## [1] "study_id"
## [2] "birthdt"
## [3] "intakedt"
## [4] "last_visit"
## [5] "last_predx_visit"
## [6] "birth_cohort"
## [7] "birth_cohort_2yr"
## [8] "last_predx_visit_age"
## [9] "onsetage"
## [10] "intakeage"
## [11] "last_visit_age"
## [12] "Onset_Age_ACT"
## [13] "male"
## [14] "hispanic"
## [15] "race"
## [16] "corrected_anydementia"
## [17] "corrected_dsmivdx"
## [18] "nindx"
## [19] "final_nindx"
## [20] "apoe"
## [21] "FamHx_AD"
## [22] "FamHx_Dx"
## [23] "FamHx_Vasc"
## [24] "cohort"
## [25] "CONSENT"
## [26] "AUTOPSY"
## [27] "corrected_onsetdate"
## [28] "income"
## [29] "livingsb"
## [30] "marital"
## [31] "smoke"
## [32] "pack_years"
## [33] "last_pack_years"
## [34] "alcohol"
## [35] "alc_problem"
## [36] "alc_prob_past"
## [37] "otherfx_new"
## [38] "tCESD_Score"
## [39] "bmi"
## [40] "bmi4"
## [41] "balance"
## [42] "Chair_Able"
## [43] "Chair_Time"
## [44] "exercise"
## [45] "exercise_reg"
## [46] "Gait_able"
## [47] "Gait_Aid"
## [48] "Gait_Time"
## [49] "grip_able"
## [50] "grip_strength"
## [51] "gripsmall"
## [52] "halfmile"
## [53] "heavyhouse"
## [54] "lift10lbs"
## [55] "reachabove"
## [56] "stairs"
## [57] "VitaminA"
## [58] "VitaminC"
## [59] "VitaminE"
## [60] "VitaminMulti"
## [61] "Fishoil"
## [62] "CHF"
## [63] "CV_DIS"
## [64] "Heart_Dis"
## [65] "Stroke"
## [66] "TIA"
## [67] "MI"
## [68] "avedia"
## [69] "avesys"
## [70] "cholmed"
## [71] "Hypertension"
## [72] "htnmed"
## [73] "Diabetes"
## [74] "CESD_Flag"
## [75] "CESD_Miss"
## [76] "CESD_Score"
## [77] "Asthma"
## [78] "COPD"
## [79] "IADL_Bills"
## [80] "iadl_flag"
## [81] "IADL_House"
## [82] "IADL_Meals"
## [83] "IADL_phone"
## [84] "IADL_shopping"
## [85] "iadl_sum"
## [86] "ForeFX"
## [87] "ForeFx_New"
## [88] "HipFX"
## [89] "HipFX_New"
## [90] "SpineFX"
## [91] "SpineFX_New"
## [92] "OtherFX"
## [93] "CASI_SC"
## [94] "casi_valid"
## [95] "casi_irt"
## [96] "CASI_IRT_SE"
## [97] "casi_ataj"
## [98] "casi_att"
## [99] "casi_draw"
## [100] "casi_flu"
## [101] "casi_lang"
## [102] "casi_ltm"
## [103] "casi_mmc"
## [104] "casi_ori"
## [105] "casi_stm"
## [106] "ratehealth"
## [107] "visits_db_visit_type"
## [108] "employment_status"
## [109] "occupation"
## [110] "DIAGCRIT"
## [111] "education"
## [112] "degree"
## [113] "deathdt"
## [114] "discontinue_reason"
## [115] "tr_med_inc_hshld"
## [116] "geo_income_65th"
## [117] "age_on_income"
## [118] "status"
## [119] "status_date"
## [120] "one_location"
## [121] "frontal_ripa_ab40"
## [122] "frontal_ripa_ab42"
## [123] "frontal_guhcl_ab40"
## [124] "frontal_guhcl_ab42"
## [125] "occipital_ripa_ab40"
## [126] "occipital_ripa_ab42"
## [127] "occipital_guhcl_ab40"
## [128] "occipital_guhcl_ab42"
## [129] "temporal_ripa_ab40"
## [130] "temporal_ripa_ab42"
## [131] "temporal_guhcl_ab40"
## [132] "temporal_guhcl_ab42"
## [133] "parietal_ripa_ab40"
## [134] "parietal_ripa_ab42"
## [135] "parietal_guhcl_ab40"
## [136] "parietal_guhcl_ab42"
## [137] "pmi_hrs"
## [138] "freshbrainwt_grams"
## [139] "cerad_score"
## [140] "braak_stg"
## [141] "amylangi_score"
## [142] "athero_level"
## [143] "arteriolo_level"
## [144] "late_stg"
## [145] "thal_phase"
## [146] "hipscl_any"
## [147] "lewybodydis_cat"
## [148] "chronic_grossinfarcts_any"
## [149] "chronic_grossinfarcts_num"
## [150] "chronic_grossinfarcts_num_ctx"
## [151] "chronic_grossinfarcts_num_wm"
## [152] "chronic_grossinfarcts_num_gm"
## [153] "chronic_grossinfarcts_num_bs"
## [154] "chronic_microinfarcts_any"
## [155] "chronic_microinfarcts_num"
## [156] "chronic_microinfarcts_num_cere"
## [157] "chronic_microinfarcts_num_deep"
## [158] "apoe_status"
## [159] "cognitive_status"
## [160] "thal"
## [161] "atherosclerosis"
## [162] "arteriolosclerosis"
## [163] "Hippocampal_Sclerosis"
## [164] "Consensus_Clinical_Dx"
## [165] "If_other_Consensus__describe"
## [166] "Primary_Neuropath_Dx"
## [167] "Contributing_Neuropath_Dx"
## [168] "Contributing_Other"
## [169] "Fresh_Brain_Weight"
## [170] "BRAAK"
## [171] "C_score"
## [172] "Micro_unclassified"
## [173] "micro_deep_unclassified"
## [174] "Total_Microinfarcts"
## [175] "Primary_Other"
## [176] "overall_ad_neuro_change"
## [177] "hlb_disease"
## [178] "caa_score"
## [179] "pmi"
## [180] "year_65"
## [181] "has_neuro"
## [182] "exp_avg65th_bday_01_yr_MM_bc"
## [183] "exp_avg65th_bday_01_yr_MM_co2"
## [184] "exp_avg65th_bday_01_yr_MM_no2"
## [185] "exp_avg65th_bday_01_yr_MM_pm25"
## [186] "exp_avg65th_bday_01_yr_MM_ufp_10_42"
## [187] "exp_avg65th_bday_01_yr_MM_ufp_10_70"
## [188] "exp_avg65th_bday_01_yr_MM_ufp_20_1k"
## [189] "exp_avg65th_bday_01_yr_MM_ufp_36_1k"
## [190] "exp_avg65th_bday_10_yr_MM_bc"
## [191] "exp_avg65th_bday_10_yr_MM_co2"
## [192] "exp_avg65th_bday_10_yr_MM_no2"
## [193] "exp_avg65th_bday_10_yr_MM_pm25"
## [194] "exp_avg65th_bday_10_yr_MM_ufp_10_42"
## [195] "exp_avg65th_bday_10_yr_MM_ufp_10_70"
## [196] "exp_avg65th_bday_10_yr_MM_ufp_20_1k"
## [197] "exp_avg65th_bday_10_yr_MM_ufp_36_1k"
## [198] "exp_avgdeath_01_yr_MM_bc"
## [199] "exp_avgdeath_01_yr_MM_co2"
## [200] "exp_avgdeath_01_yr_MM_no2"
## [201] "exp_avgdeath_01_yr_MM_pm25"
## [202] "exp_avgdeath_01_yr_MM_ufp_10_42"
## [203] "exp_avgdeath_01_yr_MM_ufp_10_70"
## [204] "exp_avgdeath_01_yr_MM_ufp_20_1k"
## [205] "exp_avgdeath_01_yr_MM_ufp_36_1k"
## [206] "exp_avgdeath_05_yr_MM_bc"
## [207] "exp_avgdeath_05_yr_MM_co2"
## [208] "exp_avgdeath_05_yr_MM_no2"
## [209] "exp_avgdeath_05_yr_MM_pm25"
## [210] "exp_avgdeath_05_yr_MM_ufp_10_42"
## [211] "exp_avgdeath_05_yr_MM_ufp_10_70"
## [212] "exp_avgdeath_05_yr_MM_ufp_20_1k"
## [213] "exp_avgdeath_05_yr_MM_ufp_36_1k"
## [214] "exp_avgdeath_05_yr10yrlag_MM_bc"
## [215] "exp_avgdeath_05_yr10yrlag_MM_co2"
## [216] "exp_avgdeath_05_yr10yrlag_MM_no2"
## [217] "exp_avgdeath_05_yr10yrlag_MM_pm25"
## [218] "exp_avgdeath_05_yr10yrlag_MM_ufp_10_42"
## [219] "exp_avgdeath_05_yr10yrlag_MM_ufp_10_70"
## [220] "exp_avgdeath_05_yr10yrlag_MM_ufp_20_1k"
## [221] "exp_avgdeath_05_yr10yrlag_MM_ufp_36_1k"
## [222] "exp_avgdeath_10_yr_MM_bc"
## [223] "exp_avgdeath_10_yr_MM_co2"
## [224] "exp_avgdeath_10_yr_MM_no2"
## [225] "exp_avgdeath_10_yr_MM_pm25"
## [226] "exp_avgdeath_10_yr_MM_ufp_10_42"
## [227] "exp_avgdeath_10_yr_MM_ufp_10_70"
## [228] "exp_avgdeath_10_yr_MM_ufp_20_1k"
## [229] "exp_avgdeath_10_yr_MM_ufp_36_1k"
## [230] "exp_avgdeath_20_yr_MM_bc"
## [231] "exp_avgdeath_20_yr_MM_co2"
## [232] "exp_avgdeath_20_yr_MM_no2"
## [233] "exp_avgdeath_20_yr_MM_pm25"
## [234] "exp_avgdeath_20_yr_MM_ufp_10_42"
## [235] "exp_avgdeath_20_yr_MM_ufp_10_70"
## [236] "exp_avgdeath_20_yr_MM_ufp_20_1k"
## [237] "exp_avgdeath_20_yr_MM_ufp_36_1k"
## [238] "exp_avgdeath_01_yr_SP_bc"
## [239] "exp_avgdeath_01_yr_SP_co2"
## [240] "exp_avgdeath_01_yr_SP_no2"
## [241] "exp_avgdeath_01_yr_SP_pm25"
## [242] "exp_avgdeath_01_yr_SP_ufp_10_42"
## [243] "exp_avgdeath_01_yr_SP_ufp_10_70"
## [244] "exp_avgdeath_01_yr_SP_ufp_20_1k"
## [245] "exp_avgdeath_01_yr_SP_ufp_36_1k"
## [246] "exp_avgdeath_01_yr_ST_bc"
## [247] "exp_avgdeath_01_yr_ST_co2"
## [248] "exp_avgdeath_01_yr_ST_no2"
## [249] "exp_avgdeath_01_yr_ST_pm25"
## [250] "exp_avgdeath_01_yr_ST_ufp_10_42"
## [251] "exp_avgdeath_01_yr_ST_ufp_10_70"
## [252] "exp_avgdeath_01_yr_ST_ufp_20_1k"
## [253] "exp_avgdeath_01_yr_ST_ufp_36_1k"
## [254] "exp_avgdeath_05_yr_SP_bc"
## [255] "exp_avgdeath_05_yr_SP_co2"
## [256] "exp_avgdeath_05_yr_SP_no2"
## [257] "exp_avgdeath_05_yr_SP_pm25"
## [258] "exp_avgdeath_05_yr_SP_ufp_10_42"
## [259] "exp_avgdeath_05_yr_SP_ufp_10_70"
## [260] "exp_avgdeath_05_yr_SP_ufp_20_1k"
## [261] "exp_avgdeath_05_yr_SP_ufp_36_1k"
## [262] "exp_avgdeath_05_yr_ST_bc"
## [263] "exp_avgdeath_05_yr_ST_co2"
## [264] "exp_avgdeath_05_yr_ST_no2"
## [265] "exp_avgdeath_05_yr_ST_pm25"
## [266] "exp_avgdeath_05_yr_ST_ufp_10_42"
## [267] "exp_avgdeath_05_yr_ST_ufp_10_70"
## [268] "exp_avgdeath_05_yr_ST_ufp_20_1k"
## [269] "exp_avgdeath_05_yr_ST_ufp_36_1k"
## [270] "exp_avgdeath_05_yr10yrlag_SP_bc"
## [271] "exp_avgdeath_05_yr10yrlag_SP_co2"
## [272] "exp_avgdeath_05_yr10yrlag_SP_no2"
## [273] "exp_avgdeath_05_yr10yrlag_SP_pm25"
## [274] "exp_avgdeath_05_yr10yrlag_SP_ufp_10_42"
## [275] "exp_avgdeath_05_yr10yrlag_SP_ufp_10_70"
## [276] "exp_avgdeath_05_yr10yrlag_SP_ufp_20_1k"
## [277] "exp_avgdeath_05_yr10yrlag_SP_ufp_36_1k"
## [278] "exp_avgdeath_05_yr10yrlag_ST_bc"
## [279] "exp_avgdeath_05_yr10yrlag_ST_co2"
## [280] "exp_avgdeath_05_yr10yrlag_ST_no2"
## [281] "exp_avgdeath_05_yr10yrlag_ST_pm25"
## [282] "exp_avgdeath_05_yr10yrlag_ST_ufp_10_42"
## [283] "exp_avgdeath_05_yr10yrlag_ST_ufp_10_70"
## [284] "exp_avgdeath_05_yr10yrlag_ST_ufp_20_1k"
## [285] "exp_avgdeath_05_yr10yrlag_ST_ufp_36_1k"
## [286] "exp_avgdeath_10_yr_SP_bc"
## [287] "exp_avgdeath_10_yr_SP_co2"
## [288] "exp_avgdeath_10_yr_SP_no2"
## [289] "exp_avgdeath_10_yr_SP_pm25"
## [290] "exp_avgdeath_10_yr_SP_ufp_10_42"
## [291] "exp_avgdeath_10_yr_SP_ufp_10_70"
## [292] "exp_avgdeath_10_yr_SP_ufp_20_1k"
## [293] "exp_avgdeath_10_yr_SP_ufp_36_1k"
## [294] "exp_avgdeath_10_yr_ST_bc"
## [295] "exp_avgdeath_10_yr_ST_co2"
## [296] "exp_avgdeath_10_yr_ST_no2"
## [297] "exp_avgdeath_10_yr_ST_pm25"
## [298] "exp_avgdeath_10_yr_ST_ufp_10_42"
## [299] "exp_avgdeath_10_yr_ST_ufp_10_70"
## [300] "exp_avgdeath_10_yr_ST_ufp_20_1k"
## [301] "exp_avgdeath_10_yr_ST_ufp_36_1k"
## [302] "exp_avgdeath_20_yr_SP_bc"
## [303] "exp_avgdeath_20_yr_SP_co2"
## [304] "exp_avgdeath_20_yr_SP_no2"
## [305] "exp_avgdeath_20_yr_SP_pm25"
## [306] "exp_avgdeath_20_yr_SP_ufp_10_42"
## [307] "exp_avgdeath_20_yr_SP_ufp_10_70"
## [308] "exp_avgdeath_20_yr_SP_ufp_20_1k"
## [309] "exp_avgdeath_20_yr_SP_ufp_36_1k"
## [310] "exp_avgdeath_20_yr_ST_bc"
## [311] "exp_avgdeath_20_yr_ST_co2"
## [312] "exp_avgdeath_20_yr_ST_no2"
## [313] "exp_avgdeath_20_yr_ST_pm25"
## [314] "exp_avgdeath_20_yr_ST_ufp_10_42"
## [315] "exp_avgdeath_20_yr_ST_ufp_10_70"
## [316] "exp_avgdeath_20_yr_ST_ufp_20_1k"
## [317] "exp_avgdeath_20_yr_ST_ufp_36_1k"
## [318] "exp_wks_cvg65th_bday_01_yr_MM_bc"
## [319] "exp_wks_cvg65th_bday_01_yr_MM_co2"
## [320] "exp_wks_cvg65th_bday_01_yr_MM_no2"
## [321] "exp_wks_cvg65th_bday_01_yr_MM_pm25"
## [322] "exp_wks_cvg65th_bday_01_yr_MM_ufp_10_42"
## [323] "exp_wks_cvg65th_bday_01_yr_MM_ufp_10_70"
## [324] "exp_wks_cvg65th_bday_01_yr_MM_ufp_20_1k"
## [325] "exp_wks_cvg65th_bday_01_yr_MM_ufp_36_1k"
## [326] "exp_wks_cvg65th_bday_10_yr_MM_bc"
## [327] "exp_wks_cvg65th_bday_10_yr_MM_co2"
## [328] "exp_wks_cvg65th_bday_10_yr_MM_no2"
## [329] "exp_wks_cvg65th_bday_10_yr_MM_pm25"
## [330] "exp_wks_cvg65th_bday_10_yr_MM_ufp_10_42"
## [331] "exp_wks_cvg65th_bday_10_yr_MM_ufp_10_70"
## [332] "exp_wks_cvg65th_bday_10_yr_MM_ufp_20_1k"
## [333] "exp_wks_cvg65th_bday_10_yr_MM_ufp_36_1k"
## [334] "exp_wks_cvgdeath_01_yr_MM_bc"
## [335] "exp_wks_cvgdeath_01_yr_MM_co2"
## [336] "exp_wks_cvgdeath_01_yr_MM_no2"
## [337] "exp_wks_cvgdeath_01_yr_MM_pm25"
## [338] "exp_wks_cvgdeath_01_yr_MM_ufp_10_42"
## [339] "exp_wks_cvgdeath_01_yr_MM_ufp_10_70"
## [340] "exp_wks_cvgdeath_01_yr_MM_ufp_20_1k"
## [341] "exp_wks_cvgdeath_01_yr_MM_ufp_36_1k"
## [342] "exp_wks_cvgdeath_05_yr_MM_bc"
## [343] "exp_wks_cvgdeath_05_yr_MM_co2"
## [344] "exp_wks_cvgdeath_05_yr_MM_no2"
## [345] "exp_wks_cvgdeath_05_yr_MM_pm25"
## [346] "exp_wks_cvgdeath_05_yr_MM_ufp_10_42"
## [347] "exp_wks_cvgdeath_05_yr_MM_ufp_10_70"
## [348] "exp_wks_cvgdeath_05_yr_MM_ufp_20_1k"
## [349] "exp_wks_cvgdeath_05_yr_MM_ufp_36_1k"
## [350] "exp_wks_cvgdeath_05_yr10yrlag_MM_bc"
## [351] "exp_wks_cvgdeath_05_yr10yrlag_MM_co2"
## [352] "exp_wks_cvgdeath_05_yr10yrlag_MM_no2"
## [353] "exp_wks_cvgdeath_05_yr10yrlag_MM_pm25"
## [354] "exp_wks_cvgdeath_05_yr10yrlag_MM_ufp_10_42"
## [355] "exp_wks_cvgdeath_05_yr10yrlag_MM_ufp_10_70"
## [356] "exp_wks_cvgdeath_05_yr10yrlag_MM_ufp_20_1k"
## [357] "exp_wks_cvgdeath_05_yr10yrlag_MM_ufp_36_1k"
## [358] "exp_wks_cvgdeath_10_yr_MM_bc"
## [359] "exp_wks_cvgdeath_10_yr_MM_co2"
## [360] "exp_wks_cvgdeath_10_yr_MM_no2"
## [361] "exp_wks_cvgdeath_10_yr_MM_pm25"
## [362] "exp_wks_cvgdeath_10_yr_MM_ufp_10_42"
## [363] "exp_wks_cvgdeath_10_yr_MM_ufp_10_70"
## [364] "exp_wks_cvgdeath_10_yr_MM_ufp_20_1k"
## [365] "exp_wks_cvgdeath_10_yr_MM_ufp_36_1k"
## [366] "exp_wks_cvgdeath_20_yr_MM_bc"
## [367] "exp_wks_cvgdeath_20_yr_MM_co2"
## [368] "exp_wks_cvgdeath_20_yr_MM_no2"
## [369] "exp_wks_cvgdeath_20_yr_MM_pm25"
## [370] "exp_wks_cvgdeath_20_yr_MM_ufp_10_42"
## [371] "exp_wks_cvgdeath_20_yr_MM_ufp_10_70"
## [372] "exp_wks_cvgdeath_20_yr_MM_ufp_20_1k"
## [373] "exp_wks_cvgdeath_20_yr_MM_ufp_36_1k"
## [374] "exp_wks_cvgdeath_01_yr_SP_bc"
## [375] "exp_wks_cvgdeath_01_yr_SP_co2"
## [376] "exp_wks_cvgdeath_01_yr_SP_no2"
## [377] "exp_wks_cvgdeath_01_yr_SP_pm25"
## [378] "exp_wks_cvgdeath_01_yr_SP_ufp_10_42"
## [379] "exp_wks_cvgdeath_01_yr_SP_ufp_10_70"
## [380] "exp_wks_cvgdeath_01_yr_SP_ufp_20_1k"
## [381] "exp_wks_cvgdeath_01_yr_SP_ufp_36_1k"
## [382] "exp_wks_cvgdeath_01_yr_ST_bc"
## [383] "exp_wks_cvgdeath_01_yr_ST_co2"
## [384] "exp_wks_cvgdeath_01_yr_ST_no2"
## [385] "exp_wks_cvgdeath_01_yr_ST_pm25"
## [386] "exp_wks_cvgdeath_01_yr_ST_ufp_10_42"
## [387] "exp_wks_cvgdeath_01_yr_ST_ufp_10_70"
## [388] "exp_wks_cvgdeath_01_yr_ST_ufp_20_1k"
## [389] "exp_wks_cvgdeath_01_yr_ST_ufp_36_1k"
## [390] "exp_wks_cvgdeath_05_yr_SP_bc"
## [391] "exp_wks_cvgdeath_05_yr_SP_co2"
## [392] "exp_wks_cvgdeath_05_yr_SP_no2"
## [393] "exp_wks_cvgdeath_05_yr_SP_pm25"
## [394] "exp_wks_cvgdeath_05_yr_SP_ufp_10_42"
## [395] "exp_wks_cvgdeath_05_yr_SP_ufp_10_70"
## [396] "exp_wks_cvgdeath_05_yr_SP_ufp_20_1k"
## [397] "exp_wks_cvgdeath_05_yr_SP_ufp_36_1k"
## [398] "exp_wks_cvgdeath_05_yr_ST_bc"
## [399] "exp_wks_cvgdeath_05_yr_ST_co2"
## [400] "exp_wks_cvgdeath_05_yr_ST_no2"
## [401] "exp_wks_cvgdeath_05_yr_ST_pm25"
## [402] "exp_wks_cvgdeath_05_yr_ST_ufp_10_42"
## [403] "exp_wks_cvgdeath_05_yr_ST_ufp_10_70"
## [404] "exp_wks_cvgdeath_05_yr_ST_ufp_20_1k"
## [405] "exp_wks_cvgdeath_05_yr_ST_ufp_36_1k"
## [406] "exp_wks_cvgdeath_05_yr10yrlag_SP_bc"
## [407] "exp_wks_cvgdeath_05_yr10yrlag_SP_co2"
## [408] "exp_wks_cvgdeath_05_yr10yrlag_SP_no2"
## [409] "exp_wks_cvgdeath_05_yr10yrlag_SP_pm25"
## [410] "exp_wks_cvgdeath_05_yr10yrlag_SP_ufp_10_42"
## [411] "exp_wks_cvgdeath_05_yr10yrlag_SP_ufp_10_70"
## [412] "exp_wks_cvgdeath_05_yr10yrlag_SP_ufp_20_1k"
## [413] "exp_wks_cvgdeath_05_yr10yrlag_SP_ufp_36_1k"
## [414] "exp_wks_cvgdeath_05_yr10yrlag_ST_bc"
## [415] "exp_wks_cvgdeath_05_yr10yrlag_ST_co2"
## [416] "exp_wks_cvgdeath_05_yr10yrlag_ST_no2"
## [417] "exp_wks_cvgdeath_05_yr10yrlag_ST_pm25"
## [418] "exp_wks_cvgdeath_05_yr10yrlag_ST_ufp_10_42"
## [419] "exp_wks_cvgdeath_05_yr10yrlag_ST_ufp_10_70"
## [420] "exp_wks_cvgdeath_05_yr10yrlag_ST_ufp_20_1k"
## [421] "exp_wks_cvgdeath_05_yr10yrlag_ST_ufp_36_1k"
## [422] "exp_wks_cvgdeath_10_yr_SP_bc"
## [423] "exp_wks_cvgdeath_10_yr_SP_co2"
## [424] "exp_wks_cvgdeath_10_yr_SP_no2"
## [425] "exp_wks_cvgdeath_10_yr_SP_pm25"
## [426] "exp_wks_cvgdeath_10_yr_SP_ufp_10_42"
## [427] "exp_wks_cvgdeath_10_yr_SP_ufp_10_70"
## [428] "exp_wks_cvgdeath_10_yr_SP_ufp_20_1k"
## [429] "exp_wks_cvgdeath_10_yr_SP_ufp_36_1k"
## [430] "exp_wks_cvgdeath_10_yr_ST_bc"
## [431] "exp_wks_cvgdeath_10_yr_ST_co2"
## [432] "exp_wks_cvgdeath_10_yr_ST_no2"
## [433] "exp_wks_cvgdeath_10_yr_ST_pm25"
## [434] "exp_wks_cvgdeath_10_yr_ST_ufp_10_42"
## [435] "exp_wks_cvgdeath_10_yr_ST_ufp_10_70"
## [436] "exp_wks_cvgdeath_10_yr_ST_ufp_20_1k"
## [437] "exp_wks_cvgdeath_10_yr_ST_ufp_36_1k"
## [438] "exp_wks_cvgdeath_20_yr_SP_bc"
## [439] "exp_wks_cvgdeath_20_yr_SP_co2"
## [440] "exp_wks_cvgdeath_20_yr_SP_no2"
## [441] "exp_wks_cvgdeath_20_yr_SP_pm25"
## [442] "exp_wks_cvgdeath_20_yr_SP_ufp_10_42"
## [443] "exp_wks_cvgdeath_20_yr_SP_ufp_10_70"
## [444] "exp_wks_cvgdeath_20_yr_SP_ufp_20_1k"
## [445] "exp_wks_cvgdeath_20_yr_SP_ufp_36_1k"
## [446] "exp_wks_cvgdeath_20_yr_ST_bc"
## [447] "exp_wks_cvgdeath_20_yr_ST_co2"
## [448] "exp_wks_cvgdeath_20_yr_ST_no2"
## [449] "exp_wks_cvgdeath_20_yr_ST_pm25"
## [450] "exp_wks_cvgdeath_20_yr_ST_ufp_10_42"
## [451] "exp_wks_cvgdeath_20_yr_ST_ufp_10_70"
## [452] "exp_wks_cvgdeath_20_yr_ST_ufp_20_1k"
## [453] "exp_wks_cvgdeath_20_yr_ST_ufp_36_1k"
## [454] "exact_covgdeath_01_yr_bc"
## [455] "exact_covgdeath_01_yr_co2"
## [456] "exact_covgdeath_01_yr_no2"
## [457] "exact_covgdeath_01_yr_pm25"
## [458] "exact_covgdeath_01_yr_ufp_10_42"
## [459] "exact_covgdeath_01_yr_ufp_10_70"
## [460] "exact_covgdeath_01_yr_ufp_20_1k"
## [461] "exact_covgdeath_01_yr_ufp_36_1k"
## [462] "exact_covgdeath_05_yr_bc"
## [463] "exact_covgdeath_05_yr_co2"
## [464] "exact_covgdeath_05_yr_no2"
## [465] "exact_covgdeath_05_yr_pm25"
## [466] "exact_covgdeath_05_yr_ufp_10_42"
## [467] "exact_covgdeath_05_yr_ufp_10_70"
## [468] "exact_covgdeath_05_yr_ufp_20_1k"
## [469] "exact_covgdeath_05_yr_ufp_36_1k"
## [470] "exact_covgdeath_05_yr10yrlag_bc"
## [471] "exact_covgdeath_05_yr10yrlag_co2"
## [472] "exact_covgdeath_05_yr10yrlag_no2"
## [473] "exact_covgdeath_05_yr10yrlag_pm25"
## [474] "exact_covgdeath_05_yr10yrlag_ufp_10_42"
## [475] "exact_covgdeath_05_yr10yrlag_ufp_10_70"
## [476] "exact_covgdeath_05_yr10yrlag_ufp_20_1k"
## [477] "exact_covgdeath_05_yr10yrlag_ufp_36_1k"
## [478] "exact_covgdeath_10_yr_bc"
## [479] "exact_covgdeath_10_yr_co2"
## [480] "exact_covgdeath_10_yr_no2"
## [481] "exact_covgdeath_10_yr_pm25"
## [482] "exact_covgdeath_10_yr_ufp_10_42"
## [483] "exact_covgdeath_10_yr_ufp_10_70"
## [484] "exact_covgdeath_10_yr_ufp_20_1k"
## [485] "exact_covgdeath_10_yr_ufp_36_1k"
## [486] "exact_covgdeath_20_yr_bc"
## [487] "exact_covgdeath_20_yr_co2"
## [488] "exact_covgdeath_20_yr_no2"
## [489] "exact_covgdeath_20_yr_pm25"
## [490] "exact_covgdeath_20_yr_ufp_10_42"
## [491] "exact_covgdeath_20_yr_ufp_10_70"
## [492] "exact_covgdeath_20_yr_ufp_20_1k"
## [493] "exact_covgdeath_20_yr_ufp_36_1k"
## [494] "imptd_covgdeath_01_yr_bc"
## [495] "imptd_covgdeath_01_yr_co2"
## [496] "imptd_covgdeath_01_yr_no2"
## [497] "imptd_covgdeath_01_yr_pm25"
## [498] "imptd_covgdeath_01_yr_ufp_10_42"
## [499] "imptd_covgdeath_01_yr_ufp_10_70"
## [500] "imptd_covgdeath_01_yr_ufp_20_1k"
## [501] "imptd_covgdeath_01_yr_ufp_36_1k"
## [502] "imptd_covgdeath_05_yr_bc"
## [503] "imptd_covgdeath_05_yr_co2"
## [504] "imptd_covgdeath_05_yr_no2"
## [505] "imptd_covgdeath_05_yr_pm25"
## [506] "imptd_covgdeath_05_yr_ufp_10_42"
## [507] "imptd_covgdeath_05_yr_ufp_10_70"
## [508] "imptd_covgdeath_05_yr_ufp_20_1k"
## [509] "imptd_covgdeath_05_yr_ufp_36_1k"
## [510] "imptd_covgdeath_05_yr10yrlag_bc"
## [511] "imptd_covgdeath_05_yr10yrlag_co2"
## [512] "imptd_covgdeath_05_yr10yrlag_no2"
## [513] "imptd_covgdeath_05_yr10yrlag_pm25"
## [514] "imptd_covgdeath_05_yr10yrlag_ufp_10_42"
## [515] "imptd_covgdeath_05_yr10yrlag_ufp_10_70"
## [516] "imptd_covgdeath_05_yr10yrlag_ufp_20_1k"
## [517] "imptd_covgdeath_05_yr10yrlag_ufp_36_1k"
## [518] "imptd_covgdeath_10_yr_bc"
## [519] "imptd_covgdeath_10_yr_co2"
