, 'probability of the severity of encephalopathy in a newborn who is encephalopathic'), 'prob_mild_impairment_post_enceph': Parameter( Types.LIST, 'probability of the mild neurodevelopmental impairment in survivors of encephalopathy'), 'prob_mod_severe_impairment_post_enceph': Parameter( Types.LIST, 'probability of the moderate or severe neurodevelopmental impairment in survivors of ' 'encephalopathy'), 'cfr_enceph': Parameter( Types.LIST, 'case fatality rate for a neonate due to mild encephalopathy'), 'cfr_multiplier_severe_enceph': Parameter( Types.LIST, 'multiplier for risk of death in a neonate with severe encephalopathy'), # COMPLICATIONS OF PREMATURITY... 'prob_respiratory_distress_preterm': Parameter( Types.LIST, 'probability that a preterm infant will experience respiratory distress at birth'), 'rr_rds_maternal_diabetes_mellitus': Parameter( Types.LIST, 'relative risk of RDS in a preterm newborn whose mother has DM'), 'rr_rds_maternal_gestational_diab': Parameter( Types.LIST, 'relative risk of RDS in a preterm newborn whose mother has gestational diabetes'), 'cfr_respiratory_distress_syndrome': Parameter( Types.LIST, 'case fatality rate for respiratory distress syndrome of prematurity'), 'prob_mild_disability_preterm_<32weeks': Parameter( Types.LIST, 'probability of mild long term neurodevelopmental disability in preterm infants born at less ' 'than 32 weeks gestation'), 'prob_mod_severe_disability_preterm_<32weeks': Parameter( Types.LIST, 'probability of moderate/severe longterm neurodevelopmental disability in preterm infants born ' 'at less than 32 weeks gestation'), 'prob_mild_disability_preterm_32_36weeks': Parameter( Types.LIST, 'probability of mild long term neurodevelopmental disability in preterm infants born between 32' ' and 36 weeks gestation'), 'prob_mod_severe_disability_preterm_32_36weeks': Parameter( Types.LIST, 'probability of moderate/severe long term neurodevelopmental disability in preterm infants born' ' between 32 and 36 weeks gestation'), 'prob_retinopathy_preterm': Parameter( Types.LIST, 'baseline probability of a preterm neonate developing retinopathy of prematurity '), 'prob_retinopathy_severity': Parameter( Types.LIST, 'probabilities of severity of retinopathy'), 'cfr_preterm_birth': Parameter( Types.LIST, 'case fatality rate for a neonate born prematurely'), 'rr_preterm_death_early_preterm': Parameter( Types.LIST, 'relative risk of preterm death in early preterm neonates '), 'prob_preterm_death_by_day': Parameter( Types.LIST, 'probability of death due to preterm complications occurring on days 0-14'), # CONGENITAL ANOMALIES... 'prob_congenital_heart_anomaly': Parameter( Types.LIST, 'Probability of a congenital heart anomaly in the newborn'), 'prob_limb_musc_skeletal_anomaly': Parameter( Types.LIST, 'Probability of a congenital limb or musculoskeletal anomaly in the newborn'), 'prob_urogenital_anomaly': Parameter( Types.LIST, 'Probability of a congenital urogenital anomaly in the newborn'), 'prob_digestive_anomaly': Parameter( Types.LIST, 'Probability of a congenital digestive anomaly in the newborn'), 'prob_other_anomaly': Parameter( Types.LIST, 'Probability of a other congenital anomalies in the newborn'), 'cfr_congenital_heart_anomaly': Parameter( Types.LIST, 'case fatality rate for newborns with congenital heart anomalies'), 'cfr_limb_or_musculoskeletal_anomaly': Parameter( Types.LIST, 'case fatality rate for newborns with congenital limb/musculoskeletal anomalies'), 'cfr_urogenital_anomaly': Parameter( Types.LIST, 'case fatality rate for newborns with congenital urogenital anomalies'), 'cfr_digestive_anomaly': Parameter( Types.LIST, 'case fatality rate for newborns with congenital digestive anomalies'), 'cfr_other_anomaly': Parameter( Types.LIST, 'case fatality rate for newborns with other congenital anomalies'), 'prob_cba_death_by_age_group': Parameter( Types.LIST, 'probabilities that death from a congenital birth anomaly will occur once the individual ' 'reaches the following age groups: early neonatal, late neonatal, post neonatal 1-4 years,' ' 5-9 years, 10 -14 years or 15-69 years'), # BREASTFEEDING... 'prob_early_breastfeeding_hb': Parameter( Types.LIST, 'probability that a neonate will be breastfed within the first hour following birth when ' 'delivered at home'), 'prob_breastfeeding_type': Parameter( Types.LIST, 'probabilities that a woman is 1.) not breastfeeding 2.) non-exclusively breastfeeding ' '3.)exclusively breastfeeding at birth (until 6 months)'), 'prob_early_breastfeeding_hf': Parameter( Types.LIST, 'probability that a neonate will be breastfed within the first hour following birth when ' 'delivered at a health facility'), # POSTNATAL CARE.. 'prob_pnc_check_newborn': Parameter( Types.LIST, 'probability that a neonate will receive a full postnatal check'), 'prob_timings_pnc_newborns': Parameter( Types.LIST, 'probabilities that a postnatal check will happen before or after 48 hours alive'), # DISABILITY WEIGHT PROBABILITIES 'probs_for_mild_preterm_daly_wts_<32wks': Parameter( Types.LIST, 'Probabilities (sum to one) that a newborn delivered at less than 32 weeks AND experiencing ' 'mild impairment will accrue the DALY weight 357 ("mild motor and cognitive impairment") or ' 'DALY weight 371 ("mild motor impairment")'), 'probs_for_sev_preterm_daly_wts_<32wks': Parameter( Types.LIST, 'Probabilities (sum to one) that a newborn delivered at less than 32 weeks AND experiencing ' 'moderate/severe impairment will accrue the DALY weight 378 ("moderate motor impairment") or ' 'DALY weight 383 ("severe motor impairment")'), 'probs_for_mild_preterm_daly_wts_>32wks': Parameter( Types.LIST, 'Probabilities (sum to one) that a newborn delivered at greater than 32 weeks AND experiencing ' 'mild impairment will accrue the DALY weight 357 ("mild motor and cognitive impairment") or ' 'DALY weight 371 ("mild motor impairment")'), 'probs_for_sev_preterm_daly_wts_>32wks': Parameter( Types.LIST, 'Probabilities (sum to one) that a newborn delivered at less than 32 weeks AND experiencing ' 'moderate/severe impairment will accrue the DALY weight 378 ("moderate motor impairment") or ' 'DALY weight 383 ("severe motor impairment")'), 'probs_for_mild_enceph_daly_wts': Parameter( Types.LIST, 'Probabilities (sum to one) that a newborn delivered who develops encephalopathy AND ' 'experiencing mild impairment will accrue the DALY weight 419 ("mild motor and cognitive' ' impairment") or DALY weight 416 ("mild motor impairment")'), 'probs_for_sev_enceph_daly_wts': Parameter( Types.LIST, 'Probabilities (sum to one) that a newborn delivered who develops encephalopathy AND ' 'experiencing moderate/severe impairment will accrue the DALY weight 411 ("moderate motor ' ' impairment") or DALY weight 410 ("severe motor impairment")'), 'probs_for_mild_sepsis_daly_wts': Parameter( Types.LIST, 'Probabilities (sum to one) that a newborn delivered who develops sepsis AND ' 'experiencing mild impairment will accrue the DALY weight 411 ("mild motor and cognitive' ' impairment") or DALY weight 431 ("mild motor impairment")'), 'probs_for_sev_sepsis_daly_wts': Parameter( Types.LIST, 'Probabilities (sum to one) that a newborn delivered who develops sepsis AND ' 'experiencing moderate/severe impairment will accrue the DALY weight 438 ("moderate motor ' ' impairment") or DALY weight 435 ("severe motor impairment")'), # TREATMENT... 'treatment_effect_inj_abx_sep': Parameter( Types.LIST, 'effect of injectable antibiotics treatment on reducing mortality from sepsis'), 'treatment_effect_supp_care_sep': Parameter( Types.LIST, 'effect of full supportive care treatment on reducing mortality from sepsis'), 'treatment_effect_cord_care': Parameter( Types.LIST, 'effect of full supportive care treatment on reducing incidence of sepsis'), 'treatment_effect_clean_birth': Parameter( Types.LIST, 'effect of clean birth practices on reducing incidence of sepsis'), 'treatment_effect_early_init_bf': Parameter( Types.LIST, 'effect of early initiation of breastfeeding on reducing incidence of sepsis'), 'treatment_effect_resuscitation': Parameter( Types.LIST, 'effect of resuscitation on newborn mortality associated with encephalopathy'), 'treatment_effect_resuscitation_preterm': Parameter( Types.LIST, 'effect of delayed resuscitation on newborn mortality associated with prematurity'), 'treatment_effect_abx_prom': Parameter( Types.LIST, 'effect of antibiotics given to mothers experience PROM on risk of newborn sepsis '), 'treatment_effect_steroid_preterm': Parameter( Types.LIST, 'relative risk of death for preterm neonates following administration of antenatal ' 'corticosteroids'), 'treatment_effect_kmc': Parameter( Types.LIST, 'treatment effect of kangaroo mother care on preterm mortality'), 