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update parameters to enable interventions: a simpler and more reader-friendly way #1081

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Feb 11, 2024
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update parameters to enable interventions: a simpler and more reader-friendly way #1081

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3d0f2d4
co: more-readable update_params_for_interventions() fnc
EvaJanouskova Aug 23, 2023
84d9b16
co: do not save init probs for years before current sim year
EvaJanouskova Aug 23, 2023
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147 changes: 31 additions & 116 deletions src/tlo/methods/contraception.py
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Original file line number Diff line number Diff line change
Expand Up @@ -596,122 +596,37 @@ def update_params_for_interventions(self):

processed_params = self.processed_params

def expand_to_age_years(values_by_age_groups, ages_by_year):
_d = dict(zip(['15-19', '20-24', '25-29', '30-34', '35-39', '40-44', '45-49'], values_by_age_groups))
return np.array(
[_d[self.sim.modules['Demography'].AGE_RANGE_LOOKUP[_age_year]] for _age_year in ages_by_year]
)

def time_age_trend_in_initiation():
"""The age-specific effect of calendar year on the probability of starting use of contraceptive
(multiplicative effect). Values are chosen to induce a trend in age-specific fertility consistent with
the WPP estimates."""

_years = np.arange(2010, 2101)
_ages = np.arange(15, 50)

_init_over_time = np.exp(+0.05 * np.minimum(2020 - 2010, (_years - 2010))) * np.maximum(1.0, np.exp(
+0.01 * (_years - 2020)))
_init_over_time_modification_by_age = 1.0 / expand_to_age_years([1.0, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5], _ages)
_init = np.outer(_init_over_time, _init_over_time_modification_by_age)

return pd.DataFrame(index=_years, columns=_ages, data=_init)

def avoid_sterilization_below30(probs):
"""Prevent women below 30 years having female sterilization and adjust the probability for women 30 and over
to preserve the overall probability of initiating sterilization."""
# Input 'probs' must include probs for all methods including 'not_using'
assert set(probs.index) == set(self.all_contraception_states)

# Prevent women below 30 years having 'female_sterilization'
probs_below30 = probs.copy()
probs_below30['female_sterilization'] = 0.0
# Scale so that the probability of all outcomes sum to 1.0
probs_below30 = probs_below30 / probs_below30.sum()
assert np.isclose(1.0, probs_below30.sum())

# Increase prob of 'female_sterilization' in older women accordingly
probs_30plus = probs.copy()
probs_30plus['female_sterilization'] = (
probs.loc['female_sterilization'] /
self.ratio_n_females_30_49_to_15_49_in_2010
)
# Scale so that the probability of all outcomes sum to 1.0
probs_30plus = probs_30plus / probs_30plus.sum()
assert np.isclose(1.0, probs_30plus.sum())

return probs_below30, probs_30plus

def contraception_initiation_with_interv():
"""Generate the probability per month of a woman initiating onto each contraceptive, by the age (in whole
years) if FP interventions are applied."""

# Probability of initiation by method per month (average over all ages)
p_init_by_method = self.parameters['Initiation_ByMethod'].loc[0]

# Prevent women below 30 years having 'female_sterilization' while preserving the overall probability of
# 'female_sterilization' initiation
p_init_by_method_below30, p_init_by_method_30plus = avoid_sterilization_below30(p_init_by_method)

# Effect of age
age_effect = 1.0 + self.parameters['Initiation_ByAge'].set_index('age')['r_init1_age'].rename_axis(
"age_years")

# Year effect
year_effect = time_age_trend_in_initiation()

def apply_intervention_age_year_effects(probs_below30, probs_30plus):
# Apply Pop intervention
probs_by_method_below30 = \
probs_below30.copy().drop('not_using').mul(self.parameters['Interventions_Pop'].loc[0])
probs_by_method_30plus = \
probs_30plus.copy().drop('not_using').mul(self.parameters['Interventions_Pop'].loc[0])
# Assemble into age-specific data-frame:
p_init = dict()
for year in year_effect.index:

p_init_this_year = dict()
for a in age_effect.index:
if a < 30:
p_init_this_year[a] = probs_by_method_below30 * age_effect.at[a] * year_effect.at[year, a]
else:
p_init_this_year[a] = probs_by_method_30plus * age_effect.at[a] * year_effect.at[year, a]
p_init_this_year_df = pd.DataFrame.from_dict(p_init_this_year, orient='index')

# Check correct format of age/method data-frame
assert set(p_init_this_year_df.columns) == set(self.all_contraception_states - {'not_using'})
assert (p_init_this_year_df.index == range(15, 50)).all()
assert (p_init_this_year_df >= 0.0).all().all()

p_init[year] = p_init_this_year_df

return p_init

return apply_intervention_age_year_effects(p_init_by_method_below30, p_init_by_method_30plus)

def contraception_initiation_after_birth_with_interv():
"""Get the probability of a woman starting a contraceptive following giving birth if FP interventions are
applied. Avoid sterilization in women below 30 years old."""

# Get initiation probabilities of contraception methods after birth from read-in Excel sheet
p_start_after_birth = self.parameters['Initiation_AfterBirth'].loc[0].drop('not_using')

# Apply PPFP intervention multipliers
p_start_after_birth = p_start_after_birth.mul(self.parameters['Interventions_PPFP'].loc[0])

# Add 'not_using' to initiation probabilities of contraception methods after birth
p_start_after_birth = pd.concat(
(
pd.Series((1.0 - p_start_after_birth.sum()), index=['not_using']),
p_start_after_birth
)
)

return avoid_sterilization_below30(p_start_after_birth)

processed_params['p_start_per_month'] = contraception_initiation_with_interv()
processed_params['p_start_after_birth_below30'], processed_params['p_start_after_birth_30plus'] =\
contraception_initiation_after_birth_with_interv()
def contraception_initiation_with_interv(p_start_per_month_without_interv):
"""Increase the probabilities of a woman starting modern contraceptives due to Pop intervention being
applied."""
p_start_per_month_with_interv = {}
for year, age_method_df in p_start_per_month_without_interv.items():
if year >= self.sim.date.year:
p_start_per_month_with_interv[year] = age_method_df * self.parameters['Interventions_Pop'].loc[0]
return p_start_per_month_with_interv

def contraception_initiation_after_birth_with_interv(p_start_after_birth_without_interv):
"""Increase the probabilities of a woman starting modern contraceptives following giving birth due to PPFP
intervention being applied."""
# Exclude prob of 'not_using'
p_start_after_birth_with_interv = p_start_after_birth_without_interv.copy().drop('not_using')

# Apply PPFP intervention multipliers (ie increase probs of modern methods)
p_start_after_birth_with_interv = \
p_start_after_birth_with_interv.mul(self.parameters['Interventions_PPFP'].loc[0])

# Return reduced prob of 'not_using'
p_start_after_birth_with_interv = pd.Series((1.0 - p_start_after_birth_with_interv.sum()),
index=['not_using']).append(p_start_after_birth_with_interv)

return p_start_after_birth_with_interv

processed_params['p_start_per_month'] = \
contraception_initiation_with_interv(processed_params['p_start_per_month'])
processed_params['p_start_after_birth_below30'] = \
contraception_initiation_after_birth_with_interv(processed_params['p_start_after_birth_below30'])
processed_params['p_start_after_birth_30plus'] = \
contraception_initiation_after_birth_with_interv(processed_params['p_start_after_birth_30plus'])

return processed_params

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