## [520] "imptd_covgdeath_10_yr_no2"
## [521] "imptd_covgdeath_10_yr_pm25"
## [522] "imptd_covgdeath_10_yr_ufp_10_42"
## [523] "imptd_covgdeath_10_yr_ufp_10_70"
## [524] "imptd_covgdeath_10_yr_ufp_20_1k"
## [525] "imptd_covgdeath_10_yr_ufp_36_1k"
## [526] "imptd_covgdeath_20_yr_bc"
## [527] "imptd_covgdeath_20_yr_co2"
## [528] "imptd_covgdeath_20_yr_no2"
## [529] "imptd_covgdeath_20_yr_pm25"
## [530] "imptd_covgdeath_20_yr_ufp_10_42"
## [531] "imptd_covgdeath_20_yr_ufp_10_70"
## [532] "imptd_covgdeath_20_yr_ufp_20_1k"
## [533] "imptd_covgdeath_20_yr_ufp_36_1k"
## [534] "imp_qualdeath_01_yr_bc"
## [535] "imp_qualdeath_01_yr_co2"
## [536] "imp_qualdeath_01_yr_no2"
## [537] "imp_qualdeath_01_yr_pm25"
## [538] "imp_qualdeath_01_yr_ufp_10_42"
## [539] "imp_qualdeath_01_yr_ufp_10_70"
## [540] "imp_qualdeath_01_yr_ufp_20_1k"
## [541] "imp_qualdeath_01_yr_ufp_36_1k"
## [542] "imp_qualdeath_05_yr_bc"
## [543] "imp_qualdeath_05_yr_co2"
## [544] "imp_qualdeath_05_yr_no2"
## [545] "imp_qualdeath_05_yr_pm25"
## [546] "imp_qualdeath_05_yr_ufp_10_42"
## [547] "imp_qualdeath_05_yr_ufp_10_70"
## [548] "imp_qualdeath_05_yr_ufp_20_1k"
## [549] "imp_qualdeath_05_yr_ufp_36_1k"
## [550] "imp_qualdeath_05_yr10yrlag_bc"
## [551] "imp_qualdeath_05_yr10yrlag_co2"
## [552] "imp_qualdeath_05_yr10yrlag_no2"
## [553] "imp_qualdeath_05_yr10yrlag_pm25"
## [554] "imp_qualdeath_05_yr10yrlag_ufp_10_42"
## [555] "imp_qualdeath_05_yr10yrlag_ufp_10_70"
## [556] "imp_qualdeath_05_yr10yrlag_ufp_20_1k"
## [557] "imp_qualdeath_05_yr10yrlag_ufp_36_1k"
## [558] "imp_qualdeath_10_yr_bc"
## [559] "imp_qualdeath_10_yr_co2"
## [560] "imp_qualdeath_10_yr_no2"
## [561] "imp_qualdeath_10_yr_pm25"
## [562] "imp_qualdeath_10_yr_ufp_10_42"
## [563] "imp_qualdeath_10_yr_ufp_10_70"
## [564] "imp_qualdeath_10_yr_ufp_20_1k"
## [565] "imp_qualdeath_10_yr_ufp_36_1k"
## [566] "imp_qualdeath_20_yr_bc"
## [567] "imp_qualdeath_20_yr_co2"
## [568] "imp_qualdeath_20_yr_no2"
## [569] "imp_qualdeath_20_yr_pm25"
## [570] "imp_qualdeath_20_yr_ufp_10_42"
## [571] "imp_qualdeath_20_yr_ufp_10_70"
## [572] "imp_qualdeath_20_yr_ufp_20_1k"
## [573] "imp_qualdeath_20_yr_ufp_36_1k"
## [574] "exp_avg65th_bday_01_yr_SP_bc"
## [575] "exp_avg65th_bday_01_yr_SP_co2"
## [576] "exp_avg65th_bday_01_yr_SP_no2"
## [577] "exp_avg65th_bday_01_yr_SP_pm25"
## [578] "exp_avg65th_bday_01_yr_SP_ufp_10_42"
## [579] "exp_avg65th_bday_01_yr_SP_ufp_10_70"
## [580] "exp_avg65th_bday_01_yr_SP_ufp_20_1k"
## [581] "exp_avg65th_bday_01_yr_SP_ufp_36_1k"
## [582] "exp_avg65th_bday_01_yr_ST_bc"
## [583] "exp_avg65th_bday_01_yr_ST_co2"
## [584] "exp_avg65th_bday_01_yr_ST_no2"
## [585] "exp_avg65th_bday_01_yr_ST_pm25"
## [586] "exp_avg65th_bday_01_yr_ST_ufp_10_42"
## [587] "exp_avg65th_bday_01_yr_ST_ufp_10_70"
## [588] "exp_avg65th_bday_01_yr_ST_ufp_20_1k"
## [589] "exp_avg65th_bday_01_yr_ST_ufp_36_1k"
## [590] "exp_avg65th_bday_10_yr_SP_bc"
## [591] "exp_avg65th_bday_10_yr_SP_co2"
## [592] "exp_avg65th_bday_10_yr_SP_no2"
## [593] "exp_avg65th_bday_10_yr_SP_pm25"
## [594] "exp_avg65th_bday_10_yr_SP_ufp_10_42"
## [595] "exp_avg65th_bday_10_yr_SP_ufp_10_70"
## [596] "exp_avg65th_bday_10_yr_SP_ufp_20_1k"
## [597] "exp_avg65th_bday_10_yr_SP_ufp_36_1k"
## [598] "exp_avg65th_bday_10_yr_ST_bc"
## [599] "exp_avg65th_bday_10_yr_ST_co2"
## [600] "exp_avg65th_bday_10_yr_ST_no2"
## [601] "exp_avg65th_bday_10_yr_ST_pm25"
## [602] "exp_avg65th_bday_10_yr_ST_ufp_10_42"
## [603] "exp_avg65th_bday_10_yr_ST_ufp_10_70"
## [604] "exp_avg65th_bday_10_yr_ST_ufp_20_1k"
## [605] "exp_avg65th_bday_10_yr_ST_ufp_36_1k"
## [606] "exp_wks_cvg65th_bday_01_yr_SP_bc"
## [607] "exp_wks_cvg65th_bday_01_yr_SP_co2"
## [608] "exp_wks_cvg65th_bday_01_yr_SP_no2"
## [609] "exp_wks_cvg65th_bday_01_yr_SP_pm25"
## [610] "exp_wks_cvg65th_bday_01_yr_SP_ufp_10_42"
## [611] "exp_wks_cvg65th_bday_01_yr_SP_ufp_10_70"
## [612] "exp_wks_cvg65th_bday_01_yr_SP_ufp_20_1k"
## [613] "exp_wks_cvg65th_bday_01_yr_SP_ufp_36_1k"
## [614] "exp_wks_cvg65th_bday_01_yr_ST_bc"
## [615] "exp_wks_cvg65th_bday_01_yr_ST_co2"
## [616] "exp_wks_cvg65th_bday_01_yr_ST_no2"
## [617] "exp_wks_cvg65th_bday_01_yr_ST_pm25"
## [618] "exp_wks_cvg65th_bday_01_yr_ST_ufp_10_42"
## [619] "exp_wks_cvg65th_bday_01_yr_ST_ufp_10_70"
## [620] "exp_wks_cvg65th_bday_01_yr_ST_ufp_20_1k"
## [621] "exp_wks_cvg65th_bday_01_yr_ST_ufp_36_1k"
## [622] "exp_wks_cvg65th_bday_10_yr_SP_bc"
## [623] "exp_wks_cvg65th_bday_10_yr_SP_co2"
## [624] "exp_wks_cvg65th_bday_10_yr_SP_no2"
## [625] "exp_wks_cvg65th_bday_10_yr_SP_pm25"
## [626] "exp_wks_cvg65th_bday_10_yr_SP_ufp_10_42"
## [627] "exp_wks_cvg65th_bday_10_yr_SP_ufp_10_70"
## [628] "exp_wks_cvg65th_bday_10_yr_SP_ufp_20_1k"
## [629] "exp_wks_cvg65th_bday_10_yr_SP_ufp_36_1k"
## [630] "exp_wks_cvg65th_bday_10_yr_ST_bc"
## [631] "exp_wks_cvg65th_bday_10_yr_ST_co2"
## [632] "exp_wks_cvg65th_bday_10_yr_ST_no2"
## [633] "exp_wks_cvg65th_bday_10_yr_ST_pm25"
## [634] "exp_wks_cvg65th_bday_10_yr_ST_ufp_10_42"
## [635] "exp_wks_cvg65th_bday_10_yr_ST_ufp_10_70"
## [636] "exp_wks_cvg65th_bday_10_yr_ST_ufp_20_1k"
## [637] "exp_wks_cvg65th_bday_10_yr_ST_ufp_36_1k"
## [638] "exact_covg65th_bday_01_yr_bc"
## [639] "exact_covg65th_bday_01_yr_co2"
## [640] "exact_covg65th_bday_01_yr_no2"
## [641] "exact_covg65th_bday_01_yr_pm25"
## [642] "exact_covg65th_bday_01_yr_ufp_10_42"
## [643] "exact_covg65th_bday_01_yr_ufp_10_70"
## [644] "exact_covg65th_bday_01_yr_ufp_20_1k"
## [645] "exact_covg65th_bday_01_yr_ufp_36_1k"
## [646] "exact_covg65th_bday_10_yr_bc"
## [647] "exact_covg65th_bday_10_yr_co2"
## [648] "exact_covg65th_bday_10_yr_no2"
## [649] "exact_covg65th_bday_10_yr_pm25"
## [650] "exact_covg65th_bday_10_yr_ufp_10_42"
## [651] "exact_covg65th_bday_10_yr_ufp_10_70"
## [652] "exact_covg65th_bday_10_yr_ufp_20_1k"
## [653] "exact_covg65th_bday_10_yr_ufp_36_1k"
## [654] "imptd_covg65th_bday_01_yr_bc"
## [655] "imptd_covg65th_bday_01_yr_co2"
## [656] "imptd_covg65th_bday_01_yr_no2"
## [657] "imptd_covg65th_bday_01_yr_pm25"
## [658] "imptd_covg65th_bday_01_yr_ufp_10_42"
## [659] "imptd_covg65th_bday_01_yr_ufp_10_70"
## [660] "imptd_covg65th_bday_01_yr_ufp_20_1k"
## [661] "imptd_covg65th_bday_01_yr_ufp_36_1k"
## [662] "imptd_covg65th_bday_10_yr_bc"
## [663] "imptd_covg65th_bday_10_yr_co2"
## [664] "imptd_covg65th_bday_10_yr_no2"
## [665] "imptd_covg65th_bday_10_yr_pm25"
## [666] "imptd_covg65th_bday_10_yr_ufp_10_42"
## [667] "imptd_covg65th_bday_10_yr_ufp_10_70"
## [668] "imptd_covg65th_bday_10_yr_ufp_20_1k"
## [669] "imptd_covg65th_bday_10_yr_ufp_36_1k"
## [670] "imp_qual65th_bday_01_yr_bc"
## [671] "imp_qual65th_bday_01_yr_co2"
## [672] "imp_qual65th_bday_01_yr_no2"
## [673] "imp_qual65th_bday_01_yr_pm25"
## [674] "imp_qual65th_bday_01_yr_ufp_10_42"
## [675] "imp_qual65th_bday_01_yr_ufp_10_70"
## [676] "imp_qual65th_bday_01_yr_ufp_20_1k"
## [677] "imp_qual65th_bday_01_yr_ufp_36_1k"
## [678] "imp_qual65th_bday_10_yr_bc"
## [679] "imp_qual65th_bday_10_yr_co2"
## [680] "imp_qual65th_bday_10_yr_no2"
## [681] "imp_qual65th_bday_10_yr_pm25"
## [682] "imp_qual65th_bday_10_yr_ufp_10_42"
## [683] "imp_qual65th_bday_10_yr_ufp_10_70"
## [684] "imp_qual65th_bday_10_yr_ufp_20_1k"
## [685] "imp_qual65th_bday_10_yr_ufp_36_1k"
## [686] "autopsy_cat"
## [687] "autopsy_flag"
## [688] "APOE"
## [689] "birth_cohort_cat"
## [690] "birth_cohort_cat_binned"
## [691] "death_date"
## [692] "death_year"
## [693] "deathyr"
## [694] "dead"
## [695] "death_yr_cat"
## [696] "death_yr_cat_simp"
## [697] "age_death_yrs"
## [698] "age_baseline"
## [699] "degree_cat"
## [700] "degree_cat_simp"
## [701] "male_cat"
## [702] "income_factor"
## [703] "income_cat"
## [704] "income_cat_combined"
## [705] "race_cat"
## [706] "hispanic_cat"
## [707] "marital_cat"
## [708] "smoke_cat"
## [709] "smoke_ever"
## [710] "smoke_current"
## [711] "pack_years_cat"
## [712] "pack_years_cat_50"
## [713] "pack_years_cat_5"
## [714] "pack_years_50_binary"
## [715] "last_pack_years_cat"
## [716] "last_pack_years_cat_50"
## [717] "last_pack_years_cat_5"
## [718] "last_pack_years_50_binary"
## [719] "exercise_cat"
## [720] "alcohol_cat"
## [721] "cohort_cat"
## [722] "consent_cat"
## [723] "bmi_cat"
## [724] "diabetes_cat"
## [725] "HTN_cat"
## [726] "CVD_cat"
## [727] "Fishoil_cat"
## [728] "VitaminE_cat"
## [729] "VitaminC_cat"
## [730] "VitaminMulti_cat"
## [731] "tr_med_inc_hshld_cat"
## [732] "tr_med_inc_hshld_cat_abbrev"
## [733] "tr_med_inc_hshld_cat_2"
## [734] "tr_med_inc_hshld_cat_3"
## [735] "tr_med_inc_hshld_cat_4"
## [736] "FamHx_Dx_cat"
## [737] "BRAAK_cat"
## [738] "Braak_cat"
## [739] "Braak_cat_ordered"
## [740] "Braak_binary_2"
## [741] "CERAD_cat"
## [742] "CERAD_cat_V1"
## [743] "CERAD_cat_ordered"
## [744] "CERAD_binary"
## [745] "calc_NIA_AA_cat"
## [746] "calc_NIA_AA_cat_simplified"
## [747] "calc_NIA_AA_cat_simulated"
## [748] "calc_NIA_AA_cat_sim_simplified"
## [749] "CognitiveStatus_cat"
## [750] "dementia_cat"
## [751] "race_cat_adjusted"
## [752] "hispanic_cat_adjusted"
## [753] "smoke_cat_adjusted"
## [754] "alcohol_cat_adjusted"
## [755] "exercise_cat_adjusted"
## [756] "APOE_adjusted"
## [757] "bmi_adjusted"
## [758] "bmi_cat_adjusted"
## [759] "diabetes_cat_adjusted"
## [760] "HTN_cat_adjusted"
## [761] "CVD_cat_adjusted"
## [762] "degree_cat_simp_adjusted"
## [763] "VitaminC_adjusted"
## [764] "VitaminE_adjusted"
## [765] "VitaminMulti_cat_adjusted"
## [766] "Fishoil_cat_adjusted"
## [767] "FamHx_Dx_cat_adjusted"
## [768] "tr_med_inc_hshld_cat_adjusted"
## [769] "race_cat_simp"
## [770] "race_cat_simp_adjusted"
## [771] "bmi_cat_simp"
## [772] "bmi_cat_simp_adjusted"
Table 1
Â
|
No
(N=4788) |
Yes
(N=921) |
Overall
(N=5709) |
| Cohort |
|
|
|
| Original |
2022 (42.2%) |
537 (58.3%) |
2559 (44.8%) |
| Expansion |
597 (12.5%) |
210 (22.8%) |
807 (14.1%) |
| Replacement |
2169 (45.3%) |
174 (18.9%) |
2343 (41.0%) |
| Gender |
|
|
|
| Female |
2776 (58.0%) |
538 (58.4%) |
3314 (58.0%) |
| Male |
2012 (42.0%) |
383 (41.6%) |
2395 (42.0%) |
| Age at Death |
|
|
|
| Mean (SD) |
87.0 (7.15) |
89.4 (6.62) |
87.7 (7.09) |
| Median [Min, Max] |
87.6 [66.4, 108] |
90.0 [68.9, 106] |
88.2 [66.4, 108] |
| Missing |
2325 (48.6%) |
0 (0%) |
2325 (40.7%) |
| Race |
|
|
|
| White |
4231 (88.4%) |
865 (93.9%) |
5096 (89.3%) |
| Black |
195 (4.1%) |
12 (1.3%) |
207 (3.6%) |
| Asian |
0 (0%) |
0 (0%) |
0 (0%) |
| American Indian or Alaskan Native |
0 (0%) |
0 (0%) |
0 (0%) |
| Native Hawaiian or Pacific Islander |
3 (0.1%) |
0 (0%) |
3 (0.1%) |
| Other including mixed |
159 (3.3%) |
27 (2.9%) |
186 (3.3%) |
| Missing |
200 (4.2%) |
17 (1.8%) |
217 (3.8%) |
| Dementia Status |
|
|
|
| No Dementia |
3868 (80.8%) |
501 (54.4%) |
4369 (76.5%) |
| Dementia |
920 (19.2%) |
420 (45.6%) |
1340 (23.5%) |
| APOE Status |
|
|
|
| - APOE e4 |
2779 (58.0%) |
639 (69.4%) |
3418 (59.9%) |
| + APOE e4 |
979 (20.4%) |
245 (26.6%) |
1224 (21.4%) |
| Missing |
1030 (21.5%) |
37 (4.0%) |
1067 (18.7%) |
| Smoking Status |
|
|
|
| Never |
2374 (49.6%) |
416 (45.2%) |
2790 (48.9%) |
| Former |
2181 (45.6%) |
452 (49.1%) |
2633 (46.1%) |
| Current |
221 (4.6%) |
51 (5.5%) |
272 (4.8%) |
| Missing |
12 (0.3%) |
2 (0.2%) |
14 (0.2%) |
| Pack Years |
|
|
|
| Mean (SD) |
18.6 (47.7) |
22.2 (29.9) |
19.2 (45.3) |
| Median [Min, Max] |
0 [0, 1520] |
6.00 [0, 228] |
1.00 [0, 1520] |
| Missing |
51 (1.1%) |
5 (0.5%) |
56 (1.0%) |
| Alcohol Use |
|
|
|
| never |
858 (17.9%) |
171 (18.6%) |
1029 (18.0%) |
| previous |
1087 (22.7%) |
233 (25.3%) |
1320 (23.1%) |
| current |
2657 (55.5%) |
515 (55.9%) |
3172 (55.6%) |
| Missing |
186 (3.9%) |
2 (0.2%) |
188 (3.3%) |
| BMI |
|
|
|
| Mean (SD) |
27.5 (5.01) |
26.9 (4.84) |
27.4 (4.99) |
| Median [Min, Max] |
26.8 [14.9, 61.3] |
26.2 [15.9, 48.9] |
26.7 [14.9, 61.3] |
| Missing |
127 (2.7%) |
22 (2.4%) |
149 (2.6%) |
| Cardiovascular Disease |
|
|
|
| No |
4350 (90.9%) |
808 (87.7%) |
5158 (90.3%) |
| Yes |
397 (8.3%) |
103 (11.2%) |
500 (8.8%) |
| Missing |
41 (0.9%) |
10 (1.1%) |
51 (0.9%) |
| Hypertension |
|
|
|
| No |
2728 (57.0%) |
571 (62.0%) |
3299 (57.8%) |
| Yes |
2022 (42.2%) |
343 (37.2%) |
2365 (41.4%) |
| Missing |
38 (0.8%) |
7 (0.8%) |
45 (0.8%) |
| CASI Score (IRT) |
|
|
|
| Mean (SD) |
0.330 (0.719) |
0.322 (0.680) |
0.329 (0.713) |
| Median [Min, Max] |
0.370 [-2.69, 1.75] |
0.343 [-1.75, 1.75] |
0.366 [-2.69, 1.75] |
| Education |
|
|
|
| Less than High School |
392 (8.2%) |
67 (7.3%) |
459 (8.0%) |
| GED |
70 (1.5%) |
12 (1.3%) |
82 (1.4%) |
| High School |
1650 (34.5%) |
371 (40.3%) |
2021 (35.4%) |
| Bachelor's |
1124 (23.5%) |
230 (25.0%) |
1354 (23.7%) |
| Master's |
789 (16.5%) |
121 (13.1%) |
910 (15.9%) |
| Doctorate |
290 (6.1%) |
48 (5.2%) |
338 (5.9%) |
| Other |
471 (9.8%) |
72 (7.8%) |
543 (9.5%) |
| Missing |
2 (0.0%) |
0 (0%) |
2 (0.0%) |
| Neighborhood Median Household Income |
|
|
|
| <35,000 |
449 (9.4%) |
84 (9.1%) |
533 (9.3%) |
| 35,000-49,999 |
1430 (29.9%) |
287 (31.2%) |
1717 (30.1%) |
| 50,000-74,999 |
2231 (46.6%) |
457 (49.6%) |
2688 (47.1%) |
| >75,000 |
506 (10.6%) |
91 (9.9%) |
597 (10.5%) |
| Missing |
172 (3.6%) |
2 (0.2%) |
174 (3.0%) |
| Gross Infarcts |
|
|
|
| No |
0 (0%) |
583 (63.3%) |
583 (10.2%) |
| Yes |
0 (0%) |
266 (28.9%) |
266 (4.7%) |
| Missing |
4788 (100%) |
72 (7.8%) |
4860 (85.1%) |
| Number of Gross Infarcts |
|
|
|
| Mean (SD) |
NA (NA) |
0.718 (1.53) |
0.718 (1.53) |
| Median [Min, Max] |
NA [NA, NA] |
0 [0, 16.0] |
0 [0, 16.0] |
| Missing |
4788 (100%) |
72 (7.8%) |
4860 (85.1%) |
| Microinfarcts |
|
|
|
| No |
0 (0%) |
425 (46.1%) |
425 (7.4%) |
| Yes |
0 (0%) |
416 (45.2%) |
416 (7.3%) |
| Missing |
4788 (100%) |
80 (8.7%) |
4868 (85.3%) |
| Number of Microinfarcts |
|
|
|
| Mean (SD) |
NA (NA) |
1.36 (2.24) |
1.36 (2.24) |
| Median [Min, Max] |
NA [NA, NA] |
0 [0, 15.0] |
0 [0, 15.0] |
| Missing |
4788 (100%) |
80 (8.7%) |
4868 (85.3%) |
| Atherosclerosis |
|
|
|
| None |
0 (0%) |
36 (3.9%) |
36 (0.6%) |
| Mild |
0 (0%) |
214 (23.2%) |
214 (3.7%) |
| Moderate |
0 (0%) |
510 (55.4%) |
510 (8.9%) |
| Severe |
0 (0%) |
59 (6.4%) |
59 (1.0%) |
| Missing |
4788 (100%) |
102 (11.1%) |
4890 (85.7%) |
| Arteriolosclerosis |
|
|
|
| Absent |
0 (0%) |
8 (0.9%) |
8 (0.1%) |
| Mild |
0 (0%) |
172 (18.7%) |
172 (3.0%) |
| Moderate |
0 (0%) |
349 (37.9%) |
349 (6.1%) |
| Severe |
0 (0%) |
173 (18.8%) |
173 (3.0%) |
| Missing |
4788 (100%) |
219 (23.8%) |
5007 (87.7%) |
| Total Microinfarcts |
|
|
|
| Mean (SD) |
NA (NA) |
1.47 (2.39) |
1.47 (2.39) |
| Median [Min, Max] |
NA [NA, NA] |
1.00 [0, 17.0] |
1.00 [0, 17.0] |
| Missing |
4788 (100%) |
99 (10.7%) |
4887 (85.6%) |
| PM2.5 Exposure from Death (1 year) |
|
|
|
| Mean (SD) |
7.51 (1.47) |
7.05 (1.34) |
7.38 (1.45) |
| Median [Min, Max] |
7.39 [2.64, 12.6] |
6.85 [2.75, 11.0] |
7.21 [2.64, 12.6] |
| Missing |
2421 (50.6%) |
30 (3.3%) |
2451 (42.9%) |
| PM2.5 Exposure from Death (5 year) |
|
|
|
| Mean (SD) |
7.91 (1.59) |
7.33 (1.33) |
7.75 (1.54) |
| Median [Min, Max] |
7.90 [3.31, 13.7] |
7.05 [3.41, 11.5] |
7.63 [3.31, 13.7] |
| Missing |
2379 (49.7%) |
12 (1.3%) |
2391 (41.9%) |
| PM2.5 Exposure from Death (10 year) |
|
|
|
| Mean (SD) |
8.59 (1.97) |
7.86 (1.54) |
8.39 (1.89) |
| Median [Min, Max] |
8.38 [3.40, 16.4] |
7.68 [4.33, 13.6] |
8.20 [3.40, 16.4] |
| Missing |
2344 (49.0%) |
6 (0.7%) |
2350 (41.2%) |
| PM2.5 Exposure from Death (20 year) |
|
|
|
| Mean (SD) |
10.4 (2.54) |
9.42 (2.19) |
10.1 (2.48) |
| Median [Min, Max] |
9.97 [4.81, 18.7] |
8.85 [5.37, 16.3] |
9.62 [4.81, 18.7] |
| Missing |
2326 (48.6%) |
0 (0%) |
2326 (40.7%) |
| exp_avgdeath_01_yr_MM_ufp_10_42 |
|
|
|
| Mean (SD) |
10600 (2130) |
10600 (2160) |
10600 (2140) |
| Median [Min, Max] |
10300 [4350, 21100] |
10200 [4510, 20700] |
10300 [4350, 21100] |
| Missing |
2569 (53.7%) |
86 (9.3%) |
2655 (46.5%) |
| exp_avgdeath_01_yr_MM_ufp_10_70 |
|
|
|
| Mean (SD) |
9500 (3060) |
9290 (2950) |
9440 (3030) |
| Median [Min, Max] |
8970 [2100, 31100] |
8560 [2650, 25800] |
8880 [2100, 31100] |
| Missing |
2569 (53.7%) |
86 (9.3%) |
2655 (46.5%) |
| exp_avgdeath_01_yr_MM_ufp_20_1k |
|
|
|
| Mean (SD) |
7380 (1890) |
7290 (1880) |
7360 (1890) |
| Median [Min, Max] |
7090 [2330, 15900] |
6900 [2740, 15000] |
7050 [2330, 15900] |
| Missing |
2569 (53.7%) |
86 (9.3%) |
2655 (46.5%) |
| exp_avgdeath_01_yr_MM_ufp_36_1k |
|
|
|
| Mean (SD) |
3300 (684) |
3290 (710) |
3290 (691) |
| Median [Min, Max] |
3240 [1320, 7120] |
3220 [1480, 6960] |
3230 [1320, 7120] |
| Missing |
2569 (53.7%) |
86 (9.3%) |
2655 (46.5%) |
| CERAD Score |
|
|
|
| 0 |
0 (0%) |
187 (20.3%) |
187 (3.3%) |
| 1 |
0 (0%) |
205 (22.3%) |
205 (3.6%) |
| 2 |
0 (0%) |
198 (21.5%) |
198 (3.5%) |
| 3 |
0 (0%) |
238 (25.8%) |
238 (4.2%) |
| Missing |
4788 (100%) |
93 (10.1%) |
4881 (85.5%) |
| BRAAK Stage |
|
|
|
| 0 |
0 (0%) |
24 (2.6%) |
24 (0.4%) |
| 1 |
0 (0%) |
68 (7.4%) |
68 (1.2%) |
| 2 |
0 (0%) |
136 (14.8%) |
136 (2.4%) |
| 3 |
0 (0%) |
142 (15.4%) |
142 (2.5%) |
| 4 |
0 (0%) |
148 (16.1%) |
148 (2.6%) |
| 5 |
0 (0%) |
173 (18.8%) |
173 (3.0%) |
| 6 |
0 (0%) |
131 (14.2%) |
131 (2.3%) |
| 98 |
0 (0%) |
1 (0.1%) |
1 (0.0%) |
| Missing |
4788 (100%) |
98 (10.6%) |
4886 (85.6%) |
| amylangi_score |
|
|
|
| 0 |
0 (0%) |
526 (57.1%) |
526 (9.2%) |
| 1 |
0 (0%) |
152 (16.5%) |
152 (2.7%) |
| 2 |
0 (0%) |
145 (15.7%) |
145 (2.5%) |
| 3 |
0 (0%) |
24 (2.6%) |
24 (0.4%) |
| Missing |
4788 (100%) |
74 (8.0%) |
4862 (85.2%) |
| Late Stage |
|
|
|
| 0 |
0 (0%) |
432 (46.9%) |
432 (7.6%) |
| 1 |
0 (0%) |
178 (19.3%) |
178 (3.1%) |
| 2 |
0 (0%) |
204 (22.1%) |
204 (3.6%) |
| 3 |
0 (0%) |
13 (1.4%) |
13 (0.2%) |
| Missing |
4788 (100%) |
94 (10.2%) |
4882 (85.5%) |
| Thal Phase |
|
|
|
| 0 |
0 (0%) |
50 (5.4%) |
50 (0.9%) |
| 1 |
0 (0%) |
34 (3.7%) |
34 (0.6%) |
| 2 |
0 (0%) |
39 (4.2%) |
39 (0.7%) |
| 3 |
0 (0%) |
88 (9.6%) |
88 (1.5%) |
| 4 |
0 (0%) |
108 (11.7%) |
108 (1.9%) |
| 5 |
0 (0%) |
67 (7.3%) |
67 (1.2%) |
| Missing |
4788 (100%) |
535 (58.1%) |
5323 (93.2%) |
| Any Hippocampal Sclerosis |
|
|
|
| 0 |
0 (0%) |
714 (77.5%) |
714 (12.5%) |
| 1 |
0 (0%) |
115 (12.5%) |
115 (2.0%) |
| Missing |
4788 (100%) |
92 (10.0%) |
4880 (85.5%) |
| Any Microinfarcts (cere) |
|
|
|
| 0 |
0 (0%) |
541 (58.7%) |
541 (9.5%) |
| 1 |
0 (0%) |
300 (32.6%) |
300 (5.3%) |
| Missing |
4788 (100%) |
80 (8.7%) |
4868 (85.3%) |
| Any Microinfarcts (deep) |
|
|
|
| 0 |
0 (0%) |
592 (64.3%) |
592 (10.4%) |
| 1 |
0 (0%) |
249 (27.0%) |
249 (4.4%) |
| Missing |
4788 (100%) |
80 (8.7%) |
4868 (85.3%) |
| Any Gross infarcts (bs) |
|
|
|
| 0 |
0 (0%) |
766 (83.2%) |
766 (13.4%) |
| 1 |
0 (0%) |
83 (9.0%) |
83 (1.5%) |
| Missing |
4788 (100%) |
72 (7.8%) |
4860 (85.1%) |
| Any Gross infarcts (ctx) |
|
|
|
| 0 |
0 (0%) |
754 (81.9%) |
754 (13.2%) |
| 1 |
0 (0%) |
95 (10.3%) |
95 (1.7%) |
| Missing |
4788 (100%) |
72 (7.8%) |
4860 (85.1%) |
| Any Gross infarcts (gm) |
|
|
|
| 0 |
0 (0%) |
701 (76.1%) |
701 (12.3%) |
| 1 |
0 (0%) |
148 (16.1%) |
148 (2.6%) |
| Missing |
4788 (100%) |
72 (7.8%) |