'squeeze_threshold_for_delay_three_nb_care': Parameter( Types.LIST, 'squeeze factor value over which an individual within a newborn HSI is said to experience ' 'type 3 delay i.e. delay in receiving appropriate care'), 'treatment_effect_modifier_one_delay': Parameter( Types.LIST, 'factor by which treatment effectiveness is reduced in the presences of one delays'), } PROPERTIES = { 'nb_is_twin': Property(Types.BOOL, 'whether this is part of a twin pair'), 'nb_twin_sibling_id': Property(Types.INT, 'id number of this twins sibling'), 'nb_early_preterm': Property(Types.BOOL, 'whether this neonate has been born early preterm (24-33 weeks ' 'gestation)'), 'nb_late_preterm': Property(Types.BOOL, 'whether this neonate has been born late preterm (34-36 weeks ' 'gestation)'), 'nb_preterm_birth_disab': Property(Types.CATEGORICAL, 'Disability associated with preterm delivery', categories=['none', 'mild_motor_and_cog', 'mild_motor', 'moderate_motor', 'severe_motor']), 'nb_congenital_anomaly': Property(Types.INT, 'Types of congenital anomaly of the newborn stored as bitset'), 'nb_early_onset_neonatal_sepsis': Property(Types.BOOL, 'whether this neonate has developed neonatal sepsis' ' following birth'), 'nb_inj_abx_neonatal_sepsis': Property(Types.BOOL, 'If this neonate has injectable antibiotics as treatment ' 'for neonatal sepsis'), 'nb_supp_care_neonatal_sepsis': Property(Types.BOOL, 'If this neonate has received full supportive care for ' 'neonatal sepsis (in hospital)'), 'nb_neonatal_sepsis_disab': Property(Types.CATEGORICAL, 'Disability associated neonatal sepsis', categories=['none', 'mild_motor_and_cog', 'mild_motor', 'moderate_motor', 'severe_motor']), 'nb_preterm_respiratory_distress': Property(Types.BOOL, 'whether this preterm newborn has respiratory ' 'distress syndrome (RDS)'), 'nb_not_breathing_at_birth': Property(Types.BOOL, 'whether this neonate has failed to transition to breathing ' 'on their own following birth'), 'nb_received_neonatal_resus': Property(Types.BOOL, 'If this neonate has received resuscitation'), 'nb_encephalopathy': Property(Types.CATEGORICAL, 'None, mild encephalopathy, moderate encephalopathy, ' 'severe encephalopathy', categories=['none', 'mild_enceph', 'moderate_enceph', 'severe_enceph']), 'nb_encephalopathy_disab': Property(Types.CATEGORICAL, 'Disability associated neonatal sepsis', categories=['none', 'mild_motor_and_cog', 'mild_motor', 'moderate_motor', 'severe_motor']), 'nb_retinopathy_prem': Property(Types.CATEGORICAL, 'Level of visual disturbance due to retinopathy of' ' prematurity: None, mild, moderate, severe, blindness', categories=['none', 'mild', 'moderate', 'severe', 'blindness']), 'nb_low_birth_weight_status': Property(Types.CATEGORICAL, 'extremely low birth weight (<1000g), ' ' very low birth weight (<1500g), ' 'low birth weight (<2500g),' 'normal birth weight (>2500g), macrosomia (>4000g)', categories=['extremely_low_birth_weight', 'very_low_birth_weight', 'low_birth_weight', 'normal_birth_weight', 'macrosomia']), 'nb_size_for_gestational_age': Property(Types.CATEGORICAL, 'size for gestational age categories', categories=['small_for_gestational_age', 'average_for_gestational_age', 'large_for_gestational_age']), 'nb_early_init_breastfeeding': Property(Types.BOOL, 'whether this neonate initiated breastfeeding ' 'within 1 hour of birth '), 'nb_breastfeeding_status': Property(Types.CATEGORICAL, 'How this neonate is being breastfed', categories=['none', 'non_exclusive', 'exclusive']), 'nb_kangaroo_mother_care': Property(Types.BOOL, 'whether this neonate received kangaroo mother care following ' 'birth'), 'nb_clean_birth': Property(Types.BOOL, 'whether this neonate received clean birth practices at delivery'), 'nb_received_cord_care': Property(Types.BOOL, 'whether this neonate received chlorhexidine cord care'), 'nb_death_after_birth': Property(Types.BOOL, 'whether this child has died following complications after birth'), 'nb_pnc_check': Property(Types.INT, 'Number of postnatal checks received in the postnatal period'), }
[docs] def read_parameters(self, data_folder): parameter_dataframe = pd.read_excel(Path(self.resourcefilepath) / 'ResourceFile_NewbornOutcomes.xlsx', sheet_name='parameter_values') self.load_parameters_from_dataframe(parameter_dataframe) # Here we map 'disability' parameters to associated DALY weights to be passed to the health burden module if 'HealthBurden' in self.sim.modules: self.parameters['nb_daly_weights'] = { 'mild_motor_cognitive_preterm': self.sim.modules['HealthBurden'].get_daly_weight(357), 'mild_motor_preterm': self.sim.modules['HealthBurden'].get_daly_weight(371), 'moderate_motor_preterm': self.sim.modules['HealthBurden'].get_daly_weight(378), 'severe_motor_preterm': self.sim.modules['HealthBurden'].get_daly_weight(383), # n.b. DALY weight for prematurity are separated by disability and gestation (<28wks, 28-32wks etc) but # the weight doesnt differ by gestation, only severity- so has been condensed here 'mild_vision_rptb': self.sim.modules['HealthBurden'].get_daly_weight(404), 'moderate_vision_rptb': self.sim.modules['HealthBurden'].get_daly_weight(405), 'severe_vision_rptb': self.sim.modules['HealthBurden'].get_daly_weight(402), 'blindness_rptb': self.sim.modules['HealthBurden'].get_daly_weight(386), 'mild_motor_cognitive_enceph': self.sim.modules['HealthBurden'].get_daly_weight(419), 'mild_motor_enceph': self.sim.modules['HealthBurden'].get_daly_weight(416), 'moderate_motor_enceph': self.sim.modules['HealthBurden'].get_daly_weight(411), 'severe_motor_enceph': self.sim.modules['HealthBurden'].get_daly_weight(410), 'mild_motor_sepsis': self.sim.modules['HealthBurden'].get_daly_weight(431), 'moderate_motor_sepsis': self.sim.modules['HealthBurden'].get_daly_weight(438), 'severe_motor_sepsis': self.sim.modules['HealthBurden'].get_daly_weight(435), 'mild_motor_cognitive_sepsis': self.sim.modules['HealthBurden'].get_daly_weight(441)}
[docs] def initialise_population(self, population): df = population.props df.loc[df.is_alive, 'nb_is_twin'] = False df.loc[df.is_alive, 'nb_twin_sibling_id'] = -1 df.loc[df.is_alive, 'nb_early_preterm'] = False df.loc[df.is_alive, 'nb_late_preterm'] = False df.loc[df.is_alive, 'nb_preterm_birth_disab'] = 'none' df.loc[df.is_alive, 'nb_congenital_anomaly'] = 0 df.loc[df.is_alive, 'nb_early_onset_neonatal_sepsis'] = False df.loc[df.is_alive, 'nb_inj_abx_neonatal_sepsis'] = False df.loc[df.is_alive, 'nb_supp_care_neonatal_sepsis'] = False df.loc[df.is_alive, 'nb_neonatal_sepsis_disab'] = 'none' df.loc[df.is_alive, 'nb_preterm_respiratory_distress'] = False df.loc[df.is_alive, 'nb_not_breathing_at_birth'] = False df.loc[df.is_alive, 'nb_received_neonatal_resus'] = False df.loc[df.is_alive, 'nb_encephalopathy'] = 'none' df.loc[df.is_alive, 'nb_encephalopathy_disab'] = 'none' df.loc[df.is_alive, 'nb_retinopathy_prem'] = 'none' df.loc[df.is_alive, 'nb_low_birth_weight_status'] = 'normal_birth_weight' df.loc[df.is_alive, 'nb_size_for_gestational_age'] = 'average_for_gestational_age' df.loc[df.is_alive, 'nb_early_init_breastfeeding'] = False df.loc[df.is_alive, 'nb_breastfeeding_status'] = 'none' df.loc[df.is_alive, 'nb_kangaroo_mother_care'] = False df.loc[df.is_alive, 'nb_clean_birth'] = False df.loc[df.is_alive, 'nb_received_cord_care'] = False df.loc[df.is_alive, 'nb_death_after_birth'] = False df.loc[df.is_alive, 'nb_pnc_check'] = 0 # We store congenital anomalies as bitset self.congeintal_anomalies = BitsetHandler(self.sim.population, 'nb_congenital_anomaly', ['heart', 'limb_musc_skeletal', 'urogenital', 'digestive', 'other'])
