Table 1.
Characteristics of POD distribution among CVD death from NMSS in China, 2008-2020 (Death counts (person), %)
| Characteristics | Total (Death counts (person), %) | Medical and healthcare institutions (Death counts (person), %) | Out of medical and healthcare institutions (Death counts (person), %) |
|||
|---|---|---|---|---|---|---|
| Home | Nursing homes | On the way to hospitals | Others/Unknown | |||
| Total | 7101871 (100·00) | 1312850 (18·49) | 5477427 (77·13) | 119896 (1·69) | 80578 (1·13) | 111120 (1·56) |
| Location | ||||||
| Rural | 4972998 (70·02) | 695221 (52·96) | 4083287 (74·55) | 86134 (71·84) | 34430 (42·73) | 73926 (66·53) |
| Urban | 2128873 (29·98) | 617629 (47·04) | 1394140 (25·45) | 33762 (28·16) | 46148 (57·27) | 37194 (33·47) |
| P for differenceb | <0·001 | <0·001 | <0·001 | <0·001 | <0·001 | <0·001 |
| Region a | ||||||
| Western | 1669095 (23·50) | 256303 (19·52) | 1340500 (24·47) | 26219 (21·87) | 13715 (17·02) | 32358 (29·12) |
| Central | 2672896 (37·64) | 549425 (41·85) | 2002981 (36·57) | 56582 (47·19) | 25788 (32·00) | 38120 (34·31) |
| Eastern | 2759880 (38·86) | 507122 (38·63) | 2133946 (38·96) | 37095 (30·94) | 41075 (50·98) | 40642 (36·57) |
| P for difference | <0·001 | <0·001 | <0·001 | <0·001 | <0·001 | <0·001 |
| Sex | ||||||
| Male | 3860831 (54·36) | 771294 (58·75) | 2891030 (52·78) | 73731 (61·50) | 46523 (57·74) | 78253 (70·42) |
| Female | 3241040 (45·64) | 541556 (41·25) | 2586397 (47·22) | 46165 (38·50) | 34055 (42·26) | 32867 (29·58) |
| P for difference | <0·001 | <0·001 | <0·001 | <0·001 | <0·001 | <0·001 |
| Age groups, years old | ||||||
| 0-14 | 3721 (0·05) | 2068 (0·16) | 1043 (0·02) | 335 (0·28) | 1 (0·00) | 274 (0·25) |
| 15-64 | 1331828 (18·75) | 350745 (26·72) | 874114 (15·96) | 43117 (35·96) | 10281 (12·76) | 53571 (48·21) |
| 65-84 | 4004276 (56·38) | 707383 (53·88) | 3151142 (57·53) | 59445 (49·58) | 42569 (52·83) | 43737 (39·36) |
| 85 and above | 1762046 (24·81) | 252654 (19·24) | 1451128 (26·49) | 16999 (14·18) | 27727 (34·41) | 13538 (12·18) |
| P for trendc | <0·001 | <0·001 | <0·001 | <0·001 | <0·001 | <0·001 |
| Ethnicity | ||||||
| Han | 6681073 (94·07) | 1256756 (95·73) | 5134034 (93·73) | 112926 (94·19) | 74500 (92·46) | 102857 (92·56) |
| Other ethnics | 420798 (5·93) | 56094 (4·27) | 343393 (6·27) | 6970 (5·81) | 6078 (7·54) | 8263 (7·44) |
| P for difference | <0·001 | <0·001 | <0·001 | <0·001 | <0·001 | <0·001 |
| Marital status | ||||||
| Married | 4460593 (62·81) | 961501 (73·24) | 3311712 (60·46) | 88165 (73·53) | 25329 (31·43) | 73886 (66·49) |
| Unmarried | 194097 (2·73) | 37993 (2·89) | 130946 (2·39) | 3633 (3·03) | 13327 (16·54) | 8198 (7·38) |
| Widowed/Divorced | 2397127 (33·75) | 304469 (23·19) | 2002333 (36·56) | 27247 (22·73) | 40927 (50·79) | 22151 (19·93) |
| Unknown | 50054 (0·70) | 8887 (0·68) | 32436 (0·59) | 851 (0·71) | 995 (1·23) | 6885 (6·20) |
| P for difference | <0·001 | <0·001 | <0·001 | <0·001 | <0·001 | <0·001 |
| Education | ||||||
| Junior high school and below | 6451663 (90·84) | 1021065 (77·77) | 5170385 (94·39) | 99515 (83·00) | 70497 (87·49) | 90201 (81·17) |
| Senior high school | 529794 (7·46) | 222315 (16·93) | 265834 (4·85) | 17200 (14·35) | 7394 (9·18) | 17051 (15·34) |
| College and above | 120414 (1·70) | 69470 (5·29) | 41208 (0·75) | 3181 (2·65) | 2687 (3·33) | 3868 (3·48) |
| P for trend | <0·001 | <0·001 | <0·001 | <0·001 | <0·001 | <0·001 |
| Occupation | ||||||
| Agricultural-related personnel | 5253275 (73·97) | 568941 (43·34) | 4505919 (82·26) | 80973 (67·54) | 30332 (37·64) | 67110 (60·39) |
| Retired | 611319 (8·61) | 274054 (20·87) | 299824 (5·47) | 9229 (7·70) | 21212 (26·32) | 7000 (6·30) |