4860 (85.1%) |
| Any Gross infarcts (wm) |
|
|
|
| 0 |
0 (0%) |
763 (82.8%) |
763 (13.4%) |
| 1 |
0 (0%) |
86 (9.3%) |
86 (1.5%) |
| Missing |
4788 (100%) |
72 (7.8%) |
4860 (85.1%) |
Looking into UFP
Exposure Plots
Green = 5 year average exposure, red = 10 year average exposure
Outcome Co-Occurence
Pie Charts: Microinfarcts
|
Outcomes
|
N Dementia
|
N No Dementia
|
|
Arteriolosclerosis + Microinfarcts
|
31
|
47
|
|
Arteriolosclerosis only
|
35
|
63
|
|
Atherosclerosis + Arteriolosclerosis
|
136
|
148
|
|
Atherosclerosis + Arteriolosclerosis + Microinfarcts
|
163
|
118
|
|
Atherosclerosis + Microinfarcts
|
15
|
24
|
|
Atherosclerosis only
|
27
|
40
|
|
None
|
13
|
61
|
Pie Charts: Gross Infarcts
|
Outcomes
|
N Dementia
|
N No Dementia
|
|
Arteriolosclerosis + Gross infarcts
|
17
|
17
|
|
Arteriolosclerosis only
|
49
|
93
|
|
Atherosclerosis + Arteriolosclerosis
|
176
|
195
|
|
Atherosclerosis + Arteriolosclerosis + Gross infarcts
|
123
|
71
|
|
Atherosclerosis + Gross infarcts
|
13
|
14
|
|
Atherosclerosis only
|
29
|
50
|
|
None
|
13
|
61
|
Log Linear Analysis
## Call:
## MASS::loglm(formula = ~atherosclerosis_binary + arteriolosclerosis_binary,
## data = outs)
##
## Statistics:
## X^2 df P(> X^2)
## Likelihood Ratio 572.8033 45 0
## Pearson 564.2073 45 0
## Call:
## MASS::loglm(formula = ~atherosclerosis_binary + chronic_microinfarcts_any,
## data = outs)
##
## Statistics:
## X^2 df P(> X^2)
## Likelihood Ratio 841.8655 45 0
## Pearson 914.4342 45 0
## Call:
## MASS::loglm(formula = ~atherosclerosis_binary + chronic_grossinfarcts_any,
## data = outs)
##
## Statistics:
## X^2 df P(> X^2)
## Likelihood Ratio 722.0615 45 0
## Pearson 858.2416 45 0
## Call:
## MASS::loglm(formula = ~arteriolosclerosis_binary + chronic_microinfarcts_any,
## data = outs)
##
## Statistics:
## X^2 df P(> X^2)
## Likelihood Ratio 702.3974 45 0
## Pearson 735.4973 45 0
## Call:
## MASS::loglm(formula = ~arteriolosclerosis_binary + chronic_grossinfarcts_any,
## data = outs)
##
## Statistics:
## X^2 df P(> X^2)
## Likelihood Ratio 582.5935 45 0
## Pearson 685.7489 45 0
## Call:
## MASS::loglm(formula = ~chronic_microinfarcts_any + chronic_grossinfarcts_any,
## data = outs)
##
## Statistics:
## X^2 df P(> X^2)
## Likelihood Ratio 851.6556 45 0
## Pearson 1213.7905 45 0
## Call:
## MASS::loglm(formula = ~chronic_microinfarcts_any + deathage_group2,
## data = outs)
##
## Statistics:
## X^2 df P(> X^2)
## Likelihood Ratio 745.3532 44 0
## Pearson 815.4693 44 0
## Call:
## MASS::loglm(formula = ~chronic_grossinfarcts_any + +deathage_group2,
## data = outs)
##
## Statistics:
## X^2 df P(> X^2)
## Likelihood Ratio 625.5492 44 0
## Pearson 766.0614 44 0
Modeling
Risk Factor Modeling
## No Yes
## 4788 921
emmeans for Microinfarct models
## CVD_cat = 0:
## deathage_group2 prob SE df asymp.LCL asymp.UCL
## < 80 0.288 0.0377 Inf 0.220 0.367
## 80-89 0.433 0.0322 Inf 0.371 0.496
## 90+ 0.547 0.0318 Inf 0.484 0.608
##
## CVD_cat = 1:
## deathage_group2 prob SE df asymp.LCL asymp.UCL
## < 80 0.287 0.0830 Inf 0.154 0.471
## 80-89 0.798 0.0697 Inf 0.629 0.903
## 90+ 0.485 0.1116 Inf 0.282 0.694
##
## Results are averaged over the levels of: male_cat
## Confidence level used: 0.95
## Intervals are back-transformed from the logit scale
## diabetes_cat = 0:
## deathage_group2 prob SE df asymp.LCL asymp.UCL
## < 80 0.291 0.0402 Inf 0.219 0.376
## 80-89 0.494 0.0334 Inf 0.430 0.560
## 90+ 0.541 0.0313 Inf 0.480 0.602
##
## diabetes_cat = 1:
## deathage_group2 prob SE df asymp.LCL asymp.UCL
## < 80 0.238 0.0577 Inf 0.144 0.369
## 80-89 0.438 0.0725 Inf 0.304 0.581
## 90+ 0.590 0.1336 Inf 0.328 0.809
##
## Results are averaged over the levels of: male_cat
## Confidence level used: 0.95
## Intervals are back-transformed from the logit scale
## HTN_cat = 0:
## deathage_group2 prob SE df asymp.LCL asymp.UCL
## < 80 0.301 0.0445 Inf 0.221 0.394
## 80-89 0.461 0.0402 Inf 0.384 0.540
## 90+ 0.519 0.0397 Inf 0.442 0.596
##
## HTN_cat = 1:
## deathage_group2 prob SE df asymp.LCL asymp.UCL
## < 80 0.248 0.0504 Inf 0.163 0.359
## 80-89 0.507 0.0462 Inf 0.417 0.597
## 90+ 0.591 0.0468 Inf 0.497 0.678
##
## Results are averaged over the levels of: male_cat
## Confidence level used: 0.95
## Intervals are back-transformed from the logit scale
emmeans for Gross Infarct models
## CVD_cat = 0:
## deathage_group2 prob SE df asymp.LCL asymp.UCL
## < 80 0.106 0.0255 Inf 0.0658 0.168
## 80-89 0.281 0.0290 Inf 0.2275 0.341
## 90+ 0.377 0.0308 Inf 0.3190 0.439
##
## CVD_cat = 1:
## deathage_group2 prob SE df asymp.LCL asymp.UCL
## < 80 0.178 0.0705 Inf 0.0779 0.358
## 80-89 0.517 0.0864 Inf 0.3521 0.678
## 90+ 0.440 0.1108 Inf 0.2451 0.654
##
## Results are averaged over the levels of: male_cat
## Confidence level used: 0.95
## Intervals are back-transformed from the logit scale
## diabetes_cat = 0:
## deathage_group2 prob SE df asymp.LCL asymp.UCL
## < 80 0.1539 0.0319 Inf 0.10123 0.227
## 80-89 0.3090 0.0306 Inf 0.25239 0.372
## 90+ 0.3832 0.0304 Inf 0.32561 0.444
##
## diabetes_cat = 1:
## deathage_group2 prob SE df asymp.LCL asymp.UCL
## < 80 0.0359 0.0252 Inf 0.00884 0.134
## 80-89 0.2989 0.0669 Inf 0.18573 0.443
## 90+ 0.3443 0.1287 Inf 0.14654 0.616
##
## Results are averaged over the levels of: male_cat
## Confidence level used: 0.95
## Intervals are back-transformed from the logit scale
## HTN_cat = 0:
## deathage_group2 prob SE df asymp.LCL asymp.UCL
## < 80 0.1644 0.0358 Inf 0.1055 0.247
## 80-89 0.2694 0.0354 Inf 0.2058 0.344
## 90+ 0.3752 0.0383 Inf 0.3036 0.453
##
## HTN_cat = 1:
## deathage_group2 prob SE df asymp.LCL asymp.UCL
## < 80 0.0557 0.0268 Inf 0.0213 0.138
## 80-89 0.3474 0.0440 Inf 0.2668 0.438
## 90+ 0.3886 0.0462 Inf 0.3028 0.482
##
## Results are averaged over the levels of: male_cat
## Confidence level used: 0.95
## Intervals are back-transformed from the logit scale
emmeans for simple model + Hypertension with gender interaction
Outome = arteriolosclerosis
## HTN_cat = 0:
## male_cat emmean SE df asymp.LCL asymp.UCL
## Female 0.891 0.160 Inf 0.577 1.204
## Male 0.588 0.174 Inf 0.247 0.929
##
## HTN_cat = 1:
## male_cat emmean SE df asymp.LCL asymp.UCL
## Female 1.262 0.178 Inf 0.914 1.610
## Male 1.654 0.218 Inf 1.227 2.081
##
## Results are averaged over the levels of: deathage_group2
## Confidence level used: 0.95
Outome = microinfarcts
## HTN_cat = 0:
## male_cat prob SE df asymp.LCL asymp.UCL
## Female 0.479 0.0346 Inf 0.413 0.547
## Male 0.348 0.0350 Inf 0.283 0.420
##
## HTN_cat = 1:
## male_cat prob SE df asymp.LCL asymp.UCL
## Female 0.411 0.0377 Inf 0.339 0.486
## Male 0.519 0.0464 Inf 0.428 0.608
##
## Results are averaged over the levels of: deathage_group2
## Confidence level used: 0.95
## Intervals are back-transformed from the logit scale
Simple Model (Age, Sex, Race)
|
Â
|
Atherosclerosis
|
Arteriolosclerosis
|
Microinfacts (binary)
|
Gross Infarcts (binary)
|
|
Predictors
|
Odds Ratios
|
CI
|
p
|
Odds Ratios
|
CI
|
p
|
Odds Ratios
|
CI
|
p
|
Odds Ratios
|
CI
|
p
|
|
None|Mild
|
117.226
|
21.102 – 651.222
|
<0.001
|
|
|
|
|
|
|
|
|
|
|
Mild|Moderate
|
796.360
|
139.800 – 4536.420
|
<0.001
|
971.759
|
167.108 – 5650.927
|
<0.001
|
|
|
|
|
|
|
|
Moderate|Severe
|
33050.538
|
5181.644 – 210809.162
|
<0.001
|
8904.467
|
1431.032 – 55407.261
|
<0.001
|
|
|
|
|
|
|
|
Age at Death
|
1.090
|
1.069 – 1.112
|
<0.001
|
1.092
|
1.071 – 1.115
|
<0.001
|
1.049
|
1.029 – 1.070
|
<0.001
|
1.063
|
1.040 – 1.088
|
<0.001
|
|
Gender: Male
|
0.882
|
0.649 – 1.199
|
0.422
|
1.001
|
0.736 – 1.362
|
0.993
|
0.862
|
0.637 – 1.166
|
0.335
|
1.035
|
0.741 – 1.445
|
0.840
|
|
Absent|Mild
|
|
|
|
33.876
|
5.732 – 200.204
|
<0.001
|
|
|
|
|
|
|
|
(Intercept)
|
|
|
|
|
|
|
0.014
|
0.002 – 0.075
|
<0.001
|
0.002
|
0.000 – 0.013
|
<0.001
|
|
Observations
|
696
|
611
|
841
|
849
|
|
R2 Nagelkerke
|
0.122
|
0.136
|
0.026
|
0.031
|
Simple Model + Year of Death
|
Â
|
Atherosclerosis
|
Arteriolosclerosis
|
Microinfacts (binary)
|
Gross Infarcts (binary)
|
|
Predictors
|
Odds Ratios
|
CI
|
p
|
Odds Ratios
|
CI
|
p
|
Odds Ratios
|
CI
|
p
|
Odds Ratios
|
CI
|
p
|
|
None|Mild
|
57414549658824687484928.000
|
56225099811995936358400.000 – 58629162483448975196160.000
|
<0.001
|
|
|
|
|
|
|
|
|
|
|
Mild|Moderate
|
393400502309139513344000.000
|
323309937628883705659392.000 – 478686044580329289678848.000
|
<0.001
|
1924564.120
|
1062387.993 – 3486435.347
|
<0.001
|
|
|
|
|
|
|
|
Moderate|Severe
|
16641719353300590429995008.000
|
11133106541762300407185408.000 – 24875969882722463658278912.000
|
<0.001
|
17647811.979
|
9166089.845 – 33977985.480
|
<0.001
|
|
|
|
|
|
|
|
Age at Death
|
1.090
|
NaN – NaN
|
NaN
|
1.093
|
1.063 – 1.124
|
<0.001
|
1.049
|
1.029 – 1.070
|
<0.001
|
1.068
|
1.044 – 1.093
|
<0.001
|
|
Gender: Male
|
0.886
|
0.660 – 1.191
|
0.423
|
1.003
|
0.732 – 1.376
|
0.983
|
0.864
|
0.639 – 1.169
|
0.343
|
1.016
|
0.720 – 1.432
|
0.928
|
|
Year of Death
|
1.024
|
NaN – NaN
|
NaN
|
1.004
|
1.003 – 1.005
|
<0.001
|
0.985
|
0.961 – 1.010
|
0.242
|
1.100
|
1.066 – 1.136
|
<0.001
|
|
Absent|Mild
|
|
|
|
67290.656
|
66111.865 – 68490.466
|
<0.001
|
|
|
|
|
|
|
|
(Intercept)
|
|
|
|
|
|
|
92885745978.876
|
0.000 – 307464850842620912485561259786240.000
|
0.317
|
0.000
|
0.000 – 0.000
|
<0.001
|
|
Observations
|
696
|
611
|
841
|
849
|
|
R2 Nagelkerke
|
0.127
|
0.135
|
0.025
|
0.097
|
Simple Model + Year of Death (as a factor)
|
Â
|
Atherosclerosis
|
Arteriolosclerosis
|
Microinfacts (binary)
|
Gross Infarcts (binary)
|
|
Predictors
|
Odds Ratios
|
CI
|
p
|
Odds Ratios
|
CI
|
p
|
Odds Ratios
|
CI
|
p
|
Odds Ratios
|
CI
|
p
|
|
None|Mild
|
651.463
|
89.336 – 4750.631
|
<0.001
|
|
|
|
|
|
|
|
|
|
|
Mild|Moderate
|
5119.185
|
678.511 – 38622.911
|
<0.001
|
1977.902
|
275.576 – 14196.093
|
<0.001
|
|
|
|
|
|
|
|
Moderate|Severe
|
273298.007
|
31252.655 – 2389934.602
|
<0.001
|
19451.772
|
2530.116 – 149547.051
|
<0.001
|
|
|
|
|
|
|
|
Age at Death
|
1.109
|
1.085 – 1.134
|
<0.001
|
1.100
|
1.076 – 1.123
|
<0.001
|
1.046
|
1.025 – 1.068
|
<0.001
|
1.065
|
1.040 – 1.091
|
<0.001
|
|
Gender: Male
|
0.923
|
0.671 – 1.269
|
0.621
|
1.057
|
0.768 – 1.455
|
0.735
|
0.886
|
0.647 – 1.213
|
0.451
|
0.948
|
0.660 – 1.359
|
0.772
|
|
as.factor(death_year)1996
|
21.591
|
0.695 – 670.913
|
0.080
|
|
|
|
|
|
|
|
|
|
|
as.factor(death_year)1997
|
0.238
|
0.039 – 1.452
|
0.120
|
|
|
|
0.302
|
0.002 – 42.560
|
0.585
|
0.000
|
0.000 – 24112256426836.176
|
0.988
|
|
as.factor(death_year)1998
|
1.414
|
0.308 – 6.491
|
0.656
|
2.063
|
0.030 – 143.803
|
0.738
|
0.477
|
0.010 – 51.040
|
0.693
|
0.166
|
0.001 – 22.257
|
0.404
|
|
as.factor(death_year)1999
|
2.516
|
0.702 – 9.011
|
0.156
|
0.122
|
0.005 – 2.768
|
0.186
|
2.084
|
0.065 – 194.962
|
0.672
|
0.000
|
0.000 – 24.208
|
0.979
|
|
as.factor(death_year)2000
|
0.578
|
0.138 – 2.429
|
0.454
|
0.060
|
0.001 – 4.522
|
0.202
|
1.262
|
0.037 – 121.686
|
0.895
|
0.258
|
0.005 – 28.160
|
0.476
|
|
as.factor(death_year)2001
|
0.486
|
0.159 – 1.481
|
0.204
|
1635227.378
|
1635225.360 – 1635229.396
|
<0.001
|
1.432
|
0.046 – 130.877
|
0.833
|
0.478
|
0.014 – 45.605
|
0.674
|
|
as.factor(death_year)2002
|
0.471
|
0.179 – 1.241
|
0.127
|
8.098
|
1.295 – 50.649
|
0.025
|
0.815
|
0.026 – 74.394
|
0.904
|
0.175
|
0.004 – 17.808
|
0.340
|
|
as.factor(death_year)2003
|
1.138
|
0.384 – 3.370
|
0.816
|
2.620
|
0.874 – 7.851
|
0.085
|
1.864
|
0.060 – 169.832
|
0.715
|
0.108
|
0.002 – 11.545
|
0.237
|
|
as.factor(death_year)2004
|
1.473
|
0.515 – 4.212
|
0.469
|
0.822
|
0.298 – 2.269
|
0.705
|
1.577
|
0.052 – 141.880
|
0.788
|
0.073
|
0.001 – 8.053
|
0.173
|
|
as.factor(death_year)2005
|
1.419
|
0.480 – 4.196
|
0.527
|
1.312
|
0.473 – 3.641
|
0.602
|
3.520
|
0.114 – 320.821
|
0.460
|
0.351
|
0.010 – 33.412
|
0.551
|
|
as.factor(death_year)2006
|
1.349
|
0.509 – 3.573
|
0.547
|
2.016
|
0.750 – 5.420
|
0.164
|
2.445
|
0.082 – 218.862
|
0.596
|
0.233
|
0.007 – 22.386
|
0.409
|
|
as.factor(death_year)2007
|
1.469
|
0.504 – 4.287
|
0.481
|
1.651
|
0.579 – 4.705
|
0.348
|
1.614
|
0.053 – 145.812
|
0.778
|
0.248
|
0.007 – 23.939
|
0.431
|
|
as.factor(death_year)2008
|
1.503
|
0.591 – 3.819
|
0.391
|
0.782
|
0.299 – 2.045
|
0.616
|
3.617
|
0.121 – 323.655
|
0.446
|
0.751
|
0.024 – 68.261
|
0.867
|
|
as.factor(death_year)2009
|
0.550
|
0.192 – 1.575
|
0.265
|
0.998
|
0.352 – 2.832
|
0.997
|
1.408
|
0.046 – 127.724
|
0.841
|
0.469
|
0.014 – 44.004
|
0.663
|
|
as.factor(death_year)2010
|
2.390
|
0.904 – 6.323
|
0.079
|
0.919
|
0.369 – 2.287
|
0.855
|
1.524
|
0.051 – 135.423
|
0.802
|
0.690
|
0.022 – 62.687
|
0.828
|
|
as.factor(death_year)2011
|
1.131
|
0.450 – 2.846
|
0.793
|
1.185
|
0.499 – 2.816
|
0.700
|
1.883
|
0.064 – 166.837
|
0.706
|
0.549
|
0.018 – 49.726
|
0.725
|
|
as.factor(death_year)2012
|
2.074
|
0.820 – 5.246
|
0.123
|
1.140
|
0.489 – 2.660
|
0.761
|
2.065
|
0.070 – 182.666
|
0.665
|
0.711
|
0.023 – 64.315
|
0.841
|
|
as.factor(death_year)2013
|
2.518
|
1.028 – 6.168
|
0.043
|
0.891
|
0.374 – 2.122
|
0.793
|
1.179
|
0.040 – 104.170
|
0.922
|
1.883
|
0.062 – 168.958
|
0.708
|
|
as.factor(death_year)2014
|
4.616
|
2.024 – 10.529
|
<0.001
|
1.522
|
0.707 – 3.276
|
0.283
|
1.363
|
0.047 – 119.079
|
0.852
|
0.889
|
0.030 – 79.057
|
0.944
|
|
as.factor(death_year)2015
|
2.704
|
1.163 – 6.289
|
0.021
|
0.601
|
0.268 – 1.348
|
0.216
|
1.155
|
0.040 – 101.489
|
0.931
|
1.504
|
0.050 – 134.238
|
0.809
|
|
as.factor(death_year)2016
|
0.944
|
0.394 – 2.261
|
0.896
|
0.706
|
0.302 – 1.651
|
0.421
|
1.637
|
0.056 – 144.063
|
0.768
|
1.842
|
0.061 – 164.819
|
0.718
|
|
as.factor(death_year)2017
|
1.875
|
0.877 – 4.009
|
0.105
|
1.398
|
0.671 – 2.913
|
0.371
|
1.665
|
0.058 – 144.586
|
0.758
|
0.505
|
0.017 – 44.928
|
0.685
|
|
as.factor(death_year)2018
|
0.643
|
0.281 – 1.467
|
0.293
|
1.242
|
0.549 – 2.811
|
0.602
|
1.007
|
0.035 – 88.372
|
0.997
|
1.517
|
0.051 – 135.046
|
0.805
|
|
Absent|Mild
|
|
|
|
63.145
|
8.741 – 456.152
|
<0.001
|
|
|
|
|
|
|
|
(Intercept)
|
|
|
|
|
|
|
0.012
|
0.000 – 0.475
|
0.018
|
0.002
|
0.000 – 0.116
|
0.002
|
|
as.factor(death_year)2019
|
|
|
|
|
|
|
1.367
|
0.047 – 119.463
|
0.851
|
0.823
|
0.027 – 73.270
|
0.908
|
|
as.factor(death_year)2020
|
|
|
|
|
|
|
0.362
|
0.010 – 35.177
|
0.567
|
0.737
|
0.022 – 70.078
|
0.862
|
|
Observations
|
696
|
611
|
841
|
849
|
|
R2 Nagelkerke
|
0.211
|
0.181
|
0.054
|
0.140
|
Simple Model + Year of Death (centered)
|
Â
|
Atherosclerosis
|
Arteriolosclerosis
|
Microinfacts (binary)
|
Gross Infarcts (binary)
|
|
Predictors
|
Odds Ratios
|
CI
|
p
|
Odds Ratios
|
CI
|
p
|
Odds Ratios
|
CI
|
p
|
Odds Ratios
|
CI
|
p
|
|
None|Mild
|
129.368
|
23.140 – 723.262
|
<0.001
|
|
|
|
|
|
|
|
|
|
|
Mild|Moderate
|
886.601
|
154.613 – 5084.048
|
<0.001
|
829.664
|
139.858 – 4921.730
|
<0.001
|
|
|
|
|
|
|
|
Moderate|Severe
|
37515.213
|
5823.767 – 241663.399
|
<0.001
|
7621.246
|
1203.190 – 48274.508
|
<0.001
|
|
|
|
|
|
|
|
Age at Death
|
1.090
|
1.069 – 1.113
|
<0.001
|
1.092
|
1.070 – 1.114
|
<0.001
|
1.049
|
1.029 – 1.070
|
<0.001
|
1.068
|
1.044 – 1.093
|
<0.001
|
|
Gender: Male
|
0.887
|
0.652 – 1.206
|
0.442
|
1.001
|
0.736 – 1.361
|
0.997
|
0.864
|
0.639 – 1.169
|
0.343
|
1.016
|
0.720 – 1.432
|
0.928
|
|
death year centered
|
1.024
|
0.998 – 1.051
|
0.072
|
0.981
|
0.950 – 1.013
|
0.249
|
0.985
|
0.961 – 1.010
|
0.242
|
1.100
|
1.066 – 1.136
|
<0.001
|
|
Absent|Mild
|
|
|
|
28.702
|
4.760 – 173.091
|
<0.001
|
|
|
|
|
|
|
|
(Intercept)
|
|
|
|
|
|
|
0.015
|
0.003 – 0.081
|
<0.001
|
0.001
|
0.000 – 0.006
|
<0.001
|
|
Observations
|
696
|
611
|
841
|
849
|
|
R2 Nagelkerke
|
0.127
|
0.138
|
0.025
|
0.097
|
Simple Model + CVD
|
Â
|
Atherosclerosis
|
Arteriolosclerosis
|
Microinfacts (binary)
|
Gross Infarcts (binary)
|
|
Predictors
|
Odds Ratios
|
CI
|
p
|
Odds Ratios
|
CI
|
p
|
Odds Ratios
|
CI
|
p
|
Odds Ratios
|
CI
|
p
|
|
None|Mild
|
109.946
|
19.181 – 630.210
|
<0.001
|
|
|
|
|
|
|
|
|
|
|
Mild|Moderate
|
727.027
|
123.697 – 4273.074
|
<0.001
|
1141.530
|
187.910 – 6934.637
|
<0.001
|
|
|
|
|
|
|
|
Moderate|Severe
|
30396.487
|
4624.349 – 199800.322
|
<0.001
|
11063.246
|
1699.582 – 72014.993
|
<0.001
|
|
|
|
|
|
|
|
Age at Death
|
1.089
|
1.067 – 1.111
|
<0.001
|
1.093
|
1.071 – 1.116
|
<0.001
|
1.049
|
1.029 – 1.070
|
<0.001
|
1.066
|
1.042 – 1.091
|
<0.001
|
|
Gender: Male
|
0.864
|
0.633 – 1.179
|
0.356
|
1.022
|
0.748 – 1.397
|
0.891
|
0.832
|
0.613 – 1.129
|
0.238
|
1.048
|
0.747 – 1.468
|
0.786
|
|
CVD cat: CVD cat 1
|
1.625
|
0.991 – 2.697
|
0.057
|
2.606
|
1.571 – 4.352
|
<0.001
|
1.660
|
1.039 – 2.668
|
0.035
|
1.868
|
1.133 – 3.048
|
0.013
|
|
Absent|Mild
|
|
|
|
41.348
|
6.764 – 252.746
|
<0.001
|
|
|
|
|
|
|
|
(Intercept)
|
|
|
|
|
|
|
0.013
|
0.002 – 0.077
|
<0.001
|
0.001
|
0.000 – 0.011
|
<0.001
|
|
Observations
|
683
|
598
|
832
|
840
|
|
R2 Nagelkerke
|
0.166
|
0.196
|
0.033
|
0.039
|
Simple Model + CVD (Interaction with Age at Death)
|
Â
|
Atherosclerosis
|
Arteriolosclerosis
|
Microinfacts (binary)
|
Gross Infarcts (binary)
|
|
Predictors
|
Odds Ratios
|
CI
|
p
|
Odds Ratios
|
CI
|
p
|
Odds Ratios
|
CI
|
p
|
Odds Ratios
|
CI
|
p
|
|
None|Mild
|
122.184
|
19.743 – 756.158
|
<0.001
|
|
|
|
|
|
|
|
|
|
|
Mild|Moderate
|
808.999
|
127.347 – 5139.327
|
<0.001
|
2365.167
|
345.993 – 16168.013
|
<0.001
|
|
|
|
|
|
|
|
Moderate|Severe
|
33748.887
|
4785.297 – 238018.087
|
<0.001
|
23170.088
|
3163.441 – 169705.384
|
<0.001
|
|
|
|
|
|
|
|
Gender: Male
|
0.865
|
0.634 – 1.180
|
0.360
|
1.054
|
0.770 – 1.443
|
0.744
|
0.827
|
0.609 – 1.123
|
0.224
|
1.043
|
0.743 – 1.461
|
0.809
|
|
CVD cat: CVD cat 1
|
5.084
|
0.019 – 1537.100
|
0.573
|
1539.632
|
6.410 – 371849.404
|
0.009
|
0.182
|
0.001 – 45.496
|
0.554
|
0.314
|
0.001 – 104.473
|
0.704
|
|
Age at Death
|
1.090
|
1.067 – 1.114
|
<0.001
|
1.102
|
1.079 – 1.127
|
<0.001
|
1.046
|
1.025 – 1.069
|
<0.001
|
1.063
|
1.038 – 1.089
|
<0.001
|
|
CVD_cat1:age_death_yrs
|
0.987
|
0.922 – 1.054
|
0.691
|
0.928
|
0.870 – 0.989
|
0.022
|
1.027
|
0.962 – 1.100
|
0.442
|
1.021
|
0.954 – 1.097
|
0.557
|
|
Absent|Mild
|
|
|
|
82.739
|
12.240 – 559.304
|
<0.001
|
|
|
|
|
|
|
|
(Intercept)
|
|
|
|
|
|
|
0.017
|
0.003 – 0.107
|
<0.001
|
0.002
|
0.000 – 0.015
|
<0.001
|
|
Observations
|
683
|
598
|
832
|
840
|
|
R2 Nagelkerke
|
0.166
|
0.204
|
0.033
|
0.040
|
Simple Model + CVD (Interaction with Gender)
|
Â
|
Atherosclerosis
|
Arteriolosclerosis
|
Microinfacts (binary)
|
Gross Infarcts (binary)
|
|
Predictors
|
Odds Ratios
|
CI
|
p
|
Odds Ratios
|
CI
|
p
|
Odds Ratios
|
CI
|
p
|
Odds Ratios
|
CI
|
p
|
|
None|Mild
|
108.613
|
18.916 – 623.627
|
<0.001
|
|
|
|
|
|
|
|
|
|
|
Mild|Moderate
|
718.284
|
122.010 – 4228.596
|
<0.001
|
1061.793
|
173.388 – 6502.207
|
<0.001
|
|
|
|
|
|
|
|
Moderate|Severe
|
30034.185
|
4562.339 – 197717.047
|
<0.001
|
10308.772
|
1572.141 – 67596.204
|
<0.001
|
|
|
|
|
|
|
|
Age at Death
|
1.089
|
1.067 – 1.111
|
<0.001
|
1.093
|
1.071 – 1.116
|
<0.001
|
1.048
|
1.028 – 1.070
|
<0.001
|
1.065
|
1.042 – 1.090
|
<0.001
|
|
CVD cat: CVD cat 1