[docs] def get_and_store_newborn_item_codes(self): """ This function defines the required consumables for each intervention delivered during this module and stores them in a module level dictionary called within HSIs """ get_list_of_items = pregnancy_helper_functions.get_list_of_items # ---------------------------------- IV DRUG ADMIN EQUIPMENT ------------------------------------------------- self.item_codes_nb_consumables['iv_drug_equipment'] = \ get_list_of_items(self, ['Cannula iv (winged with injection pot) 18_each_CMST', 'Giving set iv administration + needle 15 drops/ml_each_CMST', 'Disposables gloves, powder free, 100 pieces per box']) # ---------------------------------- BLOOD TEST EQUIPMENT --------------------------------------------------- self.item_codes_nb_consumables['blood_test_equipment'] = \ get_list_of_items(self, ['Disposables gloves, powder free, 100 pieces per box']) # -------------------------------------------- VITAMIN K ------------------------------------------ self.item_codes_nb_consumables['vitamin_k'] = \ get_list_of_items(self, ['vitamin K1 (phytomenadione) 1 mg/ml, 1 ml, inj._100_IDA']) # -------------------------------------------- EYE CARE ------------------------------------------ self.item_codes_nb_consumables['eye_care'] = get_list_of_items( self, ['Tetracycline eye ointment, 1 %, tube 5 mg']) # -------------------------------------------- RESUSCITATION ------------------------------------------ self.item_codes_nb_consumables['resuscitation'] = \ get_list_of_items(self, ['Infant resuscitator, clear plastic + mask + bag_each_CMST']) # ------------------------------------- SEPSIS - FULL SUPPORTIVE CARE --------------------------------------- self.item_codes_nb_consumables['sepsis_supportive_care_core'] = \ get_list_of_items(self, ['Benzylpenicillin 1g (1MU), PFR_Each_CMST', 'Gentamicin 40mg/ml, 2ml_each_CMST', 'Oxygen, 1000 liters, primarily with oxygen cylinders']) self.item_codes_nb_consumables['sepsis_supportive_care_optional'] = \ get_list_of_items(self, ['Dextrose (glucose) 5%, 1000ml_each_CMST', 'Tube, feeding CH 8_each_CMST', 'Cannula iv (winged with injection pot) 18_each_CMST', 'Giving set iv administration + needle 15 drops/ml_each_CMST', 'Disposables gloves, powder free, 100 pieces per box']) # ---------------------------------------- SEPSIS - ANTIBIOTICS --------------------------------------------- self.item_codes_nb_consumables['sepsis_abx'] =\ get_list_of_items(self, ['Benzylpenicillin 1g (1MU), PFR_Each_CMST', 'Gentamicin 40mg/ml, 2ml_each_CMST'])
[docs] def initialise_simulation(self, sim): # For the first period (2010-2015) we use the first value in each list as a parameter pregnancy_helper_functions.update_current_parameter_dictionary(self, list_position=0) # We call the following function to store the required consumables for the simulation run within the appropriate # dictionary self.get_and_store_newborn_item_codes() # ======================================= LINEAR MODEL EQUATIONS ============================================== # All linear equations used in this module are stored within the nb_newborn_equations # parameter below params = self.current_parameters self.nb_linear_models = { # This equation is used to determine a newborns risk of early onset neonatal sepsis. Risk of early onset # sepsis is mitigated by a number of interventions 'early_onset_neonatal_sepsis': LinearModel.custom(newborn_outcomes_lm.predict_early_onset_neonatal_sepsis, parameters=params), # This equation is used to determine a newborns risk of encephalopathy 'encephalopathy': LinearModel.custom(newborn_outcomes_lm.predict_encephalopathy, module=self, parameters=params), # This equation is used to determine a preterm newborns risk of respiratory distress syndrome # (secondary to incomplete lung development) 'rds_preterm': LinearModel.custom(newborn_outcomes_lm.predict_rds_preterm, module=self, parameters=params), # This equation is used to determine a preterm newborns risk of death due to 'complications of prematurity' # not explicitly modelled here (therefore excluding sepsis, encephalopathy and RDS) 'preterm_other_death': LinearModel.custom( newborn_outcomes_lm.predict_preterm_birth_other_death, parameters=params), # This equation is used to determine a the risk of death for a newborn who doesnt breathe at birth. 'neonatal_respiratory_depression_death': LinearModel.custom( newborn_outcomes_lm.predict_not_breathing_at_birth_death, parameters=params), # This equations is used to determine the risk of death for encephalopathic newborns. 'encephalopathy_death': LinearModel.custom( newborn_outcomes_lm.predict_enceph_death, parameters=params), # This equation is used to determine a newborns risk of death from sepsis 'early_onset_sepsis_death': LinearModel.custom( newborn_outcomes_lm.predict_neonatal_sepsis_death, parameters=params), # This equation is used to determine a preterm newborns risk of death due to respiratory distress syndrome 'respiratory_distress_syndrome_death': LinearModel.custom( newborn_outcomes_lm.predict_respiratory_distress_death, parameters=params)} # Finally we add this warning note to document that HIV testing will not occur if the correct module isnt # registered if 'Hiv' not in self.sim.modules: logger.debug(key='message', data='HIV module is not registered in this simulation run and therefore HIV ' 'testing will not happen in newborn care')
[docs] def eval(self, eq, person_id): """ This function compares the result of a specific linear equation with a random draw providing a boolean for the outcome under examination. For equations that require external variables, they are also defined here :param eq: The linear model equation being evaluated :param person_id: person_id :return: BOOL outcome """ df = self.sim.population.props nci = self.newborn_care_info person = df.loc[[person_id]] # Here we define all the possible external variables used in the linear model steroid_status = nci[person_id]['corticosteroids_given'] abx_for_prom = nci[person_id]['abx_for_prom_given'] chorio = nci[person_id]['maternal_chorio'] # We return a BOOLEAN return self.rng.random_sample(size=1) < eq.predict(person, received_corticosteroids=steroid_status, received_abx_for_prom=abx_for_prom, maternal_chorioamnionitis=chorio, )[person_id]
# ========================================= OUTCOME FUNCTIONS =================================================== # These functions are called within the on_birth function or # HSI_NewbornOutcomes_CareOfTheNewbornBySkilledAttendant depending on location of delivery. Generally they # output an individuals probability of an outcome (complication, disability, death) and make the relevant changes # to the data frame
[docs] def apply_risk_of_congenital_anomaly(self, child_id): """ This function determines if a neonate has been born with one of the common congenital anomalies. It is called during the on_birth function. :param child_id: child_id """ params = self.current_parameters if self.rng.random_sample() < params['prob_congenital_heart_anomaly']: self.congeintal_anomalies.set(child_id, 'heart') logger.info(key='newborn_complication', data={'newborn': child_id, 'type': 'congenital_heart_anomaly'}) if self.rng.random_sample() < params['prob_limb_musc_skeletal_anomaly']: self.congeintal_anomalies.set(child_id, 'limb_musc_skeletal') logger.info(key='newborn_complication', data={'newborn': child_id, 'type': 'limb_or_musculoskeletal_anomaly'}) if self.rng.random_sample() < params['prob_urogenital_anomaly']: self.congeintal_anomalies.set(child_id, 'urogenital') logger.info(key='newborn_complication', data={'newborn': child_id, 'type': 'urogenital_anomaly'}) if self.rng.random_sample() < params['prob_digestive_anomaly']: self.congeintal_anomalies.set(child_id, 'digestive') logger.info(key='newborn_complication', data={'newborn': child_id, 'type': 'digestive_anomaly'}) if self.rng.random_sample() < params['prob_other_anomaly']: self.congeintal_anomalies.set(child_id, 'other') logger.info(key='newborn_complication', data={'newborn': child_id, 'type': 'other_anomaly'})
[docs] def apply_risk_of_neonatal_infection_and_sepsis(self, child_id): """ This function uses the linear model to determines if a neonate will develop early onset neonatal sepsis. It is called during the on_birth function or during HSI_NewbornOutcomes_CareOfTheNewbornBySkilledAttendantAt Birth dependent on delivery setting. :param child_id: child_id """ df = self.sim.population.props # The linear model calculates the individuals probability of early_onset_neonatal_sepsis if self.eval(self.nb_linear_models['early_onset_neonatal_sepsis'], child_id): df.at[child_id, 'nb_early_onset_neonatal_sepsis'] = True logger.info(key='newborn_complication', data={'newborn': child_id, 'type': 'early_onset_sepsis'})
[docs] def apply_risk_of_encephalopathy(self, child_id, timing): """ This function determines if a neonate will develop neonatal encephalopathy on birth or after birth (if they were not breathing), at what severity, and makes the appropriate changes to the data frame. It is called during the on_birth function or during HSI_NewbornOutcomes_CareOfTheNewbornBySkilledAttendantAt Birth dependent on delivery setting. :param child_id: child_id :param timing: on_birth or after_birth """ params = self.current_parameters df = self.sim.population.props # We use a linear model equation to determine risk of encephalopathy on birth if timing == 'on_birth': result = self.eval(self.nb_linear_models['encephalopathy'], child_id) else: # Or, if we are applying risk to a non-encephalopathic newborn who was not breathing at birth result = self.rng.random_sample() < params['prob_enceph_no_resus'] if result: # For a newborn who is encephalopathic we then set the severity using a weighted probability derived from # the prevalence of severity of encephalopathy in the encephalopathic population severity_enceph = self.rng.choice(('mild', 'moderate', 'severe'), p=params['prob_enceph_severity']) if severity_enceph == 'mild': df.at[child_id, 'nb_encephalopathy'] = 'mild_enceph' elif severity_enceph == 'moderate': df.at[child_id, 'nb_encephalopathy'] = 'moderate_enceph' else: df.at[child_id, 'nb_encephalopathy'] = 'severe_enceph' logger.info(key='newborn_complication', data={'newborn': child_id, 'type': f'{df.at[child_id, "nb_encephalopathy"]}'}) # Check all encephalopathy cases receive a grade if df.at[child_id, 'nb_encephalopathy'] == 'none': logger.info(key='error', data=f'Child {child_id} should have developed encephalopathy but didnt')
