tables for manuscript. recreated ruca table

analysis/new analysis Aug 2025/appendix_table2_manuscript.csv

@@ -0,0 +1,7 @@
+Period,Rurality,Poisson Δ,Poisson p,NB Δ,NB p
+Before 2020,Rural,"0.01 (-0.10, 0.12)",0.828,"-9.83 (-14.87, -4.51)",<0.001
+Before 2020,Suburban,"0.02 (-0.21, 0.25)",0.862,"-7.10 (-15.48, 1.48)",0.107
+Before 2020,Urban,"0.13 (0.05, 0.21)",<0.001,"-1.64 (-2.86, -0.46)",0.008
+2020 and After,Rural,"0.07 (0.00, 0.14)",0.049,"-13.92 (-18.48, -9.26)",<0.001
+2020 and After,Suburban,"0.14 (0.02, 0.26)",0.022,"-7.23 (-13.89, -0.40)",0.037
+2020 and After,Urban,"0.29 (0.24, 0.34)",<0.001,"-2.87 (-3.73, -2.01)",<0.001

analysis/new analysis Aug 2025/appendix_table2_model_comparison.csv

@@ -0,0 +1,7 @@
+Period,Rurality,Poisson Δ (days),NB Δ (days),Difference (NB - Poisson),Poisson p,NB p
+Before 2020,Rural,0.0125075677145938,-9.826219107892053,-9.838726675606646,0.828,0.0004
+Before 2020,Suburban,0.0192862765678301,-7.100280417538358,-7.119566694106188,0.862,0.1072
+Before 2020,Urban,0.1305571823770012,-1.6388416472647436,-1.7693988296417449,0.0,0.008
+2020 and After,Rural,0.0727709345083023,-13.917287633807826,-13.990058568316128,0.049,0.0
+2020 and After,Suburban,0.1438421422124148,-7.230488621220758,-7.374330763433172,0.022,0.0368
+2020 and After,Urban,0.2921678186765097,-2.869383763253332,-3.161551581929842,0.0,0.0

analysis/new analysis Aug 2025/appendix_table2_sensitivity_full.csv

@@ -0,0 +1,13 @@
+Period,Rurality,Model,Δ (days),95% CI Lower,95% CI Upper,p-value,Δ (hours),Hours CI Lower,Hours CI Upper
+Before 2020,Rural,Poisson,0.0125075677145938,-0.1007299005887473,0.1242653832992618,0.828,0.3001816251502512,-2.417517614129935,2.9823691991822834
+Before 2020,Rural,Negative Binomial,-9.826219107892053,-14.869599396708646,-4.507633417615492,0.0004,-235.82925858940928,-356.8703855210075,-108.1832020227718
+Before 2020,Suburban,Poisson,0.0192862765678301,-0.2070683547625284,0.2474055366045697,0.862,0.4628706376279224,-4.969640514300682,5.937732878509673
+Before 2020,Suburban,Negative Binomial,-7.100280417538358,-15.484324609933072,1.4802600357437907,0.1072,-170.4067300209206,-371.62379063839376,35.52624085785098
+Before 2020,Urban,Poisson,0.1305571823770012,0.0529612911724684,0.2084385013879577,0.0,3.133372377048029,1.2710709881392417,5.002524033310984
+Before 2020,Urban,Negative Binomial,-1.6388416472647436,-2.8560915098428508,-0.4644060319162429,0.008,-39.33219953435385,-68.54619623622841,-11.14574476598983
+2020 and After,Rural,Poisson,0.0727709345083023,0.0005333461979639,0.144294044390127,0.049,1.7465024281992552,0.0128003087511336,3.463057065363048
+2020 and After,Rural,Negative Binomial,-13.917287633807826,-18.48415448898385,-9.260262673475022,0.0,-334.01490321138783,-443.6197077356125,-222.2463041634005
+2020 and After,Suburban,Poisson,0.1438421422124148,0.0193728479256509,0.2640026911376328,0.022,3.452211413097955,0.4649483502156215,6.336064587303188
+2020 and After,Suburban,Negative Binomial,-7.230488621220758,-13.88880956784627,-0.396495094273276,0.0368,-173.5317269092982,-333.3314296283105,-9.515882262558623
+2020 and After,Urban,Poisson,0.2921678186765097,0.2442461679633707,0.3427039893841879,0.0,7.0120276482362325,5.861908031120897,8.22489574522051
+2020 and After,Urban,Negative Binomial,-2.869383763253332,-3.7289121485454073,-2.005130365939876,0.0,-68.86521031807997,-89.49389156508977,-48.12312878255703

