indicator_code stringclasses 1
value | country_iso3 stringclasses 47
values | who_region stringclasses 1
value | year int64 1.96k 2.02k | dim1_type stringclasses 1
value | dim1 stringclasses 1
value | dim2_type stringclasses 1
value | dim2 stringclasses 1
value | value_numeric float64 12 276k | value_low float64 11 238k | value_high float64 14 380k | value_display stringlengths 10 25 | last_updated stringclasses 1
value |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
CM_03 | GHA | AFR | 1,972 | SEX | SEX_BTSX | AGEGROUP | AGEGROUP_DAYS0-27 | 25,673 | 23,618 | 28,120 | 25 673 [23 618-28 120] | 2025-04-15T16:15:21.28+02:00 |
CM_03 | ZWE | AFR | 1,991 | SEX | SEX_BTSX | AGEGROUP | AGEGROUP_DAYS0-27 | 8,535 | 7,662 | 9,472 | 8535 [7662-9472] | 2025-04-15T16:15:21.28+02:00 |
CM_03 | CPV | AFR | 1,996 | SEX | SEX_BTSX | AGEGROUP | AGEGROUP_DAYS0-27 | 261 | 221 | 300 | 261 [221-300] | 2025-04-15T16:15:21.28+02:00 |
CM_03 | ZWE | AFR | 2,022 | SEX | SEX_BTSX | AGEGROUP | AGEGROUP_DAYS0-27 | 11,344 | 7,072 | 17,827 | 11 344 [7072-17 827] | 2025-04-15T16:15:21.28+02:00 |
CM_03 | CPV | AFR | 1,990 | SEX | SEX_BTSX | AGEGROUP | AGEGROUP_DAYS0-27 | 279 | 241 | 319 | 279 [241-319] | 2025-04-15T16:15:21.28+02:00 |
CM_03 | GIN | AFR | 1,994 | SEX | SEX_BTSX | AGEGROUP | AGEGROUP_DAYS0-27 | 19,109 | 17,057 | 21,517 | 19 109 [17 057-21 517] | 2025-04-15T16:15:21.28+02:00 |
CM_03 | CIV | AFR | 2,003 | SEX | SEX_BTSX | AGEGROUP | AGEGROUP_DAYS0-27 | 32,221 | 28,853 | 35,766 | 32 221 [28 853-35 766] | 2025-04-15T16:15:21.28+02:00 |
CM_03 | MUS | AFR | 2,015 | SEX | SEX_BTSX | AGEGROUP | AGEGROUP_DAYS0-27 | 119 | 110 | 127 | 119 [110-127] | 2025-04-15T16:15:21.28+02:00 |
CM_03 | SWZ | AFR | 2,005 | SEX | SEX_BTSX | AGEGROUP | AGEGROUP_DAYS0-27 | 858 | 743 | 990 | 858 [743-990] | 2025-04-15T16:15:21.28+02:00 |
CM_03 | BDI | AFR | 1,975 | SEX | SEX_BTSX | AGEGROUP | AGEGROUP_DAYS0-27 | 8,858 | 7,590 | 10,340 | 8858 [7590-10 340] | 2025-04-15T16:15:21.28+02:00 |
CM_03 | TZA | AFR | 2,023 | SEX | SEX_BTSX | AGEGROUP | AGEGROUP_DAYS0-27 | 48,216 | 36,677 | 63,210 | 48 216 [36 677-63 210] | 2025-04-15T16:15:21.28+02:00 |
CM_03 | GNQ | AFR | 2,008 | SEX | SEX_BTSX | AGEGROUP | AGEGROUP_DAYS0-27 | 1,601 | 1,198 | 2,069 | 1601 [1198-2069] | 2025-04-15T16:15:21.28+02:00 |
CM_03 | LSO | AFR | 1,965 | SEX | SEX_BTSX | AGEGROUP | AGEGROUP_DAYS0-27 | 2,385 | 2,074 | 2,732 | 2385 [2074-2732] | 2025-04-15T16:15:21.28+02:00 |
CM_03 | BFA | AFR | 2,016 | SEX | SEX_BTSX | AGEGROUP | AGEGROUP_DAYS0-27 | 20,965 | 14,630 | 28,807 | 20 965 [14 630-28 807] | 2025-04-15T16:15:21.28+02:00 |
CM_03 | MWI | AFR | 1,973 | SEX | SEX_BTSX | AGEGROUP | AGEGROUP_DAYS0-27 | 17,382 | 15,201 | 19,979 | 17 382 [15 201-19 979] | 2025-04-15T16:15:21.28+02:00 |
CM_03 | BFA | AFR | 1,987 | SEX | SEX_BTSX | AGEGROUP | AGEGROUP_DAYS0-27 | 20,033 | 17,798 | 22,519 | 20 033 [17 798-22 519] | 2025-04-15T16:15:21.28+02:00 |