[docs] def apply_risk_of_preterm_respiratory_distress_syndrome(self, child_id): """ This function uses the linear model to determine if a preterm neonate will develop respiratory distress syndrome. It is called during the on_birth function or during HSI_NewbornOutcomes_CareOfTheNewbornBySkilledAttendantAtBirth dependent on delivery setting. :param child_id: child_id """ df = self.sim.population.props child = df.loc[child_id] # Ensure only preterm infants have risk of RDS applied if not child.nb_early_preterm and not child.nb_late_preterm: return # Use the linear model to calculate individual risk and make changes if self.eval(self.nb_linear_models['rds_preterm'], child_id): df.at[child_id, 'nb_preterm_respiratory_distress'] = True logger.info(key='newborn_complication', data={'newborn': child_id, 'type': 'respiratory_distress_syndrome'})
[docs] def apply_risk_of_not_breathing_at_birth(self, child_id): """ This function uses the linear model to determines if a neonate will not sufficiently initiate breathing at birth and makes the appropriate changes to the data frame. It is called during the on_birth function or during HSI_NewbornOutcomes_CareOfTheNewbornBySkilledAttendant dependent on delivery setting. :param child_id: child_id """ df = self.sim.population.props params = self.current_parameters # We assume all newborns with encephalopathy and respiratory distress syndrome will require some form of # resuscitation and will not be effectively breathing at birth if df.at[child_id, 'nb_encephalopathy'] != 'none' or df.at[child_id, 'nb_preterm_respiratory_distress']: df.at[child_id, 'nb_not_breathing_at_birth'] = True # Otherwise we use a probability to demonstrate risk of inadequate breathing due to other causes not # explicitly modelled elif self.rng.random_sample() < params['prob_failure_to_transition']: df.at[child_id, 'nb_not_breathing_at_birth'] = True logger.info(key='newborn_complication', data={'newborn': child_id, 'type': 'not_breathing_at_birth'})
[docs] def scheduled_week_one_postnatal_event(self, individual_id): """ This function schedules PostnatalWeekOneNeonatalEvent within the Postnatal Supervisor module. Date on which the newborn attends is randomly spread across the first week of life :param individual_id: individual_id """ nci = self.newborn_care_info df = self.sim.population.props params = self.current_parameters # Only run this function on newborns are yet to reach the postnatal period if nci[individual_id]['passed_through_week_one']: return days_post_birth_td = self.sim.date - df.at[individual_id, 'date_of_birth'] days_post_birth_int = int(days_post_birth_td / np.timedelta64(1, 'D')) # Ensure that these newborns are less than one week old and scheduled the event accordingly if not days_post_birth_int < 6: logger.info(key='error', data=f'Child {individual_id} was older than 6 days when ' f'PostnatalWeekOneNeonatalEvent was scheduled') day_for_event = int(self.rng.choice([2, 3, 4, 5, 6], p=params['prob_day_reaches_week_one_event'])) # Ensure no newborns go to this event after week 1 if day_for_event + days_post_birth_int > 6: day_for_event = 1 self.sim.schedule_event( PostnatalWeekOneNeonatalEvent(self.sim.modules['PostnatalSupervisor'], individual_id), self.sim.date + DateOffset(days=day_for_event))
[docs] def set_death_status(self, individual_id): """ This function cycles through each complication of which a newborn may die, if the newborn has experienced one or more of these complication it calculates overall risk of death and then the probability that each complication was the primary cause of death. This information is logged and the death event is scheduled. :param individual_id: individual_id """ df = self.sim.population.props nci = self.newborn_care_info mni = self.sim.modules['PregnancySupervisor'].mother_and_newborn_info params = self.current_parameters # Function checks df for any potential cause of death, uses CFR parameters to determine risk of death # (either from one or multiple causes) and if death occurs returns the cause potential_cause_of_death = pregnancy_helper_functions.check_for_risk_of_death_from_cause_neonatal( self, individual_id=individual_id) # If a cause is returned death is scheduled if potential_cause_of_death: df.at[individual_id, 'nb_death_after_birth'] = True if individual_id in nci: del nci[individual_id] # If the death is associated with prematurity we scatter those deaths across the first 2 weeks if potential_cause_of_death == 'preterm_other': random_draw = self.rng.choice([0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14], p=params['prob_preterm_death_by_day']) death_date = self.sim.date + DateOffset(days=int(random_draw)) # Similarly, if death is associated with congential anomaly we schedule death across the life coarse to # align with GBD estimates elif 'anomaly' in potential_cause_of_death: days_per_year = 365.25 # Generate the minimum and maximum number of days within the age group to allow for random # distribution within each group days_per_age_group = \ {'early_n': [0, 6], 'late_n': [7, 28], 'post_n': [29, 364], '1-4': [days_per_year, ((5 * days_per_year) - 1)], '5-9': [(5 * days_per_year), ((10 * days_per_year) - 1)], '10-14': [(10 * days_per_year), ((15 * days_per_year) - 1)], '15-69': [(15 * days_per_year), ((70 * days_per_year) - 1)]} random_draw = self.rng.choice(list(days_per_age_group.keys()), p=params['prob_cba_death_by_age_group']) onset_day = self.rng.randint(days_per_age_group[random_draw][0], days_per_age_group[random_draw][1]) death_date = self.sim.date + DateOffset(days=onset_day) else: death_date = self.sim.date self.sim.schedule_event(demography.InstantaneousDeath(self, individual_id, cause=potential_cause_of_death), death_date) # Reset variables else: df.at[individual_id, 'nb_early_onset_neonatal_sepsis'] = False df.at[individual_id, 'pn_sepsis_early_neonatal'] = False df.at[individual_id, 'pn_sepsis_late_neonatal'] = False # Finally we schedule the postnatal week one event if individual_id in nci: nci[individual_id]['third_delay'] = False if not nci[individual_id]['passed_through_week_one']: self.scheduled_week_one_postnatal_event(individual_id) # We now delete the MNI dictionary for mothers who have died in labour but their children have survived, # this is done here as we use variables from the mni as predictors in some of the above equations mother_id = df.loc[individual_id, 'mother_id'] if not df.at[mother_id, 'is_alive']: if (mother_id in mni and (not df.at[mother_id, 'ps_multiple_pregnancy'] or (df.at[mother_id, 'ps_multiple_pregnancy'] and (mni[mother_id]['twin_count'] == 2)))): del mni[mother_id]
[docs] def set_disability_status(self, individual_id): """ This function cycles through each complication experience by a newborn during birth/neonatal period and determines if this newborn will experience long term developmental impairment due to said complication. It is called at the end of the neonatal period by the PostnatalSupervisor event for survivors of the neonatal period. :param individual_id: individual_id """ nci = self.newborn_care_info params = self.current_parameters df = self.sim.population.props child = df.loc[individual_id] logger.debug(key='message', data=f'Child {individual_id} will have now their DALYs calculated following ' f'complications after birth') if individual_id not in nci: return if nci[individual_id]['ga_at_birth'] < 32: if self.rng.random_sample() < params['prob_mild_disability_preterm_<32weeks']: df.at[individual_id, 'nb_preterm_birth_disab'] = self.rng.choice( ('mild_motor_and_cog', 'mild_motor'), p=params['probs_for_mild_preterm_daly_wts_<32wks']) elif self.rng.random_sample() < params['prob_mod_severe_disability_preterm_<32weeks']: df.at[individual_id, 'nb_preterm_birth_disab'] = self.rng.choice( ('moderate_motor', 'severe_motor'), p=params['probs_for_sev_preterm_daly_wts_<32wks']) elif 32 <= nci[individual_id]['ga_at_birth'] < 37: if self.rng.random_sample() < params['prob_mild_disability_preterm_32_36weeks']: df.at[individual_id, 'nb_preterm_birth_disab'] = self.rng.choice( ('mild_motor_and_cog', 'mild_motor'), p=params['probs_for_mild_preterm_daly_wts_>32wks']) elif self.rng.random_sample() < params['prob_mod_severe_disability_preterm_32_36weeks']: df.at[individual_id, 'nb_preterm_birth_disab'] = self.rng.choice( ('moderate_motor', 'severe_motor'), p=params['probs_for_sev_preterm_daly_wts_>32wks']) if child.nb_encephalopathy != 'none': if self.rng.random_sample() < params['prob_mild_impairment_post_enceph']: df.at[individual_id, 'nb_encephalopathy_disab'] = self.rng.choice( ('mild_motor_and_cog', 'mild_motor'), p=params['probs_for_mild_enceph_daly_wts']) elif self.rng.random_sample() < params['prob_mod_severe_impairment_post_enceph']: df.at[individual_id, 'nb_encephalopathy_disab'] = self.rng.choice( ('moderate_motor', 'severe_motor'), p=params['probs_for_sev_enceph_daly_wts']) # n.b. no data data on sepsis long term outcomes, using encephalopathy data as a proxy for now... if child.nb_early_onset_neonatal_sepsis or nci[individual_id]['sepsis_postnatal']: if self.rng.random_sample() < params['prob_mild_impairment_post_enceph']: df.at[individual_id, 'nb_neonatal_sepsis_disab'] = self.rng.choice( ('mild_motor_and_cog', 'mild_motor'), p=params['probs_for_mild_sepsis_daly_wts']) elif self.rng.random_sample() < params['prob_mod_severe_impairment_post_enceph']: df.at[individual_id, 'nb_neonatal_sepsis_disab'] = self.rng.choice( ('moderate_motor', 'severe_motor'), p=params['probs_for_sev_sepsis_daly_wts']) del nci[individual_id]