analysis/new analysis Aug 2025/create_table3.py

@@ -0,0 +1,53 @@
+import pandas as pd
+
+# Load the data with counts by strata
+df = pd.read_csv("poisson_predicted_groups_boot_final.csv")
+
+# Create a pivot table with counts
+counts_table = df.pivot_table(
+    index=["spill_type", "Period"],
+    columns="rurality",
+    values="n",
+    aggfunc="first"
+)
+
+# Reorder columns
+counts_table = counts_table[["Urban", "Suburban", "Rural"]]
+
+# Add total column
+counts_table["Total"] = counts_table.sum(axis=1)
+
+# Reset index to make it cleaner
+counts_table = counts_table.reset_index()
+
+# Rename columns for publication
+counts_table.columns.name = None
+counts_table.rename(columns={
+    "spill_type": "Spill Type",
+    "Period": "Period"
+}, inplace=True)
+
+# Save to CSV
+counts_table.to_csv("table3_cell_counts_by_strata.csv", index=False)
+
+print("Table 3: Cell Counts by Strata")
+print("="*80)
+print(counts_table.to_string(index=False))
+print("\n")
+print(f"Total observations: {counts_table['Total'].sum():,.0f}")
+
+# Also create a formatted version for Word
+counts_table_formatted = counts_table.copy()
+for col in ["Urban", "Suburban", "Rural", "Total"]:
+    counts_table_formatted[col] = counts_table_formatted[col].apply(lambda x: f"{int(x):,}")
+
+print("\n" + "="*80)
+print("Formatted for Word/LaTeX:")
+print("="*80)
+print(counts_table_formatted.to_string(index=False))
+
+# Save formatted version
+counts_table_formatted.to_csv("table3_cell_counts_formatted.csv", index=False)
+
+print("\n✓ Saved: table3_cell_counts_by_strata.csv")
+print("✓ Saved: table3_cell_counts_formatted.csv")

analysis/new analysis Aug 2025/its_coef_bootstrap.csv

@@ -1,7 +1,7 @@
 spill_type,rurality,n_months,cov_type_used,immediate_median,immediate_ci_lower,immediate_ci_upper,immediate_p_boot,slope_median,slope_ci_lower,slope_ci_upper,slope_p_boot
-Historical,Urban,117,HAC(3),-0.6250991837929991,-0.9772868254796147,-0.2619678514805641,0.0040000000000000036,0.008677356602081509,0.0024123586979186433,0.01534362269873128,0.012
-Historical,Suburban,65,HAC(3),-1.5555203555268773,-2.760016526399644,-0.37589988751216996,0.008000000000000007,0.018111983033016497,-0.002105555534072708,0.03884445206697719,0.084
-Historical,Rural,105,HAC(3),-0.24590595010391308,-0.824718160817529,0.40663717560118645,0.45799999999999996,0.00013021988464623603,-0.0107828487359158,0.010256027360183254,0.976
-Recent,Urban,118,HAC(3),-0.15628489133359408,-0.5468189876262599,0.26330563995054274,0.44799999999999995,0.001369074917704279,-0.005773664319305678,0.008682893452063814,0.7
-Recent,Suburban,92,HAC(3),-0.23244391793278704,-1.2135798544269085,0.67739991550723,0.6359999999999999,-0.003999467175621145,-0.017363911188202664,0.010956067039010234,0.562
-Recent,Rural,118,HAC(3),-0.02527235325752801,-0.36146457009829014,0.35820151045927684,0.8859999999999999,-0.002781214701541218,-0.008208128151727126,0.002389512920216333,0.27
+Historical,Urban,117,HAC(3),-0.6143298365704635,-0.9701974488679479,-0.2576102437589131,0.0020000000000000018,0.008548526458438652,0.0025645936683214026,0.014992999515163894,0.008
+Historical,Suburban,65,HAC(3),-1.5419172264856993,-2.8005057370759436,-0.5092818284564702,0.0020000000000000018,0.018288851239152915,-0.001413811154498385,0.04054252593164273,0.076
+Historical,Rural,105,HAC(3),-0.2319822274291954,-0.8686730523534315,0.39757251088646645,0.44599999999999995,-6.973679570138947e-05,-0.011328809843695048,0.011024892423937206,0.98
+Recent,Urban,118,HAC(3),-0.17454324356137912,-0.5863332553476475,0.2245678547101131,0.44599999999999995,0.0016865010996214614,-0.005893803703454753,0.009254460119298775,0.686
+Recent,Suburban,92,HAC(3),-0.17958384840540031,-1.2130013501446437,0.7370245819198297,0.6759999999999999,-0.0047429405935390115,-0.01925760998354386,0.00966656492524187,0.528
+Recent,Rural,118,HAC(3),-0.015990389127948444,-0.3694722611284897,0.34107009362805324,0.9039999999999999,-0.003043894243530523,-0.008093280725626546,0.002427039704067753,0.252