CM_03 | CMR | AFR | 1,989 | SEX | SEX_BTSX | AGEGROUP | AGEGROUP_DAYS0-27 | 19,574 | 17,603 | 21,799 | 19 574 [17 603-21 799] | 2025-04-15T16:15:21.28+02:00 |
CM_03 | GIN | AFR | 2,001 | SEX | SEX_BTSX | AGEGROUP | AGEGROUP_DAYS0-27 | 15,525 | 13,785 | 17,399 | 15 525 [13 785-17 399] | 2025-04-15T16:15:21.28+02:00 |
CM_03 | BEN | AFR | 1,987 | SEX | SEX_BTSX | AGEGROUP | AGEGROUP_DAYS0-27 | 10,623 | 9,609 | 11,743 | 10 623 [9609-11 743] | 2025-04-15T16:15:21.28+02:00 |
CM_03 | CAF | AFR | 1,986 | SEX | SEX_BTSX | AGEGROUP | AGEGROUP_DAYS0-27 | 6,569 | 5,620 | 7,698 | 6569 [5620-7698] | 2025-04-15T16:15:21.28+02:00 |
CM_03 | MRT | AFR | 2,023 | SEX | SEX_BTSX | AGEGROUP | AGEGROUP_DAYS0-27 | 3,710 | 2,607 | 5,287 | 3710 [2607-5287] | 2025-04-15T16:15:21.28+02:00 |
CM_03 | KEN | AFR | 1,971 | SEX | SEX_BTSX | AGEGROUP | AGEGROUP_DAYS0-27 | 23,632 | 21,034 | 26,390 | 23 632 [21 034-26 390] | 2025-04-15T16:15:21.28+02:00 |
CM_03 | LSO | AFR | 1,970 | SEX | SEX_BTSX | AGEGROUP | AGEGROUP_DAYS0-27 | 2,563 | 2,209 | 2,971 | 2563 [2209-2971] | 2025-04-15T16:15:21.28+02:00 |
CM_03 | LSO | AFR | 1,981 | SEX | SEX_BTSX | AGEGROUP | AGEGROUP_DAYS0-27 | 2,455 | 2,051 | 2,897 | 2455 [2051-2897] | 2025-04-15T16:15:21.28+02:00 |
CM_03 | MRT | AFR | 1,998 | SEX | SEX_BTSX | AGEGROUP | AGEGROUP_DAYS0-27 | 3,944 | 3,520 | 4,425 | 3944 [3520-4425] | 2025-04-15T16:15:21.28+02:00 |
CM_03 | SYC | AFR | 2,016 | SEX | SEX_BTSX | AGEGROUP | AGEGROUP_DAYS0-27 | 16 | 14 | 18 | 16 [14-18] | 2025-04-15T16:15:21.28+02:00 |
CM_03 | TCD | AFR | 2,006 | SEX | SEX_BTSX | AGEGROUP | AGEGROUP_DAYS0-27 | 21,167 | 18,310 | 24,276 | 21 167 [18 310-24 276] | 2025-04-15T16:15:21.28+02:00 |
CM_03 | CPV | AFR | 2,001 | SEX | SEX_BTSX | AGEGROUP | AGEGROUP_DAYS0-27 | 209 | 182 | 237 | 209 [182-237] | 2025-04-15T16:15:21.28+02:00 |
CM_03 | CAF | AFR | 1,978 | SEX | SEX_BTSX | AGEGROUP | AGEGROUP_DAYS0-27 | 5,663 | 4,776 | 6,691 | 5663 [4776-6691] | 2025-04-15T16:15:21.28+02:00 |
CM_03 | MWI | AFR | 2,005 | SEX | SEX_BTSX | AGEGROUP | AGEGROUP_DAYS0-27 | 16,708 | 15,127 | 18,349 | 16 708 [15 127-18 349] | 2025-04-15T16:15:21.28+02:00 |
CM_03 | TGO | AFR | 1,985 | SEX | SEX_BTSX | AGEGROUP | AGEGROUP_DAYS0-27 | 6,981 | 6,194 | 7,881 | 6981 [6194-7881] | 2025-04-15T16:15:21.28+02:00 |
CM_03 | CIV | AFR | 1,997 | SEX | SEX_BTSX | AGEGROUP | AGEGROUP_DAYS0-27 | 30,314 | 27,201 | 33,713 | 30 314 [27 201-33 713] | 2025-04-15T16:15:21.28+02:00 |
CM_03 | ETH | AFR | 1,987 | SEX | SEX_BTSX | AGEGROUP | AGEGROUP_DAYS0-27 | 135,017 | 121,175 | 149,806 | 135 017 [121 175-149 806] | 2025-04-15T16:15:21.28+02:00 |
CM_03 | MRT | AFR | 1,989 | SEX | SEX_BTSX | AGEGROUP | AGEGROUP_DAYS0-27 | 3,595 | 3,175 | 4,082 | 3595 [3175-4082] | 2025-04-15T16:15:21.28+02:00 |
CM_03 | GIN | AFR | 2,010 | SEX | SEX_BTSX | AGEGROUP | AGEGROUP_DAYS0-27 | 14,864 | 13,059 | 16,873 | 14 864 [13 059-16 873] | 2025-04-15T16:15:21.28+02:00 |