# ======================================== HSI INTERVENTION FUNCTIONS ============================================= # These functions are called by HSI_NewbornOutcomes_CareOfTheNewbornBySkilledAttendant and broadly represent # interventions/packages of interventions to manage the main complications in the module
[docs] def essential_newborn_care(self, hsi_event): """ This function contains interventions delivered as part of 'essential newborn care'. These include clean birth practices, cord care, vitamin k and eye care :param hsi_event: The HSI event in which this function is called (HSI_NewbornOutcomes_CareOfTheNewbornBySkilledAttendant) """ df = self.sim.population.props nci = self.newborn_care_info person_id = hsi_event.target cons = self.item_codes_nb_consumables # -------------------------------------- CHLORHEXIDINE CORD CARE ---------------------------------------------- # Next we determine if cord care with chlorhexidine is applied (consumables are counted during labour) # todo: condition on use of clean birth kit? df.at[person_id, 'nb_received_cord_care'] = True # ---------------------------------- VITAMIN D AND EYE CARE ----------------------------------------------- # We define the consumables avail_eyecare = hsi_event.get_consumables(item_codes=cons['eye_care']) avail_vit_k = hsi_event.get_consumables(item_codes=cons['vitamin_k'], optional_item_codes=cons['iv_drug_equipment']) # If they are available the intervention is delivered, there is limited evidence of the effect of these # interventions so currently we are just mapping the consumables if avail_eyecare: nci[person_id]['tetra_eye_d'] = True # This process is repeated for vitamin K if avail_vit_k: nci[person_id]['vit_k'] = True
[docs] def breast_feeding(self, person_id, birth_setting): """ This function is used to set breastfeeding status for newborns. It schedules the BreastfeedingStatusUpdateEvent for breastfed newborns. It is called during the on_birth function or during HSI_NewbornOutcomes_CareOfTheNewbornBySkilledAttendant dependent on delivery setting. :param person_id: person_id :param birth_setting: hf (health facility) or hb (home birth) """ df = self.sim.population.props params = self.current_parameters mni = self.sim.modules['PregnancySupervisor'].mother_and_newborn_info mother_id = df.at[person_id, 'mother_id'] # We determine breastfeeding for non-twins or first born twins (second born twins will match first born for # breastfeeding type) if not df.at[person_id, 'nb_is_twin'] or (df.at[mother_id, 'ps_multiple_pregnancy'] and mni[mother_id]['twin_count'] == 1): # First we determine the 'type' of breastfeeding this newborn will receive random_draw = self.rng.choice(('none', 'non_exclusive', 'exclusive'), p=params['prob_breastfeeding_type']) df.at[person_id, 'nb_breastfeeding_status'] = random_draw # For newborns that are breastfed, we apply a probability that breastfeeding was initiated within one hour. # This varies between home births and facility deliveries if df.at[person_id, 'nb_breastfeeding_status'] != 'none': if self.rng.random_sample() < params[f'prob_early_breastfeeding_{birth_setting}']: df.at[person_id, 'nb_early_init_breastfeeding'] = True # We store the breastfeeding information of the first twin in a twin pair to ensure twins have the same # breastfeeding status if df.at[mother_id, 'ps_multiple_pregnancy'] and mni[mother_id]['twin_count'] == 1: mni[mother_id]['bf_status_twin_one'] = random_draw mni[mother_id]['eibf_status_twin_one'] = df.at[person_id, 'nb_early_init_breastfeeding'] # Here we set the breastfeeding status of any second born twins elif df.at[mother_id, 'ps_multiple_pregnancy'] and mni[mother_id]['twin_count'] == 2: df.at[person_id, 'nb_breastfeeding_status'] = mni[mother_id]['bf_status_twin_one'] df.at[person_id, 'nb_early_init_breastfeeding'] = mni[mother_id]['eibf_status_twin_one'] if df.at[person_id, 'nb_breastfeeding_status'] != 'none': # For breastfed neonates we schedule a future event where breastfeeding status is updated self.sim.schedule_event(BreastfeedingStatusUpdateEventSixMonths(self, person_id), self.sim.date + DateOffset(months=6))
[docs] def kangaroo_mother_care(self, hsi_event): """ This function manages the diagnosis and treatment of low birth weight neonates who have delivered in a facility. It is called by the HSI_NewbornOutcomes_CareOfTheNewbornBySkilledAttendant. The intervention delivered is Kangaroo Mother Care (KMC) which includes skin-to-skin nursing and encouragement of frequent and exclusive breastfeeding :param hsi_event: The HSI event in which this function is called (HSI_NewbornOutcomes_CareOfTheNewbornBySkilledAttendant) """ df = self.sim.population.props person_id = hsi_event.target if (df.at[person_id, 'nb_low_birth_weight_status'] != 'normal_birth_weight') or \ (df.at[person_id, 'nb_low_birth_weight_status'] != 'macrosomia'): # Store treatment as a property of the newborn used to apply treatment effect df.at[person_id, 'nb_kangaroo_mother_care'] = True
[docs] def hiv_screening_for_at_risk_newborns(self, child_id): """ This function schedules immediate HIV screening for newborns of mothers who are HIV positive. Additional treatment is managed within the HIV module :param child_id: child_id """ df = self.sim.population.props child_id = int(child_id) if 'Hiv' in self.sim.modules: if not df.at[child_id, 'hv_diagnosed']: if df.at[child_id, 'nb_pnc_check'] == 1: for days in 0, 41: self.sim.modules['HealthSystem'].schedule_hsi_event( HSI_Hiv_TestAndRefer(person_id=child_id, module=self.sim.modules['Hiv']), topen=self.sim.date + pd.DateOffset(days=days), tclose=None, priority=0 )
[docs] def assessment_and_initiation_of_neonatal_resus(self, hsi_event): """ This function manages the diagnosis of failure to transition/encephalopathy and the administration of neonatal resuscitation for neonates delivered in a facility. It is called by the HSI_NewbornOutcomes_CareOfTheNewborn BySkilledAttendant. :param hsi_event: The HSI event in which this function is called (HSI_NewbornOutcomes_CareOfTheNewbornBySkilledAttendant) """ df = self.sim.population.props person_id = hsi_event.target if df.at[person_id, 'nb_not_breathing_at_birth']: # check consumables avail = pregnancy_helper_functions.return_cons_avail( self, hsi_event, self.item_codes_nb_consumables, core='resuscitation') # Run HCW check sf_check = pregnancy_helper_functions.check_emonc_signal_function_will_run(self.sim.modules['Labour'], sf='neo_resus', hsi_event=hsi_event) # Then, if the consumables are available,resuscitation is started. We assume this is delayed in # deliveries that are not attended if avail and sf_check: df.at[person_id, 'nb_received_neonatal_resus'] = True pregnancy_helper_functions.log_met_need(self, 'neo_resus', hsi_event) else: self.apply_risk_of_encephalopathy(person_id, timing='after_birth')