analysis/new analysis Aug 2025/its_poisson_summary.csv

@@ -1,7 +1,7 @@
 spill_type,rurality,n_months,cov_type,coef_post,p_post,coef_timepost,p_timepost
-Historical,Urban,118,OLS-GLM,-2.2558997513868926,1.3476569456688608e-63,0.030369443639893377,1.2996649901502465e-48
-Historical,Suburban,118,OLS-GLM,-1.34383735287832,0.009457923117687337,0.016932518245171085,0.0417093239079251
-Historical,Rural,118,OLS-GLM,-2.7164423892570597,1.3022260138535194e-21,0.04369289171690502,3.1047832219194077e-23
-Recent,Urban,118,OLS-GLM,-0.23865705652862768,0.14267759889367287,0.0008995270283667922,0.7140399407335487
-Recent,Suburban,118,OLS-GLM,0.4297773652745585,0.16457183794635,-0.0008188605523384556,0.8724230164238238
-Recent,Rural,118,OLS-GLM,0.734195574412651,4.848782193840399e-09,-0.011298705684153811,7.335729970141464e-09
+Historical,Urban,118,OLS-GLM,-2.255899751382564,2.7246131918171756e-125,0.03036944363983375,2.2088667696862934e-95
+Historical,Suburban,118,OLS-GLM,-1.3438373528783196,0.00024260336632732281,0.01693251824517155,0.003977799711160122
+Historical,Rural,118,OLS-GLM,-2.716442389257058,1.4582406399745034e-41,0.04369289171690913,8.608891702162188e-45
+Recent,Urban,118,OLS-GLM,-0.238657056528626,0.0381643759487025,0.0008995270283673768,0.6043058843976774
+Recent,Suburban,118,OLS-GLM,0.4297773652745612,0.04934982898288095,-0.0008188605523375405,0.8203486639361263
+Recent,Rural,118,OLS-GLM,0.7341955744126516,1.2698239565857607e-16,-0.011298705684153568,2.8746860921086824e-16

analysis/new analysis Aug 2025/its_summary_with_boot.csv

@@ -1,7 +1,7 @@
 spill_type,rurality,n_months,status,cov_type,R_squared,cov_type_used,immediate_median,immediate_ci_lower,immediate_ci_upper,immediate_p_boot,slope_median,slope_ci_lower,slope_ci_upper,slope_p_boot
-Historical,Urban,117,ok,HAC(3),0.4510030209910569,HAC(3),-0.6250991837929991,-0.9772868254796147,-0.2619678514805641,0.0040000000000000036,0.008677356602081509,0.0024123586979186433,0.01534362269873128,0.012
-Historical,Suburban,65,ok,HAC(3),0.1452643863997323,HAC(3),-1.5555203555268773,-2.760016526399644,-0.37589988751216996,0.008000000000000007,0.018111983033016497,-0.002105555534072708,0.03884445206697719,0.084
-Historical,Rural,105,ok,HAC(3),0.0788182219726172,HAC(3),-0.24590595010391308,-0.824718160817529,0.40663717560118645,0.45799999999999996,0.00013021988464623603,-0.0107828487359158,0.010256027360183254,0.976
-Recent,Urban,118,ok,HAC(3),0.1959564116019398,HAC(3),-0.15628489133359408,-0.5468189876262599,0.26330563995054274,0.44799999999999995,0.001369074917704279,-0.005773664319305678,0.008682893452063814,0.7
-Recent,Suburban,92,ok,HAC(3),0.0658293631915236,HAC(3),-0.23244391793278704,-1.2135798544269085,0.67739991550723,0.6359999999999999,-0.003999467175621145,-0.017363911188202664,0.010956067039010234,0.562
-Recent,Rural,118,ok,HAC(3),0.1709315051206097,HAC(3),-0.02527235325752801,-0.36146457009829014,0.35820151045927684,0.8859999999999999,-0.002781214701541218,-0.008208128151727126,0.002389512920216333,0.27
+Historical,Urban,117,ok,HAC(3),0.4510030209910569,HAC(3),-0.6143298365704635,-0.9701974488679479,-0.2576102437589131,0.0020000000000000018,0.008548526458438652,0.0025645936683214026,0.014992999515163894,0.008
+Historical,Suburban,65,ok,HAC(3),0.1452643863997323,HAC(3),-1.5419172264856993,-2.8005057370759436,-0.5092818284564702,0.0020000000000000018,0.018288851239152915,-0.001413811154498385,0.04054252593164273,0.076
+Historical,Rural,105,ok,HAC(3),0.0788182219726172,HAC(3),-0.2319822274291954,-0.8686730523534315,0.39757251088646645,0.44599999999999995,-6.973679570138947e-05,-0.011328809843695048,0.011024892423937206,0.98
+Recent,Urban,118,ok,HAC(3),0.1959564116019398,HAC(3),-0.17454324356137912,-0.5863332553476475,0.2245678547101131,0.44599999999999995,0.0016865010996214614,-0.005893803703454753,0.009254460119298775,0.686
+Recent,Suburban,92,ok,HAC(3),0.0658293631915236,HAC(3),-0.17958384840540031,-1.2130013501446437,0.7370245819198297,0.6759999999999999,-0.0047429405935390115,-0.01925760998354386,0.00966656492524187,0.528
+Recent,Rural,118,ok,HAC(3),0.1709315051206097,HAC(3),-0.015990389127948444,-0.3694722611284897,0.34107009362805324,0.9039999999999999,-0.003043894243530523,-0.008093280725626546,0.002427039704067753,0.252