|
1.530
|
0.804 – 2.964
|
0.201
|
2.250
|
1.207 – 4.224
|
0.011
|
1.075
|
0.585 – 1.969
|
0.815
|
1.656
|
0.857 – 3.127
|
0.124
|
|
Gender: Male
|
0.851
|
0.612 – 1.182
|
0.334
|
0.980
|
0.705 – 1.363
|
0.905
|
0.734
|
0.529 – 1.015
|
0.062
|
1.008
|
0.701 – 1.447
|
0.965
|
|
CVD_cat1:male_catMale
|
1.156
|
0.423 – 3.190
|
0.778
|
1.530
|
0.531 – 4.450
|
0.432
|
2.996
|
1.144 – 8.092
|
0.027
|
1.340
|
0.494 – 3.620
|
0.563
|
|
Absent|Mild
|
|
|
|
38.554
|
6.256 – 237.602
|
<0.001
|
|
|
|
|
|
|
|
(Intercept)
|
|
|
|
|
|
|
0.015
|
0.003 – 0.088
|
<0.001
|
0.001
|
0.000 – 0.011
|
<0.001
|
|
Observations
|
683
|
598
|
832
|
840
|
|
R2 Nagelkerke
|
0.166
|
0.197
|
0.037
|
0.039
|
Simple Model + Diabetes
|
Â
|
Atherosclerosis
|
Arteriolosclerosis
|
Microinfacts (binary)
|
Gross Infarcts (binary)
|
|
Predictors
|
Odds Ratios
|
CI
|
p
|
Odds Ratios
|
CI
|
p
|
Odds Ratios
|
CI
|
p
|
Odds Ratios
|
CI
|
p
|
|
None|Mild
|
321.889
|
53.357 – 1941.871
|
<0.001
|
|
|
|
|
|
|
|
|
|
|
Mild|Moderate
|
2330.138
|
372.359 – 14581.473
|
<0.001
|
2816.791
|
419.032 – 18934.838
|
<0.001
|
|
|
|
|
|
|
|
Moderate|Severe
|
101048.660
|
14336.725 – 712215.101
|
<0.001
|
26573.830
|
3682.419 – 191767.562
|
<0.001
|
|
|
|
|
|
|
|
Age at Death
|
1.102
|
1.079 – 1.125
|
<0.001
|
1.104
|
1.081 – 1.129
|
<0.001
|
1.046
|
1.026 – 1.068
|
<0.001
|
1.059
|
1.035 – 1.083
|
<0.001
|
|
Gender: Male
|
0.916
|
0.672 – 1.248
|
0.576
|
1.030
|
0.756 – 1.403
|
0.852
|
0.859
|
0.634 – 1.162
|
0.323
|
1.034
|
0.740 – 1.445
|
0.843
|
diabetes cat: diabetes
cat 1
|
2.718
|
1.698 – 4.400
|
<0.001
|
2.068
|
1.320 – 3.248
|
0.002
|
0.818
|
0.529 – 1.256
|
0.363
|
0.677
|
0.391 – 1.127
|
0.146
|
|
Absent|Mild
|
|
|
|
94.916
|
14.188 – 634.952
|
<0.001
|
|
|
|
|
|
|
|
(Intercept)
|
|
|
|
|
|
|
0.018
|
0.003 – 0.108
|
<0.001
|
0.003
|
0.000 – 0.022
|
<0.001
|
|
Observations
|
694
|
609
|
838
|
846
|
|
R2 Nagelkerke
|
0.156
|
0.159
|
0.024
|
0.030
|
Simple Model + Diabetes (Interaction with Age at Death)
|
Â
|
Atherosclerosis
|
Arteriolosclerosis
|
Microinfacts (binary)
|
Gross Infarcts (binary)
|
|
Predictors
|
Odds Ratios
|
CI
|
p
|
Odds Ratios
|
CI
|
p
|
Odds Ratios
|
CI
|
p
|
Odds Ratios
|
CI
|
p
|
|
None|Mild
|
856.039
|
125.929 – 5819.196
|
<0.001
|
|
|
|
|
|
|
|
|
|
|
Mild|Moderate
|
6405.177
|
899.500 – 45610.102
|
<0.001
|
4245.996
|
542.227 – 33248.944
|
<0.001
|
|
|
|
|
|
|
|
Moderate|Severe
|
280976.350
|
35170.108 – 2244738.902
|
<0.001
|
40090.306
|
4790.126 – 335530.374
|
<0.001
|
|
|
|
|
|
|
|
Gender: Male
|
0.938
|
0.688 – 1.281
|
0.689
|
1.040
|
0.763 – 1.418
|
0.804
|
0.857
|
0.633 – 1.160
|
0.317
|
1.028
|
0.735 – 1.438
|
0.870
|
diabetes cat: diabetes
cat 1
|
7880.803
|
42.483 – 1555064.140
|
0.001
|
27.915
|
0.234 – 3310.562
|
0.172
|
0.341
|
0.002 – 48.417
|
0.675
|
0.000
|
0.000 – 0.217
|
0.023
|
|
Age at Death
|
1.115
|
1.090 – 1.140
|
<0.001
|
1.109
|
1.084 – 1.136
|
<0.001
|
1.045
|
1.023 – 1.068
|
<0.001
|
1.049
|
1.025 – 1.075
|
<0.001
|
|
diabetes_cat1:age_death_yrs
|
0.907
|
0.851 – 0.967
|
0.003
|
0.969
|
0.914 – 1.027
|
0.283
|
1.011
|
0.952 – 1.075
|
0.732
|
1.097
|
1.014 – 1.200
|
0.029
|
|
Absent|Mild
|
|
|
|
141.048
|
18.345 – 1084.474
|
<0.001
|
|
|
|
|
|
|
|
(Intercept)
|
|
|
|
|
|
|
0.020
|
0.003 – 0.137
|
<0.001
|
0.006
|
0.001 – 0.053
|
<0.001
|
|
Observations
|
694
|
609
|
838
|
846
|
|
R2 Nagelkerke
|
0.169
|
0.161
|
0.024
|
0.027
|
Simple Model + Diabetes (Interaction with Gender)
|
Â
|
Atherosclerosis
|
Arteriolosclerosis
|
Microinfacts (binary)
|
Gross Infarcts (binary)
|
|
Predictors
|
Odds Ratios
|
CI
|
p
|
Odds Ratios
|
CI
|
p
|
Odds Ratios
|
CI
|
p
|
Odds Ratios
|
CI
|
p
|
|
None|Mild
|
299.647
|
49.426 – 1816.620
|
<0.001
|
|
|
|
|
|
|
|
|
|
|
Mild|Moderate
|
2173.049
|
345.664 – 13661.085
|
<0.001
|
2543.958
|
376.978 – 17167.365
|
<0.001
|
|
|
|
|
|
|
|
Moderate|Severe
|
94686.712
|
13381.092 – 670018.047
|
<0.001
|
24150.734
|
3336.137 – 174830.317
|
<0.001
|
|
|
|
|
|
|
|
Age at Death
|
1.101
|
1.079 – 1.125
|
<0.001
|
1.104
|
1.080 – 1.128
|
<0.001
|
1.047
|
1.027 – 1.069
|
<0.001
|
1.059
|
1.035 – 1.084
|
<0.001
|
diabetes cat: diabetes
cat 1
|
2.195
|
1.204 – 4.064
|
0.011
|
1.594
|
0.902 – 2.818
|
0.109
|
1.154
|
0.651 – 2.040
|
0.621
|
0.871
|
0.430 – 1.673
|
0.688
|
|
Gender: Male
|
0.857
|
0.616 – 1.194
|
0.362
|
0.934
|
0.667 – 1.307
|
0.690
|
0.965
|
0.695 – 1.341
|
0.833
|
1.107
|
0.776 – 1.580
|
0.574
|
|
diabetes_cat1:male_catMale
|
1.675
|
0.662 – 4.273
|
0.278
|
1.907
|
0.804 – 4.540
|
0.144
|
0.457
|
0.190 – 1.071
|
0.075
|
0.556
|
0.186 – 1.569
|
0.275
|
|
Absent|Mild
|
|
|
|
85.265
|
12.690 – 572.896
|
<0.001
|
|
|
|
|
|
|
|
(Intercept)
|
|
|
|
|
|
|
0.016
|
0.003 – 0.095
|
<0.001
|
0.003
|
0.000 – 0.021
|
<0.001
|
|
Observations
|
694
|
609
|
838
|
846
|
|
R2 Nagelkerke
|
0.158
|
0.162
|
0.024
|
0.030
|
Simple Model + Hypertension
|
Â
|
Atherosclerosis
|
Arteriolosclerosis
|
Microinfacts (binary)
|
Gross Infarcts (binary)
|
|
Predictors
|
Odds Ratios
|
CI
|
p
|
Odds Ratios
|
CI
|
p
|
Odds Ratios
|
CI
|
p
|
Odds Ratios
|
CI
|
p
|
|
None|Mild
|
123.036
|
21.742 – 696.245
|
<0.001
|
|
|
|
|
|
|
|
|
|
|
Mild|Moderate
|
847.430
|
145.801 – 4925.456
|
<0.001
|
1296.307
|
215.589 – 7794.521
|
<0.001
|
|
|
|
|
|
|
|
Moderate|Severe
|
35852.304
|
5507.275 – 233398.120
|
<0.001
|
12340.094
|
1913.601 – 79576.618
|
<0.001
|
|
|
|
|
|
|
|
Age at Death
|
1.089
|
1.067 – 1.111
|
<0.001
|
1.093
|
1.071 – 1.115
|
<0.001
|
1.049
|
1.029 – 1.070
|
<0.001
|
1.063
|
1.040 – 1.087
|
<0.001
|
|
Gender: Male
|
0.893
|
0.655 – 1.217
|
0.474
|
1.036
|
0.759 – 1.414
|
0.824
|
0.875
|
0.645 – 1.185
|
0.388
|
1.038
|
0.741 – 1.452
|
0.828
|
|
HTN cat: HTN cat 1
|
1.556
|
1.140 – 2.131
|
0.006
|
1.898
|
1.392 – 2.596
|
<0.001
|
1.150
|
0.851 – 1.556
|
0.363
|
1.062
|
0.761 – 1.479
|
0.723
|
|
Absent|Mild
|
|
|
|
44.558
|
7.371 – 269.352
|
<0.001
|
|
|
|
|
|
|
|
(Intercept)
|
|
|
|
|
|
|
0.013
|
0.002 – 0.073
|
<0.001
|
0.002
|
0.000 – 0.013
|
<0.001
|
|
Observations
|
689
|
604
|
834
|
842
|
|
R2 Nagelkerke
|
0.156
|
0.180
|
0.028
|
0.031
|
Simple Model + Hypertension (Interaction with Age at Death)
|
Â
|
Atherosclerosis
|
Arteriolosclerosis
|
Microinfacts (binary)
|
Gross Infarcts (binary)
|
|
Predictors
|
Odds Ratios
|
CI
|
p
|
Odds Ratios
|
CI
|
p
|
Odds Ratios
|
CI
|
p
|
Odds Ratios
|
CI
|
p
|
|
None|Mild
|
129.478
|
15.962 – 1050.303
|
<0.001
|
|
|
|
|
|
|
|
|
|
|
Mild|Moderate
|
892.237
|
106.970 – 7442.156
|
<0.001
|
8214.035
|
844.712 – 79873.803
|
<0.001
|
|
|
|
|
|
|
|
Moderate|Severe
|
37713.125
|
4160.422 – 341859.485
|
<0.001
|
78562.762
|
7617.058 – 810300.760
|
<0.001
|
|
|
|
|
|
|
|
Gender: Male
|
0.893
|
0.655 – 1.217
|
0.473
|
1.041
|
0.762 – 1.423
|
0.800
|
0.870
|
0.641 – 1.180
|
0.371
|
1.032
|
0.736 – 1.445
|
0.856
|
|
HTN cat: HTN cat 1
|
1.808
|
0.057 – 58.822
|
0.738
|
247.064
|
7.915 – 9216.540
|
0.002
|
0.027
|
0.001 – 0.917
|
0.046
|
0.017
|
0.000 – 1.007
|
0.053
|
|
Age at Death
|
1.089
|
1.063 – 1.116
|
<0.001
|
1.116
|
1.088 – 1.146
|
<0.001
|
1.033
|
1.009 – 1.059
|
0.008
|
1.045
|
1.018 – 1.075
|
0.001
|
|
HTN_cat1:age_death_yrs
|
0.998
|
0.959 – 1.039
|
0.932
|
0.946
|
0.907 – 0.984
|
0.007
|
1.044
|
1.003 – 1.089
|
0.038
|
1.048
|
1.001 – 1.099
|
0.049
|
|
Absent|Mild
|
|
|
|
262.308
|
28.217 – 2438.406
|
<0.001
|
|
|
|
|
|
|
|
(Intercept)
|
|
|
|
|
|
|
0.050
|
0.006 – 0.408
|
0.006
|
0.008
|
0.001 – 0.089
|
<0.001
|
|
Observations
|
689
|
604
|
834
|
842
|
|
R2 Nagelkerke
|
0.156
|
0.192
|
0.030
|
0.034
|
Simple Model + Hypertension (Interaction with Gender)
|
Â
|
Atherosclerosis
|
Arteriolosclerosis
|
Microinfacts (binary)
|
Gross Infarcts (binary)
|
|
Predictors
|
Odds Ratios
|
CI
|
p
|
Odds Ratios
|
CI
|
p
|
Odds Ratios
|
CI
|
p
|
Odds Ratios
|
CI
|
p
|
|
None|Mild
|
118.658
|
20.925 – 672.864
|
<0.001
|
|
|
|
|
|
|
|
|
|
|
Mild|Moderate
|
818.719
|
140.627 – 4766.525
|
<0.001
|
1142.629
|
189.137 – 6902.950
|
<0.001
|
|
|
|
|
|
|
|
Moderate|Severe
|
34687.121
|
5319.944 – 226167.131
|
<0.001
|
11045.282
|
1705.459 – 71533.958
|
<0.001
|
|
|
|
|
|
|
|
Age at Death
|
1.089
|
1.067 – 1.111
|
<0.001
|
1.093
|
1.071 – 1.116
|
<0.001
|
1.049
|
1.029 – 1.070
|
<0.001
|
1.063
|
1.040 – 1.088
|
<0.001
|
|
HTN cat: HTN cat 1
|
1.385
|
0.921 – 2.089
|
0.119
|
1.412
|
0.947 – 2.108
|
0.092
|
0.762
|
0.511 – 1.134
|
0.181
|
1.127
|
0.729 – 1.740
|
0.589
|
|
Gender: Male
|
0.800
|
0.537 – 1.191
|
0.272
|
0.766
|
0.512 – 1.146
|
0.196
|
0.581
|
0.388 – 0.866
|
0.008
|
1.102
|
0.710 – 1.707
|
0.664
|
|
HTN_cat1:male_catMale
|
1.319
|
0.703 – 2.481
|
0.389
|
2.102
|
1.119 – 3.962
|
0.022
|
2.653
|
1.438 – 4.911
|
0.002
|
0.866
|
0.440 – 1.698
|
0.677
|
|
Absent|Mild
|
|
|
|
39.077
|
6.420 – 237.849
|
<0.001
|
|
|
|
|
|
|
|
(Intercept)
|
|
|
|
|
|
|
0.016
|
0.003 – 0.090
|
<0.001
|
0.002
|
0.000 – 0.013
|
<0.001
|
|
Observations
|
689
|
604
|
834
|
842
|
|
R2 Nagelkerke
|
0.157
|
0.189
|
0.032
|
0.031
|
Micro Infarct Model + CVD (one model for each age group)
|
Â
|
< 80
|
80-89
|
90+
|
|
Predictors
|
Odds Ratios
|
CI
|
p
|
Odds Ratios
|
CI
|
p
|
Odds Ratios
|
CI
|
p
|
|
(Intercept)
|
0.546
|
0.326 – 0.895
|
0.018
|
0.785
|
0.558 – 1.100
|
0.162
|
1.274
|
0.940 – 1.732
|
0.120
|
|
Gender: Male
|
0.568
|
0.290 – 1.103
|
0.096
|
0.955
|
0.582 – 1.567
|
0.856
|
0.919
|
0.560 – 1.511
|
0.738
|
|
CVD cat: CVD cat 1
|
0.934
|
0.368 – 2.201
|
0.880
|
5.239
|
2.279 – 13.792
|
<0.001
|
0.779
|
0.309 – 1.955
|
0.591
|
|
Observations
|
91
|
333
|
408
|
|
R2 Tjur
|
0.015
|
0.050
|
0.000
|
Micro Infarct Model + Diabetes (one model for each age group)
|
Â
|
< 80
|
80-89
|
90+
|
|
Predictors
|
Odds Ratios
|
CI
|
p
|
Odds Ratios
|
CI
|
p
|
Odds Ratios
|
CI
|
p
|
|
(Intercept)
|
0.517
|
0.307 – 0.852
|
0.011
|
1.022
|
0.732 – 1.426
|
0.899
|
1.234
|
0.913 – 1.675
|
0.173
|
|
Gender: Male
|
0.641
|
0.331 – 1.232
|
0.184
|
0.926
|
0.573 – 1.494
|
0.752
|
0.936
|
0.571 – 1.536
|
0.793
|
diabetes cat: diabetes
cat 1
|
0.744
|
0.347 – 1.524
|
0.431
|
0.794
|
0.418 – 1.493
|
0.476
|
1.225
|
0.409 – 3.946
|
0.720
|
|
Observations
|
93
|
334
|
411
|
|
R2 Tjur
|
0.013
|
0.001
|
0.001
|
Micro Infarct Model + Hypertension (one model for each age
group)
|
Â
|
< 80
|
80-89
|
90+
|
|
Predictors
|
Odds Ratios
|
CI
|
p
|
Odds Ratios
|
CI
|
p
|
Odds Ratios
|
CI
|
p
|
|
(Intercept)
|
0.562
|
0.321 – 0.963
|
0.039
|
0.876
|
0.594 – 1.287
|
0.499
|
1.120
|
0.780 – 1.611
|
0.540
|
|
Gender: Male
|
0.607
|
0.311 – 1.172
|
0.139
|
0.965
|
0.597 – 1.559
|
0.883
|
0.952
|
0.579 – 1.568
|
0.846
|
|
HTN cat: HTN cat 1
|
0.736
|
0.367 – 1.441
|
0.377
|
1.207
|
0.746 – 1.954
|
0.444
|
1.341
|
0.825 – 2.189
|
0.238
|
|
Observations
|
92
|
333
|
409
|
|
R2 Tjur
|
0.011
|
0.003
|
0.006
|
Gross Infarct Model + CVD (one model for each age group)
|
Â
|
< 80
|
80-89
|
90+
|
|
Predictors
|
Odds Ratios
|
CI
|
p
|
Odds Ratios
|
CI
|
p
|
Odds Ratios
|
CI
|
p
|
|
(Intercept)
|
0.027
|
0.005 – 0.082
|
<0.001
|
0.404
|
0.278 – 0.577
|
<0.001
|
0.659
|
0.482 – 0.895
|
0.008
|
|
Gender: Male
|
7.437
|
2.238 – 36.840
|
0.004
|
0.918
|
0.540 – 1.550
|
0.748
|
0.771
|
0.459 – 1.281
|
0.319
|
|
CVD cat: CVD cat 1
|
2.566
|
0.753 – 8.018
|
0.112
|
2.714
|
1.300 – 5.698
|
0.008
|
1.313
|
0.510 – 3.286
|
0.561
|
|
Observations
|
91
|
337
|
412
|
|
R2 Tjur
|
0.054
|
0.027
|
0.006
|
Gross Infarct Model + Diabetes (one model for each age group)
|
Â
|
< 80
|
80-89
|
90+
|
|
Predictors
|
Odds Ratios
|
CI
|
p
|
Odds Ratios
|
CI
|
p
|
Odds Ratios
|
CI
|
p
|
|
(Intercept)
|
0.059
|
0.016 – 0.151
|
<0.001
|
0.474
|
0.330 – 0.672
|
<0.001
|
0.678
|
0.497 – 0.919
|
0.013
|
|
Gender: Male
|
5.176
|
1.746 – 20.286
|
0.007
|
0.868
|
0.515 – 1.453
|
0.591
|
0.780
|
0.466 – 1.293
|
0.338
|
diabetes cat: diabetes
cat 1
|
0.213
|
0.032 – 0.793
|
0.047
|
0.962
|
0.472 – 1.879
|
0.912
|
0.833
|
0.242 – 2.526
|
0.754
|
|
Observations
|
93
|
338
|
415
|
|
R2 Tjur
|
0.034
|
0.000
|
0.005
|
Gross Infarct Model + Hypertension (one model for each age
group)
|
Â
|
< 80
|
80-89
|
90+
|
|
Predictors
|
Odds Ratios
|
CI
|
p
|
Odds Ratios
|
CI
|
p
|
Odds Ratios
|
CI
|
p
|
|
(Intercept)
|
0.065
|
0.018 – 0.171
|
<0.001
|
0.383
|
0.248 – 0.578
|
<0.001
|
0.667
|
0.459 – 0.962
|
0.031
|
|
Gender: Male
|
4.840
|
1.632 – 18.963
|
0.009
|
0.920
|
0.544 – 1.547
|
0.753
|
0.752
|
0.448 – 1.250
|
0.275
|
|
HTN cat: HTN cat 1
|
0.329
|
0.092 – 0.948
|
0.056
|
1.438
|
0.856 – 2.419
|
0.169
|
1.045
|
0.635 – 1.712
|
0.863
|
|
Observations
|
92
|
337
|
413
|
|
R2 Tjur
|
0.033
|
0.008
|
0.006
|
Micro Infarct Model, gender only (one model for each age group)
|
Â
|
< 80
|
80-89
|
90+
|
|
Predictors
|
Odds Ratios
|
CI
|
p
|
Odds Ratios
|
CI
|
p
|
Odds Ratios
|
CI
|
p
|
|
(Intercept)
|
0.471
|
0.295 – 0.735
|
0.001
|
0.973
|
0.708 – 1.336
|
0.866
|
1.248
|
0.928 – 1.683
|
0.145
|
|
Gender: Male
|
0.642
|
0.333 – 1.231
|
0.183
|
0.931
|
0.577 – 1.500
|
0.768
|
0.939
|
0.573 – 1.539
|
0.801
|
|
Observations
|
94
|
335
|
412
|
|
R2 Tjur
|
0.011
|
0.001
|
0.000
|
Gross Infarct Model, gender only (one model for each age group)
|
Â
|
< 80
|
80-89
|
90+
|
|
Predictors
|
Odds Ratios
|
CI
|
p
|
Odds Ratios
|
CI
|
p
|
Odds Ratios
|
CI
|
p
|
|
(Intercept)
|
0.043
|
0.012 – 0.108
|
<0.001
|
0.467
|
0.331 – 0.651
|
<0.001
|
0.672
|
0.495 – 0.905
|
0.009
|
|
Gender: Male
|
5.242
|
1.789 – 20.392
|
0.006
|
0.875
|
0.519 – 1.463
|
0.611
|
0.778
|
0.465 – 1.289
|
0.334
|
|
Observations
|
94
|
339
|
416
|
|
R2 Tjur
|
0.018
|
0.001
|
0.004
|
Location-specific Infarct Outcomes
Microinfarcts
|
Â
|
Microinfacts
|
Microinfarcts (cere)
|
Microinfarcts (deep)
|
|
Predictors
|
Odds Ratios
|
CI
|
p
|
Odds Ratios
|
CI
|
p
|
Odds Ratios
|
CI
|
p
|
|
(Intercept)
|
0.013
|
0.002 – 0.077
|
<0.001
|
0.002
|
0.000 – 0.012
|
<0.001
|
0.029
|
0.004 – 0.200
|
<0.001
|
|
Age at Death
|
1.049
|
1.029 – 1.070
|
<0.001
|
1.066
|
1.043 – 1.090
|
<0.001
|
1.031
|
1.009 – 1.054
|
0.006
|
|
Gender: Male
|
0.832
|
0.613 – 1.129
|
0.238
|
0.920
|
0.658 – 1.284
|
0.624
|
0.830
|
0.590 – 1.163
|
0.281
|
|
CVD cat: CVD cat 1
|
1.660
|
1.039 – 2.668
|
0.035
|
2.047
|
1.253 – 3.324
|
0.004
|
0.973
|
0.565 – 1.623
|
0.920
|
|
Observations
|
832
|
832
|
832
|
|
R2 Tjur
|
0.033
|
0.037
|
0.007
|
|
Â
|
Microinfacts
|
Microinfarcts (cere)
|
Microinfarcts (deep)
|
|
Predictors
|
Odds Ratios
|
CI
|
p
|
Odds Ratios
|
CI
|
p
|
Odds Ratios
|
CI
|
p
|
|
(Intercept)
|
0.018
|
0.003 – 0.108
|
<0.001
|
0.003
|
0.000 – 0.022
|
<0.001
|
0.019
|
0.003 – 0.141
|
<0.001
|
|
Age at Death
|
1.046
|
1.026 – 1.068
|
<0.001
|
1.060
|
1.036 – 1.084
|
<0.001
|
1.035
|
1.013 – 1.059
|
0.002
|
|
Gender: Male
|
0.859
|
0.634 – 1.162
|
0.323
|
0.942
|
0.677 – 1.310
|
0.724
|
0.842
|
0.600 – 1.179
|
0.319
|
diabetes cat: diabetes
cat 1
|
0.818
|
0.529 – 1.256
|
0.363
|
0.757
|
0.451 – 1.235
|
0.277
|
1.141
|
0.702 – 1.820
|
0.586
|
|
Observations
|
838
|
838
|
838
|
|
R2 Tjur
|
0.024
|
0.026
|
0.008
|
|
Â
|
Microinfacts
|
Microinfarcts (cere)
|
Microinfarcts (deep)
|
|
Predictors
|
Odds Ratios
|
CI
|
p
|
Odds Ratios
|
CI
|
p
|
Odds Ratios
|
CI
|
p
|
|
(Intercept)
|
0.013
|
0.002 – 0.073
|
<0.001
|
0.002
|
0.000 – 0.015
|
<0.001
|
0.019
|
0.003 – 0.129
|
<0.001
|
|
Age at Death
|
1.049
|
1.029 – 1.070
|
<0.001
|
1.064
|
1.041 – 1.087
|
<0.001
|
1.034
|
1.012 – 1.057
|
0.003
|
|
Gender: Male
|
0.875
|
0.645 – 1.185
|
0.388
|
0.957
|
0.686 – 1.332
|
0.793
|
0.851
|
0.605 – 1.194
|
0.352
|
|
HTN cat: HTN cat 1
|
1.150
|
0.851 – 1.556
|
0.363
|
0.939
|
0.676 – 1.302
|
0.707
|
1.464
|
1.051 – 2.039
|
0.024
|
|
Observations
|
834
|
834
|
834
|
|
R2 Tjur
|
0.028
|
0.028
|
0.016
|
Gross infarcts
|
Â
|
Gross infarcts
|
Gross infarcts (bs)
|
Gross infarcts (ctx)
|
Gross infarcts (gm)
|
Gross infarcts (wm)
|
|
Predictors
|
Odds Ratios
|
CI
|
p
|
Odds Ratios
|
CI
|
p
|
Odds Ratios
|
CI
|
p
|
Odds Ratios
|
CI
|
p
|
Odds Ratios
|
CI
|
p
|
|
(Intercept)
|
0.001
|
0.000 – 0.011
|
<0.001
|
0.003
|
0.000 – 0.056
|
<0.001
|
0.000
|
0.000 – 0.006
|
<0.001
|
0.001
|
0.000 – 0.007
|
<0.001
|
0.000
|
0.000 – 0.001
|
<0.001
|
|
Age at Death
|
1.066
|
1.042 – 1.091
|
<0.001
|
1.040
|
1.006 – 1.076
|
0.022
|
1.071
|
1.033 – 1.112
|
<0.001
|
1.067
|
1.038 – 1.099
|
<0.001
|
1.091
|
1.051 – 1.135
|
<0.001
|
|
Gender: Male
|
1.048
|
0.747 – 1.468
|
0.786
|
1.390
|
0.839 – 2.302
|
0.200
|
0.882
|
0.511 – 1.497
|
0.646
|
1.092
|
0.717 – 1.656
|
0.680
|
0.913
|
0.526 – 1.560
|
0.743
|
|
CVD cat: CVD cat 1
|
1.868
|
1.133 – 3.048
|
0.013
|
1.154
|
0.498 – 2.377
|
0.717
|
1.856
|
0.864 – 3.687
|
0.092
|
2.125
|
1.180 – 3.710
|
0.010
|
1.686
|
0.751 – 3.456
|
0.175
|
|
Observations
|
840
|
840
|
840
|
840
|
840
|
|
R2 Tjur
|
0.039
|
0.008
|
0.015
|
0.023
|
0.018
|
|
Â
|
Gross infarcts
|
Gross infarcts (bs)
|
Gross infarcts (ctx)
|
Gross infarcts (gm)
|
Gross infarcts (wm)
|
|
Predictors
|
Odds Ratios
|
CI
|
p
|
Odds Ratios
|
CI
|
p
|
Odds Ratios
|
CI
|
p
|
Odds Ratios
|
CI
|
p
|
Odds Ratios
|
CI
|
p
|
|
(Intercept)
|
0.003
|
0.000 – 0.022
|
<0.001
|
0.004
|
0.000 – 0.076
|
<0.001
|
0.000
|
0.000 – 0.009
|
<0.001
|
0.002
|
0.000 – 0.021
|
<0.001
|
0.000
|
0.000 – 0.002
|
<0.001
|
|
Age at Death
|
1.059
|
1.035 – 1.083
|
<0.001
|
1.038
|
1.004 – 1.075
|
0.033
|
1.067
|
1.029 – 1.109
|
0.001
|
1.056
|
1.027 – 1.087
|
<0.001
|
1.087
|
1.047 – 1.132
|
<0.001
|
|
Gender: Male
|
1.034
|
0.740 – 1.445
|
0.843
|
1.386
|
0.838 – 2.292
|
0.202
|
0.871
|
0.505 – 1.476
|
0.612
|
1.052
|
0.693 – 1.590
|
0.810
|
0.934
|
0.540 – 1.590
|
0.802
|
diabetes cat: diabetes
cat 1
|
0.677
|
0.391 – 1.127
|
0.146
|
0.731
|
0.297 – 1.570
|
0.454
|
0.861
|
0.333 – 1.917
|
0.732
|
0.542
|
0.242 – 1.083
|
0.105
|
0.842
|
0.308 – 1.945
|
0.710
|
|
Observations
|
846
|
846
|
846
|
846
|
846
|
|
R2 Tjur
|
0.030
|
0.007
|
0.011
|
0.019
|
0.017
|
|
Â
|
Gross infarcts
|
Gross infarcts (bs)
|
Gross infarcts (ctx)
|
Gross infarcts (gm)
|
Gross infarcts (wm)
|
|
Predictors
|
Odds Ratios
|
CI
|
p
|
Odds Ratios
|
CI
|
p
|
Odds Ratios
|
CI
|
p
|
Odds Ratios
|
CI
|
p
|
Odds Ratios
|
CI
|
p
|
|
(Intercept)
|
0.002
|
0.000 – 0.013
|
<0.001
|
0.004
|
0.000 – 0.067
|
<0.001
|
0.000
|
0.000 – 0.008
|
<0.001
|
0.001
|
0.000 – 0.009
|
<0.001
|
0.000
|
0.000 – 0.001
|
<0.001
|
|
Age at Death
|
1.063
|
1.040 – 1.087
|
<0.001
|
1.039
|
1.006 – 1.075
|
0.023
|
1.068
|
1.031 – 1.108
|
<0.001
|
1.063
|
1.034 – 1.094
|
<0.001
|
1.094
|
1.053 – 1.138
|
<0.001
|
|
Gender: Male
|
1.038
|
0.741 – 1.452
|
0.828
|
1.316
|
0.792 – 2.184
|
0.287
|
0.856
|
0.496 – 1.453
|
0.570
|
1.051
|
0.690 – 1.592
|
0.817
|
0.959
|
0.548 – 1.651
|
0.882
|
|
HTN cat: HTN cat 1
|
1.062
|
0.761 – 1.479
|
0.723
|
0.745
|
0.438 – 1.241
|
0.266
|
0.783
|
0.455 – 1.321
|
0.366
|
1.572
|
1.046 – 2.364
|
0.029
|
1.455
|
0.856 – 2.473
|
0.164
|
|
Observations
|
842
|
842
|
842
|
842
|
842
|
|
R2 Tjur
|
0.031
|
0.005
|
0.013
|
0.026
|
0.022
|
Primary results for model 2
## < 90 90+ NA's
## 2020 1364 2325
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 4.88 10.92 12.53 13.18 14.58 30.72 6