[docs] def assessment_and_treatment_newborn_sepsis(self, hsi_event, facility_type): """ This function manages the treatment of early onset neonatal sepsis for neonates delivered in a facility. It is called by the HSI_NewbornOutcomes_CareOfTheNewbornBySkilledAttendant. Treatment for sepsis includes either injectable antibiotics or full supportive care (antibiotics, fluids, oxygen etc) and varies between facility level. :param hsi_event: The HSI event in which this function is called (HSI_NewbornOutcomes_CareOfTheNewbornBySkilledAttendant) :param facility_type: health centre (hc) or hospital (hp) """ df = self.sim.population.props person_id = int(hsi_event.target) # We assume that only hospitals are able to deliver full supportive care for neonatal sepsis, full supportive # care evokes a stronger treatment effect than injectable antibiotics alone if df.at[person_id, 'nb_early_onset_neonatal_sepsis'] or df.at[person_id, 'pn_sepsis_late_neonatal'] or\ df.at[person_id, 'pn_sepsis_early_neonatal']: # Run HCW check sf_check = pregnancy_helper_functions.check_emonc_signal_function_will_run(self.sim.modules['Labour'], sf='iv_abx', hsi_event=hsi_event) if facility_type != '1a': # check consumables avail = pregnancy_helper_functions.return_cons_avail( self, hsi_event, self.item_codes_nb_consumables, core='sepsis_supportive_care_core', optional='sepsis_supportive_care_optional') # Then, if the consumables are available, treatment for sepsis is delivered if avail and sf_check: df.at[person_id, 'nb_supp_care_neonatal_sepsis'] = True pregnancy_helper_functions.log_met_need(self, 'neo_sep_supportive_care', hsi_event) # The same pattern is then followed for health centre care else: avail = pregnancy_helper_functions.return_cons_avail( self, hsi_event, self.item_codes_nb_consumables, core='sepsis_abx', optional='iv_drug_equipment') if avail and sf_check: df.at[person_id, 'nb_inj_abx_neonatal_sepsis'] = True pregnancy_helper_functions.log_met_need(self, 'neo_sep_abx', hsi_event)
[docs] def schedule_pnc(self, child_id): """ This function is called by the on_birth function for all newborns who will receive post natal care following birth. The function determines if PNC will be received early or let following birth and schedules HSI_NewbornOutcomes_ReceivesPostnatalCheck accordingly :param child_id: child's individual id """ params = self.current_parameters df = self.sim.population.props mother_id = df.at[child_id, 'mother_id'] m = self.sim.modules['PregnancySupervisor'].mother_and_newborn_info[mother_id] nci = self.newborn_care_info # Use a weighted random draw to determine when the HSI will occur timing = self.rng.choice(['<48', '>48'], p=params['prob_timings_pnc_newborns']) # If this is being called for the second of a twin pair, and the first twin is attending early, the second twin # will automatically attend early if (timing == '<48') or (m['pnc_twin_one'] == 'early'): nci[child_id]['will_receive_pnc'] = 'early' if df.at[mother_id, 'ps_multiple_pregnancy'] and (m['twin_count'] == 1): m['pnc_twin_one'] = 'early' early_event = HSI_NewbornOutcomes_ReceivesPostnatalCheck(module=self, person_id=child_id) self.sim.modules['HealthSystem'].schedule_hsi_event( early_event, priority=0, topen=self.sim.date, tclose=self.sim.date + pd.DateOffset(days=1)) else: # 'Late' PNC is scheduled in the postnatal supevervisor module nci[child_id]['will_receive_pnc'] = 'late'
[docs] def on_birth(self, mother_id, child_id): """The on_birth function of this module sets key properties of all newborns, including prematurity status and schedules functions to set weight and size. For newborns delivered at home it determines if they will experience complications following birth (early onset sepsis, encephalopathy, failure to transition) and if these complications will lead to death or disability .For newborns delivered in facility it schedules HSI_NewbornOutcomes_CareOfTheNewbornBySkilledAttendant which represents the care newborns should receive after being delivered in a facility. :param mother_id: mother_id :param child_id: child_id """ df = self.sim.population.props params = self.current_parameters nci = self.newborn_care_info if mother_id == -1: # The child has been born without a mother identified (from contraception.DirectBirth), so give the child # the default properties: child = { 'nb_is_twin': False, 'nb_twin_sibling_id': -1, 'nb_early_preterm': False, 'nb_late_preterm': False, 'nb_preterm_birth_disab': 'none', 'nb_congenital_anomaly': 0, 'nb_early_onset_neonatal_sepsis': False, 'nb_inj_abx_neonatal_sepsis': False, 'nb_supp_care_neonatal_sepsis': False, 'nb_neonatal_sepsis_disab': 'none', 'nb_preterm_respiratory_distress': False, 'nb_not_breathing_at_birth': False, 'nb_received_neonatal_resus': False, 'nb_encephalopathy': 'none', 'nb_encephalopathy_disab': 'none', 'nb_retinopathy_prem': 'none', 'nb_low_birth_weight_status': 'normal_birth_weight', 'nb_size_for_gestational_age': 'average_for_gestational_age', 'nb_early_init_breastfeeding': False, 'nb_breastfeeding_status': 'none', 'nb_kangaroo_mother_care': False, 'nb_clean_birth': False, 'nb_received_cord_care': False, 'nb_death_after_birth': False, 'nb_pnc_check': 0 } df.loc[child_id, child.keys()] = child.values() return self.sim.modules['Labour'].further_on_birth_labour(mother_id) mni = self.sim.modules['PregnancySupervisor'].mother_and_newborn_info m = mni[mother_id] # For twins, each baby has the 'twin_count' variable updated to ensure certain processes are/are not repeated # for each birth if df.at[mother_id, 'ps_multiple_pregnancy'] and m['twin_count'] == 0: df.at[child_id, 'nb_is_twin'] = True m['twin_count'] = 1 if m['single_twin_still_birth']: df.at[child_id, 'nb_twin_sibling_id'] = -1 elif df.at[mother_id, 'ps_multiple_pregnancy'] and (m['twin_count'] == 1): df.at[child_id, 'nb_is_twin'] = True m['twin_count'] = 2 elif not df.at[mother_id, 'ps_multiple_pregnancy']: df.at[child_id, 'nb_is_twin'] = False df.at[child_id, 'nb_twin_sibling_id'] = -1 df.at[child_id, 'nb_early_preterm'] = False df.at[child_id, 'nb_late_preterm'] = False df.at[child_id, 'nb_preterm_birth_disab'] = 'none' df.at[child_id, 'nb_congenital_anomaly'] = 0 df.at[child_id, 'nb_early_onset_neonatal_sepsis'] = False df.at[child_id, 'nb_inj_abx_neonatal_sepsis'] = False df.at[child_id, 'nb_supp_care_neonatal_sepsis'] = False df.at[child_id, 'nb_neonatal_sepsis_disab'] = 'none' df.at[child_id, 'nb_preterm_respiratory_distress'] = False df.at[child_id, 'nb_not_breathing_at_birth'] = False df.at[child_id, 'nb_received_neonatal_resus'] = False df.at[child_id, 'nb_encephalopathy'] = 'none' df.at[child_id, 'nb_encephalopathy_disab'] = 'none' df.at[child_id, 'nb_retinopathy_prem'] = 'none' # Set the childs birthweight, decided in the labour module, and log accordingly df.at[child_id, 'nb_low_birth_weight_status'] = mni[mother_id]['birth_weight'] if (df.at[child_id, 'nb_low_birth_weight_status'] == 'low_birth_weight') or\ (df.at[child_id, 'nb_low_birth_weight_status'] == 'very_low_birth_weight') or\ (df.at[child_id, 'nb_low_birth_weight_status'] == 'extremely_low_birth_weight'): logger.info(key='newborn_complication', data={'newborn': child_id, 'type': 'low_birth_weight'}) elif df.at[child_id, 'nb_low_birth_weight_status'] == 'macrosomia': logger.info(key='newborn_complication', data={'newborn': child_id, 'type': 'macrosomia'}) df.at[child_id, 'nb_size_for_gestational_age'] = mni[mother_id]['birth_size'] if df.at[child_id, 'nb_size_for_gestational_age'] == 'small_for_gestational_age': logger.info(key='newborn_complication', data={'newborn': child_id, 'type': 'small_for_gestational_age'}) df.at[child_id, 'nb_early_init_breastfeeding'] = False df.at[child_id, 'nb_breastfeeding_status'] = 'none' df.at[child_id, 'nb_kangaroo_mother_care'] = False df.at[child_id, 'nb_clean_birth'] = False df.at[child_id, 'nb_received_cord_care'] = False df.at[child_id, 'nb_death_after_birth'] = False df.at[child_id, 'nb_pnc_check'] = 0 # 'Category' of prematurity (early/late) is stored as a temporary property of the mother via the MNI dictionary # generated in labour (this is because some interventions delivered to the mother are based on prematurity) # We now store a newborns 'category of prematurity' within the main data frame if m['labour_state'] == 'early_preterm_labour': df.at[child_id, 'nb_early_preterm'] = True if m['labour_state'] == 'late_preterm_labour': df.at[child_id, 'nb_late_preterm'] = True # Check no children born at term or postterm women are incorrectly categorised as preterm if (m['labour_state'] == 'term_labour') or (m['labour_state'] == 'postterm_labour'): if df.at[child_id, 'nb_early_preterm'] or df.at[child_id, 'nb_late_preterm']: logger.info(key='error', data=f'Child {child_id} has been registered as preterm despite their mother ' f'delivering at term') if df.at[child_id, 'is_alive']: # Next we populate the newborn info dictionary with relevant parameters nci[child_id] = {'ga_at_birth': int(df.at[mother_id, 'ps_gestational_age_in_weeks']), 'maternal_chorio': mni[mother_id]['chorio_in_preg'], 'maternal_gest_diab': df.at[mother_id, 'ps_gest_diab'], 'vit_k': False, 'tetra_eye_d': False, 'proph_abx': False, 'abx_for_prom_given': mni[mother_id]['abx_for_prom_given'], 'corticosteroids_given': mni[mother_id]['corticosteroids_given'], 'delivery_setting': mni[mother_id]['delivery_setting'], 'cause_of_death_after_birth': [], 