analysis/new analysis Aug 2025/pct_change_period_boot.csv

@@ -1,7 +1,7 @@
 spill_type,rurality,median_days,ci_lower_days,ci_upper_days,median_hours
-Historical,Rural,0.2764117602827311,0.15162950848434606,0.4062470235857321,6.6338822467855465
-Historical,Suburban,0.36538461538461653,0.14574604189988757,0.5849992003838161,8.769230769230797
-Historical,Urban,0.4586609329592761,0.4092283066452901,0.5158414737344518,11.007862391022627
-Recent,Rural,0.2208484333698994,0.16234478809493721,0.280090515780138,5.300362400877585
-Recent,Suburban,0.23802863254895285,0.09616929954473215,0.381120070210082,5.712687181174868
-Recent,Urban,0.2985415486404179,0.22800245049959508,0.3745836583142008,7.164997167370029
+Historical,Rural,0.2792941825199912,0.18920250896057408,0.36402752713236397,6.703060380479789
+Historical,Suburban,0.3649248360786824,0.210704861666399,0.5258276027506784,8.758196065888377
+Historical,Urban,0.459127653406465,0.42214597011939387,0.5004701859256199,11.01906368175516
+Recent,Rural,0.21930069265228186,0.17925021985486714,0.26096825583475497,5.263216623654765
+Recent,Suburban,0.23813833580165716,0.13572486424222685,0.33975885853765675,5.715320059239772
+Recent,Urban,0.29845327415934986,0.24341382135127804,0.3515924814466669,7.162878579824397

analysis/new analysis Aug 2025/pct_change_spilltype_boot.csv

@@ -1,7 +1,7 @@
 Period,rurality,median_abs_days,ci_lower_days,ci_upper_days,median_hours,median_pct
-2020 and After,Rural,0.0741556691931361,0.004211424865189661,0.14743252321887296,1.7797360606352663,12.14421494676097
-2020 and After,Suburban,0.14399257847758565,0.01829931802226303,0.26364122622271274,3.4558218834620558,34.39021282018639
-2020 and After,Urban,0.2907137007004865,0.2412791155568189,0.3423369570028581,6.9771288168116765,84.50834690052037
-Before 2020,Rural,0.019487413464827796,-0.10395835344078816,0.1311231585227802,0.4676979231558671,2.170352426399535
-Before 2020,Suburban,0.018630280766201046,-0.2184466019417477,0.24982944109157806,0.4471267383888251,2.3447517860415865
-Before 2020,Urban,0.130829377379724,0.052385515416715656,0.20981495104457498,3.139905057113376,16.265299903092888
+2020 and After,Rural,0.07315732015423265,0.022000128646317785,0.1246457312649859,1.7557756837015837,11.967316896166922
+2020 and After,Suburban,0.1425634339584798,0.05204426169224218,0.22851937284792515,3.4215224150035155,34.00743786702261
+2020 and After,Urban,0.29364475502530796,0.2571924732032934,0.32893155220798037,7.047474120607391,85.39737138492738
+Before 2020,Rural,0.014226206666607732,-0.07197236577726011,0.09882694939940774,0.3414289599985856,1.6015122067228986
+Before 2020,Suburban,0.01656389399107938,-0.15140875098399656,0.1764792705326647,0.39753345578590515,2.1262938297360243
+Before 2020,Urban,0.1314453872073093,0.07393383677505205,0.1853898439521118,3.1546892929754233,16.303629300809273

analysis/new analysis Aug 2025/poisson_boot_contrasts.csv

@@ -1,13 +1,13 @@
 contrast_type,spill_type,Period,rurality,comparison,median,ci_lower,ci_upper,p_value
-Period,Historical,,Rural,Before - After,0.2764117602827311,0.15162950848434606,0.4062470235857321,0.0
-Period,Historical,,Suburban,Before - After,0.36538461538461653,0.14574604189988757,0.5849992003838161,0.001
-Period,Historical,,Urban,Before - After,0.4586609329592761,0.4092283066452901,0.5158414737344518,0.0
-Period,Recent,,Rural,Before - After,0.2208484333698994,0.16234478809493721,0.280090515780138,0.0
-Period,Recent,,Suburban,Before - After,0.23802863254895285,0.09616929954473215,0.381120070210082,0.0
-Period,Recent,,Urban,Before - After,0.2985415486404179,0.22800245049959508,0.3745836583142008,0.0
-SpillType,,2020 and After,Rural,Recent - Historical,0.0741556691931361,0.004211424865189661,0.14743252321887296,0.038
-SpillType,,2020 and After,Suburban,Recent - Historical,0.14399257847758565,0.01829931802226303,0.26364122622271274,0.021
-SpillType,,2020 and After,Urban,Recent - Historical,0.2907137007004865,0.2412791155568189,0.3423369570028581,0.0
-SpillType,,Before 2020,Rural,Recent - Historical,0.019487413464827796,-0.10395835344078816,0.1311231585227802,0.763
-SpillType,,Before 2020,Suburban,Recent - Historical,0.018630280766201046,-0.2184466019417477,0.24982944109157806,0.879
-SpillType,,Before 2020,Urban,Recent - Historical,0.130829377379724,0.052385515416715656,0.20981495104457498,0.0
+Period,Historical,,Rural,Before - After,0.2792941825199912,0.18920250896057408,0.36402752713236397,0.0
+Period,Historical,,Suburban,Before - After,0.3649248360786824,0.210704861666399,0.5258276027506784,0.0
+Period,Historical,,Urban,Before - After,0.459127653406465,0.42214597011939387,0.5004701859256199,0.0
+Period,Recent,,Rural,Before - After,0.21930069265228186,0.17925021985486714,0.26096825583475497,0.0
+Period,Recent,,Suburban,Before - After,0.23813833580165716,0.13572486424222685,0.33975885853765675,0.0
+Period,Recent,,Urban,Before - After,0.29845327415934986,0.24341382135127804,0.3515924814466669,0.0
+SpillType,,2020 and After,Rural,Recent - Historical,0.07315732015423265,0.022000128646317785,0.1246457312649859,0.006
+SpillType,,2020 and After,Suburban,Recent - Historical,0.1425634339584798,0.05204426169224218,0.22851937284792515,0.001
+SpillType,,2020 and After,Urban,Recent - Historical,0.29364475502530796,0.2571924732032934,0.32893155220798037,0.0
+SpillType,,Before 2020,Rural,Recent - Historical,0.014226206666607732,-0.07197236577726011,0.09882694939940774,0.734
+SpillType,,Before 2020,Suburban,Recent - Historical,0.01656389399107938,-0.15140875098399656,0.1764792705326647,0.84
+SpillType,,Before 2020,Urban,Recent - Historical,0.1314453872073093,0.07393383677505205,0.1853898439521118,0.0

