fpsim.locations.ethiopia module

Set the parameters for FPsim, specifically for Ethiopia.

data2interp(data, ages, normalize=False)[source]

Convert unevenly spaced data into an even spline interpolation


Data files for use with calibration, etc – not needed for running a sim


Starting age bin, male population, female population Data are from World Population Prospects https://population.un.org/wpp/Download/Standard/Population/


Age-dependent mortality rates taken from UN World Population Prospects 2022. From probability of dying each year. https://population.un.org/wpp/ Used CSV WPP2022_Life_Table_Complete_Medium_Female_1950-2021, Ethiopia, 2020 Used CSV WPP2022_Life_Table_Complete_Medium_Male_1950-2021, Ethiopia, 2020 Mortality rate trend from crude death rate per 1000 people, also from UN Data Portal, 1950-2030: https://population.un.org/dataportal/data/indicators/59/locations/231/start/1950/end/2030/table/pivotbylocation Projections go out until 2030, but the csv file can be manually adjusted to remove any projections and stop at your desired year


From World Bank indicators for maternal mortality ratio (modeled estimate) per 100,000 live births: https://data.worldbank.org/indicator/SH.STA.MMRT?locations=ET


From World Bank indicators for infant mortality (< 1 year) for Ethiopia, per 1000 live births From API_SP.DYN.IMRT.IN_DS2_en_csv_v2_5358355 Adolescent increased risk of infant mortality gradient taken from Noori et al for Sub-Saharan African from 2014-2018. Odds ratios with age 23-25 as reference group: https://www.medrxiv.org/content/10.1101/2021.06.10.21258227v1


Returns a linear interpolation of the likelihood of a miscarriage by age, taken from data from Magnus et al BMJ 2019: https://pubmed.ncbi.nlm.nih.gov/30894356/ Data to be fed into likelihood of continuing a pregnancy once initialized in model Age 0 and 5 set at 100% likelihood. Age 10 imputed to be symmetrical with probability at age 45 for a parabolic curve


From Report of the UN Inter-agency Group for Child Mortality Estimation, 2020 https://childmortality.org/wp-content/uploads/2020/10/UN-IGME-2020-Stillbirth-Report.pdf

Age adjustments come from an extension of Noori et al., which were conducted June 2022.


Use fecundity rates from PRESTO study: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5712257/ Fecundity rate assumed to be approximately linear from onset of fecundity around age 10 (average age of menses 12.5) to first data point at age 20 45-50 age bin estimated at 0.10 of fecundity of 25-27 yr olds


Returns an array of fecundity ratios for a nulliparous woman vs a gravid woman from PRESTO study: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5712257/ Approximates primary infertility and its increasing likelihood if a woman has never conceived by age


Returns an array of the percent of breastfeeding women by month postpartum 0-11 months who meet criteria for LAM: Exclusively breastfeeding (bf + water alone), menses have not returned. Extended out 5-11 months to better match data as those women continue to be postpartum insusceptible. From DHS Ethiopia 2016 calendar data


Returns a linear interpolation of rates of female sexual activity, defined as percentage women who have had sex within the last four weeks. From STAT Compiler DHS https://www.statcompiler.com/en/ Using indicator “Timing of sexual intercourse” Includes women who have had sex “within the last four weeks” Excludes women who answer “never had sex”, probabilities are only applied to agents who have sexually debuted Data taken from 2018 DHS, no trend over years for now Onset of sexual activity probabilities assumed to be linear from age 10 to first data point at age 15 Last value duplicated so that it interpolates out to 50 and then stops


Returns an array of monthly likelihood of having resumed sexual activity within 0-35 months postpartum Uses 2016 Ethiopia DHS individual recode (postpartum (v222), months since last birth, and sexual activity within 30 days. Data is weighted. Limited to 23 months postpartum (can use any limit you want 0-23 max) Postpartum month 0 refers to the first month after delivery TODO– Add code for processing this for other countries to data_processing


Returns an array of weighted probabilities of sexual debut by a certain age 10-45. Data taken from DHS variable v531 (imputed age of sexual debut, imputed with data from age at first union) Use sexual_debut_age_probs.py under locations/data_processing to output for other DHS countries


Returns an array of experimental factors to be applied to account for residual exposure to either pregnancy or live birth by age. Exposure to pregnancy will increase factor number and residual likelihood of avoiding live birth (mostly abortion, also miscarriage), will decrease factor number


Returns an array of experimental factors to be applied to account for residual exposure to either pregnancy or live birth by parity.


Returns an array of birth spacing preferences by closest postpartum month. Applied to postpartum pregnancy likelihoods.

NOTE: spacing bins must be uniform!


Names, indices, modern/traditional flag, and efficacies of contraceptive methods – see also parameters.py Efficacy from Guttmacher, fp_prerelease/docs/gates_review/contraceptive-failure-rates-in-developing-world_1.pdf BTL failure rate from general published data Pooled efficacy rates for all women in this study: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4970461/


Define “raw” (un-normalized, un-trended) matrices to give transitional probabilities from PMA Ethiopia contraceptive calendar data.

Probabilities in this function are annual probabilities of initiating (top row), discontinuing (first column), continuing (diagonal), or switching methods (all other entries).

Probabilities at postpartum month 1 are 1 month transitional probabilities for starting a method after delivery.

Probabilities at postpartum month 6 are 5 month transitional probabilities for starting or changing methods over the first 6 months postpartum.

Data from Ethiopia PMA contraceptive calendars, 2019-2020 Processed from matrices_ethiopia_pma_2019_20.csv using process_matrices.py


Reasons for nonuse – taken from Ethiopia DHS 2005.


Take all parameters and construct into a dictionary