diff --git a/crcsim/analysis.py b/crcsim/analysis.py index 4a55fc4..d11e5fd 100644 --- a/crcsim/analysis.py +++ b/crcsim/analysis.py @@ -970,11 +970,11 @@ def compute_pop_rates(self, status_arrays: list): """ # Sum all of the person status arrays to get an array of counts of the number of # people in each status for each year. - statuses: np.ndarray = sum(status_arrays) + status_array: np.ndarray = sum(status_arrays) # Convert to DataFrame so we can index by column name statuses = pd.DataFrame( - statuses, + status_array, columns=[ "alive", "crc_death", diff --git a/crcsim/experiment/README.md b/crcsim/experiment/README.md index 85a2687..37c1340 100644 --- a/crcsim/experiment/README.md +++ b/crcsim/experiment/README.md @@ -1,17 +1,8 @@ -# Replication of the CRCCP intervention scenarios +# Testing changes to expected lifespan calculation -This experiment is a replication of the CRCCP compliance intervention experiment, which was conducted prior to open-sourcing the model and making some changes to the AWS infrastructure. We are replicating this experiment to ensure continuity after those changes. +This experiment tests new `crcsim` functionality to make expected lifespans consistent when the model is run with a given random seed. -The CRCCP compliance intervention experiment examines the cost-effectiveness of interventions designed to improve compliance with routine screening. - -The experiment is designed around 8 health centers (labeled FHQC1-FHQC8), each having its own baseline compliance rate, intervention cost, and post-intervention compliance rate. - -We don't model the intervention explicitly. In other words, we didn't add any code to the model to implement the intervention. Instead, we model the intervention by assuming it leads to a change in the compliance rate, and so we run a pair of simulations: one using the baseline compliance rate and another using the post-intervention compliance rate. Any differences in outcomes can therefore be attributed to the intervention. -## Scenarios - -Includes 2 scenarios per health center: one baseline scenario and one intervention scenario. The baseline scenarios are based on real data and the intervention scenarios include a hypothetical increase in screening compliance rates. - -The scenarios are created by `prepare.py`. This script reads a set of base parameters defined in `crcsim/experiment/parameters.json`, modifies them to create the scenarios, and saves them in a directory structure that will eventually be copied to AWS. +See https://github.com/RTIInternational/crcsim/pull/18 for rationale. ## Defining new experiments @@ -53,7 +44,7 @@ The subdirectories and files in `scenarios/` must be uploaded to AWS S3 for the To upload the files to S3, run ``` -aws s3 cp ./scenarios s3://crcsim-exp-crccp-replication/scenarios --recursive +aws s3 cp ./scenarios s3://crcsim-exp-consistent-lifespans/scenarios --recursive ``` *(Another note: this manual step is necessary because `boto3` does not include functionality to upload a directory to S3 recursively. Future experiments could improve this workflow by writing a function to upload the directory recursively in `prepare.py`. Or submit a patch to resolve https://github.com/boto/boto3/issues/358)* @@ -98,7 +89,7 @@ Most of the AWS architecture was built