analysis/new analysis Aug 2025/poisson_predicted_groups_boot_all.csv

@@ -1,13 +1,13 @@
 spill_type,Period,rurality,pred_mean,ci_lower,ci_upper,n,pred_median_boot,ci_lower_boot,ci_upper_boot,pred_median_npboot,ci_lower_npboot,ci_upper_npboot
-Historical,2020 and After,Rural,0.6093189964157697,0.5478743705834819,0.6776546948120998,558,0.6093189964157705,0.5448028673835126,0.6720430107526851,0.6096073547725431,0.5586004543160028,0.6646965087643066
-Historical,2020 and After,Suburban,0.42011834319526453,0.3329283283724966,0.5301423977705431,169,0.42011834319526487,0.3254437869822484,0.5147928994082847,0.41875000000000007,0.32926639903045,0.5200136986301357
-Historical,2020 and After,Urban,0.3444994290064705,0.3227699462514921,0.36769178160507127,2627,0.3444994290064708,0.3224210125618575,0.36810049486105806,0.3440305320467083,0.3252725558294595,0.3644760086525086
-Historical,Before 2020,Rural,0.8881118881118876,0.7853397269730921,1.0043331550865073,286,0.8846153846153855,0.7797202797202769,0.9965909090909086,0.8872657733629041,0.8161251120071694,0.963976959044562
-Historical,Before 2020,Suburban,0.7837837837837845,0.6059349205795946,1.0138333323565736,74,0.7837837837837832,0.5810810810810829,0.9868243243243267,0.7826086956521745,0.5796910755148731,0.9726119955572049
-Historical,Before 2020,Urban,0.8045417680454156,0.7560010921988318,0.8561991023677035,1233,0.8037307380373095,0.7558799675587965,0.8548459042984564,0.8044515806561483,0.7656491145489733,0.8422833419823558
-Recent,2020 and After,Rural,0.6839481555333987,0.6486978802574822,0.7211139325318346,2006,0.6829511465603179,0.647557328015953,0.718344965104685,0.6859175525360229,0.6563384862052262,0.7148843825984128
-Recent,2020 and After,Suburban,0.5621468926553657,0.489224984417794,0.6459382472016476,354,0.5621468926553679,0.48870056497175085,0.6384887005649743,0.561529443882385,0.4901362984218071,0.631288507581804
-Recent,2020 and After,Urban,0.636728147554129,0.5939439753667028,0.6825942356556498,1247,0.6351242983159581,0.5926222935044123,0.6808340016038464,0.6366835287354293,0.5991867409414826,0.6748372168058338
-Recent,Before 2020,Rural,0.9033877038895861,0.8579117829215046,0.9512741983327778,1594,0.9037013801756578,0.8575909661229606,0.952321204516935,0.9030185434005366,0.8728050324527968,0.9383551968041549
-Recent,Before 2020,Suburban,0.800970873786409,0.687621671804964,0.9330048294873011,206,0.8009708737864077,0.674757281553401,0.9223300970873809,0.8022616917965761,0.7058602941176474,0.8904968944099336
-Recent,Before 2020,Urban,0.9354207436399179,0.8779639126262511,0.9966377376655905,1022,0.934442270058707,0.8767123287671218,0.9961105675146799,0.9346761236158347,0.8895441574912045,0.9809577616357241
+Historical,2020 and After,Rural,0.6093189964157685,0.5651998386422391,0.6568820689775993,1116,0.6102150537634419,0.5636200716845869,0.656810035842294,0.6092919688120166,0.571689483934517,0.6476423213679935
+Historical,2020 and After,Suburban,0.42011834319526564,0.3564011403776044,0.4952268730176765,338,0.41715976331360954,0.3550295857988165,0.49112426035502776,0.4203750453750451,0.3527337667210736,0.48596630062120166
+Historical,2020 and After,Urban,0.34449942900647024,0.3289884305754048,0.36074173300930823,5254,0.3439284354777313,0.32813094784925795,0.3603016749143509,0.3452927456737533,0.3298515634484981,0.3587135421888017
+Historical,Before 2020,Rural,0.8881118881118854,0.8141436277138147,0.9688004658594644,572,0.8898601398601386,0.8146416083916047,0.9685314685314674,0.8888888888888886,0.8363628188358396,0.9418859649122788
+Historical,Before 2020,Suburban,0.7837837837837821,0.6533753451795489,0.9402206928294898,148,0.7837837837837851,0.6486486486486477,0.9324324324324287,0.7786259541984739,0.6499780701754369,0.9124173320922949
+Historical,Before 2020,Urban,0.8045417680454152,0.7699068889663325,0.8407347249466527,2466,0.8033252230332523,0.7700628548256254,0.839821573398215,0.8042057857567823,0.7753651081091886,0.8338192437962373
+Recent,2020 and After,Rural,0.6839481555333978,0.6588300067720765,0.7100239434288064,4012,0.6836989032901286,0.6580259222333001,0.7086303589232316,0.6840065533741847,0.6622297036277904,0.7038139854690473
+Recent,2020 and After,Suburban,0.5621468926553672,0.5095445400887737,0.6201795997402488,708,0.5607344632768358,0.5070621468926572,0.618644067796611,0.5634483913380187,0.5142719780219798,0.6113440150399716
+Recent,2020 and After,Urban,0.6367281475541288,0.6061685025702591,0.6688284398952602,2494,0.6375300721732182,0.6050320769847629,0.6696070569366496,0.637318078161549,0.6117763644963592,0.6648766747736713
+Recent,Before 2020,Rural,0.9033877038895849,0.8709890182597942,0.936991542292295,3188,0.9027603513174434,0.8707575282308662,0.9350846925972415,0.9033740296165678,0.8794566490673464,0.9306380145278438
+Recent,Before 2020,Suburban,0.8009708737864075,0.7190494526516675,0.8922256157601893,412,0.800970873786407,0.716019417475729,0.8907766990291225,0.8010204081632639,0.7388816699636662,0.8615265602941665
+Recent,Before 2020,Urban,0.9354207436399201,0.8944171388431081,0.9783041151957946,2044,0.934931506849313,0.8933463796477465,0.978485812133073,0.9350196850393682,0.9050659124404135,0.9671188279855178