'sepsis_postnatal': False, 'passed_through_week_one': False, 'will_receive_pnc': 'none', 'third_delay': False} if mni[mother_id]['clean_birth_practices']: df.at[child_id, 'nb_clean_birth'] = True # Check these variables are not unassigned if nci[child_id]['delivery_setting'] == 'none': logger.info(key='error', data=f'Child {child_id} does not have a delivery setting stored') # --------------------------------------- Breastfeeding ------------------------------------------------- # Check see if this newborn will start breastfeeding if m['delivery_setting'] == 'home_birth': self.breast_feeding(child_id, birth_setting='hb') # hb = home birth else: self.breast_feeding(child_id, birth_setting='hf') # hf = health facility # ====================================== COMPLICATIONS ==================================================== # Here we apply risk of complications following birth -first we determine if this child has been born with # a congenital anomaly self.apply_risk_of_congenital_anomaly(child_id) # Next, for all preterm newborns we apply a risk of retinopathy of prematurity if df.at[child_id, 'nb_early_preterm'] or df.at[child_id, 'nb_late_preterm']: if self.rng.random_sample() < params['prob_retinopathy_preterm']: # For newborns with retinopathy we then use a weighted random draw to determine the severity of the # retinopathy to map to DALY weights random_draw = self.rng.choice(['mild', 'moderate', 'severe', 'blindness'], p=params['prob_retinopathy_severity']) df.at[child_id, 'nb_retinopathy_prem'] = random_draw # and respiratory distress syndrome self.apply_risk_of_preterm_respiratory_distress_syndrome(child_id) # Finally apply risk of infect, encephalopathy and respiratory depression self.apply_risk_of_neonatal_infection_and_sepsis(child_id) self.apply_risk_of_encephalopathy(child_id, timing='on_birth') self.apply_risk_of_not_breathing_at_birth(child_id) # Neonates who were delivered in a facility are automatically scheduled to receive care at birth at the # same level of facility that they were delivered in # ===================================== HSI SCHEDULING ==================================================== # If delivered in a health facility schedule immediate post-delivery care if m['delivery_setting'] != 'home_birth': if m['delivery_setting'] == 'health_centre': f_level = '1a' elif m['delivery_setting'] == 'hospital': f_level = self.rng.choice(['1a', '1b']) event = HSI_NewbornOutcomes_CareOfTheNewbornBySkilledAttendantAtBirth( self, person_id=child_id, facility_level_of_this_hsi=f_level) self.sim.modules['HealthSystem'].schedule_hsi_event(event, priority=0, topen=self.sim.date, tclose=self.sim.date + DateOffset(days=1)) # Followed by a full postnatal check up if (self.rng.random_sample() < params['prob_pnc_check_newborn']) or (m['pnc_twin_one'] != 'none'): self.schedule_pnc(child_id) # If delivered at home, determine if a postnatal check will be sought for this newborn- with the probability # being higher if complications have occurred elif m['delivery_setting'] == 'home_birth': if df.at[child_id, 'nb_early_onset_neonatal_sepsis'] or \ (df.at[child_id, 'nb_encephalopathy'] != 'none') or \ df.at[child_id, 'nb_early_preterm'] or \ df.at[child_id, 'nb_late_preterm']: care_seeking = params['prob_care_seeking_for_complication'] else: care_seeking = params['prob_pnc_check_newborn'] # If indicated we schedule either and early or late PNC visit for this individual if (self.rng.random_sample() < care_seeking) or (m['pnc_twin_one'] != 'none'): self.schedule_pnc(child_id) # If this child will not receive any early care following delivery we determine if they will die # following any complications immediately after birth if nci[child_id]['will_receive_pnc'] != 'early': self.set_death_status(child_id) # Finally we call the following functions to conduct logging/update variables related to pregnancy if not df.at[mother_id, 'ps_multiple_pregnancy'] or\ (df.at[mother_id, 'ps_multiple_pregnancy'] and (m['twin_count'] == 2)) or \ (df.at[mother_id, 'ps_multiple_pregnancy'] and m['single_twin_still_birth']): self.sim.modules['PregnancySupervisor'].further_on_birth_pregnancy_supervisor(mother_id) self.sim.modules['PostnatalSupervisor'].further_on_birth_postnatal_supervisor(mother_id) self.sim.modules['CareOfWomenDuringPregnancy'].further_on_birth_care_of_women_in_pregnancy(mother_id)
[docs] def on_hsi_alert(self, person_id, treatment_id): logger.debug(key='message', data=f'This is NewbornOutcomes, being alerted about a health system interaction ' f'person {person_id} for: {treatment_id}')
[docs] def report_daly_values(self): """ This function reports the DALY weights for this module generated in the previous month :return: data frame containing the DALY weights """ logger.debug(key='message', data='This is Newborn Outcomes reporting my health values') df = self.sim.population.props p = self.parameters['nb_daly_weights'] # Disability properties are mapped to DALY weights and stored for the health burden module health_values_1 = df.loc[df.is_alive, 'nb_retinopathy_prem'].map( {'none': 0, 'mild': p['mild_vision_rptb'], 'moderate': p['moderate_vision_rptb'], 'severe': p['severe_vision_rptb'], 'blindness': p['blindness_rptb']}) health_values_1.name = 'Retinopathy of Prematurity' health_values_1 = pd.to_numeric(health_values_1) health_values_2 = df.loc[df.is_alive, 'nb_encephalopathy_disab'].map( {'none': 0, 'mild_motor': p['mild_motor_enceph'], 'mild_motor_and_cog': p['mild_motor_cognitive_enceph'], 'moderate_motor': p['moderate_motor_enceph'], 'severe_motor': p['severe_motor_enceph']}) health_values_2.name = 'Neonatal Encephalopathy' health_values_2 = pd.to_numeric(health_values_2) health_values_3 = df.loc[df.is_alive, 'nb_neonatal_sepsis_disab'].map( {'none': 0, 'mild_motor': p['mild_motor_sepsis'], 'mild_motor_and_cog': p['mild_motor_cognitive_sepsis'], 'moderate_motor': p['moderate_motor_sepsis'], 'severe_motor': p['severe_motor_sepsis']}) health_values_3.name = 'Neonatal Sepsis Long term Disability' health_values_3 = pd.to_numeric(health_values_3) health_values_4 = df.loc[df.is_alive, 'nb_preterm_birth_disab'].map( {'none': 0, 'mild_motor': p['mild_motor_preterm'], 'mild_motor_and_cog': p['mild_motor_cognitive_preterm'], 'moderate_motor': p['moderate_motor_preterm'], 'severe_motor': p['severe_motor_preterm']}) health_values_4.name = 'Preterm Birth Disability' health_values_4 = pd.to_numeric(health_values_4) health_values_df = pd.concat([health_values_1.loc[df.is_alive], health_values_2.loc[df.is_alive], health_values_3.loc[df.is_alive], health_values_4.loc[df.is_alive]], axis=1) scaling_factor = (health_values_df.sum(axis=1).clip(lower=0, upper=1) / health_values_df.sum(axis=1)).fillna(1.0) health_values_df = health_values_df.multiply(scaling_factor, axis=0) return health_values_df
[docs] def run_if_care_of_the_newborn_by_skilled_attendant_at_birth_cant_run(self, hsi_event): """ This function is called by HSI_NewbornOutcomes_CareOfTheNewbornBySkilledAttendantAtBirth if the HSI is unable to run on the date it has been scheduled for. Risk of death is applied to newborns as this would have been applied within the event :param hsi_event: HSI event in which the function has been called: """ person_id = hsi_event.target nci = self.newborn_care_info logger.debug(key='message', data=f'NewbornOutcomes_CareOfTheNewbornBySkilledAttendant did not run for ' f'{person_id}') if person_id in nci: if not nci[person_id]['will_receive_pnc'] == 'early': self.set_death_status(person_id)
[docs] def run_if_care_of_the_receives_postnatal_check_cant_run(self, hsi_event): """ This function is called by HSI_NewbornOutcomes_ReceivesPostnatalCheck if the HSI is unable to run on the date it has been scheduled for. Risk of death is applied to newborns as this would have been applied within the event :param hsi_event: HSI event in which the function has been called: """ person_id = hsi_event.target nci = self.newborn_care_info logger.debug(key='message', data=f'HSI_NewbornOutcomes_ReceivesPostnatalCheck did not run for ' f'{person_id}') if person_id in nci: self.set_death_status(person_id)
[docs]class HSI_NewbornOutcomes_CareOfTheNewbornBySkilledAttendantAtBirth(HSI_Event, IndividualScopeEventMixin): """ This is HSI_NewbornOutcomes_CareOfTheNewbornBySkilledAttendantAtBirth. It is scheduled by on_birth for any newborns who were delivered in a health facility. Currently it applies the effect of interventions that would reasonably be delivered as part of the process of birth (i.e. resuscitation of a flat newborn) but is not a full postnatal check """