## Warning in eval(family$initialize): non-integer #successes in a binomial glm!
## Warning in eval(family$initialize): non-integer #successes in a binomial glm!
## Warning in eval(family$initialize): non-integer #successes in a binomial glm!
## Warning in eval(family$initialize): non-integer #successes in a binomial glm!
## Warning in eval(family$initialize): non-integer #successes in a binomial glm!
## Warning in eval(family$initialize): non-integer #successes in a binomial glm!
## Warning in eval(family$initialize): non-integer #successes in a binomial glm!
## Warning in eval(family$initialize): non-integer #successes in a binomial glm!
## Warning in eval(family$initialize): non-integer #successes in a binomial glm!
## Warning in eval(family$initialize): non-integer #successes in a binomial glm!
## Warning in eval(family$initialize): non-integer #successes in a binomial glm!
## Warning in eval(family$initialize): non-integer #successes in a binomial glm!
## Warning in eval(family$initialize): non-integer #successes in a binomial glm!
## Warning in eval(family$initialize): non-integer #successes in a binomial glm!
## Warning in eval(family$initialize): non-integer #successes in a binomial glm!
## Warning in eval(family$initialize): non-integer #successes in a binomial glm!
## Warning in eval(family$initialize): non-integer #successes in a binomial glm!
## Warning in eval(family$initialize): non-integer #successes in a binomial glm!
## Warning in eval(family$initialize): non-integer #successes in a binomial glm!
## Warning in eval(family$initialize): non-integer #successes in a binomial glm!
Atherosclerosis
Outcome = Atherosclerosis severity
Predictors = AP Exposure + Age at Death + Gender + Race + Education +
Year of Death + Neighborhood Household Income + APOE
|
Â
|
PM 2.5
|
NO2
|
|
Predictors
|
Odds Ratios
|
CI
|
p
|
Odds Ratios
|
CI
|
p
|
|
(Intercept)
|
0.019
|
0.000 – 4.652
|
0.160
|
0.181
|
0.008 – 3.707
|
0.271
|
PM 2.5 Exposure from
Death(10 year)
|
1.221
|
0.856 – 1.748
|
0.272
|
|
|
|
|
splines::bs(age_death_yrs)1
|
67.419
|
0.486 – 12839.826
|
0.104
|
105.587
|
0.815 – 18862.875
|
0.069
|
|
splines::bs(age_death_yrs)2
|
4.176
|
0.267 – 49.628
|
0.283
|
4.249
|
0.270 – 50.821
|
0.278
|
|
splines::bs(age_death_yrs)3
|
558.075
|
5.796 – 105864.712
|
0.011
|
774.609
|
8.310 – 143168.519
|
0.007
|
|
Gender: Male
|
0.991
|
0.690 – 1.426
|
0.960
|
0.975
|
0.679 – 1.402
|
0.892
|
|
Race: nonwhite
|
1.881
|
0.977 – 3.816
|
0.068
|
1.856
|
0.962 – 3.769
|
0.074
|
|
tr_med_inc_hshld_cat35,000-49,999
|
0.662
|
0.352 – 1.222
|
0.193
|
0.691
|
0.353 – 1.326
|
0.272
|
|
tr_med_inc_hshld_cat50,000-74,999
|
0.863
|
0.465 – 1.570
|
0.634
|
0.884
|
0.456 – 1.681
|
0.711
|
Neighborhood Median
Household Income: >75,000
|
0.742
|
0.320 – 1.731
|
0.487
|
0.740
|
0.314 – 1.759
|
0.492
|
|
splines::bs(death_year)1
|
0.009
|
0.000 – 2.529
|
0.103
|
0.002
|
0.000 – 0.375
|
0.020
|
|
splines::bs(death_year)2
|
366.335
|
26.276 – 5500.046
|
<0.001
|
121.237
|
21.022 – 723.374
|
<0.001
|
|
splines::bs(death_year)3
|
0.273
|
0.004 – 20.871
|
0.557
|
0.054
|
0.003 – 1.106
|
0.058
|
|
APOE Status: +APOE e 4
|
1.263
|
0.829 – 1.945
|
0.282
|
1.263
|
0.829 – 1.945
|
0.282
|
NO 2 Exposure from
Death(10 year)
|
|
|
|
1.025
|
0.960 – 1.098
|
0.479
|
|
Observations
|
785
|
785
|
|
R2 Tjur
|
0.140
|
0.139
|
Arteriolosclerosis
Outcome = Atherosclerosis severity
Predictors = AP Exposure + Age at Death + Gender + Race + Education +
Year of Death + Neighborhood Household Income + APOE
|
Â
|
PM 2.5
|
NO2
|
|
Predictors
|
Odds Ratios
|
CI
|
p
|
Odds Ratios
|
CI
|
p
|
|
(Intercept)
|
0.001
|
0.000 – 3.846
|
0.085
|
0.042
|
0.000 – 79.421
|
0.375
|
PM 2.5 Exposure from
Death(10 year)
|
1.509
|
1.015 – 2.262
|
0.044
|
|
|
|
|
splines::bs(age_death_yrs)1
|
832913.718
|
1053.729 – 1624152124.573
|
<0.001
|
2063168.895
|
2869.569 – 3676536497.050
|
<0.001
|
|
splines::bs(age_death_yrs)2
|
6.858
|
0.292 – 117.430
|
0.208
|
6.092
|
0.259 – 105.531
|
0.238
|
|
splines::bs(age_death_yrs)3
|
410026.401
|
957.071 – 528750746.203
|
<0.001
|
903606.101
|
2193.369 – 1105270640.775
|
<0.001
|
|
Gender: Male
|
1.008
|
0.681 – 1.496
|
0.970
|
0.977
|
0.662 – 1.446
|
0.908
|
|
Race: nonwhite
|
0.777
|
0.420 – 1.477
|
0.430
|
0.752
|
0.407 – 1.426
|
0.371
|
|
tr_med_inc_hshld_cat35,000-49,999
|
0.760
|
0.390 – 1.451
|
0.413
|
0.728
|
0.358 – 1.445
|
0.371
|
|
tr_med_inc_hshld_cat50,000-74,999
|
0.621
|
0.324 – 1.157
|
0.141
|
0.560
|
0.280 – 1.088
|
0.093
|
Neighborhood Median
Household Income: >75,000
|
0.850
|
0.339 – 2.192
|
0.732
|
0.736
|
0.294 – 1.895
|
0.518
|
|
splines::bs(death_year)1
|
0.002
|
0.000 – 165.936
|
0.309
|
0.001
|
0.000 – 74.689
|
0.258
|
|
splines::bs(death_year)2
|
1.054
|
0.005 – 117.745
|
0.984
|
0.237
|
0.001 – 17.663
|
0.543
|
|
splines::bs(death_year)3
|
0.164
|
0.000 – 340.804
|
0.661
|
0.020
|
0.000 – 23.892
|
0.311
|
|
APOE Status: +APOE e 4
|
1.402
|
0.882 – 2.266
|
0.159
|
1.400
|
0.883 – 2.257
|
0.159
|
NO 2 Exposure from
Death(10 year)
|
|
|
|
1.011
|
0.944 – 1.088
|
0.758
|
|
Observations
|
677
|
677
|
|
R2 Tjur
|
0.081
|
0.076
|
Microinfarcts (binary)
Outcome = Presence of Microinfarcts
Predictors = AP Exposure + Age at Death + Gender + Race + Education +
Year of Death + Neighborhood Household Income + APOE
|
Â
|
PM 2.5
|
NO2
|
|
Predictors
|
Odds Ratios
|
CI
|
p
|
Odds Ratios
|
CI
|
p
|
|
(Intercept)
|
6.968
|
0.071 – 733.432
|
0.410
|
0.173
|
0.011 – 2.503
|
0.207
|
PM 2.5 Exposure from
Death(10 year)
|
0.718
|
0.529 – 0.966
|
0.031
|
|
|
|
|
splines::bs(age_death_yrs)1
|
0.031
|
0.000 – 2.139
|
0.107
|
0.016
|
0.000 – 1.019
|
0.051
|
|
splines::bs(age_death_yrs)2
|
12.065
|
1.825 – 82.943
|
0.010
|
11.885
|
1.801 – 81.949
|
0.011
|
|
splines::bs(age_death_yrs)3
|
0.263
|
0.008 – 8.681
|
0.454
|
0.154
|
0.005 – 4.831
|
0.288
|
|
Gender: Male
|
0.868
|
0.630 – 1.197
|
0.388
|
0.880
|
0.639 – 1.212
|
0.432
|
|
Race: nonwhite
|
1.332
|
0.787 – 2.264
|
0.286
|
1.337
|
0.789 – 2.273
|
0.281
|
|
tr_med_inc_hshld_cat35,000-49,999
|
2.717
|
1.548 – 4.879
|
0.001
|
2.599
|
1.443 – 4.780
|
0.002
|
|
tr_med_inc_hshld_cat50,000-74,999
|
1.601
|
0.929 – 2.821
|
0.096
|
1.568
|
0.885 – 2.835
|
0.128
|
Neighborhood Median
Household Income: >75,000
|
1.515
|
0.719 – 3.211
|
0.275
|
1.557
|
0.734 – 3.321
|
0.249
|
|
splines::bs(death_year)1
|
13.972
|
0.096 – 2499.475
|
0.308
|
85.766
|
0.868 – 11204.436
|
0.064
|
|
splines::bs(death_year)2
|
1.302
|
0.125 – 13.735
|
0.826
|
8.313
|
1.706 – 43.917
|
0.010
|
|
splines::bs(death_year)3
|
0.503
|
0.013 – 19.727
|
0.712
|
5.939
|
0.407 – 102.750
|
0.204
|
|
APOE Status: +APOE e 4
|
1.094
|
0.756 – 1.584
|
0.632
|
1.100
|
0.760 – 1.591
|
0.613
|
NO 2 Exposure from
Death(10 year)
|
|
|
|
0.969
|
0.918 – 1.023
|
0.256
|
|
Observations
|
806
|
806
|
|
R2 Tjur
|
0.057
|
0.055
|
Gross Infarcts (binary)
Outcome = Presence of Gross infarcts
Predictors = AP Exposure + Age at Death + Gender + Race + Education +
Year of Death + Neighborhood Household Income + APOE
|
Â
|
PM 2.5
|
NO2
|
|
Predictors
|
Odds Ratios
|
CI
|
p
|
Odds Ratios
|
CI
|
p
|
|
(Intercept)
|
0.062
|
0.000 – 17.805
|
0.340
|
0.051
|
0.001 – 2.267
|
0.148
|
PM 2.5 Exposure from
Death(10 year)
|
0.915
|
0.654 – 1.274
|
0.601
|
|
|
|
|
splines::bs(age_death_yrs)1
|
10.530
|
0.044 – 3270.904
|
0.407
|
10.332
|
0.047 – 3061.684
|
0.406
|
|
splines::bs(age_death_yrs)2
|
180.691
|
21.076 – 1785.382
|
<0.001
|
171.172
|
19.958 – 1688.032
|
<0.001
|
|
splines::bs(age_death_yrs)3
|
3.987
|
0.045 – 337.444
|
0.540
|
4.140
|
0.050 – 335.110
|
0.523
|
|
Gender: Male
|
0.982
|
0.683 – 1.412
|
0.923
|
0.994
|
0.690 – 1.429
|
0.973
|
|
Race: nonwhite
|
0.450
|
0.229 – 0.834
|
0.015
|
0.465
|
0.236 – 0.866
|
0.020
|
|
tr_med_inc_hshld_cat35,000-49,999
|
0.738
|
0.412 – 1.331
|
0.308
|
0.629
|
0.340 – 1.169
|
0.141
|
|
tr_med_inc_hshld_cat50,000-74,999
|
0.558
|
0.316 – 0.992
|
0.045
|
0.469
|
0.254 – 0.862
|
0.015
|
Neighborhood Median
Household Income: >75,000
|
0.977
|
0.447 – 2.124
|
0.953
|
0.889
|
0.404 – 1.937
|
0.767
|
|
splines::bs(death_year)1
|
0.027
|
0.000 – 37.634
|
0.293
|
0.039
|
0.000 – 44.697
|
0.326
|
|
splines::bs(death_year)2
|
18.948
|
1.001 – 475.815
|
0.059
|
29.969
|
3.335 – 448.385
|
0.005
|
|
splines::bs(death_year)3
|
0.969
|
0.011 – 126.394
|
0.989
|
1.411
|
0.041 – 100.517
|
0.860
|
|
APOE Status: +APOE e 4
|
1.016
|
0.660 – 1.552
|
0.942
|
1.016
|
0.659 – 1.554
|
0.941
|
NO 2 Exposure from
Death(10 year)
|
|
|
|
0.947
|
0.889 – 1.005
|
0.077
|
|
Observations
|
813
|
813
|
|
R2 Tjur
|
0.130
|
0.132
|
Microinfarcts (continuous)
Outcome = Number of Microinfarcts Predictors = AP Exposure + Age at
Death + Gender + Race + Education + Year of Death + Neighborhood
Household Income + APOE
|
Â
|
PM 2.5
|
NO2
|
|
Predictors
|
Odds Ratios
|
CI
|
p
|
Odds Ratios
|
CI
|
p
|
|
0|1
|
0.159
|
0.002 – 12.190
|
0.405
|
5.100
|
0.386 – 67.463
|
0.216
|
|
1|2+
|
0.432
|
0.006 – 33.115
|
0.704
|
13.827
|
1.041 – 183.640
|
0.047
|
PM 2.5 Exposure from
Death(10 year)
|
0.731
|
0.551 – 0.965
|
0.029
|
|
|
|
|
splines::bs(age_death_yrs)1
|
0.253
|
0.006 – 11.374
|
0.477
|
0.132
|
0.003 – 5.649
|
0.288
|
|
splines::bs(age_death_yrs)2
|
11.970
|
2.140 – 71.190
|
0.005
|
11.590
|
2.069 – 69.235
|
0.006
|
|
splines::bs(age_death_yrs)3
|
0.938
|
0.040 – 20.105
|
0.968
|
0.572
|
0.025 – 11.852
|
0.720
|
|
Gender: Male
|
0.909
|
0.671 – 1.230
|
0.535
|
0.923
|
0.682 – 1.248
|
0.603
|
|
Race: nonwhite
|
1.349
|
0.820 – 2.206
|
0.235
|
1.359
|
0.825 – 2.224
|
0.225
|
|
tr_med_inc_hshld_cat35,000-49,999
|
2.200
|
1.282 – 3.869
|
0.005
|
2.102
|
1.198 – 3.776
|
0.011
|
|
tr_med_inc_hshld_cat50,000-74,999
|
1.418
|
0.836 – 2.469
|
0.205
|
1.385
|
0.795 – 2.468
|
0.259
|
Neighborhood Median
Household Income: >75,000
|
1.472
|
0.715 – 3.044
|
0.294
|
1.508
|
0.727 – 3.138
|
0.270
|
|
splines::bs(death_year)1
|
4.233
|
0.039 – 523.057
|
0.550
|
24.244
|
0.316 – 2214.437
|
0.156
|
|
splines::bs(death_year)2
|
0.610
|
0.067 – 5.604
|
0.661
|
3.487
|
0.758 – 17.043
|
0.115
|
|
splines::bs(death_year)3
|
0.193
|
0.006 – 6.136
|
0.351
|
2.041
|
0.159 – 29.148
|
0.589
|
|
APOE Status: +APOE e 4
|
1.239
|
0.874 – 1.753
|
0.227
|
1.242
|
0.876 – 1.757
|
0.222
|
NO 2 Exposure from
Death(10 year)
|
|
|
|
0.969
|
0.919 – 1.020
|
0.236
|
|
Observations
|
701
|
701
|
|
R2 Nagelkerke
|
0.200
|
0.195
|
Gross Infarcts (continuous)
Outcome = Number of Gross infarcts
Predictors = AP Exposure + Age at Death + Gender + Race + Education +
Year of Death + Neighborhood Household Income + APOE
|
Â
|
PM 2.5
|
NO2
|
|
Predictors
|
Odds Ratios
|
CI
|
p
|
Odds Ratios
|
CI
|
p
|
|
0|1
|
14.591
|
0.059 – 3599.342
|
0.340
|
13.311
|
0.267 – 662.908
|
0.194
|
|
1|2+
|
35.534
|
0.144 – 8773.081
|
0.204
|
32.529
|
0.653 – 1621.490
|
0.081
|
PM 2.5 Exposure from
Death(10 year)
|
0.934
|
0.680 – 1.279
|
0.671
|
|
|
|
|
splines::bs(age_death_yrs)1
|
6.410
|
0.035 – 1449.097
|
0.491
|
6.811
|
0.040 – 1454.269
|
0.471
|
|
splines::bs(age_death_yrs)2
|
168.640
|
21.367 – 1532.326
|
<0.001
|
160.384
|
20.372 – 1450.145
|
<0.001
|
|
splines::bs(age_death_yrs)3
|
3.699
|
0.054 – 236.808
|
0.538
|
4.067
|
0.063 – 247.576
|
0.503
|
|
Gender: Male
|
1.092
|
0.767 – 1.552
|
0.625
|
1.106
|
0.777 – 1.572
|
0.576
|
|
Race: nonwhite
|
0.452
|
0.233 – 0.827
|
0.014
|
0.468
|
0.240 – 0.859
|
0.019
|
|
tr_med_inc_hshld_cat35,000-49,999
|
0.743
|
0.424 – 1.313
|
0.301
|
0.634
|
0.353 – 1.149
|
0.131
|
|
tr_med_inc_hshld_cat50,000-74,999
|
0.567
|
0.328 – 0.990
|
0.044
|
0.476
|
0.266 – 0.855
|
0.013
|
Neighborhood Median
Household Income: >75,000
|
0.861
|
0.408 – 1.800
|
0.692
|
0.795
|
0.376 – 1.663
|
0.545
|
|
splines::bs(death_year)1
|
0.016
|
0.000 – 18.935
|
0.218
|
0.019
|
0.000 – 18.775
|
0.221
|
|
splines::bs(death_year)2
|
25.417
|
1.453 – 598.242
|
0.033
|
36.036
|
4.115 – 524.161
|
0.003
|
|
splines::bs(death_year)3
|
0.775
|
0.010 – 88.846
|
0.911
|
0.917
|
0.028 – 59.890
|
0.964
|
|
APOE Status: +APOE e 4
|
0.947
|
0.622 – 1.428
|
0.798
|
0.944
|
0.619 – 1.424
|
0.786
|
NO 2 Exposure from
Death(10 year)
|
|
|
|
0.946
|
0.892 – 1.001
|
0.061
|
|
Observations
|
705
|
705
|
|
R2 Nagelkerke
|
0.253
|
0.257
|
Location-specific infarct outcomes
Microinfarcts
|
Â
|
Microinfarcts
|
Microinfarcts (cere)
|
Microinfarcts (deep)
|
|
Predictors
|
Odds Ratios
|
CI
|
p
|
Odds Ratios
|
CI
|
p
|
Odds Ratios
|
CI
|
p
|
|
(Intercept)
|
6.968
|
0.071 – 733.432
|
0.410
|
0.881
|
0.006 – 141.548
|
0.961
|
1.521
|
0.009 – 256.749
|
0.872
|
PM 2.5 Exposure from
Death(10 year)
|
0.718
|
0.529 – 0.966
|
0.031
|
0.765
|
0.553 – 1.051
|
0.102
|
0.793
|
0.570 – 1.097
|
0.165
|
|
splines::bs(age_death_yrs)1
|
0.031
|
0.000 – 2.139
|
0.107
|
0.665
|
0.006 – 92.371
|
0.868
|
0.148
|
0.001 – 21.789
|
0.449
|
|
splines::bs(age_death_yrs)2
|
12.065
|
1.825 – 82.943
|
0.010
|
13.926
|
1.907 – 107.176
|
0.010
|
13.099
|
1.520 – 129.326
|
0.023
|
|
splines::bs(age_death_yrs)3
|
0.263
|
0.008 – 8.681
|
0.454
|
3.384
|
0.074 – 162.291
|
0.531
|
0.214
|
0.003 – 12.740
|
0.468
|
|
Gender: Male
|
0.868
|
0.630 – 1.197
|
0.388
|
0.966
|
0.685 – 1.361
|
0.843
|
0.802
|
0.561 – 1.142
|
0.222
|
|
Race: nonwhite
|
1.332
|
0.787 – 2.264
|
0.286
|
0.674
|
0.363 – 1.200
|
0.193
|
1.552
|
0.877 – 2.707
|
0.125
|
|
tr_med_inc_hshld_cat35,000-49,999
|
2.717
|
1.548 – 4.879
|
0.001
|
1.624
|
0.888 – 3.072
|
0.124
|
2.719
|
1.407 – 5.600
|
0.004
|
|
tr_med_inc_hshld_cat50,000-74,999
|
1.601
|
0.929 – 2.821
|
0.096
|
1.208
|
0.669 – 2.259
|
0.541
|
1.602
|
0.835 – 3.275
|
0.173
|
Neighborhood Median
Household Income: >75,000
|
1.515
|
0.719 – 3.211
|
0.275
|
1.330
|
0.596 – 2.972
|
0.485
|
2.150
|
0.916 – 5.161
|
0.081
|
|
splines::bs(death_year)1
|
13.972
|
0.096 – 2499.475
|
0.308
|
7.525
|
0.035 – 2157.866
|
0.471
|
3.353
|
0.015 – 979.156
|
0.668
|
|
splines::bs(death_year)2
|
1.302
|
0.125 – 13.735
|
0.826
|
0.752
|
0.061 – 9.477
|
0.824
|
1.662
|
0.134 – 21.317
|
0.694
|
|
splines::bs(death_year)3
|
0.503
|
0.013 – 19.727
|
0.712
|
0.560
|
0.011 – 31.257
|
0.776
|
0.181
|
0.003 – 10.801
|
0.410
|
|
APOE Status: +APOE e 4
|
1.094
|
0.756 – 1.584
|
0.632
|
1.259
|
0.849 – 1.859
|
0.249
|
1.038
|
0.691 – 1.545
|
0.856
|
|
Observations
|
806
|
806
|
806
|
|
R2 Tjur
|
0.057
|
0.039
|
0.044
|
Gross infarcts
|
Â
|
Gross infarcts
|
Gross infarcts (bs)
|
Gross infarcts (ctx)
|
Gross infarcts (gm)
|
Gross infarcts (wm)
|
|
Predictors
|
Odds Ratios
|
CI
|
p
|
Odds Ratios
|
CI
|
p
|
Odds Ratios
|
CI
|
p
|
Odds Ratios
|
CI
|
p
|
Odds Ratios
|
CI
|
p
|
|
(Intercept)
|
0.062
|
0.000 – 17.805
|
0.340
|
0.001
|
0.000 – 9.610
|
0.161
|
0.000
|
0.000 – 0.052
|
0.021
|
0.067
|
0.000 – 66.246
|
0.454
|
0.000
|
0.000 – 0.411
|
0.056
|
PM 2.5 Exposure from
Death(10 year)
|
0.915
|
0.654 – 1.274
|
0.601
|
0.927
|
0.573 – 1.491
|
0.757
|
1.097
|
0.667 – 1.798
|
0.716
|
1.020
|
0.681 – 1.528
|
0.922
|
1.049
|
0.639 – 1.722
|
0.849
|
|
splines::bs(age_death_yrs)1
|
10.530
|
0.044 – 3270.904
|
0.407
|
5758.600
|
0.562 – 152894310.248
|
0.079
|
121059728.111
|
78.581 – 6545337731475125.000
|
0.022
|
0.004
|
0.000 – 2.976
|
0.101
|
1514411.228
|
0.115 – 1148487045479331.250
|
0.128
|
|
splines::bs(age_death_yrs)2
|
180.691
|
21.076 – 1785.382
|
<0.001
|
18.284
|
0.939 – 496.308
|
0.067
|
1912.140
|
22.952 – 827209.595
|
0.004
|
488.761
|
27.480 – 12060.295
|
<0.001
|
17197.142
|
132.406 – 19867817.626
|
0.001
|
|
splines::bs(age_death_yrs)3
|
3.987
|
0.045 – 337.444
|
0.540
|
495.391
|
0.365 – 689169.520
|
0.087
|
334830.857
|
11.800 – 61751499996.994
|
0.024
|
0.014
|
0.000 – 3.014
|
0.133
|
17267.499
|
0.090 – 22399121172.553
|
0.140
|
|
Gender: Male
|
0.982
|
0.683 – 1.412
|
0.923
|
1.402
|
0.824 – 2.391
|
0.212
|
0.867
|
0.489 – 1.510
|
0.618
|
1.065
|
0.685 – 1.650
|
0.780
|
0.907
|
0.506 – 1.600
|
0.739
|
|
Race: nonwhite
|
0.450
|
0.229 – 0.834
|
0.015
|
0.516
|
0.161 – 1.303
|
0.205
|
0.979
|
0.372 – 2.240
|
0.962
|
0.429
|
0.167 – 0.951
|
0.053
|
0.992
|
0.355 – 2.367
|
0.986
|
|
tr_med_inc_hshld_cat35,000-49,999
|
0.738
|
0.412 – 1.331
|
0.308
|
0.479
|
0.228 – 1.025
|
0.054
|
1.267
|
0.495 – 3.736
|
0.641
|
1.978
|
0.902 – 4.761
|
0.105
|
0.754
|
0.318 – 1.890
|
0.531
|
|
tr_med_inc_hshld_cat50,000-74,999
|
0.558
|
0.316 – 0.992
|
0.045
|
0.310
|
0.147 – 0.664
|
0.002
|
1.116
|
0.445 – 3.250
|
0.826
|
1.386
|
0.637 – 3.316
|
0.433
|
0.672
|
0.292 – 1.653
|
0.365
|
Neighborhood Median
Household Income: >75,000
|
0.977
|
0.447 – 2.124
|
0.953
|
0.419
|
0.130 – 1.193
|
0.119
|
0.869
|
0.212 – 3.321
|
0.837
|
2.430
|
0.908 – 6.783
|
0.081
|
0.512
|
0.130 – 1.750
|
0.303
|
|
splines::bs(death_year)1
|
0.027
|
0.000 – 37.634
|
0.293
|
0.167
|
0.000 – 145311.943
|
0.763
|
0.005
|
0.000 – 266.631
|
0.276
|
0.021
|
0.000 – 528.420
|
0.397
|
0.018
|
0.000 – 51568.128
|
0.513
|
|
splines::bs(death_year)2
|
18.948
|
1.001 – 475.815
|
0.059
|
19.198
|
0.189 – 8785.229
|
0.259
|
31.681
|
0.416 – 4956.813
|
0.138
|
73.555
|
1.715 – 6778.881
|
0.036
|
67.635
|
0.517 – 70454.802
|
0.133
|
|
splines::bs(death_year)3
|
0.969
|
0.011 – 126.394
|
0.989
|
1.806
|
0.002 – 13818.554
|
0.881
|
0.482
|
0.001 – 771.376
|
0.832
|
1.121
|
0.004 – 944.652
|
0.971
|
1.652
|
0.001 – 29945.912
|
0.902
|
|
APOE Status: +APOE e 4
|
1.016
|
0.660 – 1.552
|
0.942
|
0.832
|
0.406 – 1.596
|
0.596
|
1.168
|
0.610 – 2.150
|
0.627
|
1.242
|
0.747 – 2.032
|
0.394
|
0.449
|
0.185 – 0.959
|
0.053
|
|
Observations
|
813
|
813
|
813
|
813
|
813
|
|
R2 Tjur
|
0.130
|
0.037
|
0.042
|
0.093
|
0.074
|
Exploring AP*Age Group Interaction
Atherosclerosis
|
Â
|
PM2.5
|
NO2
|
|
Predictors
|
Odds Ratios
|
CI
|
p
|
Odds Ratios
|
CI
|
p
|
|
(Intercept)
|
0.002
|
0.000 – 0.498
|
0.028
|
0.300
|
0.021 – 4.014
|
0.366
|
PM 2.5 Exposure from
Death(10 year)
|
1.583
|
1.113 – 2.268
|
0.011
|
|
|
|
|
deathage_group490+
|
14.138
|
2.029 – 101.907
|
0.008
|
11.359
|
2.413 – 53.721
|
0.002
|
|
Gender: Male
|
0.952
|
0.669 – 1.355
|
0.782
|
0.903
|
0.635 – 1.285
|
0.570
|
|
tr_med_inc_hshld_cat35,000-49,999
|
0.763
|
0.414 – 1.382
|
0.378
|
0.891
|
0.469 – 1.667
|
0.721
|
|
tr_med_inc_hshld_cat50,000-74,999
|
0.990
|
0.542 – 1.776
|
0.974
|
1.106
|
0.584 – 2.065
|
0.753
|
Neighborhood Median
Household Income: >75,000
|
0.816
|
0.361 – 1.858
|
0.626
|
0.882
|
0.388 – 2.028
|
0.765
|
|
splines::bs(death_year)1
|
0.741
|
0.004 – 131.742
|
0.909
|
0.053
|
0.000 – 6.402
|
0.225
|
|
splines::bs(death_year)2
|
1195.526
|
91.124 – 17220.165
|
<0.001
|
115.248
|
21.136 – 654.129
|
<0.001
|
|
splines::bs(death_year)3
|
6.952
|
0.127 – 405.202
|
0.345
|
0.273
|
0.015 – 4.940
|
0.375
|
|
APOE Status: +APOE e 4
|
1.277
|
0.850 – 1.936
|
0.244
|
1.250
|
0.833 – 1.892
|
0.285
|
|
exp_avgdeath_10_yr_ST_pm25:deathage_group490+
|
0.813
|
0.636 – 1.039
|
0.098
|
|
|
|
NO 2 Exposure from
Death(10 year)
|
|
|
|
1.087
|
1.005 – 1.183
|
0.043
|
|
exp_avgdeath_10_yr_ST_no2:deathage_group490+
|
|
|
|
0.901
|
0.804 – 1.011
|
0.072
|
|
Observations
|
785
|
785
|
|
R2 Tjur
|
0.119
|
0.116
|
## Warning in eval(family$initialize): non-integer #successes in a binomial glm!
## Warning in eval(family$initialize): non-integer #successes in a binomial glm!
##
## Model 1: athero_bi ~ exp_avgdeath_10_yr_ST_pm25 + deathage_group4 + exp_avgdeath_10_yr_ST_pm25:deathage_group4 +
## male_cat + tr_med_inc_hshld_cat + death_year + apoe
## Model 2: athero_bi ~ exp_avgdeath_10_yr_ST_pm25 + deathage_group4 + male_cat +
## tr_med_inc_hshld_cat + death_year + apoe
##
## L.R. Chisq d.f. P
## 1.4770402 1.0000000 0.2242382
Arteriolosclerosis
|
Â
|
PM2.5
|
NO2
|
|
Predictors
|
Odds Ratios
|
CI
|
p
|
Odds Ratios
|
CI
|
p
|
|
(Intercept)
|
0.000
|
0.000 – 0.324
|
0.021
|
0.485
|
0.001 – 308.118
|
0.813
|
PM 2.5 Exposure from
Death(10 year)
|
2.507
|
1.662 – 3.855
|
<0.001
|
|
|
|
|
deathage_group490+
|
266.923
|
18.598 – 4089.705
|
<0.001
|
20.969
|
4.222 – 104.782
|
<0.001
|
|
Gender: Male
|
0.975
|
0.671 – 1.421
|
0.896
|
0.879
|
0.607 – 1.274
|
0.495
|
|
tr_med_inc_hshld_cat35,000-49,999
|
0.865
|
0.458 – 1.606
|
0.650
|
0.935
|
0.485 – 1.778
|
0.840
|
|
tr_med_inc_hshld_cat50,000-74,999
|
0.772
|
0.416 – 1.405
|
0.403
|
0.767
|
0.403 – 1.435
|
0.412
|
Neighborhood Median
Household Income: >75,000
|
0.925
|
0.390 – 2.231
|
0.861
|
0.838
|
0.362 – 1.969
|
0.682
|
|
splines::bs(death_year)1
|
5.868
|
0.000 – 266690.363
|
0.760
|
2.069
|
0.000 – 56673.126
|
0.891
|
|
splines::bs(death_year)2
|
14.207
|
0.090 – 1245.522
|
0.268
|
0.699
|
0.008 – 38.954
|
0.864
|
|
splines::bs(death_year)3
|
56.974
|
0.018 – 74048.022
|
0.291
|
1.131
|
0.001 – 749.968
|
0.971
|
|
APOE Status: +APOE e 4
|
1.298
|
0.843 – 2.021
|
0.241
|
1.223
|
0.802 – 1.881
|
0.355
|
|
exp_avgdeath_10_yr_ST_pm25:deathage_group490+
|
0.560
|
0.392 – 0.798
|
0.001
|
|
|
|
NO 2 Exposure from
Death(10 year)
|
|
|
|
1.088
|
1.005 – 1.186
|
0.045
|
|
exp_avgdeath_10_yr_ST_no2:deathage_group490+
|
|
|
|
0.874
|
0.776 – 0.987
|
0.027
|
|
Observations
|
677
|
677
|
|
R2 Tjur
|
0.070
|
0.055
|
## Warning in eval(family$initialize): non-integer #successes in a binomial glm!
## Warning in eval(family$initialize): non-integer #successes in a binomial glm!
##
## Model 1: arteriolo_bi ~ exp_avgdeath_10_yr_ST_pm25 * deathage_group4 +
## male_cat + tr_med_inc_hshld_cat + death_year + apoe
## Model 2: arteriolo_bi ~ exp_avgdeath_10_yr_ST_pm25 + deathage_group4 +
## male_cat + tr_med_inc_hshld_cat + death_year + apoe
##
## L.R. Chisq d.f. P
## 10.260566647 1.000000000 0.001359039
Microinfarcts
|
Â
|
PM2.5
|
NO2
|
|
Predictors
|
Odds Ratios
|
CI
|
p
|
Odds Ratios
|
CI
|
p
|
|
(Intercept)
|
1.049
|
0.010 – 112.441
|
0.984
|
0.053
|
0.004 – 0.600
|
0.020
|
PM 2.5 Exposure from
Death(10 year)
|
0.775
|
0.566 – 1.054
|
0.107
|
|
|
|
|
deathage_group490+
|
4.690
|
0.770 – 29.733
|
0.097
|
1.400
|
0.381 – 5.127
|
0.611
|
|
Gender: Male
|
0.827
|
0.603 – 1.133
|
0.237
|
0.840
|
0.612 – 1.152
|
0.280
|
|
tr_med_inc_hshld_cat35,000-49,999
|
2.754
|
1.580 – 4.912
|
<0.001
|
2.682
|
1.498 – 4.905
|
0.001
|
|
tr_med_inc_hshld_cat50,000-74,999
|
1.594
|
0.931 – 2.793
|
0.095
|
1.589
|
0.903 – 2.857
|
0.114
|
Neighborhood Median
Household Income: >75,000
|
1.556
|
0.743 – 3.278
|
0.242
|
1.626
|
0.771 – 3.447
|
0.202
|
|
splines::bs(death_year)1
|
34.084
|
0.284 – 5064.403
|
0.157
|
117.792
|
1.345 – 13704.821
|
0.042
|
|
splines::bs(death_year)2
|
2.083
|
0.206 – 21.365
|
0.535
|
9.774
|
2.017 – 51.492
|
0.006
|
|
splines::bs(death_year)3
|
0.933
|
0.029 – 30.871
|
0.969
|
6.980
|
0.513 – 112.869
|
0.156
|
|
APOE Status: +APOE e 4
|
1.134
|
0.786 – 1.635
|
0.502
|
1.129
|
0.783 – 1.625
|
0.516
|
|
exp_avgdeath_10_yr_ST_pm25:deathage_group490+
|
0.883
|
0.696 – 1.114
|
0.297
|
|
|
|
NO 2 Exposure from
Death(10 year)
|
|
|
|
0.966
|
0.899 – 1.035
|
0.332
|
|
exp_avgdeath_10_yr_ST_no2:deathage_group490+
|
|
|
|
1.018
|
0.924 – 1.122
|
0.721
|
|
Observations
|
806
|
806
|
|
R2 Tjur
|
0.051
|
0.048
|
## Warning in eval(family$initialize): non-integer #successes in a binomial glm!
## Warning in eval(family$initialize): non-integer #successes in a binomial glm!
##
## Model 1: chronic_microinfarcts_any ~ exp_avgdeath_10_yr_ST_pm25 * deathage_group4 +
## male_cat + tr_med_inc_hshld_cat + death_year + apoe
## Model 2: chronic_microinfarcts_any ~ exp_avgdeath_10_yr_ST_pm25 + deathage_group4 +
## male_cat + tr_med_inc_hshld_cat + death_year + apoe
##
## L.R. Chisq d.f. P
## 0.4422174 1.0000000 0.5060541
Gross Infarcts
|
Â
|
PM2.5
|
NO2
|
|
Predictors
|
Odds Ratios
|
CI
|
p
|
Odds Ratios
|
CI
|
p
|
|
(Intercept)
|
0.022
|
0.000 – 5.100
|
0.174
|
0.232
|
0.004 – 6.448
|
0.428
|
PM 2.5 Exposure from
Death(10 year)
|
1.155
|
0.818 – 1.630
|
0.410
|
|
|
|
|
deathage_group490+
|
23.474
|
2.525 – 244.896
|
0.007
|
2.632
|
0.633 – 11.007
|
0.183
|
|
Gender: Male
|
0.921
|
0.648 – 1.307
|
0.645
|
0.921
|
0.647 – 1.310
|
0.649
|
|
tr_med_inc_hshld_cat35,000-49,999
|
0.794
|
0.451 – 1.410
|
0.426
|
0.690
|
0.379 – 1.264
|
0.227
|
|
tr_med_inc_hshld_cat50,000-74,999
|
0.625
|
0.362 – 1.089
|
0.094
|
0.536
|
0.299 – 0.964
|
0.036
|
Neighborhood Median
Household Income: >75,000
|
0.979
|
0.456 – 2.085
|
0.955
|
0.906
|
0.419 – 1.938
|
0.799
|
|
splines::bs(death_year)1
|
0.518
|
0.001 – 641.991
|
0.846
|
0.276
|
0.001 – 335.654
|
0.697
|
|
splines::bs(death_year)2
|
64.446
|
3.379 – 1668.401
|
0.008
|
38.528
|
4.123 – 636.175
|
0.004
|
|
splines::bs(death_year)3
|
5.277
|
0.074 – 571.221
|
0.462
|
2.616
|
0.076 – 197.293
|
0.627
|
|
APOE Status: +APOE e 4
|
0.937
|
0.614 – 1.416
|
0.760
|
0.913
|
0.599 – 1.378
|
0.669
|
|
exp_avgdeath_10_yr_ST_pm25:deathage_group490+
|
0.715
|
0.520 – 0.965
|
0.033
|
|
|
|
NO 2 Exposure from
Death(10 year)