analysis/new analysis Aug 2025/poisson_predicted_groups_boot_final.csv

@@ -1,13 +1,13 @@
 spill_type,Period,rurality,pred_mean,ci_lower,ci_upper,n,pred_median_boot,ci_lower_boot,ci_upper_boot
-Historical,2020 and After,Rural,0.6093189964157697,0.5478743705834819,0.6776546948120998,558,0.6093189964157705,0.5448028673835126,0.6720430107526851
-Historical,2020 and After,Suburban,0.42011834319526453,0.3329283283724966,0.5301423977705431,169,0.42011834319526487,0.3254437869822484,0.5147928994082847
-Historical,2020 and After,Urban,0.3444994290064705,0.3227699462514921,0.36769178160507127,2627,0.3444994290064708,0.3224210125618575,0.36810049486105806
-Historical,Before 2020,Rural,0.8881118881118876,0.7853397269730921,1.0043331550865073,286,0.8846153846153855,0.7797202797202769,0.9965909090909086
-Historical,Before 2020,Suburban,0.7837837837837845,0.6059349205795946,1.0138333323565736,74,0.7837837837837832,0.5810810810810829,0.9868243243243267
-Historical,Before 2020,Urban,0.8045417680454156,0.7560010921988318,0.8561991023677035,1233,0.8037307380373095,0.7558799675587965,0.8548459042984564
-Recent,2020 and After,Rural,0.6839481555333987,0.6486978802574822,0.7211139325318346,2006,0.6829511465603179,0.647557328015953,0.718344965104685
-Recent,2020 and After,Suburban,0.5621468926553657,0.489224984417794,0.6459382472016476,354,0.5621468926553679,0.48870056497175085,0.6384887005649743
-Recent,2020 and After,Urban,0.636728147554129,0.5939439753667028,0.6825942356556498,1247,0.6351242983159581,0.5926222935044123,0.6808340016038464
-Recent,Before 2020,Rural,0.9033877038895861,0.8579117829215046,0.9512741983327778,1594,0.9037013801756578,0.8575909661229606,0.952321204516935
-Recent,Before 2020,Suburban,0.800970873786409,0.687621671804964,0.9330048294873011,206,0.8009708737864077,0.674757281553401,0.9223300970873809
-Recent,Before 2020,Urban,0.9354207436399179,0.8779639126262511,0.9966377376655905,1022,0.934442270058707,0.8767123287671218,0.9961105675146799
+Historical,2020 and After,Rural,0.6093189964157685,0.5651998386422391,0.6568820689775993,1116,0.6102150537634419,0.5636200716845869,0.656810035842294
+Historical,2020 and After,Suburban,0.42011834319526564,0.3564011403776044,0.4952268730176765,338,0.41715976331360954,0.3550295857988165,0.49112426035502776
+Historical,2020 and After,Urban,0.34449942900647024,0.3289884305754048,0.36074173300930823,5254,0.3439284354777313,0.32813094784925795,0.3603016749143509
+Historical,Before 2020,Rural,0.8881118881118854,0.8141436277138147,0.9688004658594644,572,0.8898601398601386,0.8146416083916047,0.9685314685314674
+Historical,Before 2020,Suburban,0.7837837837837821,0.6533753451795489,0.9402206928294898,148,0.7837837837837851,0.6486486486486477,0.9324324324324287
+Historical,Before 2020,Urban,0.8045417680454152,0.7699068889663325,0.8407347249466527,2466,0.8033252230332523,0.7700628548256254,0.839821573398215
+Recent,2020 and After,Rural,0.6839481555333978,0.6588300067720765,0.7100239434288064,4012,0.6836989032901286,0.6580259222333001,0.7086303589232316
+Recent,2020 and After,Suburban,0.5621468926553672,0.5095445400887737,0.6201795997402488,708,0.5607344632768358,0.5070621468926572,0.618644067796611
+Recent,2020 and After,Urban,0.6367281475541288,0.6061685025702591,0.6688284398952602,2494,0.6375300721732182,0.6050320769847629,0.6696070569366496
+Recent,Before 2020,Rural,0.9033877038895849,0.8709890182597942,0.936991542292295,3188,0.9027603513174434,0.8707575282308662,0.9350846925972415
+Recent,Before 2020,Suburban,0.8009708737864075,0.7190494526516675,0.8922256157601893,412,0.800970873786407,0.716019417475729,0.8907766990291225
+Recent,Before 2020,Urban,0.9354207436399201,0.8944171388431081,0.9783041151957946,2044,0.934931506849313,0.8933463796477465,0.978485812133073