| Unemployment/Student | 409474 (5·77) | 122344 (9·32) | 260643 (4·76) | 8542 (7·12) | 9511 (11·80) | 8434 (7·59) |
| Worker/Self-employed/Enterprise manager | 319004 (4·49) | 135422 (10·32) | 155978 (2·85) | 9294 (7·75) | 6661 (8·27) | 11649 (10·48) |
| Professional/Staff/Civil servant | 132868 (1·87) | 67153 (5·12) | 55276 (1·01) | 4065 (3·39) | 1955 (2·43) | 4419 (3·98) |
| Others/Unknown | 375931 (5·29) | 144936 (11·04) | 199787 (3·65) | 7793 (6·50) | 10907 (13·54) | 12508 (11·26) |
| P for difference | <0·001 | <0·001 | <0·001 | <0·001 | <0·001 | <0·001 |
| Underlying cause of death | ||||||
| Rheumatic heart disease | 95272 (1·34) | 17546 (1·34) | 74826 (1·37) | 1255 (1·05) | 472 (0·59) | 1173 (1·06) |
| Hypertensive heart disease | 404892 (5·70) | 47809 (3·64) | 341582 (6·24) | 5871 (4·90) | 4467 (5·54) | 5163 (4·65) |
| Ischemic heart disease | 2569847 (36·19) | 517092 (39·39) | 1920166 (35·06) | 53946 (44·99) | 30537 (37·90) | 48106 (43·29) |
| Stroke | 3213270 (45·25) | 564317 (42·98) | 2528016 (46·15) | 43666 (36·42) | 37242 (46·22) | 40029 (36·02) |
| Myocarditis and myocardial disease | 12672 (0·18) | 2876 (0·22) | 9208 (0·17) | 315 (0·26) | 31 (0·04) | 242 (0·22) |
| Aortic aneurysm | 15750 (0·22) | 11699 (0·89) | 3526 (0·06) | 273 (0·23) | 35 (0·04) | 217 (0·20) |
| Other cardiovascular diseases | 790168 (11·13) | 151511 (11·54) | 600103 (10·96) | 14570 (12·15) | 7794 (9·67) | 16190 (14·57) |
| P for difference | <0·001 | <0·001 | <0·001 | <0·001 | <0·001 | <0·001 |
| Highest diagnostic institutions | ||||||
| Village clinics | 1302619 (18·34) | 162073 (12·35) | 1086865 (19·84) | 29283 (24·42) | 8924 (11·07) | 15474 (13·93) |
| Primary institutions | 3024159 (42·58) | 530942 (40·44) | 2378482 (43·42) | 47212 (39·38) | 25749 (31·96) | 41774 (37·59) |
| Secondary institutions | 1892106 (26·64) | 592140 (45·10) | 1208397 (22·06) | 27050 (22·56) | 37982 (47·14) | 26537 (23·88) |
| Tertiary institutions | 489988 (6·90) | 20685 (1·58) | 453089 (8·27) | 8919 (7·44) | 1263 (1·57) | 6032 (5·43) |
| Other institutions | 41330 (0·58) | 5201 (0·40) | 28031 (0·51) | 720 (0·60) | 2083 (2·59) | 5295 (4·77) |
| Undiagnosed | 351669 (4·95) | 1809 (0·14) | 322563 (5·89) | 6712 (5·60) | 4577 (5·68) | 16008 (14·41) |
| P for difference | <0·001 | <0·001 | <0·001 | <0·001 | <0·001 | <0·001 |
| Highest diagnosis basis | ||||||
| Autopsy | 3610 (0·05) | 398 (0·03) | 1598 (0·03) | 152 (0·13) | 14 (0·02) | 1448 (1·30) |
| Pathology | 29138 (0·41) | 4316 (0·33) | 23652 (0·43) | 474 (0·40) | 199 (0·25) | 497 (0·45) |
| Surgery | 33918 (0·48) | 14113 (1·07) | 18637 (0·34) | 405 (0·34) | 285 (0·35) | 478 (0·43) |
| Clinical and physicochemical examination | 4391504 (61·84) | 949831 (72·35) | 3290991 (60·08) | 54386 (45·36) | 50416 (62·57) | 45880 (41·29) |
| Clinical examination | 1918375 (27·01) | 310917 (23·68) | 1523166 (27·81) | 39602 (33·03) | 17065 (21·18) | 27625 (24·86) |
| Diagnosis after death | 670670 (9·44) | 30382 (2·31) | 574711 (10·49) | 23889 (19·92) | 11994 (14·88) | 29694 (26·72) |
| Other ways | 54656 (0·77) | 2893 (0·22) | 44672 (0·82) | 988 (0·82) | 605 (0·75) | 5498 (4·95) |
| P for difference | <0·001 | <0·001 | <0·001 | <0·001 | <0·001 | <0·001 |
a
Region: Western: Inner Mongolia, Guangxi, Chongqing, Sichuan, Guizhou, Yunnan, Tibet, Shaanxi, Gansu, Qinghai, Ningxia, Xinjiang; Central: Shanxi, Jilin, Heilongjiang, Anhui, Jiangxi, Henan, Hubei, Hunan; Eastern: Beijing, Tianjin, Hebei, Liaoning, Shanghai, Jiangsu, Zhejiang, Fujian, Shandong, Guangdong, Hainan.
b
P for difference: We used χ2 test to compare the differences of characteristics distribution among nominal categories
c
P for trend: We used logistic regression to test the trends of characteristics distribution for ordered variables.