|
|
|
|
0.954
|
0.877 – 1.032
|
0.255
|
|
exp_avgdeath_10_yr_ST_no2:deathage_group490+
|
|
|
|
0.979
|
0.877 – 1.092
|
0.696
|
|
Observations
|
813
|
813
|
|
R2 Tjur
|
0.113
|
0.109
|
## Warning in eval(family$initialize): non-integer #successes in a binomial glm!
## Warning in eval(family$initialize): non-integer #successes in a binomial glm!
##
## Model 1: chronic_grossinfarcts_any ~ exp_avgdeath_10_yr_ST_pm25 * deathage_group4 +
## male_cat + tr_med_inc_hshld_cat + death_year + apoe
## Model 2: chronic_grossinfarcts_any ~ exp_avgdeath_10_yr_ST_pm25 + deathage_group4 +
## male_cat + tr_med_inc_hshld_cat + death_year + apoe
##
## L.R. Chisq d.f. P
## 3.76168370 1.00000000 0.05243975
Atherosclerosis
## Warning in eval(family$initialize): non-integer #successes in a binomial glm!
Arteriolosclerosis
## Warning in eval(family$initialize): non-integer #successes in a binomial glm!
Microinfarcts
## Warning in eval(family$initialize): non-integer #successes in a binomial glm!
Gross infarcts
## Warning in eval(family$initialize): non-integer #successes in a binomial glm!
Adding Quadratic Terms
Atherosclerosis
Arteriolosclerosis
Microinfarcts
Gross infarcts
Primary Models Stratified by age group
Atherosclerosis ~ PM2.5
|
Â
|
< 90
|
90+
|
|
Predictors
|
Odds Ratios
|
CI
|
p
|
Odds Ratios
|
CI
|
p
|
|
(Intercept)
|
0.001
|
0.000 – 0.620
|
0.038
|
1.353
|
0.000 – 34018.988
|
0.952
|
PM 2.5 Exposure from
Death(10 year)
|
1.572
|
1.023 – 2.455
|
0.042
|
1.253
|
0.679 – 2.298
|
0.468
|
|
Gender: Male
|
1.100
|
0.717 – 1.691
|
0.663
|
0.849
|
0.440 – 1.662
|
0.628
|
|
tr_med_inc_hshld_cat35,000-49,999
|
0.547
|
0.271 – 1.082
|
0.087
|
1.876
|
0.483 – 6.388
|
0.330
|
|
tr_med_inc_hshld_cat50,000-74,999
|
0.979
|
0.488 – 1.929
|
0.951
|
1.088
|
0.296 – 3.371
|
0.890
|
Neighborhood Median
Household Income: >75,000
|
0.590
|
0.217 – 1.600
|
0.299
|
1.318
|
0.273 – 6.424
|
0.726
|
|
splines::bs(death_year)1
|
3.995
|
0.008 – 2310.463
|
0.663
|
0.001
|
0.000 – 23.818
|
0.227
|
|
splines::bs(death_year)2
|
4873.040
|
207.402 – 138467.676
|
<0.001
|
32.672
|
0.322 – 3141.999
|
0.135
|
|
splines::bs(death_year)3
|
11.836
|
0.090 – 1772.656
|
0.326
|
0.254
|
0.000 – 488.395
|
0.732
|
|
APOE Status: +APOE e 4
|
1.462
|
0.906 – 2.380
|
0.123
|
1.052
|
0.472 – 2.547
|
0.905
|
|
Observations
|
399
|
386
|
|
R2 Tjur
|
0.110
|
0.071
|
Atherosclerosis ~ NO2
|
Â
|
< 90
|
90+
|
|
Predictors
|
Odds Ratios
|
CI
|
p
|
Odds Ratios
|
CI
|
p
|
|
(Intercept)
|
0.177
|
0.007 – 3.630
|
0.271
|
40.358
|
0.321 – 21019.057
|
0.187
|
NO 2 Exposure from
Death(10 year)
|
1.061
|
0.977 – 1.159
|
0.169
|
0.993
|
0.895 – 1.113
|
0.893
|
|
Gender: Male
|
1.068
|
0.696 – 1.641
|
0.764
|
0.820
|
0.425 – 1.601
|
0.555
|
|
tr_med_inc_hshld_cat35,000-49,999
|
0.624
|
0.293 – 1.305
|
0.214
|
1.698
|
0.426 – 5.922
|
0.422
|
|
tr_med_inc_hshld_cat50,000-74,999
|
1.061
|
0.500 – 2.222
|
0.876
|
0.956
|
0.248 – 3.112
|
0.943
|
Neighborhood Median
Household Income: >75,000
|
0.603
|
0.216 – 1.679
|
0.330
|
1.149
|
0.240 – 5.535
|
0.860
|
|
splines::bs(death_year)1
|
0.298
|
0.001 – 121.329
|
0.685
|
0.000
|
0.000 – 1.558
|
0.104
|
|
splines::bs(death_year)2
|
396.208
|
50.470 – 3436.612
|
<0.001
|
9.581
|
0.306 – 224.103
|
0.173
|
|
splines::bs(death_year)3
|
0.385
|
0.012 – 14.706
|
0.598
|
0.034
|
0.000 – 6.665
|
0.270
|
|
APOE Status: +APOE e 4
|
1.426
|
0.885 – 2.317
|
0.148
|
1.065
|
0.479 – 2.572
|
0.883
|
|
Observations
|
399
|
386
|
|
R2 Tjur
|
0.107
|
0.066
|
Arteriosclerosis ~ PM2.5
|
Â
|
< 90
|
90+
|
|
Predictors
|
Odds Ratios
|
CI
|
p
|
Odds Ratios
|
CI
|
p
|
|
(Intercept)
|
0.000
|
0.000 – 0.001
|
0.001
|
142765.421
|
0.067 – 1658548025010.898
|
0.131
|
PM 2.5 Exposure from
Death(10 year)
|
3.470
|
2.049 – 6.126
|
<0.001
|
0.835
|
0.435 – 1.579
|
0.583
|
|
Gender: Male
|
1.128
|
0.716 – 1.781
|
0.604
|
0.572
|
0.281 – 1.166
|
0.122
|
|
tr_med_inc_hshld_cat35,000-49,999
|
0.990
|
0.480 – 2.017
|
0.978
|
0.568
|
0.121 – 2.015
|
0.418
|
|
tr_med_inc_hshld_cat50,000-74,999
|
0.747
|
0.370 – 1.490
|
0.411
|
0.857
|
0.182 – 3.037
|
0.824
|
Neighborhood Median
Household Income: >75,000
|
1.027
|
0.354 – 3.023
|
0.961
|
0.708
|
0.119 – 3.923
|
0.689
|
|
splines::bs(death_year)1
|
1428.292
|
0.003 – 171579124.235
|
0.238
|
0.000
|
0.000 – 1006.062
|
0.206
|
|
splines::bs(death_year)2
|
230.768
|
0.879 – 40277.745
|
0.042
|
0.013
|
0.000 – 51.164
|
0.347
|
|
splines::bs(death_year)3
|
9648.030
|
1.614 – 29975780.922
|
0.028
|
0.000
|
0.000 – 100.311
|
0.211
|
|
APOE Status: +APOE e 4
|
1.510
|
0.913 – 2.522
|
0.111
|
0.908
|
0.394 – 2.289
|
0.827
|
|
Observations
|
324
|
353
|
|
R2 Tjur
|
0.055
|
0.024
|
Arteriosclerosis ~ NO2
|
Â
|
< 90
|
90+
|
|
Predictors
|
Odds Ratios
|
CI
|
p
|
Odds Ratios
|
CI
|
p
|
|
(Intercept)
|
0.050
|
0.000 – 41.084
|
0.358
|
120160.092
|
0.243 – 519616226744.387
|
0.107
|
NO 2 Exposure from
Death(10 year)
|
1.071
|
0.986 – 1.171
|
0.113
|
0.946
|
0.851 – 1.060
|
0.313
|
|
Gender: Male
|
1.050
|
0.674 – 1.636
|
0.828
|
0.558
|
0.273 – 1.139
|
0.108
|
|
tr_med_inc_hshld_cat35,000-49,999
|
1.027
|
0.482 – 2.170
|
0.944
|
0.507
|
0.104 – 1.860
|
0.342
|
|
tr_med_inc_hshld_cat50,000-74,999
|
0.657
|
0.313 – 1.362
|
0.261
|
0.748
|
0.151 – 2.775
|
0.687
|
Neighborhood Median
Household Income: >75,000
|
0.690
|
0.248 – 1.936
|
0.477
|
0.685
|
0.115 – 3.782
|
0.662
|
|
splines::bs(death_year)1
|
338.640
|
0.003 – 36895242.849
|
0.309
|
0.000
|
0.000 – 447.314
|
0.178
|
|
splines::bs(death_year)2
|
1.892
|
0.017 – 191.699
|
0.779
|
0.015
|
0.000 – 46.680
|
0.356
|
|
splines::bs(death_year)3
|
17.663
|
0.010 – 27757.236
|
0.428
|
0.000
|
0.000 – 69.770
|
0.198
|
|
APOE Status: +APOE e 4
|
1.389
|
0.854 – 2.278
|
0.189
|
0.916
|
0.397 – 2.314
|
0.844
|
|
Observations
|
324
|
353
|
|
R2 Tjur
|
0.016
|
0.026
|
Microinfarcts ~ PM2.5
|
Â
|
< 90
|
90+
|
|
Predictors
|
Odds Ratios
|
CI
|
p
|
Odds Ratios
|
CI
|
p
|
|
(Intercept)
|
0.358
|
0.002 – 84.434
|
0.710
|
42.899
|
0.015 – 138026.937
|
0.356
|
PM 2.5 Exposure from
Death(10 year)
|
0.813
|
0.556 – 1.177
|
0.277
|
0.605
|
0.364 – 0.976
|
0.045
|
|
Gender: Male
|
0.785
|
0.523 – 1.177
|
0.243
|
0.869
|
0.508 – 1.486
|
0.608
|
|
tr_med_inc_hshld_cat35,000-49,999
|
3.169
|
1.592 – 6.613
|
0.001
|
1.916
|
0.707 – 5.292
|
0.202
|
|
tr_med_inc_hshld_cat50,000-74,999
|
1.726
|
0.885 – 3.529
|
0.119
|
1.264
|
0.481 – 3.375
|
0.634
|
Neighborhood Median
Household Income: >75,000
|
1.539
|
0.562 – 4.173
|
0.396
|
1.393
|
0.416 – 4.741
|
0.591
|
|
splines::bs(death_year)1
|
174.872
|
0.739 – 51748.498
|
0.069
|
3.646
|
0.001 – 68009.484
|
0.780
|
|
splines::bs(death_year)2
|
0.872
|
0.052 – 14.756
|
0.924
|
6.255
|
0.139 – 312.661
|
0.348
|
|
splines::bs(death_year)3
|
3.810
|
0.071 – 204.324
|
0.509
|
0.090
|
0.000 – 68.202
|
0.459
|
|
APOE Status: +APOE e 4
|
1.427
|
0.913 – 2.229
|
0.118
|
0.709
|
0.366 – 1.367
|
0.303
|
|
Observations
|
411
|
395
|
|
R2 Tjur
|
0.028
|
0.073
|
Microinfarcts ~ NO2
|
Â
|
< 90
|
90+
|
|
Predictors
|
Odds Ratios
|
CI
|
p
|
Odds Ratios
|
CI
|
p
|
|
(Intercept)
|
0.040
|
0.002 – 0.624
|
0.025
|
0.061
|
0.000 – 3.484
|
0.220
|
NO 2 Exposure from
Death(10 year)
|
0.968
|
0.898 – 1.042
|
0.396
|
0.983
|
0.905 – 1.068
|
0.687
|
|
Gender: Male
|
0.794
|
0.528 – 1.195
|
0.269
|
0.929
|
0.547 – 1.580
|
0.785
|
|
tr_med_inc_hshld_cat35,000-49,999
|
2.895
|
1.391 – 6.286
|
0.005
|
2.165
|
0.792 – 6.054
|
0.133
|
|
tr_med_inc_hshld_cat50,000-74,999
|
1.604
|
0.790 – 3.398
|
0.202
|
1.488
|
0.557 – 4.058
|
0.428
|
Neighborhood Median
Household Income: >75,000
|
1.483
|
0.529 – 4.110
|
0.448
|
1.685
|
0.515 – 5.653
|
0.390
|
|
splines::bs(death_year)1
|
462.798
|
2.283 – 126082.544
|
0.027
|
52.322
|
0.035 – 460193.237
|
0.338
|
|
splines::bs(death_year)2
|
2.760
|
0.420 – 19.694
|
0.299
|
81.652
|
5.780 – 1691.382
|
0.002
|
|
splines::bs(death_year)3
|
15.445
|
0.742 – 382.293
|
0.084
|
3.898
|
0.050 – 904.372
|
0.581
|
|
APOE Status: +APOE e 4
|
1.448
|
0.927 – 2.262
|
0.103
|
0.697
|
0.361 – 1.340
|
0.279
|
|
Observations
|
411
|
395
|
|
R2 Tjur
|
0.031
|
0.059
|
Gross infarcts ~ PM2.5
|
Â
|
< 90
|
90+
|
|
Predictors
|
Odds Ratios
|
CI
|
p
|
Odds Ratios
|
CI
|
p
|
|
(Intercept)
|
0.015
|
0.000 – 10.516
|
0.213
|
1.600
|
0.000 – 5863.709
|
0.912
|
PM 2.5 Exposure from
Death(10 year)
|
1.075
|
0.699 – 1.639
|
0.739
|
0.881
|
0.535 – 1.442
|
0.613
|
|
Gender: Male
|
1.064
|
0.669 – 1.692
|
0.794
|
0.775
|
0.439 – 1.357
|
0.375
|
|
tr_med_inc_hshld_cat35,000-49,999
|
0.587
|
0.295 – 1.178
|
0.131
|
1.398
|
0.503 – 4.069
|
0.526
|
|
tr_med_inc_hshld_cat50,000-74,999
|
0.481
|
0.249 – 0.937
|
0.029
|
1.036
|
0.383 – 2.936
|
0.945
|
Neighborhood Median
Household Income: >75,000
|
0.967
|
0.356 – 2.545
|
0.947
|
1.136
|
0.329 – 4.008
|
0.840
|
|
splines::bs(death_year)1
|
11.462
|
0.006 – 87299.500
|
0.556
|
0.004
|
0.000 – 314.676
|
0.242
|
|
splines::bs(death_year)2
|
46.963
|
1.222 – 2731.081
|
0.047
|
67.899
|
0.939 – 11690.953
|
0.068
|
|
splines::bs(death_year)3
|
19.946
|
0.115 – 6433.409
|
0.275
|
0.385
|
0.001 – 772.679
|
0.777
|
|
APOE Status: +APOE e 4
|
1.062
|
0.621 – 1.790
|
0.822
|
0.829
|
0.403 – 1.666
|
0.603
|
|
Observations
|
415
|
398
|
|
R2 Tjur
|
0.054
|
0.119
|
Gross infarcts ~ NO2
|
Â
|
< 90
|
90+
|
|
Predictors
|
Odds Ratios
|
CI
|
p
|
Odds Ratios
|
CI
|
p
|
|
(Intercept)
|
0.102
|
0.001 – 6.502
|
0.329
|
0.607
|
0.001 – 44.365
|
0.841
|
NO 2 Exposure from
Death(10 year)
|
0.913
|
0.830 – 0.994
|
0.046
|
0.962
|
0.879 – 1.049
|
0.383
|
|
Gender: Male
|
1.162
|
0.724 – 1.867
|
0.534
|
0.767
|
0.435 – 1.342
|
0.355
|
|
tr_med_inc_hshld_cat35,000-49,999
|
0.426
|
0.199 – 0.908
|
0.027
|
1.306
|
0.460 – 3.860
|
0.620
|
|
tr_med_inc_hshld_cat50,000-74,999
|
0.350
|
0.168 – 0.724
|
0.005
|
0.953
|
0.342 – 2.762
|
0.928
|
Neighborhood Median
Household Income: >75,000
|
0.691
|
0.241 – 1.893
|
0.479
|
1.134
|
0.332 – 3.954
|
0.841
|
|
splines::bs(death_year)1
|
29.443
|
0.012 – 339659.817
|
0.434
|
0.006
|
0.000 – 303.448
|
0.248
|
|
splines::bs(death_year)2
|
36.200
|
2.082 – 1465.580
|
0.028
|
131.549
|
5.516 – 11722.664
|
0.007
|
|
splines::bs(death_year)3
|
16.623
|
0.174 – 4796.932
|
0.273
|
0.764
|
0.008 – 643.569
|
0.919
|
|
APOE Status: +APOE e 4
|
1.066
|
0.622 – 1.801
|
0.812
|
0.831
|
0.404 – 1.670
|
0.607
|
|
Observations
|
415
|
398
|
|
R2 Tjur
|
0.058
|
0.120
|
Further emmeans investiagtion
Microinfarcts
## age_death_yrs = 75:
## exp_avgdeath_10_yr_ST_pm25 prob SE df asymp.LCL asymp.UCL
## 6.90 0.455 0.0566 Inf 0.348 0.566
## 8.39 0.337 0.0511 Inf 0.245 0.443
## 9.40 0.267 0.0654 Inf 0.159 0.412
##
## age_death_yrs = 85:
## exp_avgdeath_10_yr_ST_pm25 prob SE df asymp.LCL asymp.UCL
## 6.90 0.552 0.0513 Inf 0.450 0.649
## 8.39 0.429 0.0417 Inf 0.350 0.512
## 9.40 0.349 0.0638 Inf 0.237 0.482
##
## age_death_yrs = 95:
## exp_avgdeath_10_yr_ST_pm25 prob SE df asymp.LCL asymp.UCL
## 6.90 0.711 0.0437 Inf 0.618 0.788
## 8.39 0.600 0.0430 Inf 0.513 0.680
## 9.40 0.517 0.0718 Inf 0.379 0.653
##
## Results are averaged over the levels of: male_cat, race_cat_simp, tr_med_inc_hshld_cat, apoe
## Confidence level used: 0.95
## Intervals are back-transformed from the logit scale
Gross infarcts
## age_death_yrs = 75:
## exp_avgdeath_10_yr_ST_pm25 prob SE df asymp.LCL asymp.UCL
## 6.90 0.1038 0.0280 Inf 0.0604 0.173
## 8.39 0.0922 0.0247 Inf 0.0538 0.153
## 9.40 0.0849 0.0309 Inf 0.0408 0.168
##
## age_death_yrs = 85:
## exp_avgdeath_10_yr_ST_pm25 prob SE df asymp.LCL asymp.UCL
## 6.90 0.3085 0.0506 Inf 0.2189 0.415
## 8.39 0.2810 0.0395 Inf 0.2105 0.364
## 9.40 0.2633 0.0608 Inf 0.1620 0.398
##
## age_death_yrs = 95:
## exp_avgdeath_10_yr_ST_pm25 prob SE df asymp.LCL asymp.UCL
## 6.90 0.4056 0.0558 Inf 0.3024 0.518
## 8.39 0.3741 0.0456 Inf 0.2897 0.467
## 9.40 0.3534 0.0723 Inf 0.2272 0.504
##
## Results are averaged over the levels of: male_cat, race_cat_simp, tr_med_inc_hshld_cat, apoe
## Confidence level used: 0.95
## Intervals are back-transformed from the logit scale
Continous age interaction with AP (splines, quatratics)
Atherosclerosis, spline
|
Â
|
PM2.5
|
NO2
|
|
Predictors
|
Odds Ratios
|
CI
|
p
|
Odds Ratios
|
CI
|
p
|
|
(Intercept)
|
0.012
|
0.000 – 14.954
|
0.200
|
0.004
|
0.000 – 4.262
|
0.118
|
PM 2.5 Exposure from
Death(10 year)
|
1.285
|
0.544 – 2.564
|
0.511
|
|
|
|
|
splines::bs(age_death_yrs)1
|
0.096
|
0.000 – 222923.824
|
0.760
|
2.758
|
0.000 – 64836445.420
|
0.907
|
|
splines::bs(age_death_yrs)2
|
1170.858
|
0.003 – 95601088.245
|
0.248
|
2771059.434
|
253.712 – 24979729740.410
|
0.001
|
|
splines::bs(age_death_yrs)3
|
81.967
|
0.000 – 5704086100.635
|
0.609
|
4.455
|
0.000 – 11996381.294
|
0.840
|
|
Gender: Male
|
1.012
|
0.703 – 1.461
|
0.947
|
0.942
|
0.652 – 1.361
|
0.749
|
|
Race: nonwhite
|
2.092
|
1.068 – 4.317
|
0.037
|
2.253
|
1.132 – 4.737
|
0.025
|
|
tr_med_inc_hshld_cat35,000-49,999
|
0.626
|
0.330 – 1.163
|
0.144
|
0.912
|
0.461 – 1.772
|
0.787
|
|
tr_med_inc_hshld_cat50,000-74,999
|
0.829
|
0.443 – 1.516
|
0.548
|
1.135
|
0.581 – 2.179
|
0.707
|
Neighborhood Median
Household Income: >75,000
|
0.698
|
0.296 – 1.649
|
0.409
|
1.019
|
0.426 – 2.467
|
0.966
|
|
splines::bs(death_year)1
|
0.122
|
0.002 – 6.096
|
0.290
|
0.034
|
0.001 – 1.082
|
0.056
|
|
splines::bs(death_year)2
|
578.435
|
60.154 – 5996.787
|
<0.001
|
183.650
|
36.091 – 968.460
|
<0.001
|
|
splines::bs(death_year)3
|
0.818
|
0.028 – 24.435
|
0.907
|
0.183
|
0.021 – 1.537
|
0.117
|
|
APOE Status: +APOE e 4
|
1.275
|
0.834 – 1.970
|
0.267
|
1.229
|
0.802 – 1.902
|
0.347
|
|
exp_avgdeath_10_yr_ST_pm25:splines::bs(age_death_yrs)1
|
1.863
|
0.290 – 17.083
|
0.537
|
|
|
|
|
exp_avgdeath_10_yr_ST_pm25:splines::bs(age_death_yrs)2
|
0.471
|
0.110 – 2.250
|
0.325
|
|
|
|
|
exp_avgdeath_10_yr_ST_pm25:splines::bs(age_death_yrs)3
|
1.031
|
0.114 – 9.418
|
0.978
|
|
|
|
NO 2 Exposure from
Death(10 year)
|
|
|
|
1.359
|
0.790 – 2.310
|
0.257
|
|
exp_avgdeath_10_yr_ST_no2:splines::bs(age_death_yrs)1
|
|
|
|
1.121
|
0.290 – 4.637
|
0.870
|
|
exp_avgdeath_10_yr_ST_no2:splines::bs(age_death_yrs)2
|
|
|
|
0.353
|
0.172 – 0.706
|
0.004
|
|
exp_avgdeath_10_yr_ST_no2:splines::bs(age_death_yrs)3
|
|
|
|
1.266
|
0.401 – 4.371
|
0.695
|
|
Observations
|
785
|
785
|
|
R2 Tjur
|
0.141
|
0.142
|
Atherosclerosis, quadratic
|
Â
|
PM2.5
|
NO2
|
|
Predictors
|
Odds Ratios
|
CI
|
p
|
Odds Ratios
|
CI
|
p
|
|
(Intercept)
|
0.171
|
0.001 – 22.617
|
0.479
|
1.183
|
0.164 – 8.466
|
0.867
|
PM 2.5 Exposure from
Death(10 year)
|
1.184
|
0.827 – 1.700
|
0.357
|
|
|
|
|
poly(age_death_yrs, 2)1
|
681482573187151822848.000
|
2051590.440 – 2041448706990095571361322909543956480.000
|
0.006
|
1233441957983373056.000
|
6910285.639 – 235132442515833705258253549568.000
|
0.002
|
|
poly(age_death_yrs, 2)2
|
162.490
|
0.000 – 38365986718021.945
|
0.695
|
0.000
|
0.000 – 0.213
|
0.039
|
|
Gender: Male
|
1.006
|
0.699 – 1.449
|
0.976
|
0.949
|
0.657 – 1.371
|
0.778
|
|
Race: nonwhite
|
2.122
|
1.090 – 4.343
|
0.032
|
2.198
|
1.120 – 4.541
|
0.027
|
|
tr_med_inc_hshld_cat35,000-49,999
|
0.630
|
0.333 – 1.168
|
0.148
|
0.929
|
0.471 – 1.801
|
0.829
|
|
tr_med_inc_hshld_cat50,000-74,999
|
0.830
|
0.444 – 1.518
|
0.550
|
1.146
|
0.588 – 2.196
|
0.685
|
Neighborhood Median
Household Income: >75,000
|
0.692
|
0.295 – 1.631
|
0.397
|
1.026
|
0.431 – 2.471
|
0.954
|
|
splines::bs(death_year)1
|
0.195
|
0.004 – 8.920
|
0.400
|
0.055
|
0.002 – 1.596
|
0.091
|
|
splines::bs(death_year)2
|
566.221
|
59.346 – 5814.869
|
<0.001
|
181.292
|
36.072 – 944.848
|
<0.001
|
|
splines::bs(death_year)3
|
1.049
|
0.038 – 29.434
|
0.977
|
0.238
|
0.029 – 1.920
|
0.177
|
|
APOE Status: +APOE e 4
|
1.278
|
0.838 – 1.970
|
0.259
|
1.237
|
0.809 – 1.909
|
0.331
|
|
exp_avgdeath_10_yr_ST_pm25:poly(age_death_yrs, 2)1
|
0.019
|
0.000 – 1.294
|
0.070
|
|
|
|
|
exp_avgdeath_10_yr_ST_pm25:poly(age_death_yrs, 2)2
|
0.556
|
0.021 – 13.222
|
0.719
|
|
|
|
NO 2 Exposure from
Death(10 year)
|
|
|
|
1.017
|
0.952 – 1.089
|
0.632
|
|
exp_avgdeath_10_yr_ST_no2:poly(age_death_yrs, 2)1
|
|
|
|
0.150
|
0.023 – 1.063
|
0.052
|
|
exp_avgdeath_10_yr_ST_no2:poly(age_death_yrs, 2)2
|
|
|
|
6.424
|
1.205 – 38.002
|
0.034
|
|
Observations
|
785
|
785
|
|
R2 Tjur
|
0.140
|
0.140
|
Arteriolosclerosis, spline
|
Â
|
PM2.5
|
NO2
|
|
Predictors
|
Odds Ratios
|
CI
|
p
|
Odds Ratios
|
CI
|
p
|
|
(Intercept)
|
0.000
|
0.000 – 0.463
|
0.048
|
0.000
|
0.000 – 0.000
|
0.001
|
PM 2.5 Exposure from
Death(10 year)
|
8.213
|
0.845 – 99.535
|
0.081
|
|
|
|
|
splines::bs(age_death_yrs)1
|
1538198541.620
|
0.000 – 735531005537316556568002560.000
|
0.283
|
45582149446752728.000
|
1135680.791 – 8935377047544438374396854272.000
|
0.003
|
|
splines::bs(age_death_yrs)2
|
180255128.590
|
4.470 – 1574192307671643.750
|
0.026
|
378848.464
|
8.925 – 24271409760.338
|
0.021
|
|
splines::bs(age_death_yrs)3
|
17260231.647
|
0.000 – 15779207950228311244800.000
|
0.316
|
4058405722471.616
|
3229.225 – 7513186861743388753920.000
|
0.007
|
|
Gender: Male
|
1.076
|
0.723 – 1.610
|
0.719
|
0.932
|
0.623 – 1.397
|
0.732
|
|
Race: nonwhite
|
0.877
|
0.464 – 1.712
|
0.693
|
0.847
|
0.451 – 1.639
|
0.612
|
|
tr_med_inc_hshld_cat35,000-49,999
|
0.743
|
0.377 – 1.432
|
0.382
|
1.213
|
0.597 – 2.427
|
0.589
|
|
tr_med_inc_hshld_cat50,000-74,999
|
0.583
|
0.302 – 1.096
|
0.100
|
0.831
|
0.423 – 1.598
|
0.585
|
Neighborhood Median
Household Income: >75,000
|
0.820
|
0.323 – 2.149
|
0.680
|
1.263
|
0.499 – 3.343
|
0.628
|
|
splines::bs(death_year)1
|
0.013
|
0.000 – 52.576
|
0.341
|
0.015
|
0.000 – 46.143
|
0.339
|
|
splines::bs(death_year)2
|
2.355
|
0.071 – 59.032
|
0.614
|
0.443
|
0.018 – 7.244
|
0.590
|
|
splines::bs(death_year)3
|
0.662
|
0.001 – 179.914
|
0.891
|
0.124
|
0.000 – 18.646
|
0.447
|
|
APOE Status: +APOE e 4
|
1.487
|
0.925 – 2.436
|
0.107
|
1.432
|
0.890 – 2.351
|
0.146
|
|
exp_avgdeath_10_yr_ST_pm25:splines::bs(age_death_yrs)1
|
0.128
|
0.000 – 29.094
|
0.469
|
|
|
|
|
exp_avgdeath_10_yr_ST_pm25:splines::bs(age_death_yrs)2
|
0.095
|
0.011 – 0.901
|
0.036
|
|
|
|
|
exp_avgdeath_10_yr_ST_pm25:splines::bs(age_death_yrs)3
|
0.257
|
0.002 – 23.656
|
0.559
|
|
|
|
NO 2 Exposure from
Death(10 year)
|
|
|
|
3.654
|
1.731 – 8.227
|
0.001
|
|
exp_avgdeath_10_yr_ST_no2:splines::bs(age_death_yrs)1
|
|
|
|
0.108
|
0.016 – 0.699
|
0.019
|
|
exp_avgdeath_10_yr_ST_no2:splines::bs(age_death_yrs)2
|
|
|
|
0.428
|
0.187 – 0.902
|
0.034
|
|
exp_avgdeath_10_yr_ST_no2:splines::bs(age_death_yrs)3
|
|
|
|
0.213
|
0.048 – 1.070
|
0.050
|
|
Observations
|
677
|
677
|
|
R2 Tjur
|
0.079
|
0.080
|
Arteriolosclerosis, quadratic
|
Â
|
PM2.5
|
NO2
|
|
Predictors
|
Odds Ratios
|
CI
|
p
|
Odds Ratios
|
CI
|
p
|
|
(Intercept)
|
0.267
|
0.000 – 193.725
|
0.686
|
8.883
|
0.120 – 1388.013
|
0.351
|
PM 2.5 Exposure from
Death(10 year)
|
1.504
|
0.997 – 2.278
|
0.053
|
|
|
|
|
poly(age_death_yrs, 2)1
|
56109103068627398010725203968.000
|
47128022309.636 – 515268826344175829599885234592940434043835514880.000
|
0.002
|
27679248795091314147328.000
|
54624307099.930 – 11321820586324054518037630804819968.000
|
<0.001
|
|
poly(age_death_yrs, 2)2
|
0.000
|
0.000 – 0.000
|
0.009
|
0.000
|
0.000 – 0.000
|
<0.001
|
|
Gender: Male
|
1.084
|
0.728 – 1.620
|
0.693
|
0.910
|
0.610 – 1.360
|
0.645
|
|
Race: nonwhite
|
0.923
|
0.486 – 1.806
|
0.809
|
0.936
|
0.495 – 1.821
|
0.840
|
|
tr_med_inc_hshld_cat35,000-49,999
|
0.756
|
0.386 – 1.449
|
0.406
|
1.202
|
0.593 – 2.402
|
0.606
|
|
tr_med_inc_hshld_cat50,000-74,999
|
0.595
|
0.309 – 1.115
|
0.111
|
0.838
|
0.427 – 1.611