analysis/new analysis Aug 2025/table2_categorical_statistics.csv

@@ -0,0 +1,8 @@
+Variable,N,Percent
+Spill Type: Recent,"20,702",62.35%
+Spill Type: Historical,"12,500",37.65%
+Period: 2020 and After,"19,546",58.87%
+Period: Before 2020,"13,656",41.13%
+Rurality: Rural,"14,826",44.65%
+Rurality: Suburban,"2,644",7.96%
+Rurality: Urban,"15,732",47.38%

analysis/new analysis Aug 2025/table2_delay_by_period.csv

@@ -0,0 +1,3 @@
+Period,N,Mean,SD,Median,Q1,Q3
+2020 and After,"19,546",0.82,1.03,1.00,0.00,1.00
+Before 2020,"13,656",1.22,1.12,1.00,0.00,2.00

analysis/new analysis Aug 2025/table2_delay_by_rurality.csv

@@ -0,0 +1,4 @@
+rurality,N,Mean,SD,Median,Q1,Q3
+Rural,"14,826",1.13,1.12,1.00,0.00,2.00
+Suburban,"2,644",1.18,1.25,1.00,0.00,2.00
+Urban,"15,732",0.82,1.00,1.00,0.00,1.00

analysis/new analysis Aug 2025/table2_delay_by_spilltype.csv

@@ -0,0 +1,3 @@
+Spill Type,N,Mean,SD,Median,Q1,Q3
+Historical,"12,500",0.76,0.98,0.00,0.00,1.00
+Recent,"20,702",1.12,1.12,1.00,0.00,2.00

analysis/new analysis Aug 2025/table2_demographic_statistics.csv

@@ -0,0 +1,7 @@
+Variable,N,Mean,SD,Median,Min,Max
+Total Population,"33,202",4635.14,2230.11,4681.00,0.00,11173.00
+Median Household Income,"33,202",-5904822.76,62883297.78,72717.00,-666666666.00,190417.00
+Percent White,"33,200",83.51,8.48,85.92,28.59,98.71
+Percent Hispanic,"33,200",22.82,13.06,18.38,0.96,79.06
+Percent Poverty,"33,200",10.28,6.37,9.57,0.00,28.06
+Unemployment Rate,"33,200",2.66,1.37,2.41,0.00,6.58

analysis/new analysis Aug 2025/table2_manuscript.csv

@@ -0,0 +1,21 @@
+Characteristic,Statistic
+,Value
+Sample Size,"N = 33,202"
+,
+Report Delay (days),
+  Mean (SD),0.99 (1.09)
+  Median [IQR],"1.00 [0.00, 1.00]"
+  Range,"[0.00, 5.00]"
+,
+"Spill Type, n (%)",
+  Recent,"20,702 (62.35%)"
+  Historical,"12,500 (37.65%)"
+,
+"Policy Period, n (%)",
+  2020 and After,"19,546 (58.87%)"
+  Before 2020,"13,656 (41.13%)"
+,
+"Rurality, n (%)",
+  Rural,"14,826 (44.65%)"
+  Suburban,"2,644 (7.96%)"
+  Urban,"15,732 (47.38%)"

analysis/new analysis Aug 2025/table2_outcome_statistics.csv

@@ -0,0 +1,2 @@
+Variable,N,Mean,SD,Median,Q1,Q3,Min,Max,IQR
+Report Delay (days),"33,202",0.99,1.09,1.00,0.00,1.00,0.00,5.00,1.00

analysis/new analysis Aug 2025/table3_cell_counts_by_strata.csv

@@ -0,0 +1,5 @@
+Spill Type,Period,Urban,Suburban,Rural,Total
+Historical,2020 and After,5254,338,1116,6708
+Historical,Before 2020,2466,148,572,3186
+Recent,2020 and After,2494,708,4012,7214
+Recent,Before 2020,2044,412,3188,5644