[docs] def __init__(self, module, person_id, facility_level_of_this_hsi): super().__init__(module, person_id=person_id) assert isinstance(module, NewbornOutcomes) self.TREATMENT_ID = 'DeliveryCare_Neonatal' self.EXPECTED_APPT_FOOTPRINT = self.make_appt_footprint({}) self.ACCEPTED_FACILITY_LEVEL = facility_level_of_this_hsi self.BEDDAYS_FOOTPRINT = self.make_beddays_footprint({'general_bed': 1})
[docs] def apply(self, person_id, squeeze_factor): nci = self.module.newborn_care_info df = self.sim.population.props params = self.module.current_parameters # Run some checks on the individual if (not self.sim.date == df.at[person_id, 'date_of_birth'] or df.at[person_id, 'nb_death_after_birth'] or nci[person_id]['delivery_setting'] == 'home_birth'): logger.info(key='error', data=f'Child {person_id} arrived at CareOfTheNewbornBySkilledAttendantAtBirth ' f'when they shouldnt have') return if not df.at[person_id, 'is_alive']: return # Check for delay due to high squeeze if squeeze_factor > params['squeeze_threshold_for_delay_three_nb_care']: nci[person_id]['third_delay'] = True self.module.assessment_and_initiation_of_neonatal_resus(self) if not nci[person_id]['will_receive_pnc'] == 'early': self.module.set_death_status(person_id)
[docs] def never_ran(self): self.module.run_if_care_of_the_newborn_by_skilled_attendant_at_birth_cant_run(self)
[docs] def did_not_run(self): self.module.run_if_care_of_the_newborn_by_skilled_attendant_at_birth_cant_run(self) return False
[docs] def not_available(self): self.module.run_if_care_of_the_newborn_by_skilled_attendant_at_birth_cant_run(self)
[docs]class HSI_NewbornOutcomes_ReceivesPostnatalCheck(HSI_Event, IndividualScopeEventMixin): """ This is HSI_NewbornOutcomes_ReceivesEarlyPostnatalCheck. This event is scheduled on_birth for all neonates who are predicted to receive a full postnatal checkup within the first 2 days of life following either home birth or facility delivery. Interventions within in this event include management of essential newborn care, initiation of kangaroo mother care, newborn immunisation, HIV screening and management of early onset neonatal sepsis, """
[docs] def __init__(self, module, person_id): super().__init__(module, person_id=person_id) assert isinstance(module, NewbornOutcomes) self.TREATMENT_ID = 'PostnatalCare_Neonatal' self.EXPECTED_APPT_FOOTPRINT = self.make_appt_footprint({'Under5OPD': 1}) self.ACCEPTED_FACILITY_LEVEL = self._get_facility_level_for_pnc(person_id)
[docs] def apply(self, person_id, squeeze_factor): nci = self.module.newborn_care_info df = self.sim.population.props params = self.module.current_parameters if not df.at[person_id, 'is_alive'] or df.at[person_id, 'nb_death_after_birth'] or (person_id not in nci): return if (nci[person_id]['will_receive_pnc'] == 'early') and not nci[person_id]['passed_through_week_one']: if not self.sim.date < (df.at[person_id, 'date_of_birth'] + pd.DateOffset(days=2)): logger.info(key='error', data=f'Child {person_id} arrived at early PNC too late') if not df.at[person_id, 'nb_pnc_check'] == 0: logger.info(key='error', data=f'Child {person_id} arrived at early PNC twice') elif nci[person_id]['will_receive_pnc'] == 'late' and not nci[person_id]['passed_through_week_one']: if not self.sim.date >= (df.at[person_id, 'date_of_birth'] + pd.DateOffset(days=2)): logger.info(key='error', data=f'Child {person_id} arrived at late PNC too early') if not df.at[person_id, 'nb_pnc_check'] == 0: logger.info(key='error', data=f'Child {person_id} arrived at late PNC twice') # Log the PNC check logger.info(key='postnatal_check', data={'person_id': person_id, 'delivery_setting': nci[person_id]['delivery_setting'], 'visit_number': df.at[person_id, 'nb_pnc_check'], 'timing': nci[person_id]['will_receive_pnc']}) df.at[person_id, 'nb_pnc_check'] += 1 if squeeze_factor > params['squeeze_threshold_for_delay_three_nb_care']: nci[person_id]['third_delay'] = True # First the newborn is assessed for sepsis and treated if needed self.module.assessment_and_treatment_newborn_sepsis(self, self.ACCEPTED_FACILITY_LEVEL) # Next, interventions pertaining to essential newborn care if df.at[person_id, 'nb_pnc_check'] == 1: self.module.essential_newborn_care(self) # ...and invitation of kangaroo mother care for small infants self.module.kangaroo_mother_care(self) # Finally these newborns will receive HIV screening if at risk self.module.hiv_screening_for_at_risk_newborns(person_id) self.module.set_death_status(person_id) # Surviving neonates with complications on day 1 are admitted to the inpatient event which lives in the # Postnatal Supervisor module if df.at[person_id, 'nb_early_onset_neonatal_sepsis'] or (df.at[person_id, 'nb_encephalopathy'] != 'none')\ or df.at[person_id, 'nb_early_preterm'] or df.at[person_id, 'nb_late_preterm'] or\ df.at[person_id, 'nb_kangaroo_mother_care']: event = HSI_NewbornOutcomes_NeonatalWardInpatientCare( self.module, person_id=person_id) self.sim.modules['HealthSystem'].schedule_hsi_event(event, priority=0, topen=self.sim.date, tclose=None)
[docs] def never_ran(self): self.module.run_if_care_of_the_receives_postnatal_check_cant_run(self)
[docs] def did_not_run(self): self.module.run_if_care_of_the_receives_postnatal_check_cant_run(self) return False
[docs] def not_available(self): self.module.run_if_care_of_the_receives_postnatal_check_cant_run(self)
[docs] def _get_facility_level_for_pnc(self, person_id): nci = self.module.newborn_care_info if (person_id in nci) and (nci[person_id]['delivery_setting'] == 'hospital'): return self.module.rng.choice(['1b', '2']) else: return '1a'
[docs]class HSI_NewbornOutcomes_NeonatalWardInpatientCare(HSI_Event, IndividualScopeEventMixin): """This is HSI_PostnatalSupervisor_NeonatalWardInpatientCare. It is scheduled by any of the PNC HSIs for neonates who are require inpatient care due to a complication of the postnatal period. Treatment is delivered in this event"""
[docs] def __init__(self, module, person_id): super().__init__(module, person_id=person_id) assert isinstance(module, NewbornOutcomes) self.TREATMENT_ID = 'PostnatalCare_Neonatal_Inpatient' self.EXPECTED_APPT_FOOTPRINT = self.make_appt_footprint({}) self.ACCEPTED_FACILITY_LEVEL = '1b' self.BEDDAYS_FOOTPRINT = self.make_beddays_footprint({'general_bed': 5})
[docs] def apply(self, person_id, squeeze_factor): logger.debug(key='message', data='HSI_PostnatalSupervisor_NeonatalWardInpatientCare now running to capture ' 'inpatient time for an unwell newborn')
[docs] def did_not_run(self): logger.debug(key='message', data='HSI_PostnatalSupervisor_NeonatalWardInpatientCare: did not run') return False
[docs] def not_available(self): logger.debug(key='message', data='HSI_PostnatalSupervisor_NeonatalWardInpatientCare: cannot not run with ' 'this configuration') return False
[docs]class BreastfeedingStatusUpdateEventSixMonths(Event, IndividualScopeEventMixin): """ This is BreastfeedingStatusUpdateEventSixMonths. It is scheduled via the breastfeeding function. Children who are alive and still breastfeeding by six months have their breastfeeding status updated. Those who will continue to breastfeed are scheduled BreastfeedingStatusUpdateEventTwoYears """
[docs] def __init__(self, module, individual_id): super().__init__(module, person_id=individual_id)
[docs] def apply(self, individual_id): df = self.sim.population.props child = df.loc[individual_id] if not child.is_alive: return # For infants who are exclusively breastfeeding at 6 months, we determine if they will change to non-exclusive # feeding or no breastfeeding if child.nb_breastfeeding_status == 'exclusive': random_draw = self.module.rng.choice(('non_exclusive', 'none'), p=[0.5, 0.5]) df.at[individual_id, 'nb_breastfeeding_status'] = random_draw # Similarly, for infants who are non-exclusively breastfeeding at 6 months we determine if they will continue # non-exclusively breastfeeding or stop breastfeeding all together if child.nb_breastfeeding_status == 'non_exclusive': random_draw = self.module.rng.choice(('non_exclusive', 'none'), p=[0.5, 0.5]) df.at[individual_id, 'nb_breastfeeding_status'] = random_draw # We then schedule these breastfed newborns to return at 2 years to update breastfeeding status again if child.nb_breastfeeding_status != 'none': self.sim.schedule_event(BreastfeedingStatusUpdateEventTwoYears(self.module, individual_id), self.sim.date + DateOffset(months=18))
[docs]class BreastfeedingStatusUpdateEventTwoYears(Event, IndividualScopeEventMixin): """ This is BreastfeedingStatusUpdateEventTwoYears. It is scheduled via the breastfeeding function. Children who are alive and still breastfeeding by two years months have their breastfeeding status updated."""
[docs] def __init__(self, module, individual_id): super().__init__(module, person_id=individual_id)
[docs] def apply(self, individual_id): df = self.sim.population.props if not df.at[individual_id, 'is_alive']: return df.at[individual_id, 'nb_breastfeeding_status'] = 'none'