via the AWS Console. As such, there is no ### S3 -We used the S3 console to create the `crcsim-exp-template` bucket to store output files generated by simulation jobs. +We used the S3 console to create the `crcsim-exp-consistent-lifespans` bucket to store output files generated by simulation jobs. ### IAM diff --git a/crcsim/experiment/parameters.json b/crcsim/experiment/parameters.json index 36cb1c0..3bcda1a 100644 --- a/crcsim/experiment/parameters.json +++ b/crcsim/experiment/parameters.json @@ -23,7 +23,7 @@ "proportion_survive_clin3": 0, "proportion_survive_clin4": 0, - "cost_polypectomy": 170, + "cost_polypectomy": 650, "cost_polyp_pathology": 65, "cost_treatment_stage1_initial": 34258, @@ -71,7 +71,7 @@ "tests": { "FIT": { "proportion": 1.0, - "routine_start": 50, + "routine_start": 45, "routine_end": 75, "routine_freq": 1, "specificity": 0.97, @@ -79,7 +79,7 @@ "sensitivity_polyp2": 0.07, "sensitivity_polyp3": 0.22, "sensitivity_cancer": 0.74, - "cost": 22, + "cost": 27, "proportion_perforation": 0, "cost_perforation": 0, "compliance_rate_given_prev_compliant": [ 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0 ], @@ -87,7 +87,7 @@ }, "Colonoscopy": { "proportion": 0.0, - "routine_start": 50, + "routine_start": 45, "routine_end": 75, "routine_freq": 10, "specificity": 0.86, @@ -95,7 +95,7 @@ "sensitivity_polyp2": 0.85, "sensitivity_polyp3": 0.95, "sensitivity_cancer": 0.95, - "cost": 700, + "cost": 1608, "proportion_perforation": 0.001, "cost_perforation": 6487, "compliance_rate_given_prev_compliant": [ 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0 ], @@ -107,9 +107,9 @@ "surveillance_test": "Colonoscopy", "polypectomy_proportion_lethal": 0.00002, - "cost_discount_age": 50, + "cost_discount_age": 45, "cost_discount_rate": 0.03, - "lifespan_discount_age": 50, + "lifespan_discount_age": 45, "lifespan_discount_rate": 0.03, "death_rate_black_female_ages": [ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100 ], diff --git a/crcsim/experiment/requirements.in b/crcsim/experiment/requirements.in index 2d161d3..13635a1 100644 --- a/crcsim/experiment/requirements.in +++ b/crcsim/experiment/requirements.in @@ -1,5 +1,5 @@ # Clone the specific commit of the model that you want to use for the experiment -git+https://github.com/RTIInternational/crcsim.git@55312a08eb176556555ce5ecb99ba9628d0afc3e +git+https://github.com/RTIInternational/crcsim.git@6c264ca5e35c1b9ae20a6e4b95818f6e00970d71 fire pandas==2.1.1 diff --git a/crcsim/experiment/requirements.txt b/crcsim/experiment/requirements.txt index 8708a2e..9147064 100644 --- a/crcsim/experiment/requirements.txt +++ b/crcsim/experiment/requirements.txt @@ -36,7 +36,7 @@ colorama==0.4.4 # via awscli contourpy==1.1.1 # via matplotlib -crcsim @ git+https://github.com/RTIInternational/crcsim.git@55312a08eb176556555ce5ecb99ba9628d0afc3e +crcsim @ git+https://github.com/RTIInternational/crcsim.git@6c264ca5e35c1b9ae20a6e4b95818f6e00970d71 # via -r requirements.in cycler==0.12.0 # via matplotlib diff --git a/crcsim/experiment/run_iteration.sh b/crcsim/experiment/run_iteration.sh index 89e0710..2587329 100755 --- a/crcsim/experiment/run_iteration.sh +++ b/crcsim/experiment/run_iteration.sh @@ -12,7 +12,7 @@ if [ ! -d "$output_dir" ]; then mkdir $output_dir fi -aws s3 cp "s3://crcsim-exp-crccp-replication/scenarios/$scenario/params.json" "./params.json" +aws s3 cp "s3://crcsim-exp-consistent-lifespans/scenarios/$scenario/params.json" "./params.json" crc-simulate \ --npeople=$npeople \ @@ -23,5 +23,5 @@ crc-simulate \ crc-analyze \ --params-file=./params.json && -aws s3 cp ./results.csv "s3://crcsim-exp-crccp-replication/scenarios/$scenario/results_$iteration.csv" -aws s3 cp ./output.csv "s3://crcsim-exp-crccp-replication/scenarios/$scenario/output_$iteration.csv" \ No newline at end of file +aws s3 cp ./results.csv "s3://crcsim-exp-consistent-lifespans/scenarios/$scenario/results_$iteration.csv" +aws s3 cp ./output.csv "s3://crcsim-exp-consistent-lifespans/scenarios/$scenario/output_$iteration.csv" \ No newline at end of file diff --git a/crcsim/experiment/scenarios/fqhc1_baseline/params.json b/crcsim/experiment/scenarios/fqhc1_baseline/params.json new file mode 100644 index 0000000..bce58be --- /dev/null +++ b/crcsim/experiment/scenarios/fqhc1_baseline/params.json @@ -0,0 +1,1214 @@ +{ + "max_age": 100, + "mean_duration_polyp1_polyp2": 25.5, + "mean_duration_polyp2_polyp3": 6.25, + "mean_duration_polyp2_pre": 80, + "mean_duration_polyp3_pre": 76, + "mean_duration_pre1_pre2": 3.5, + "mean_duration_pre2_pre3": 2.5, + "mean_duration_pre3_pre4": 2.5, + "mean_duration_pre1_clin1": 18, + "mean_duration_pre2_clin2": 3.9, + "mean_duration_pre3_clin3": 2.75, + "mean_duration_pre4_clin4": 1.9, + "mean_duration_clin1_dead": 135, + "mean_duration_clin2_dead": 51, + "mean_duration_clin3_dead": 19, + "mean_duration_clin4_dead": 2.7, + "proportion_survive_clin1": 0, + "proportion_survive_clin2": 0, + "proportion_survive_clin3": 0, + "proportion_survive_clin4": 0, + "cost_polypectomy": 650, + "cost_polyp_pathology": 65, + "cost_treatment_stage1_initial": 34258, + "cost_treatment_stage1_ongoing": 2685, + "cost_treatment_stage1_terminal": 68339, + "cost_treatment_stage2_initial": 47790, + "cost_treatment_stage2_ongoing": 3266, + "cost_treatment_stage2_terminal": 77098, + "cost_treatment_stage3_initial": 71594, + "cost_treatment_stage3_ongoing": 5258, + "cost_treatment_stage3_terminal": 79770, + "cost_treatment_stage4_initial": 110161, + "cost_treatment_stage4_ongoing": 26474, + "cost_treatment_stage4_terminal": 99255, + "max_ongoing_treatments": 4, + "value_loss_cancer": 0.15, + "value_life_year_ages": [ + 0, + 65, + 100 + ], + "value_life_year_dollars": [ + 217000, + 547000, + 547000 + ], + "duration_screen_skip_testing": 5, + "surveillance_freq_polyp_none": 10, + "surveillance_freq_polyp_mild": 7, + "surveillance_freq_polyp_moderate": 3, + "surveillance_freq_polyp_severe": 1, + "surveillance_freq_cancer_first": 1, + "surveillance_freq_cancer_second": 3, + "surveillance_freq_cancer_rest": 5, + "surveillance_end_age": 85, + "use_conditional_compliance": true, + "initial_compliance_rate": 0.522, + "never_compliant_rate": 0.0, + "diagnostic_compliance_rate": 1.0, + "surveillance_compliance_rate": 1.0, + "lesion_risk_alpha": 0.47, + "lesion_risk_beta": 2.45, + "lesion_incidence_ages": [ 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+} \ No newline at end of file diff --git a/crcsim/experiment/scenarios/seeds.txt b/crcsim/experiment/scenarios/seeds.txt new file mode 100644 index 0000000..2d292ea --- /dev/null +++ b/crcsim/experiment/scenarios/seeds.txt @@ -0,0 +1,10 @@ +1001608934 +1865687462 +519221856 +196202418 +1876440239 +481309692 +257361246 +893058778 +1708949663 +859680834 diff --git a/crcsim/experiment/summarize.py b/crcsim/experiment/summarize.py index 156c03b..08677ab 100644 --- a/crcsim/experiment/summarize.py +++ b/crcsim/experiment/summarize.py @@ -4,22 +4,20 @@ import pandas as pd import s3fs # noqa: F401 - -S3_BUCKET_NAME = "crcsim-exp-crccp-replication" +S3_BUCKET_NAME = "crcsim-exp-consistent-lifespans" def main() -> None: summary_dir = Path("./summary") summary_dir.mkdir(exist_ok=True, parents=True) - combined = combine_run_results() combined.to_csv(summary_dir / "combined.csv", index=False) - df = add_derived_variables(combined) - summary = summarize_results(df) + summary, summary_subset = summarize_results(df) with pd.ExcelWriter(summary_dir / "summarized.xlsx", engine="openpyxl") as writer: - summary.to_excel(writer, index=False) + summary.to_excel(writer, sheet_name="All Columns", index=False) + summary_subset.to_excel(writer, sheet_name="Select Columns", index=False) def get_scenario_list() -> list: @@ -115,25 +113,97 @@ def add_derived_variables(df: pd.DataFrame) -> pd.DataFrame: + df["discounted_cost_treatment"] ) + df["cost_treatment_per_1k_40yo"] = ( + df["cost_treatment_initial_per_1k_40yo"] + + df["cost_treatment_ongoing_per_1k_40yo"] + + df["cost_treatment_terminal_per_1k_40yo"] + ) + + df["discounted_cost_treatment_per_1k_40yo"] = ( + df["discounted_cost_treatment_initial_per_1k_40yo"] + + df["discounted_cost_treatment_ongoing_per_1k_40yo"] + + df["discounted_cost_treatment_terminal_per_1k_40yo"] + ) + + df["cost_total_per_1k_40yo"] = ( + df["cost_routine_per_1k_40yo"] + + df["cost_diagnostic_per_1k_40yo"] + + df["cost_surveillance_per_1k_40yo"] + + df["cost_treatment_per_1k_40yo"] + ) + + df["discounted_cost_total_per_1k_40yo"] = ( + df["discounted_cost_routine_per_1k_40yo"] + + df["discounted_cost_diagnostic_per_1k_40yo"] + + df["discounted_cost_surveillance_per_1k_40yo"] + + df["discounted_cost_treatment_per_1k_40yo"] + ) + + df["total_colonoscopy"] = ( + df["Colonoscopy_performed_diagnostic_per_1k_40yo_mean"] + df["Colonoscopy_performed_surveillance_per_1k_40yo_mean"] + ) + return df + def summarize_results(df: pd.DataFrame) -> pd.DataFrame: - """ - Compute the mean and standard deviation of every analysis variable, by - scenario. - """ + """ Compute the mean and standard deviation of every analysis variable, by scenario. """ groups = df.groupby("scenario") means = groups.mean() stds = groups.std() - means.columns = [f"{c}_mean" for c in means.columns] stds.columns = [f"{c}_std" for c in stds.columns] - interleaved_columns = chain.from_iterable(zip(means.columns, stds.columns)) summary = pd.concat([means, stds], axis="columns")[interleaved_columns] - - return summary.reset_index() + summary = summary.reset_index() + + # Create a second sheet with select columns + select_columns = ["scenario", + "Colonoscopy_performed_diagnostic_per_1k_40yo_mean", + "Colonoscopy_performed_surveillance_per_1k_40yo_mean", + "total_colonoscopy", + "FIT_performed_routine_per_1k_40yo_mean", + "clin_crc_per_1k_40yo_mean", + "deadcrc_per_1k_40yo_mean", + "lifeobs_if_unscreened_undiagnosed_at_40_mean", + "discounted_cost_routine_mean", + "discounted_cost_diagnostic_mean", + "discounted_cost_surveillance_mean", + "discounted_cost_treatment_initial_mean", + "discounted_cost_treatment_ongoing_mean", + "discounted_cost_treatment_terminal_mean", + "discounted_cost_treatment", + "discounted_cost_total", + "cost_routine_mean", + "cost_diagnostic_mean", + "cost_surveillance_mean", + "cost_treatment_initial_mean", + "cost_treatment_ongoing_mean", + "cost_treatment_terminal_mean", + "cost_treatment", + "cost_total", + "discounted_cost_routine_per_1k_40yo_mean", + "discounted_cost_diagnostic_per_1k_40yo_mean", + "discounted_cost_surveillance_per_1k_40yo_mean", + "discounted_cost_treatment_initial_per_1k_40yo_mean", + "discounted_cost_treatment_ongoing_per_1k_40yo_mean", + "discounted_cost_treatment_terminal_per_1k_40yo_mean", + "discounted_cost_treatment_per_1k_40yo_mean", + "discounted_cost_total_per_1k_40yo", + "cost_routine_per_1k_40yo_mean", + "cost_diagnostic_per_1k_40yo_mean", + "cost_surveillance_per_1k_40yo_mean", + "cost_treatment_initial_per_1k_40yo_mean", + "cost_treatment_ongoing_per_1k_40yo_mean", + "cost_treatment_terminal_per_1k_40yo_mean", + "cost_treatment_per_1k_40yo_mean", + "cost_total_per_1k_40yo", + "discounted_lifeobs_if_unscreened_undiagnosed_at_40_mean"] + + summary_subset = summary[select_columns] + + return summary, summary_subset if __name__ == "__main__": diff --git a/crcsim/experiment/summary/combined.csv b/crcsim/experiment/summary/combined.csv new file mode 100644 index 0000000..701031b --- /dev/null +++ b/crcsim/experiment/summary/combined.csv @@ -0,0 +1,161 @@ +n,n_unscreened_undiagnosed_40yos,polyp,polyp_per_1k_40yo,crc,crc_per_1k_40yo,clin_crc,clin_crc_per_1k_40yo,deadcrc,deadcrc_per_1k_40yo,lifeexp,lifeexp_if_unscreened_undiagnosed_at_40,discounted_lifeexp,discounted_lifeexp_if_unscreened_undiagnosed_at_40,lifeobs,lifeobs_if_unscreened_undiagnosed_at_40,discounted_lifeobs,discounted_lifeobs_if_unscreened_undiagnosed_at_40,lifelost,lifelost_per_1k_40yo,discounted_lifelost,discounted_lifelost_per_1k_40yo,cost_routine,discounted_cost_routine,cost_routine_per_1k_40yo,discounted_cost_routine_per_1k_40yo,cost_diagnostic,discounted_cost_diagnostic,cost_diagnostic_per_1k_40yo,discounted_cost_diagnostic_per_1k_40yo,cost_surveillance,discounted_cost_surveillance,cost_surveillance_per_1k_40yo,discounted_cost_surveillance_per_1k_40yo,cost_treatment_initial,discounted_cost_treatment_initial,cost_treatment_initial_per_1k_40yo,discounted_cost_treatment_initial_per_1k_40yo,cost_treatment_ongoing,discounted_cost_treatment_ongoing,cost_treatment_ongoing_per_1k_40yo,discounted_cost_treatment_ongoing_per_1k_40yo,cost_treatment_terminal,discounted_cost_treatment_terminal,cost_treatment_terminal_per_1k_40yo,discounted_cost_treatment_terminal_per_1k_40yo,prob_polyp,FIT_adopted,Colonoscopy_adopted,FIT_performed_routine,FIT_performed_routine_per_1k_40yo,Colonoscopy_performed_routine,Colonoscopy_performed_routine_per_1k_40yo,FIT_performed_diagnostic,FIT_performed_diagnostic_per_1k_40yo,Colonoscopy_performed_diagnostic,Colonoscopy_performed_diagnostic_per_1k_40yo,FIT_performed_surveillance,FIT_performed_surveillance_per_1k_40yo,Colonoscopy_performed_surveillance,Colonoscopy_performed_surveillance_per_1k_40yo,FIT_noncompliant_routine,Colonoscopy_noncompliant_routine,FIT_noncompliant_routine_50,Colonoscopy_noncompliant_routine_50,FIT_noncompliant_diagnostic,Colonoscopy_noncompliant_diagnostic,FIT_noncompliant_surveillance,Colonoscopy_noncompliant_surveillance,FIT_perforations,FIT_perforations_per_1k_40yo,Colonoscopy_perforations,Colonoscopy_perforations_per_1k_40yo,FIT_test_fatalities,FIT_test_fatalities_per_1k_40yo,Colonoscopy_test_fatalities,Colonoscopy_test_fatalities_per_1k_40yo,prob_crc,prob_crc_given_polyp,prob_dead_crc_given_crc,prob_dead_crc,prob_crc_from_medium_polyp,prob_crc_from_large_polyp,time_polyp_to_pre,time_pre_to_clin,crc_onset_proportion_stage2,crc_onset_proportion_stage3,crc_onset_proportion_stage1,crc_onset_proportion_stage4,time_pre_to_dead,time_clin_to_dead,crc_mortality_rate,polyp_prevalence_rate,crc_incidence_rate,crc_incidence_stage1_rate,crc_incidence_stage2_rate,crc_incidence_stage3_rate,crc_incidence_stage4_rate,crc_survival_rate,crc_survival_stage1_rate,crc_survival_stage2_rate,crc_survival_stage3_rate,crc_survival_stage4_rate,crc_survival_rate_20_64,crc_survival_stage1_rate_20_64,crc_survival_stage2_rate_20_64,crc_survival_stage3_rate_20_64,crc_survival_stage4_rate_20_64,crc_survival_rate_65_100,crc_survival_stage1_rate_65_100,crc_survival_stage2_rate_65_100,crc_survival_stage3_rate_65_100,crc_survival_stage4_rate_65_100,polyp_prevalence_rate_0,polyp_prevalence_rate_1,polyp_prevalence_rate_2,polyp_prevalence_rate_3,polyp_prevalence_rate_4,polyp_prevalence_rate_5,polyp_prevalence_rate_6,polyp_prevalence_rate_7,polyp_prevalence_rate_8,polyp_prevalence_rate_9,polyp_prevalence_rate_10,polyp_prevalence_rate_11,polyp_prevalence_rate_12,polyp_prevalence_rate_13,polyp_prevalence_rate_14,polyp_prevalence_rate_15,polyp_prevalence_rate_16,polyp_prevalence_rate_17,polyp_prevalence_rate_18,polyp_prevalence_rate_19,polyp_prevalence_rate_20,polyp_prevalence_rate_21,polyp_prevalence_rate_22,polyp_prevalence_rate_23,polyp_prevalence_rate_24,polyp_prevalence_rate_25,polyp_prevalence_rate_26,polyp_prevalence_rate_27,polyp_prevalence_rate_28,polyp_prevalence_rate_29,polyp_prevalence_rate_30,polyp_prevalence_rate_31,polyp_prevalence_rate_32,polyp_prevalence_rate_33,polyp_prevalence_rate_34,polyp_prevalence_rate_35,polyp_prevalence_rate_36,polyp_prevalence_rate_37,polyp_prevalence_rate_38,polyp_prevalence_rate_39,polyp_prevalence_rate_40,polyp_prevalence_rate_41,polyp_prevalence_rate_42,polyp_prevalence_rate_43,polyp_prevalence_rate_44,polyp_prevalence_rate_45,polyp_prevalence_rate_46,polyp_prevalence_rate_47,polyp_prevalence_rate_48,polyp_prevalence_rate_49,polyp_prevalence_rate_50,polyp_prevalence_rate_51,polyp_prevalence_rate_52,polyp_prevalence_rate_53,polyp_prevalence_rate_54,polyp_prevalence_rate_55,polyp_prevalence_rate_56,polyp_prevalence_rate_57,polyp_prevalence_rate_58,polyp_prevalence_rate_59,polyp_prevalence_rate_60,polyp_prevalence_rate_61,polyp_prevalence_rate_62,polyp_prevalence_rate_63,polyp_prevalence_rate_64,polyp_prevalence_rate_65,polyp_prevalence_rate_66,polyp_prevalence_rate_67,polyp_prevalence_rate_68,polyp_prevalence_rate_69,polyp_prevalence_rate_70,polyp_prevalence_rate_71,polyp_prevalence_rate_72,polyp_prevalence_rate_73,polyp_prevalence_rate_74,polyp_prevalence_rate_75,polyp_prevalence_rate_76,polyp_prevalence_rate_77,polyp_prevalence_rate_78,polyp_prev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