|
0.600
|
Neighborhood Median
Household Income: >75,000
|
0.835
|
0.332 – 2.157
|
0.704
|
1.242
|
0.498 – 3.200
|
0.646
|
|
splines::bs(death_year)1
|
0.034
|
0.000 – 115.263
|
0.445
|
0.081
|
0.000 – 195.577
|
0.552
|
|
splines::bs(death_year)2
|
2.247
|
0.070 – 55.524
|
0.631
|
0.406
|
0.017 – 6.423
|
0.543
|
|
splines::bs(death_year)3
|
1.423
|
0.003 – 349.279
|
0.904
|
0.355
|
0.001 – 46.415
|
0.695
|
|
APOE Status: +APOE e 4
|
1.485
|
0.927 – 2.423
|
0.106
|
1.361
|
0.853 – 2.208
|
0.204
|
|
exp_avgdeath_10_yr_ST_pm25:poly(age_death_yrs, 2)1
|
0.001
|
0.000 – 0.339
|
0.020
|
|
|
|
|
exp_avgdeath_10_yr_ST_pm25:poly(age_death_yrs, 2)2
|
1271.448
|
4.434 – 487472.138
|
0.015
|
|
|
|
NO 2 Exposure from
Death(10 year)
|
|
|
|
1.011
|
0.946 – 1.085
|
0.756
|
|
exp_avgdeath_10_yr_ST_no2:poly(age_death_yrs, 2)1
|
|
|
|
0.074
|
0.010 – 0.595
|
0.012
|
|
exp_avgdeath_10_yr_ST_no2:poly(age_death_yrs, 2)2
|
|
|
|
26.108
|
4.160 – 193.945
|
0.001
|
|
Observations
|
677
|
677
|
|
R2 Tjur
|
0.079
|
0.081
|
Microinfarcts, spline
|
Â
|
PM2.5
|
NO2
|
|
Predictors
|
Odds Ratios
|
CI
|
p
|
Odds Ratios
|
CI
|
p
|
|
(Intercept)
|
1408.931
|
1.035 – 4789323.595
|
0.063
|
52.285
|
0.069 – 76879.542
|
0.262
|
PM 2.5 Exposure from
Death(10 year)
|
0.333
|
0.109 – 0.819
|
0.033
|
|
|
|
|
splines::bs(age_death_yrs)1
|
0.000
|
0.000 – 17.482
|
0.129
|
0.000
|
0.000 – 139.654
|
0.192
|
|
splines::bs(age_death_yrs)2
|
0.045
|
0.000 – 633.123
|
0.524
|
0.009
|
0.000 – 9.318
|
0.187
|
|
splines::bs(age_death_yrs)3
|
0.585
|
0.000 – 1135143.410
|
0.943
|
0.042
|
0.000 – 7290.525
|
0.606
|
|
Gender: Male
|
0.866
|
0.627 – 1.197
|
0.384
|
0.871
|
0.630 – 1.204
|
0.402
|
|
Race: nonwhite
|
1.447
|
0.849 – 2.481
|
0.175
|
1.341
|
0.791 – 2.284
|
0.277
|
|
tr_med_inc_hshld_cat35,000-49,999
|
2.640
|
1.495 – 4.764
|
0.001
|
2.355
|
1.282 – 4.407
|
0.006
|
|
tr_med_inc_hshld_cat50,000-74,999
|
1.546
|
0.892 – 2.734
|
0.126
|
1.478
|
0.821 – 2.714
|
0.199
|
Neighborhood Median
Household Income: >75,000
|
1.372
|
0.645 – 2.930
|
0.412
|
1.402
|
0.651 – 3.035
|
0.388
|
|
splines::bs(death_year)1
|
6.912
|
0.200 – 271.978
|
0.292
|
24.451
|
1.002 – 738.064
|
0.056
|
|
splines::bs(death_year)2
|
0.721
|
0.099 – 5.219
|
0.746
|
3.207
|
0.821 – 12.731
|
0.095
|
|
splines::bs(death_year)3
|
0.311
|
0.018 – 5.126
|
0.415
|
2.415
|
0.372 – 17.497
|
0.367
|
|
APOE Status: +APOE e 4
|
1.050
|
0.722 – 1.525
|
0.798
|
1.111
|
0.767 – 1.609
|
0.578
|
|
exp_avgdeath_10_yr_ST_pm25:splines::bs(age_death_yrs)1
|
4.887
|
0.521 – 66.468
|
0.200
|
|
|
|
|
exp_avgdeath_10_yr_ST_pm25:splines::bs(age_death_yrs)2
|
2.071
|
0.589 – 7.501
|
0.258
|
|
|
|
|
exp_avgdeath_10_yr_ST_pm25:splines::bs(age_death_yrs)3
|
1.125
|
0.152 – 9.449
|
0.910
|
|
|
|
NO 2 Exposure from
Death(10 year)
|
|
|
|
0.613
|
0.335 – 1.039
|
0.088
|
|
exp_avgdeath_10_yr_ST_no2:splines::bs(age_death_yrs)1
|
|
|
|
2.036
|
0.561 – 8.124
|
0.296
|
|
exp_avgdeath_10_yr_ST_no2:splines::bs(age_death_yrs)2
|
|
|
|
1.727
|
1.013 – 3.024
|
0.049
|
|
exp_avgdeath_10_yr_ST_no2:splines::bs(age_death_yrs)3
|
|
|
|
1.295
|
0.481 – 3.563
|
0.611
|
|
Observations
|
806
|
806
|
|
R2 Tjur
|
0.064
|
0.061
|
Microinfarcts, quadratic
|
Â
|
PM2.5
|
NO2
|
|
Predictors
|
Odds Ratios
|
CI
|
p
|
Odds Ratios
|
CI
|
p
|
|
(Intercept)
|
15.233
|
0.270 – 939.794
|
0.190
|
0.250
|
0.040 – 1.435
|
0.127
|
PM 2.5 Exposure from
Death(10 year)
|
0.677
|
0.497 – 0.913
|
0.012
|
|
|
|
|
poly(age_death_yrs, 2)1
|
136764629769.952
|
0.059 – 791540541579124551974912.000
|
0.081
|
12.017
|
0.000 – 9754373983.394
|
0.811
|
|
poly(age_death_yrs, 2)2
|
4974612080395069.000
|
67399.743 – 1601413154013936143531245568.000
|
0.006
|
358806917757.968
|
2798.822 – 119171131292337651712.000
|
0.006
|
|
Gender: Male
|
0.854
|
0.619 – 1.177
|
0.335
|
0.866
|
0.628 – 1.195
|
0.381
|
|
Race: nonwhite
|
1.380
|
0.815 – 2.349
|
0.231
|
1.282
|
0.759 – 2.171
|
0.353
|
|
tr_med_inc_hshld_cat35,000-49,999
|
2.659
|
1.513 – 4.776
|
0.001
|
2.335
|
1.278 – 4.347
|
0.006
|
|
tr_med_inc_hshld_cat50,000-74,999
|
1.530
|
0.886 – 2.698
|
0.133
|
1.456
|
0.812 – 2.663
|
0.213
|
Neighborhood Median
Household Income: >75,000
|
1.385
|
0.653 – 2.948
|
0.396
|
1.409
|
0.657 – 3.037
|
0.378
|
|
splines::bs(death_year)1
|
4.393
|
0.134 – 163.655
|
0.412
|
17.633
|
0.755 – 504.560
|
0.082
|
|
splines::bs(death_year)2
|
0.681
|
0.093 – 4.917
|
0.703
|
3.662
|
0.950 – 14.369
|
0.061
|
|
splines::bs(death_year)3
|
0.199
|
0.012 – 3.123
|
0.251
|
2.002
|
0.316 – 14.105
|
0.472
|
|
APOE Status: +APOE e 4
|
1.068
|
0.736 – 1.548
|
0.728
|
1.135
|
0.785 – 1.642
|
0.501
|
|
exp_avgdeath_10_yr_ST_pm25:poly(age_death_yrs, 2)1
|
0.137
|
0.003 – 5.600
|
0.298
|
|
|
|
|
exp_avgdeath_10_yr_ST_pm25:poly(age_death_yrs, 2)2
|
0.008
|
0.000 – 0.220
|
0.006
|
|
|
|
NO 2 Exposure from
Death(10 year)
|
|
|
|
0.966
|
0.915 – 1.020
|
0.217
|
|
exp_avgdeath_10_yr_ST_no2:poly(age_death_yrs, 2)1
|
|
|
|
1.793
|
0.382 – 8.469
|
0.458
|
|
exp_avgdeath_10_yr_ST_no2:poly(age_death_yrs, 2)2
|
|
|
|
0.123
|
0.026 – 0.533
|
0.006
|
|
Observations
|
806
|
806
|
|
R2 Tjur
|
0.064
|
0.060
|
Gross infarcts, spline
|
Â
|
PM2.5
|
NO2
|
|
Predictors
|
Odds Ratios
|
CI
|
p
|
Odds Ratios
|
CI
|
p
|
|
(Intercept)
|
948.450
|
0.001 – 4693687405.463
|
0.364
|
5.434
|
0.001 – 49557.457
|
0.707
|
PM 2.5 Exposure from
Death(10 year)
|
0.197
|
0.018 – 1.398
|
0.156
|
|
|
|
|
splines::bs(age_death_yrs)1
|
0.000
|
0.000 – 28.583
|
0.105
|
0.000
|
0.000 – 24731.302
|
0.364
|
|
splines::bs(age_death_yrs)2
|
33147.555
|
0.053 – 22810458006.574
|
0.126
|
167.546
|
0.051 – 777339.126
|
0.221
|
|
splines::bs(age_death_yrs)3
|
0.000
|
0.000 – 1164.478
|
0.184
|
0.001
|
0.000 – 1705.307
|
0.372
|
|
Gender: Male
|
0.988
|
0.686 – 1.422
|
0.947
|
0.988
|
0.684 – 1.425
|
0.947
|
|
Race: nonwhite
|
0.478
|
0.242 – 0.893
|
0.026
|
0.466
|
0.237 – 0.869
|
0.021
|
|
tr_med_inc_hshld_cat35,000-49,999
|
0.676
|
0.372 – 1.233
|
0.199
|
0.586
|
0.309 – 1.111
|
0.101
|
|
tr_med_inc_hshld_cat50,000-74,999
|
0.520
|
0.291 – 0.931
|
0.027
|
0.441
|
0.235 – 0.823
|
0.010
|
Neighborhood Median
Household Income: >75,000
|
0.877
|
0.396 – 1.930
|
0.745
|
0.814
|
0.363 – 1.811
|
0.615
|
|
splines::bs(death_year)1
|
0.236
|
0.002 – 42.888
|
0.560
|
0.168
|
0.003 – 23.709
|
0.437
|
|
splines::bs(death_year)2
|
39.044
|
3.513 – 492.980
|
0.004
|
42.713
|
7.399 – 300.365
|
<0.001
|
|
splines::bs(death_year)3
|
3.022
|
0.098 – 114.283
|
0.536
|
2.422
|
0.215 – 45.572
|
0.510
|
|
APOE Status: +APOE e 4
|
0.963
|
0.621 – 1.479
|
0.864
|
1.005
|
0.652 – 1.538
|
0.981
|
|
exp_avgdeath_10_yr_ST_pm25:splines::bs(age_death_yrs)1
|
68.140
|
0.810 – 10946.861
|
0.089
|
|
|
|
|
exp_avgdeath_10_yr_ST_pm25:splines::bs(age_death_yrs)2
|
0.441
|
0.072 – 2.808
|
0.378
|
|
|
|
|
exp_avgdeath_10_yr_ST_pm25:splines::bs(age_death_yrs)3
|
14.806
|
0.454 – 620.600
|
0.148
|
|
|
|
NO 2 Exposure from
Death(10 year)
|
|
|
|
0.635
|
0.298 – 1.245
|
0.210
|
|
exp_avgdeath_10_yr_ST_no2:splines::bs(age_death_yrs)1
|
|
|
|
2.558
|
0.529 – 13.946
|
0.256
|
|
exp_avgdeath_10_yr_ST_no2:splines::bs(age_death_yrs)2
|
|
|
|
0.932
|
0.491 – 1.753
|
0.827
|
|
exp_avgdeath_10_yr_ST_no2:splines::bs(age_death_yrs)3
|
|
|
|
1.966
|
0.626 – 6.825
|
0.260
|
|
Observations
|
813
|
813
|
|
R2 Tjur
|
0.135
|
0.132
|
Gross infarcts, quadratic
|
Â
|
PM2.5
|
NO2
|
|
Predictors
|
Odds Ratios
|
CI
|
p
|
Odds Ratios
|
CI
|
p
|
|
(Intercept)
|
0.659
|
0.006 – 71.738
|
0.862
|
0.564
|
0.038 – 5.530
|
0.646
|
PM 2.5 Exposure from
Death(10 year)
|
0.890
|
0.636 – 1.238
|
0.491
|
|
|
|
|
poly(age_death_yrs, 2)1
|
17572315856102.600
|
0.004 – 143040034031506114893483868160.000
|
0.098
|
1728.262
|
0.000 – 12356269414073.656
|
0.519
|
|
poly(age_death_yrs, 2)2
|
11.932
|
0.000 – 177381656584200928.000
|
0.891
|
0.006
|
0.000 – 18842430.013
|
0.646
|
|
Gender: Male
|
0.977
|
0.679 – 1.405
|
0.900
|
1.001
|
0.694 – 1.442
|
0.997
|
|
Race: nonwhite
|
0.463
|
0.236 – 0.862
|
0.019
|
0.461
|
0.235 – 0.858
|
0.019
|
|
tr_med_inc_hshld_cat35,000-49,999
|
0.730
|
0.407 – 1.317
|
0.292
|
0.608
|
0.322 – 1.146
|
0.123
|
|
tr_med_inc_hshld_cat50,000-74,999
|
0.544
|
0.307 – 0.968
|
0.037
|
0.455
|
0.244 – 0.844
|
0.013
|
Neighborhood Median
Household Income: >75,000
|
0.945
|
0.431 – 2.061
|
0.886
|
0.856
|
0.384 – 1.895
|
0.703
|
|
splines::bs(death_year)1
|
0.155
|
0.002 – 25.265
|
0.440
|
0.156
|
0.002 – 21.092
|
0.414
|
|
splines::bs(death_year)2
|
33.168
|
3.090 – 400.248
|
0.005
|
44.310
|
7.786 – 307.838
|
<0.001
|
|
splines::bs(death_year)3
|
1.926
|
0.069 – 66.174
|
0.706
|
2.304
|
0.208 – 42.224
|
0.530
|
|
APOE Status: +APOE e 4
|
1.017
|
0.660 – 1.555
|
0.937
|
1.022
|
0.663 – 1.561
|
0.922
|
|
exp_avgdeath_10_yr_ST_pm25:poly(age_death_yrs, 2)1
|
0.070
|
0.000 – 9.699
|
0.291
|
|
|
|
|
exp_avgdeath_10_yr_ST_pm25:poly(age_death_yrs, 2)2
|
0.211
|
0.001 – 20.653
|
0.542
|
|
|
|
NO 2 Exposure from
Death(10 year)
|
|
|
|
0.945
|
0.887 – 1.003
|
0.067
|
|
exp_avgdeath_10_yr_ST_no2:poly(age_death_yrs, 2)1
|
|
|
|
1.344
|
0.235 – 7.908
|
0.740
|
|
exp_avgdeath_10_yr_ST_no2:poly(age_death_yrs, 2)2
|
|
|
|
0.737
|
0.131 – 3.884
|
0.722
|
|
Observations
|
813
|
813
|
|
R2 Tjur
|
0.131
|
0.131
|
Modifying effect of pathology on damage
Outcome = microinfarcts
Modifier = Atherosclerosis
|
Â
|
PM 2.5
|
NO2
|
|
Predictors
|
Odds Ratios
|
CI
|
p
|
Odds Ratios
|
CI
|
p
|
|
(Intercept)
|
0.682
|
0.003 – 150.980
|
0.889
|
0.121
|
0.005 – 2.646
|
0.186
|
PM 2.5 Exposure from
Death(10 year)
|
0.848
|
0.585 – 1.222
|
0.379
|
|
|
|
|
athero bi: athero bi 1
|
3.217
|
0.470 – 22.788
|
0.237
|
1.105
|
0.227 – 5.093
|
0.899
|
|
splines::bs(age_death_yrs)1
|
0.010
|
0.000 – 0.768
|
0.037
|
0.007
|
0.000 – 0.513
|
0.023
|
|
splines::bs(age_death_yrs)2
|
6.337
|
0.921 – 44.714
|
0.061
|
6.843
|
0.989 – 48.971
|
0.052
|
|
splines::bs(age_death_yrs)3
|
0.118
|
0.003 – 4.202
|
0.239
|
0.090
|
0.003 – 3.013
|
0.178
|
|
Gender: Male
|
0.925
|
0.663 – 1.290
|
0.644
|
0.935
|
0.671 – 1.304
|
0.693
|
|
Race: nonwhite
|
1.310
|
0.746 – 2.317
|
0.349
|
1.330
|
0.757 – 2.352
|
0.322
|
|
tr_med_inc_hshld_cat35,000-49,999
|
3.325
|
1.845 – 6.160
|
<0.001
|
3.381
|
1.817 – 6.462
|
<0.001
|
|
tr_med_inc_hshld_cat50,000-74,999
|
1.892
|
1.066 – 3.451
|
0.033
|
1.974
|
1.074 – 3.723
|
0.031
|
Neighborhood Median
Household Income: >75,000
|
1.795
|
0.831 – 3.910
|
0.137
|
1.882
|
0.865 – 4.136
|
0.112
|
|
splines::bs(death_year)1
|
115.756
|
0.414 – 43647.349
|
0.106
|
318.320
|
2.016 – 71798.415
|
0.030
|
|
splines::bs(death_year)2
|
1.478
|
0.117 – 18.893
|
0.763
|
4.198
|
0.744 – 25.485
|
0.110
|
|
splines::bs(death_year)3
|
2.930
|
0.042 – 220.734
|
0.621
|
13.141
|
0.622 – 344.964
|
0.108
|
|
APOE Status: +APOE e 4
|
1.072
|
0.733 – 1.567
|
0.718
|
1.077
|
0.737 – 1.574
|
0.702
|
|
exp_avgdeath_10_yr_ST_pm25:athero_bi1
|
0.935
|
0.733 – 1.189
|
0.586
|
|
|
|
NO 2 Exposure from
Death(10 year)
|
|
|
|
0.960
|
0.861 – 1.062
|
0.446
|
|
exp_avgdeath_10_yr_ST_no2:athero_bi1
|
|
|
|
1.042
|
0.931 – 1.173
|
0.486
|
|
Observations
|
779
|
779
|
|
R2 Tjur
|
0.068
|
0.068
|
Modifier = Arteriolosclerosis
|
Â
|
PM 2.5
|
NO2
|
|
Predictors
|
Odds Ratios
|
CI
|
p
|
Odds Ratios
|
CI
|
p
|
|
(Intercept)
|
2.652
|
0.001 – 6694.433
|
0.810
|
0.099
|
0.000 – 80.567
|
0.515
|
PM 2.5 Exposure from
Death(10 year)
|
0.681
|
0.442 – 1.038
|
0.076
|
|
|
|
arteriolo bi: arteriolo
bi 1
|
1.392
|
0.096 – 20.294
|
0.808
|
1.588
|
0.294 – 8.075
|
0.582
|
|
splines::bs(age_death_yrs)1
|
0.001
|
0.000 – 0.118
|
0.005
|
0.000
|
0.000 – 0.048
|
0.001
|
|
splines::bs(age_death_yrs)2
|
3.656
|
0.450 – 30.471
|
0.225
|
3.793
|
0.474 – 31.502
|
0.210
|
|
splines::bs(age_death_yrs)3
|
0.030
|
0.001 – 1.544
|
0.082
|
0.016
|
0.000 – 0.746
|
0.036
|
|
Gender: Male
|
0.864
|
0.602 – 1.240
|
0.426
|
0.884
|
0.617 – 1.267
|
0.501
|
|
Race: nonwhite
|
1.498
|
0.828 – 2.737
|
0.183
|
1.518
|
0.837 – 2.782
|
0.171
|
|
tr_med_inc_hshld_cat35,000-49,999
|
3.205
|
1.725 – 6.117
|
<0.001
|
3.529
|
1.836 – 6.981
|
<0.001
|
|
tr_med_inc_hshld_cat50,000-74,999
|
2.044
|
1.116 – 3.844
|
0.023
|
2.368
|
1.248 – 4.619
|
0.010
|
Neighborhood Median
Household Income: >75,000
|
1.692
|
0.743 – 3.878
|
0.210
|
1.941
|
0.852 – 4.464
|
0.115
|
|
splines::bs(death_year)1
|
1294.564
|
0.010 – 585985357.973
|
0.254
|
1711.410
|
0.018 – 555685089.626
|
0.223
|
|
splines::bs(death_year)2
|
3.506
|
0.029 – 699.794
|
0.624
|
10.839
|
0.122 – 1640.297
|
0.320
|
|
splines::bs(death_year)3
|
7.935
|
0.004 – 37777.912
|
0.614
|
38.533
|
0.026 – 129319.900
|
0.349
|
|
APOE Status: +APOE e 4
|
1.088
|
0.720 – 1.644
|
0.688
|
1.097
|
0.728 – 1.656
|
0.657
|
|
exp_avgdeath_10_yr_ST_pm25:arteriolo_bi1
|
1.110
|
0.781 – 1.582
|
0.562
|
|
|
|
NO 2 Exposure from
Death(10 year)
|
|
|
|
0.971
|
0.866 – 1.077
|
0.592
|
|
exp_avgdeath_10_yr_ST_no2:arteriolo_bi1
|
|
|
|
1.049
|
0.931 – 1.191
|
0.447
|
|
Observations
|
672
|
672
|
|
R2 Tjur
|
0.097
|
0.095
|
Outcome = gross infarcts
Modifier = Atherosclerosis
|
Â
|
PM 2.5
|
NO2
|
|
Predictors
|
Odds Ratios
|
CI
|
p
|
Odds Ratios
|
CI
|
p
|
|
(Intercept)
|
1.426
|
0.002 – 836.614
|
0.913
|
0.125
|
0.001 – 7.410
|
0.340
|
PM 2.5 Exposure from
Death(10 year)
|
0.683
|
0.429 – 1.067
|
0.100
|
|
|
|
|
athero bi: athero bi 1
|
0.070
|
0.004 – 0.945
|
0.052
|
1.033
|
0.168 – 5.854
|
0.971
|
|
splines::bs(age_death_yrs)1
|
8.496
|
0.031 – 2856.972
|
0.460
|
4.911
|
0.020 – 1594.352
|
0.579
|
|
splines::bs(age_death_yrs)2
|
177.054
|
18.676 – 1968.642
|
<0.001
|
139.264
|
14.902 – 1524.315
|
<0.001
|
|
splines::bs(age_death_yrs)3
|
3.512
|
0.035 – 327.522
|
0.588
|
2.378
|
0.025 – 211.315
|
0.705
|
|
Gender: Male
|
1.132
|
0.775 – 1.653
|
0.521
|
1.135
|
0.778 – 1.655
|
0.511
|
|
Race: nonwhite
|
0.443
|
0.213 – 0.863
|
0.022
|
0.443
|
0.212 – 0.866
|
0.022
|
|
tr_med_inc_hshld_cat35,000-49,999
|
0.837
|
0.455 – 1.553
|
0.568
|
0.740
|
0.389 – 1.414
|
0.359
|
|
tr_med_inc_hshld_cat50,000-74,999
|
0.596
|
0.327 – 1.094
|
0.092
|
0.530
|
0.280 – 1.006
|
0.051
|
Neighborhood Median
Household Income: >75,000
|
1.163
|
0.522 – 2.583
|
0.711
|
1.089
|
0.488 – 2.419
|
0.835
|
|
splines::bs(death_year)1
|
0.001
|
0.000 – 2.692
|
0.070
|
0.003
|
0.000 – 4.891
|
0.092
|
|
splines::bs(death_year)2
|
25.588
|
1.085 – 762.222
|
0.050
|
42.385
|
4.338 – 662.222
|
0.003
|
|
splines::bs(death_year)3
|
0.112
|
0.001 – 22.638
|
0.401
|
0.252
|
0.006 – 23.427
|
0.507
|
|
APOE Status: +APOE e 4
|
1.042
|
0.668 – 1.612
|
0.856
|
1.038
|
0.665 – 1.605
|
0.869
|
|
exp_avgdeath_10_yr_ST_pm25:athero_bi1
|
1.486
|
1.052 – 2.170
|
0.031
|
|
|
|
NO 2 Exposure from
Death(10 year)
|
|
|
|
0.954
|
0.838 – 1.072
|
0.446
|
|
exp_avgdeath_10_yr_ST_no2:athero_bi1
|
|
|
|
1.020
|
0.896 – 1.171
|
0.774
|
|
Observations
|
785
|
785
|
|
R2 Tjur
|
0.139
|
0.137
|
Modifier = Arteriolosclerosis
|
Â
|
PM 2.5
|
NO2
|
|
Predictors
|
Odds Ratios
|
CI
|
p
|
Odds Ratios
|
CI
|
p
|
|
(Intercept)
|
2.777
|
0.000 – 16086.109
|
0.826
|
0.534
|
0.000 – 752.271
|
0.877
|
PM 2.5 Exposure from
Death(10 year)
|
0.669
|
0.404 – 1.077
|
0.106
|
|
|
|
arteriolo bi: arteriolo
bi 1
|
0.058
|
0.002 – 1.483
|
0.094
|
1.819
|
0.327 – 9.740
|
0.487
|
|
splines::bs(age_death_yrs)1
|
3.616
|
0.010 – 1531.732
|
0.671
|
1.571
|
0.005 – 658.864
|
0.881
|
|
splines::bs(age_death_yrs)2
|
260.446
|
24.655 – 3274.559
|
<0.001
|
171.972
|
16.579 – 2124.417
|
<0.001
|
|
splines::bs(age_death_yrs)3
|
1.408
|
0.011 – 158.483
|
0.888
|
0.826
|
0.006 – 92.660
|
0.937
|
|
Gender: Male
|
1.166
|
0.788 – 1.726
|
0.443
|
1.154
|
0.783 – 1.704
|
0.470
|
|
Race: nonwhite
|
0.477
|
0.228 – 0.937
|
0.039
|
0.478
|
0.229 – 0.938
|
0.039
|
|
tr_med_inc_hshld_cat35,000-49,999
|
0.711
|
0.382 – 1.332
|
0.283
|
0.636
|
0.330 – 1.228
|
0.176
|
|
tr_med_inc_hshld_cat50,000-74,999
|
0.595
|
0.323 – 1.104
|
0.097
|
0.518
|
0.271 – 0.992
|
0.046
|
Neighborhood Median
Household Income: >75,000
|
1.122
|
0.489 – 2.572
|
0.785
|
1.014
|
0.443 – 2.309
|
0.974
|
|
splines::bs(death_year)1
|
0.001
|
0.000 – 3514.069
|
0.289
|
0.000
|
0.000 – 1457.884
|
0.246
|
|
splines::bs(death_year)2
|
24.677
|
0.109 – 25132.189
|
0.294
|
21.795
|
0.173 – 15623.146
|
0.272
|
|
splines::bs(death_year)3
|
0.079
|
0.000 – 2423.978
|
0.588
|
0.064
|
0.000 – 1250.214
|
0.528
|
|
APOE Status: +APOE e 4
|
1.041
|
0.660 – 1.633
|
0.860
|
1.054
|
0.667 – 1.654
|
0.821
|
|
exp_avgdeath_10_yr_ST_pm25:arteriolo_bi1
|
1.572
|
1.008 – 2.546
|
0.055
|
|
|
|
NO 2 Exposure from
Death(10 year)
|
|
|
|
0.968
|
0.863 – 1.075
|
0.553
|
|
exp_avgdeath_10_yr_ST_no2:arteriolo_bi1
|
|
|
|
0.986
|
0.872 – 1.122
|
0.821
|
|
Observations
|
677
|
677
|
|
R2 Tjur
|
0.122
|
0.119
|
Modifying effect of disease on pathology and damage
Outcome = Atherosclerosis
Modifier = Hypertension
|
Â
|
PM 2.5
|
NO2
|
|
Predictors
|
Odds Ratios
|
CI
|
p
|
Odds Ratios
|
CI
|
p
|
|
(Intercept)
|
0.017
|
0.000 – 4.315
|
0.151
|
0.201
|
0.009 – 4.422
|
0.312
|
PM 2.5 Exposure from
Death(10 year)
|
1.223
|
0.855 – 1.755
|
0.270
|
|
|
|
|
HTN cat: HTN cat 1
|
1.567
|
0.208 – 11.814
|
0.663
|
1.451
|
0.254 – 7.704
|
0.668
|
|
splines::bs(age_death_yrs)1
|
34.361
|
0.225 – 7305.269
|
0.181
|
52.019
|
0.367 – 10277.583
|
0.130
|
|
splines::bs(age_death_yrs)2
|
3.357
|
0.211 – 40.288
|
0.365
|
3.392
|
0.211 – 40.995
|
0.363
|
|
splines::bs(age_death_yrs)3
|
453.238
|
4.657 – 89309.312
|
0.015
|
622.020
|
6.631 – 118149.231
|
0.010
|
|
Gender: Male
|
1.020
|
0.705 – 1.480
|
0.915
|
1.008
|
0.697 – 1.461
|
0.965
|
|
Race: nonwhite
|
1.540
|
0.788 – 3.155
|
0.220
|
1.527
|
0.780 – 3.136
|
0.231
|
|
tr_med_inc_hshld_cat35,000-49,999
|
0.645
|
0.339 – 1.202
|
0.173
|
0.672
|
0.339 – 1.304
|
0.246
|
|
tr_med_inc_hshld_cat50,000-74,999
|
0.929
|
0.495 – 1.709
|
0.815
|
0.949
|
0.484 – 1.824
|
0.876
|
Neighborhood Median
Household Income: >75,000
|
0.807
|
0.344 – 1.908
|
0.623
|
0.798
|
0.335 – 1.920
|
0.611
|
|
splines::bs(death_year)1
|
0.009
|
0.000 – 3.036
|
0.113
|
0.002
|
0.000 – 0.385
|
0.021
|
|
splines::bs(death_year)2
|
594.573
|
39.768 – 9746.991
|
<0.001
|
179.703
|
29.884 – 1125.698
|
<0.001
|
|
splines::bs(death_year)3
|
0.267
|
0.003 – 23.344
|
0.561
|
0.047
|
0.002 – 1.018
|
0.052
|
|
APOE Status: +APOE e 4
|
1.279
|
0.831 – 1.990
|
0.268
|
1.279
|
0.831 – 1.991
|
0.268
|
|
exp_avgdeath_10_yr_ST_pm25:HTN_cat1
|
1.033
|
0.804 – 1.331
|
0.800
|
|
|
|
NO 2 Exposure from
Death(10 year)
|
|
|
|
1.016
|
0.941 – 1.101
|
0.693
|
|
exp_avgdeath_10_yr_ST_no2:HTN_cat1
|
|
|
|
1.026
|
0.908 – 1.170
|
0.689
|
|
Observations
|
779
|
779
|
|
R2 Tjur
|
0.158
|
0.157
|
Modifier = CVD
|
Â
|
PM 2.5
|
NO2
|
|
Predictors
|
Odds Ratios
|
CI
|
p
|
Odds Ratios
|
CI
|
p
|
|
(Intercept)
|
0.036
|
0.000 – 8.905
|
0.238
|
0.112
|
0.005 – 2.385
|
0.164
|
PM 2.5 Exposure from
Death(10 year)
|
1.139
|
0.796 – 1.634
|
0.475
|
|
|
|
|
CVD cat: CVD cat 1
|
17.073
|
0.388 – 1103.000
|
0.156
|
1.564
|
0.043 – 48.796
|
0.801
|
|
splines::bs(age_death_yrs)1
|
55.149
|
0.357 – 11721.708
|
0.130
|
86.507
|
0.589 – 17476.312
|
0.089
|
|
splines::bs(age_death_yrs)2
|
4.012
|
0.244 – 50.105
|
0.306
|
4.010
|
0.243 – 50.427
|
0.307
|
|
splines::bs(age_death_yrs)3
|
479.602
|
4.685 – 96766.185
|
0.015
|
658.995
|
6.568 – 130278.569
|
0.010
|
|
Gender: Male
|
0.944
|
0.649 – 1.376
|
0.765
|
0.935
|
0.643 – 1.362
|
0.726
|
|
Race: nonwhite
|
3.004
|
1.403 – 7.140
|
0.007
|
2.952
|
1.374 – 7.033
|
0.009
|
|
tr_med_inc_hshld_cat35,000-49,999
|
0.688
|
0.364 – 1.276
|
0.241
|
0.703
|
0.358 – 1.354
|
0.298