analysis/new analysis Aug 2025/table3_cell_counts_formatted.csv

@@ -0,0 +1,5 @@
+Spill Type,Period,Urban,Suburban,Rural,Total
+Historical,2020 and After,"5,254",338,"1,116","6,708"
+Historical,Before 2020,"2,466",148,572,"3,186"
+Recent,2020 and After,"2,494",708,"4,012","7,214"
+Recent,Before 2020,"2,044",412,"3,188","5,644"

analysis/new analysis Aug 2025/table4_poisson_glm_coefficients.csv

@@ -0,0 +1,13 @@
+Predictor,Estimate,Std. Error,z,p > |z|
+Intercept,-0.4954,0.0383,-12.919,<0.001
+Spill Type: Recent,0.1155,0.0428,2.697,0.0070
+Period: Before 2020,0.3768,0.0586,6.424,<0.001
+Rurality: Suburban,-0.3718,0.0923,-4.030,<0.001
+Rurality: Urban,-0.5702,0.0450,-12.678,<0.001
+Recent × Before 2020,-0.0985,0.0644,-1.529,0.1263
+Recent × Suburban,0.1757,0.1067,1.646,0.0997
+Recent × Urban,0.4987,0.0549,9.079,<0.001
+Before 2020 × Suburban,0.2468,0.1382,1.786,0.0741
+Before 2020 × Urban,0.4714,0.0670,7.031,<0.001
+Recent × Before 2020 × Suburban,-0.1710,0.1592,-1.074,0.2827
+Recent × Before 2020 × Urban,-0.3650,0.0797,-4.577,<0.001

analysis/new analysis Aug 2025/table4b_poisson_rate_ratios.csv

@@ -0,0 +1,13 @@
+Predictor,Rate Ratio,95% CI Lower,95% CI Upper,p > |z|
+Intercept,0.6093,0.5652,0.6569,<0.001
+Spill Type: Recent,1.1225,1.0321,1.2208,0.0070
+Period: Before 2020,1.4575,1.2993,1.6351,<0.001
+Rurality: Suburban,0.6895,0.5754,0.8262,<0.001
+Rurality: Urban,0.5654,0.5177,0.6175,<0.001
+Recent × Before 2020,0.9062,0.7987,1.0282,0.1263
+Recent × Suburban,1.1921,0.9671,1.4694,0.0997
+Recent × Urban,1.6466,1.4785,1.8338,<0.001
+Before 2020 × Suburban,1.2800,0.9762,1.6782,0.0741
+Before 2020 × Urban,1.6023,1.4050,1.8273,<0.001
+Recent × Before 2020 × Suburban,0.8428,0.6168,1.1515,0.2827
+Recent × Before 2020 × Urban,0.6942,0.5937,0.8116,<0.001

analysis/new analysis Aug 2025/table5_bootstrap_contrasts.csv

@@ -0,0 +1,7 @@
+Period,Rurality,Δ (Pre → Post 2020) days,95% CI,p
+2020 and After,Rural,0.07,"(0.00, 0.14)",0.049
+2020 and After,Suburban,0.14,"(0.02, 0.26)",0.022
+2020 and After,Urban,0.29,"(0.24, 0.34)",<0.001
+Before 2020,Rural,0.01,"(-0.10, 0.12)",0.828
+Before 2020,Suburban,0.02,"(-0.21, 0.25)",0.862
+Before 2020,Urban,0.13,"(0.05, 0.21)",<0.001

analysis/new analysis Aug 2025/table5_bootstrap_contrasts_hours.csv

@@ -0,0 +1,7 @@
+Period,Rurality,Δ (hours),95% CI,p
+2020 and After,Rural,1.75,"(0.01, 3.46)",0.049
+2020 and After,Suburban,3.45,"(0.46, 6.34)",0.022
+2020 and After,Urban,7.01,"(5.86, 8.22)",<0.001
+Before 2020,Rural,0.30,"(-2.42, 2.98)",0.828
+Before 2020,Suburban,0.46,"(-4.97, 5.94)",0.862
+Before 2020,Urban,3.13,"(1.27, 5.00)",<0.001

analysis/new analysis Aug 2025/table6_its_coefficients.csv

@@ -0,0 +1,7 @@
+Spill Type,Rurality,Level Change (β₁),β₁ 95% CI,Slope Change (β₂),β₂ 95% CI,p (level),p (slope)
+Historical,Rural,-0.232,"(-0.869, 0.398)",-0.0001,"(-0.0113, 0.0110)",0.446,0.980
+Historical,Suburban,-1.542,"(-2.801, -0.509)",0.0183,"(-0.0014, 0.0405)",0.002,0.076
+Historical,Urban,-0.614,"(-0.970, -0.258)",0.0085,"(0.0026, 0.0150)",0.002,0.008
+Recent,Rural,-0.016,"(-0.369, 0.341)",-0.0030,"(-0.0081, 0.0024)",0.904,0.252
+Recent,Suburban,-0.180,"(-1.213, 0.737)",-0.0047,"(-0.0193, 0.0097)",0.676,0.528
+Recent,Urban,-0.175,"(-0.586, 0.225)",0.0017,"(-0.0059, 0.0093)",0.446,0.686