|
|
tr_med_inc_hshld_cat50,000-74,999
|
1.052
|
0.562 – 1.935
|
0.872
|
1.071
|
0.548 – 2.059
|
0.838
|
Neighborhood Median
Household Income: >75,000
|
0.862
|
0.368 – 2.034
|
0.732
|
0.855
|
0.359 – 2.061
|
0.725
|
|
splines::bs(death_year)1
|
0.012
|
0.000 – 3.454
|
0.125
|
0.006
|
0.000 – 1.007
|
0.050
|
|
splines::bs(death_year)2
|
206.117
|
14.372 – 3173.919
|
<0.001
|
127.340
|
21.241 – 795.970
|
<0.001
|
|
splines::bs(death_year)3
|
0.273
|
0.003 – 22.936
|
0.565
|
0.118
|
0.005 – 2.676
|
0.177
|
|
APOE Status: +APOE e 4
|
1.232
|
0.796 – 1.928
|
0.355
|
1.227
|
0.792 – 1.921
|
0.364
|
|
exp_avgdeath_10_yr_ST_pm25:CVD_cat1
|
0.802
|
0.504 – 1.237
|
0.328
|
|
|
|
NO 2 Exposure from
Death(10 year)
|
|
|
|
1.020
|
0.953 – 1.096
|
0.574
|
|
exp_avgdeath_10_yr_ST_no2:CVD_cat1
|
|
|
|
1.038
|
0.813 – 1.358
|
0.773
|
|
Observations
|
776
|
776
|
|
R2 Tjur
|
0.147
|
0.148
|
Modifier = Diabetes
|
Â
|
PM 2.5
|
NO2
|
|
Predictors
|
Odds Ratios
|
CI
|
p
|
Odds Ratios
|
CI
|
p
|
|
(Intercept)
|
0.012
|
0.000 – 3.067
|
0.120
|
0.140
|
0.006 – 3.143
|
0.220
|
PM 2.5 Exposure from
Death(10 year)
|
1.170
|
0.820 – 1.675
|
0.388
|
|
|
|
diabetes cat: diabetes
cat 1
|
41.919
|
0.850 – 2864.681
|
0.070
|
0.641
|
0.037 – 7.905
|
0.743
|
|
splines::bs(age_death_yrs)1
|
268.454
|
1.472 – 71896.915
|
0.042
|
185.082
|
1.252 – 38549.787
|
0.047
|
|
splines::bs(age_death_yrs)2
|
4.894
|
0.291 – 62.716
|
0.246
|
4.601
|
0.275 – 58.674
|
0.264
|
|
splines::bs(age_death_yrs)3
|
2015.535
|
16.399 – 510216.910
|
0.004
|
1611.272
|
14.785 – 354259.985
|
0.004
|
|
Gender: Male
|
1.071
|
0.741 – 1.554
|
0.716
|
0.999
|
0.689 – 1.451
|
0.994
|
|
Race: nonwhite
|
1.894
|
0.979 – 3.859
|
0.067
|
1.852
|
0.955 – 3.778
|
0.078
|
|
tr_med_inc_hshld_cat35,000-49,999
|
0.819
|
0.429 – 1.536
|
0.538
|
0.823
|
0.415 – 1.605
|
0.570
|
|
tr_med_inc_hshld_cat50,000-74,999
|
1.052
|
0.561 – 1.937
|
0.871
|
1.069
|
0.544 – 2.070
|
0.845
|
Neighborhood Median
Household Income: >75,000
|
0.932
|
0.396 – 2.212
|
0.872
|
0.893
|
0.374 – 2.160
|
0.800
|
|
splines::bs(death_year)1
|
0.010
|
0.000 – 2.814
|
0.110
|
0.002
|
0.000 – 0.382
|
0.020
|
|
splines::bs(death_year)2
|
192.421
|
13.332 – 2966.659
|
<0.001
|
96.311
|
16.430 – 583.091
|
<0.001
|
|
splines::bs(death_year)3
|
0.223
|
0.003 – 17.136
|
0.497
|
0.053
|
0.002 – 1.110
|
0.058
|
|
APOE Status: +APOE e 4
|
1.451
|
0.940 – 2.269
|
0.097
|
1.395
|
0.906 – 2.174
|
0.135
|
|
exp_avgdeath_10_yr_ST_pm25:diabetes_cat1
|
0.691
|
0.409 – 1.125
|
0.149
|
|
|
|
NO 2 Exposure from
Death(10 year)
|
|
|
|
1.002
|
0.935 – 1.078
|
0.948
|
|
exp_avgdeath_10_yr_ST_no2:diabetes_cat1
|
|
|
|
1.095
|
0.920 – 1.347
|
0.349
|
|
Observations
|
783
|
783
|
|
R2 Tjur
|
0.139
|
0.143
|
Outcome = Arteriolosclerosis
Modifier = Hypertension
|
Â
|
PM 2.5
|
NO2
|
|
Predictors
|
Odds Ratios
|
CI
|
p
|
Odds Ratios
|
CI
|
p
|
|
(Intercept)
|
0.001
|
0.000 – 3.216
|
0.079
|
0.024
|
0.000 – 45.182
|
0.296
|
PM 2.5 Exposure from
Death(10 year)
|
1.597
|
1.067 – 2.415
|
0.024
|
|
|
|
|
HTN cat: HTN cat 1
|
18.888
|
1.235 – 294.977
|
0.035
|
2.123
|
0.357 – 11.744
|
0.395
|
|
splines::bs(age_death_yrs)1
|
923514.341
|
848.962 – 2714675371.528
|
<0.001
|
1993499.610
|
2221.326 – 4677598415.537
|
<0.001
|
|
splines::bs(age_death_yrs)2
|
6.925
|
0.280 – 121.534
|
0.211
|
5.779
|
0.236 – 102.465
|
0.257
|
|
splines::bs(age_death_yrs)3
|
506587.287
|
992.932 – 877883770.171
|
<0.001
|
968155.074
|
2094.692 – 1428768567.817
|
<0.001
|
|
Gender: Male
|
1.027
|
0.689 – 1.536
|
0.898
|
1.001
|
0.674 – 1.494
|
0.994
|
|
Race: nonwhite
|
0.594
|
0.314 – 1.150
|
0.115
|
0.627
|
0.334 – 1.206
|
0.153
|
|
tr_med_inc_hshld_cat35,000-49,999
|
0.691
|
0.348 – 1.339
|
0.282
|
0.710
|
0.346 – 1.424
|
0.342
|
|
tr_med_inc_hshld_cat50,000-74,999
|
0.588
|
0.300 – 1.120
|
0.114
|
0.579
|
0.286 – 1.140
|
0.121
|
Neighborhood Median
Household Income: >75,000
|
0.821
|
0.319 – 2.173
|
0.686
|
0.782
|
0.307 – 2.052
|
0.610
|
|
splines::bs(death_year)1
|
0.001
|
0.000 – 90.817
|
0.260
|
0.001
|
0.000 – 77.305
|
0.261
|
|
splines::bs(death_year)2
|
1.032
|
0.004 – 120.934
|
0.990
|
0.368
|
0.002 – 26.436
|
0.669
|
|
splines::bs(death_year)3
|
0.072
|
0.000 – 159.280
|
0.525
|
0.021
|
0.000 – 23.235
|
0.309
|
|
APOE Status: +APOE e 4
|
1.363
|
0.853 – 2.216
|
0.202
|
1.381
|
0.866 – 2.240
|
0.182
|
|
exp_avgdeath_10_yr_ST_pm25:HTN_cat1
|
0.723
|
0.505 – 1.037
|
0.077
|
|
|
|
NO 2 Exposure from
Death(10 year)
|
|
|
|
1.025
|
0.943 – 1.120
|
0.578
|
|
exp_avgdeath_10_yr_ST_no2:HTN_cat1
|
|
|
|
0.984
|
0.867 – 1.126
|
0.811
|
|
Observations
|
672
|
672
|
|
R2 Tjur
|
0.090
|
0.083
|
Modifier = CVD
|
Â
|
PM 2.5
|
NO2
|
|
Predictors
|
Odds Ratios
|
CI
|
p
|
Odds Ratios
|
CI
|
p
|
|
(Intercept)
|
0.000
|
0.000 – 1.681
|
0.055
|
0.006
|
0.000 – 7.057
|
0.128
|
PM 2.5 Exposure from
Death(10 year)
|
1.388
|
0.933 – 2.080
|
0.108
|
|
|
|
|
CVD cat: CVD cat 1
|
2275.513
|
3.785 – 2792181.584
|
0.022
|
10.097
|
0.152 – 582.129
|
0.259
|
|
splines::bs(age_death_yrs)1
|
671810.538
|
728.610 – 1573696995.589
|
<0.001
|
2123472.456
|
2439.936 – 4700374218.887
|
<0.001
|
|
splines::bs(age_death_yrs)2
|
7.772
|
0.317 – 139.852
|
0.186
|
6.113
|
0.249 – 111.398
|
0.244
|
|
splines::bs(age_death_yrs)3
|
315243.017
|
694.911 – 434836860.236
|
<0.001
|
810370.941
|
1800.292 – 1086575216.084
|
<0.001
|
|
Gender: Male
|
1.012
|
0.673 – 1.528
|
0.955
|
0.971
|
0.648 – 1.460
|
0.888
|
|
Race: nonwhite
|
1.061
|
0.535 – 2.215
|
0.870
|
1.038
|
0.524 – 2.163
|
0.917
|
|
tr_med_inc_hshld_cat35,000-49,999
|
0.841
|
0.427 – 1.622
|
0.610
|
0.765
|
0.376 – 1.520
|
0.450
|
|
tr_med_inc_hshld_cat50,000-74,999
|
0.817
|
0.422 – 1.547
|
0.542
|
0.718
|
0.357 – 1.407
|
0.343
|
Neighborhood Median
Household Income: >75,000
|
0.926
|
0.367 – 2.403
|
0.872
|
0.805
|
0.319 – 2.086
|
0.649
|
|
splines::bs(death_year)1
|
0.035
|
0.000 – 2794.572
|
0.581
|
0.032
|
0.000 – 1799.109
|
0.551
|
|
splines::bs(death_year)2
|
0.727
|
0.004 – 70.081
|
0.897
|
0.417
|
0.004 – 26.015
|
0.691
|
|
splines::bs(death_year)3
|
0.847
|
0.000 – 1631.027
|
0.967
|
0.237
|
0.000 – 251.887
|
0.696
|
|
APOE Status: +APOE e 4
|
1.488
|
0.914 – 2.473
|
0.117
|
1.444
|
0.889 – 2.394
|
0.145
|
|
exp_avgdeath_10_yr_ST_pm25:CVD_cat1
|
0.449
|
0.197 – 0.978
|
0.045
|
|
|
|
NO 2 Exposure from
Death(10 year)
|
|
|
|
1.009
|
0.941 – 1.086
|
0.812
|
|
exp_avgdeath_10_yr_ST_no2:CVD_cat1
|
|
|
|
0.928
|
0.707 – 1.263
|
0.600
|
|
Observations
|
668
|
668
|
|
R2 Tjur
|
0.088
|
0.083
|
Modifier = Diabetes
|
Â
|
PM 2.5
|
NO2
|
|
Predictors
|
Odds Ratios
|
CI
|
p
|
Odds Ratios
|
CI
|
p
|
|
(Intercept)
|
0.000
|
0.000 – 1.419
|
0.051
|
0.013
|
0.000 – 21.606
|
0.219
|
PM 2.5 Exposure from
Death(10 year)
|
1.395
|
0.935 – 2.094
|
0.105
|
|
|
|
diabetes cat: diabetes
cat 1
|
6.401
|
0.028 – 1349.654
|
0.495
|
0.146
|
0.005 – 3.100
|
0.241
|
|
splines::bs(age_death_yrs)1
|
2481060.086
|
2481.412 – 6300040274.742
|
<0.001
|
7442321.364
|
7339.579 – 21667741228.446
|
<0.001
|
|
splines::bs(age_death_yrs)2
|
8.431
|
0.332 – 157.919
|
0.175
|
8.120
|
0.317 – 155.445
|
0.185
|
|
splines::bs(age_death_yrs)3
|
1259144.434
|
2339.534 – 2078651121.295
|
<0.001
|
3192259.401
|
5698.276 – 5780080736.463
|
<0.001
|
|
Gender: Male
|
1.044
|
0.701 – 1.562
|
0.832
|
0.948
|
0.634 – 1.422
|
0.796
|
|
Race: nonwhite
|
0.777
|
0.416 – 1.488
|
0.435
|
0.722
|
0.386 – 1.386
|
0.316
|
|
tr_med_inc_hshld_cat35,000-49,999
|
0.923
|
0.470 – 1.780
|
0.814
|
0.936
|
0.458 – 1.877
|
0.853
|
|
tr_med_inc_hshld_cat50,000-74,999
|
0.758
|
0.396 – 1.416
|
0.392
|
0.717
|
0.357 – 1.403
|
0.338
|
Neighborhood Median
Household Income: >75,000
|
1.031
|
0.408 – 2.694
|
0.949
|
0.957
|
0.377 – 2.505
|
0.927
|
|
splines::bs(death_year)1
|
0.007
|
0.000 – 520.140
|
0.411
|
0.004
|
0.000 – 246.483
|
0.351
|
|
splines::bs(death_year)2
|
1.000
|
0.005 – 100.197
|
1.000
|
0.268
|
0.002 – 18.599
|
0.568
|
|
splines::bs(death_year)3
|
0.289
|
0.000 – 505.631
|
0.756
|
0.044
|
0.000 – 48.422
|
0.408
|
|
APOE Status: +APOE e 4
|
1.525
|
0.948 – 2.503
|
0.087
|
1.546
|
0.961 – 2.536
|
0.078
|
|
exp_avgdeath_10_yr_ST_pm25:diabetes_cat1
|
0.886
|
0.450 – 1.796
|
0.730
|
|
|
|
NO 2 Exposure from
Death(10 year)
|
|
|
|
0.974
|
0.906 – 1.050
|
0.477
|
|
exp_avgdeath_10_yr_ST_no2:diabetes_cat1
|
|
|
|
1.231
|
0.996 – 1.568
|
0.076
|
|
Observations
|
676
|
676
|
|
R2 Tjur
|
0.083
|
0.083
|
Outcome = Microinfarcts
Modifier = Hypertension
|
Â
|
PM 2.5
|
NO2
|
|
Predictors
|
Odds Ratios
|
CI
|
p
|
Odds Ratios
|
CI
|
p
|
|
(Intercept)
|
6.209
|
0.062 – 671.835
|
0.440
|
0.195
|
0.011 – 2.965
|
0.247
|
PM 2.5 Exposure from
Death(10 year)
|
0.695
|
0.509 – 0.942
|
0.021
|
|
|
|
|
HTN cat: HTN cat 1
|
0.312
|
0.052 – 1.864
|
0.202
|
0.953
|
0.252 – 3.608
|
0.943
|
|
splines::bs(age_death_yrs)1
|
0.029
|
0.000 – 2.124
|
0.106
|
0.013
|
0.000 – 0.880
|
0.043
|
|
splines::bs(age_death_yrs)2
|
10.910
|
1.632 – 75.626
|
0.014
|
10.602
|
1.585 – 74.205
|
0.016
|
|
splines::bs(age_death_yrs)3
|
0.272
|
0.008 – 9.158
|
0.468
|
0.146
|
0.004 – 4.652
|
0.277
|
|
Gender: Male
|
0.886
|
0.641 – 1.225
|
0.464
|
0.889
|
0.644 – 1.226
|
0.472
|
|
Race: nonwhite
|
1.307
|
0.766 – 2.240
|
0.326
|
1.284
|
0.753 – 2.199
|
0.359
|
|
tr_med_inc_hshld_cat35,000-49,999
|
2.643
|
1.499 – 4.762
|
0.001
|
2.502
|
1.382 – 4.623
|
0.003
|
|
tr_med_inc_hshld_cat50,000-74,999
|
1.621
|
0.936 – 2.868
|
0.090
|
1.576
|
0.884 – 2.867
|
0.128
|
Neighborhood Median
Household Income: >75,000
|
1.534
|
0.724 – 3.270
|
0.264
|
1.542
|
0.724 – 3.303
|
0.262
|
|
splines::bs(death_year)1
|
20.175
|
0.133 – 3714.286
|
0.248
|
86.322
|
0.871 – 11243.892
|
0.064
|
|
splines::bs(death_year)2
|
1.744
|
0.162 – 19.094
|
0.647
|
8.556
|
1.746 – 45.381
|
0.009
|
|
splines::bs(death_year)3
|
0.744
|
0.018 – 30.454
|
0.875
|
5.846
|
0.400 – 100.911
|
0.208
|
|
APOE Status: +APOE e 4
|
1.119
|
0.771 – 1.625
|
0.554
|
1.112
|
0.767 – 1.612
|
0.574
|
|
exp_avgdeath_10_yr_ST_pm25:HTN_cat1
|
1.199
|
0.953 – 1.509
|
0.121
|
|
|
|
NO 2 Exposure from
Death(10 year)
|
|
|
|
0.962
|
0.899 – 1.028
|
0.262
|
|
exp_avgdeath_10_yr_ST_no2:HTN_cat1
|
|
|
|
1.021
|
0.925 – 1.128
|
0.675
|
|
Observations
|
800
|
800
|
|
R2 Tjur
|
0.063
|
0.058
|
Modifier = CVD
|
Â
|
PM 2.5
|
NO2
|
|
Predictors
|
Odds Ratios
|
CI
|
p
|
Odds Ratios
|
CI
|
p
|
|
(Intercept)
|
13.297
|
0.128 – 1502.529
|
0.278
|
0.162
|
0.010 – 2.408
|
0.194
|
PM 2.5 Exposure from
Death(10 year)
|
0.691
|
0.506 – 0.935
|
0.018
|
|
|
|
|
CVD cat: CVD cat 1
|
7.725
|
0.467 – 149.298
|
0.162
|
4.027
|
0.400 – 43.955
|
0.240
|
|
splines::bs(age_death_yrs)1
|
0.018
|
0.000 – 1.347
|
0.068
|
0.009
|
0.000 – 0.624
|
0.029
|
|
splines::bs(age_death_yrs)2
|
10.935
|
1.610 – 77.495
|
0.015
|
10.983
|
1.624 – 77.913
|
0.015
|
|
splines::bs(age_death_yrs)3
|
0.209
|
0.006 – 7.126
|
0.385
|
0.116
|
0.003 – 3.764
|
0.226
|
|
Gender: Male
|
0.830
|
0.598 – 1.152
|
0.265
|
0.837
|
0.604 – 1.159
|
0.283
|
|
Race: nonwhite
|
1.560
|
0.898 – 2.737
|
0.117
|
1.532
|
0.880 – 2.691
|
0.133
|
|
tr_med_inc_hshld_cat35,000-49,999
|
2.952
|
1.663 – 5.368
|
<0.001
|
2.772
|
1.533 – 5.121
|
0.001
|
|
tr_med_inc_hshld_cat50,000-74,999
|
1.781
|
1.023 – 3.176
|
0.045
|
1.726
|
0.970 – 3.137
|
0.067
|
Neighborhood Median
Household Income: >75,000
|
1.581
|
0.737 – 3.414
|
0.240
|
1.632
|
0.759 – 3.525
|
0.210
|
|
splines::bs(death_year)1
|
9.881
|
0.062 – 1948.998
|
0.384
|
93.019
|
0.877 – 13100.944
|
0.063
|
|
splines::bs(death_year)2
|
1.190
|
0.110 – 13.003
|
0.886
|
10.940
|
2.179 – 59.847
|
0.004
|
|
splines::bs(death_year)3
|
0.324
|
0.008 – 13.596
|
0.552
|
6.405
|
0.420 – 116.225
|
0.193
|
|
APOE Status: +APOE e 4
|
1.041
|
0.710 – 1.523
|
0.838
|
1.041
|
0.711 – 1.521
|
0.837
|
|
exp_avgdeath_10_yr_ST_pm25:CVD_cat1
|
0.855
|
0.597 – 1.202
|
0.377
|
|
|
|
NO 2 Exposure from
Death(10 year)
|
|
|
|
0.974
|
0.919 – 1.031
|
0.362
|
|
exp_avgdeath_10_yr_ST_no2:CVD_cat1
|
|
|
|
0.950
|
0.796 – 1.128
|
0.558
|
|
Observations
|
797
|
797
|
|
R2 Tjur
|
0.073
|
0.069
|
Modifier = Diabetes
|
Â
|
PM 2.5
|
NO2
|
|
Predictors
|
Odds Ratios
|
CI
|
p
|
Odds Ratios
|
CI
|
p
|
|
(Intercept)
|
8.633
|
0.084 – 947.102
|
0.364
|
0.138
|
0.008 – 2.096
|
0.162
|
PM 2.5 Exposure from
Death(10 year)
|
0.712
|
0.523 – 0.962
|
0.029
|
|
|
|
diabetes cat: diabetes
cat 1
|
0.098
|
0.004 – 2.152
|
0.144
|
2.292
|
0.353 – 17.126
|
0.397
|
|
splines::bs(age_death_yrs)1
|
0.024
|
0.000 – 1.726
|
0.087
|
0.017
|
0.000 – 1.093
|
0.055
|
|
splines::bs(age_death_yrs)2
|
10.859
|
1.626 – 75.261
|
0.014
|
11.332
|
1.700 – 78.947
|
0.013
|
|
splines::bs(age_death_yrs)3
|
0.228
|
0.006 – 7.687
|
0.410
|
0.159
|
0.005 – 4.994
|
0.296
|
|
Gender: Male
|
0.857
|
0.621 – 1.183
|
0.348
|
0.903
|
0.653 – 1.248
|
0.536
|
|
Race: nonwhite
|
1.324
|
0.781 – 2.252
|
0.298
|
1.361
|
0.801 – 2.323
|
0.255
|
|
tr_med_inc_hshld_cat35,000-49,999
|
2.670
|
1.514 – 4.811
|
0.001
|
2.537
|
1.404 – 4.679
|
0.002
|
|
tr_med_inc_hshld_cat50,000-74,999
|
1.609
|
0.930 – 2.844
|
0.094
|
1.559
|
0.878 – 2.823
|
0.135
|
Neighborhood Median
Household Income: >75,000
|
1.482
|
0.701 – 3.150
|
0.303
|
1.531
|
0.719 – 3.276
|
0.269
|
|
splines::bs(death_year)1
|
11.659
|
0.075 – 2219.541
|
0.348
|
94.274
|
0.934 – 12644.625
|
0.060
|
|
splines::bs(death_year)2
|
1.463
|
0.136 – 15.929
|
0.754
|
8.562
|
1.749 – 45.473
|
0.009
|
|
splines::bs(death_year)3
|
0.502
|
0.012 – 20.836
|
0.716
|
6.528
|
0.438 – 115.836
|
0.185
|
|
APOE Status: +APOE e 4
|
1.074
|
0.738 – 1.562
|
0.708
|
1.109
|
0.764 – 1.609
|
0.585
|
|
exp_avgdeath_10_yr_ST_pm25:diabetes_cat1
|
1.350
|
0.911 – 2.022
|
0.136
|
|
|
|
NO 2 Exposure from
Death(10 year)
|
|
|
|
0.981
|
0.923 – 1.041
|
0.523
|
|
exp_avgdeath_10_yr_ST_no2:diabetes_cat1
|
|
|
|
0.941
|
0.816 – 1.071
|
0.375
|
|
Observations
|
804
|
804
|
|
R2 Tjur
|
0.060
|
0.053
|
Outcome = Gross infarcts
Modifier = Hypertension
|
Â
|
PM 2.5
|
NO2
|
|
Predictors
|
Odds Ratios
|
CI
|
p
|
Odds Ratios
|
CI
|
p
|
|
(Intercept)
|
0.048
|
0.000 – 13.698
|
0.297
|
0.045
|
0.001 – 2.065
|
0.134
|
PM 2.5 Exposure from
Death(10 year)
|
0.852
|
0.601 – 1.203
|
0.366
|
|
|
|
|
HTN cat: HTN cat 1
|
0.131
|
0.013 – 1.222
|
0.076
|
1.188
|
0.283 – 5.160
|
0.815
|
|
splines::bs(age_death_yrs)1
|
12.776
|
0.057 – 3676.311
|
0.364
|
10.890
|
0.048 – 3259.650
|
0.397
|
|
splines::bs(age_death_yrs)2
|
159.810
|
18.477 – 1582.994
|
<0.001
|
153.044
|
17.677 – 1522.863
|
<0.001
|
|
splines::bs(age_death_yrs)3
|
4.632
|
0.055 – 370.209
|
0.491
|
4.330
|
0.052 – 351.164
|
0.511
|
|
Gender: Male
|
0.988
|
0.685 – 1.424
|
0.949
|
0.986
|
0.684 – 1.421
|
0.941
|
|
Race: nonwhite
|
0.456
|
0.232 – 0.849
|
0.017
|
0.454
|
0.230 – 0.849
|
0.017
|
|
tr_med_inc_hshld_cat35,000-49,999
|
0.765
|
0.424 – 1.389
|
0.374
|
0.637
|
0.342 – 1.190
|
0.155
|
|
tr_med_inc_hshld_cat50,000-74,999
|
0.590
|
0.331 – 1.057
|
0.074
|
0.484
|
0.261 – 0.898
|
0.021
|
Neighborhood Median
Household Income: >75,000
|
1.042
|
0.473 – 2.288
|
0.918
|
0.906
|
0.411 – 1.983
|
0.805
|
|
splines::bs(death_year)1
|
0.062
|
0.000 – 79.348
|
0.416
|
0.040
|
0.000 – 45.513
|
0.328
|
|
splines::bs(death_year)2
|
32.808
|
1.608 – 879.443
|
0.029
|
31.198
|
3.461 – 468.693
|
0.005
|
|
splines::bs(death_year)3
|
2.014
|
0.021 – 264.624
|
0.769
|
1.422
|
0.041 – 101.112
|
0.857
|
|
APOE Status: +APOE e 4
|
1.030
|
0.668 – 1.575
|
0.894
|
1.017
|
0.659 – 1.557
|
0.938
|
|
exp_avgdeath_10_yr_ST_pm25:HTN_cat1
|
1.331
|
0.988 – 1.806
|
0.062
|
|
|
|
NO 2 Exposure from
Death(10 year)
|
|
|
|
0.952
|
0.882 – 1.025
|
0.199
|
|
exp_avgdeath_10_yr_ST_no2:HTN_cat1
|
|
|
|
0.993
|
0.888 – 1.107
|
0.898
|
|
Observations
|
807
|
807
|
|
R2 Tjur
|
0.132
|
0.131
|
Modifier = CVD
|
Â
|
PM 2.5
|
NO2
|
|
Predictors
|
Odds Ratios
|
CI
|
p
|
Odds Ratios
|
CI
|
p
|
|
(Intercept)
|
0.106
|
0.000 – 35.815
|
0.454
|
0.047
|
0.001 – 2.134
|
0.138
|
PM 2.5 Exposure from
Death(10 year)
|
0.865
|
0.613 – 1.213
|
0.403
|
|
|
|
|
CVD cat: CVD cat 1
|
1.656
|
0.071 – 46.871
|
0.758
|
27.435
|
2.086 – 499.477
|
0.016
|
|
splines::bs(age_death_yrs)1
|
6.156
|
0.022 – 2238.860
|
0.534
|
3.879
|
0.015 – 1315.860
|
0.638
|
|
splines::bs(age_death_yrs)2
|
243.627
|
27.083 – 2563.809
|
<0.001
|
230.880
|
25.360 – 2458.814
|
<0.001
|
|
splines::bs(age_death_yrs)3
|
3.016
|
0.030 – 284.365
|
0.633
|
2.317
|
0.025 – 207.520
|
0.713
|
|
Gender: Male
|
1.025
|
0.708 – 1.483
|
0.897
|
1.031
|
0.712 – 1.494
|
0.871
|
|
Race: nonwhite
|
0.471
|
0.236 – 0.890
|
0.025
|
0.449
|
0.221 – 0.864
|
0.021
|
|
tr_med_inc_hshld_cat35,000-49,999
|
0.750
|
0.414 – 1.371
|
0.346
|
0.681
|
0.365 – 1.274
|
0.227
|
|
tr_med_inc_hshld_cat50,000-74,999
|
0.605
|
0.338 – 1.089
|
0.091
|
0.541
|
0.291 – 1.006
|
0.051
|
Neighborhood Median
Household Income: >75,000
|
0.977
|
0.439 – 2.162
|
0.954
|
0.978
|
0.438 – 2.166
|
0.956
|
|
splines::bs(death_year)1
|
0.021
|
0.000 – 39.456
|
0.278
|
0.026
|
0.000 – 31.081
|
0.272
|
|
splines::bs(death_year)2
|
17.257
|
0.838 – 494.811
|
0.076
|
35.193
|
3.859 – 525.877
|
0.004
|
|
splines::bs(death_year)3
|
0.753
|
0.007 – 122.105
|
0.908
|
1.263
|
0.035 – 92.002
|
0.906
|
|
APOE Status: +APOE e 4
|
1.054
|
0.676 – 1.632
|
0.815
|
1.063
|
0.680 – 1.648
|
0.787
|
|
exp_avgdeath_10_yr_ST_pm25:CVD_cat1
|
1.059
|
0.691 – 1.576
|
0.782
|
|
|
|
NO 2 Exposure from
Death(10 year)
|
|
|
|
0.965
|
0.905 – 1.027
|
0.268
|
|
exp_avgdeath_10_yr_ST_no2:CVD_cat1
|
|
|
|
0.832
|
0.668 – 1.009
|
0.076
|
|
Observations
|
804
|
804
|
|
R2 Tjur
|
0.145
|
0.145
|
Modifier = Diabetes
|
Â
|
PM 2.5
|
NO2
|
|
Predictors
|
Odds Ratios
|
CI
|
p
|
Odds Ratios
|
CI
|
p
|
|
(Intercept)
|
0.060
|
0.000 – 18.353
|
0.341
|
0.049
|
0.001 – 2.202
|
0.143
|
PM 2.5 Exposure from
Death(10 year)
|
0.932
|
0.663 – 1.306
|
0.682
|
|
|
|
diabetes cat: diabetes
cat 1
|
0.111
|
0.002 – 7.595
|
0.290
|
1.236
|
0.142 – 13.165
|
0.852
|
|
splines::bs(age_death_yrs)1
|
8.548
|
0.036 – 2605.599
|
0.449
|
9.602
|
0.045 – 2753.318
|
0.418
|
|
splines::bs(age_death_yrs)2
|
143.679
|
16.473 – 1440.241
|
<0.001
|
146.678
|
16.826 – 1469.021
|
<0.001
|
|
splines::bs(age_death_yrs)3
|
3.221
|
0.037 – 270.308
|
0.603
|
3.629
|
0.044 – 289.203
|
0.561
|
|
Gender: Male
|
0.968
|
0.672 – 1.395
|
0.863
|
0.997
|
0.690 – 1.439
|
0.988
|
|
Race: nonwhite
|
0.446
|
0.227 – 0.828
|
0.014
|
0.467
|
0.237 – 0.874
|
0.022
|
|
tr_med_inc_hshld_cat35,000-49,999
|
0.708
|
0.393 – 1.282
|
0.251
|
0.612
|
0.328 – 1.141
|
0.121
|
|
tr_med_inc_hshld_cat50,000-74,999
|
0.536
|
0.301 – 0.959
|
0.035
|
0.457
|
0.246 – 0.845
|
0.012
|
Neighborhood Median
Household Income: >75,000
|
0.925
|
0.421 – 2.020
|
0.845
|
0.854
|
0.386 – 1.872
|
0.694
|
|
splines::bs(death_year)1
|
0.028
|
0.000 – 40.164
|
0.300
|
0.043
|
0.000 – 49.876
|
0.341
|
|
splines::bs(death_year)2
|
23.817
|
1.230 – 608.671
|
0.043
|
32.539
|
3.610 – 487.333
|
0.004
|
|
splines::bs(death_year)3
|
1.178
|
0.013 – 160.508
|
0.945
|
1.537
|
0.044 – 110.745
|
0.827
|
|
APOE Status: +APOE e 4
|
0.990
|
0.641 – 1.517
|
0.962
|
1.003
|
0.649 – 1.537
|
0.989
|
|
exp_avgdeath_10_yr_ST_pm25:diabetes_cat1
|
1.281
|
0.730 – 2.137
|
0.363
|
|
|
|
NO 2 Exposure from
Death(10 year)
|
|
|
|
0.956
|
0.894 – 1.021
|
0.184
|
|
exp_avgdeath_10_yr_ST_no2:diabetes_cat1
|
|
|
|
0.965
|
0.813 – 1.119
|
0.654
|
|
Observations
|
811
|
811
|
|
R2 Tjur
|
0.129
|
0.131
|
Bootstrapped Results Visualization
Atherosclerosis
Arteriolosclerosis
Microinfarcts (binary)
Gross infarcts (binary)
Microinfarcts (ordinal)