CM_03 | CAF | AFR | 1,989 | SEX | SEX_BTSX | AGEGROUP | AGEGROUP_DAYS0-27 | 6,750 | 5,794 | 7,928 | 6750 [5794-7928] | 2025-04-15T16:15:21.28+02:00 |
CM_03 | ETH | AFR | 1,986 | SEX | SEX_BTSX | AGEGROUP | AGEGROUP_DAYS0-27 | 136,148 | 122,182 | 151,070 | 136 148 [122 182-151 070] | 2025-04-15T16:15:21.28+02:00 |
CM_03 | CPV | AFR | 2,020 | SEX | SEX_BTSX | AGEGROUP | AGEGROUP_DAYS0-27 | 63 | 58 | 68 | 63 [58-68] | 2025-04-15T16:15:21.28+02:00 |
CM_03 | MUS | AFR | 2,009 | SEX | SEX_BTSX | AGEGROUP | AGEGROUP_DAYS0-27 | 145 | 135 | 155 | 145 [135-155] | 2025-04-15T16:15:21.28+02:00 |
CM_03 | CMR | AFR | 1,992 | SEX | SEX_BTSX | AGEGROUP | AGEGROUP_DAYS0-27 | 20,655 | 18,598 | 22,872 | 20 655 [18 598-22 872] | 2025-04-15T16:15:21.28+02:00 |
CM_03 | NER | AFR | 2,019 | SEX | SEX_BTSX | AGEGROUP | AGEGROUP_DAYS0-27 | 34,680 | 28,192 | 43,198 | 34 680 [28 192-43 198] | 2025-04-15T16:15:21.28+02:00 |
CM_03 | GHA | AFR | 1,996 | SEX | SEX_BTSX | AGEGROUP | AGEGROUP_DAYS0-27 | 24,609 | 22,534 | 26,663 | 24 609 [22 534-26 663] | 2025-04-15T16:15:21.28+02:00 |
CM_03 | MUS | AFR | 1,975 | SEX | SEX_BTSX | AGEGROUP | AGEGROUP_DAYS0-27 | 562 | 532 | 593 | 562 [532-593] | 2025-04-15T16:15:21.28+02:00 |
CM_03 | ERI | AFR | 1,984 | SEX | SEX_BTSX | AGEGROUP | AGEGROUP_DAYS0-27 | 3,304 | 2,888 | 3,755 | 3304 [2888-3755] | 2025-04-15T16:15:21.28+02:00 |
CM_03 | BDI | AFR | 1,997 | SEX | SEX_BTSX | AGEGROUP | AGEGROUP_DAYS0-27 | 9,776 | 8,406 | 11,270 | 9776 [8406-11 270] | 2025-04-15T16:15:21.28+02:00 |
CM_03 | NER | AFR | 1,972 | SEX | SEX_BTSX | AGEGROUP | AGEGROUP_DAYS0-27 | 16,682 | 14,279 | 19,475 | 16 682 [14 279-19 475] | 2025-04-15T16:15:21.28+02:00 |
CM_03 | GHA | AFR | 2,022 | SEX | SEX_BTSX | AGEGROUP | AGEGROUP_DAYS0-27 | 19,084 | 15,764 | 22,927 | 19 084 [15 764-22 927] | 2025-04-15T16:15:21.28+02:00 |
CM_03 | MRT | AFR | 2,012 | SEX | SEX_BTSX | AGEGROUP | AGEGROUP_DAYS0-27 | 3,791 | 3,335 | 4,298 | 3791 [3335-4298] | 2025-04-15T16:15:21.28+02:00 |
CM_03 | NAM | AFR | 1,997 | SEX | SEX_BTSX | AGEGROUP | AGEGROUP_DAYS0-27 | 1,449 | 1,252 | 1,680 | 1449 [1252-1680] | 2025-04-15T16:15:21.28+02:00 |
CM_03 | BWA | AFR | 1,985 | SEX | SEX_BTSX | AGEGROUP | AGEGROUP_DAYS0-27 | 1,254 | 1,058 | 1,486 | 1254 [1058-1486] | 2025-04-15T16:15:21.28+02:00 |
CM_03 | MRT | AFR | 2,017 | SEX | SEX_BTSX | AGEGROUP | AGEGROUP_DAYS0-27 | 3,834 | 3,235 | 4,550 | 3834 [3235-4550] | 2025-04-15T16:15:21.28+02:00 |
CM_03 | ZWE | AFR | 2,008 | SEX | SEX_BTSX | AGEGROUP | AGEGROUP_DAYS0-27 | 14,160 | 12,624 | 15,831 | 14 160 [12 624-15 831] | 2025-04-15T16:15:21.28+02:00 |
CM_03 | MOZ | AFR | 2,003 | SEX | SEX_BTSX | AGEGROUP | AGEGROUP_DAYS0-27 | 30,305 | 26,973 | 33,726 | 30 305 [26 973-33 726] | 2025-04-15T16:15:21.28+02:00 |
CM_03 | LSO | AFR | 1,996 | SEX | SEX_BTSX | AGEGROUP | AGEGROUP_DAYS0-27 | 2,259 | 2,035 | 2,501 | 2259 [2035-2501] | 2025-04-15T16:15:21.28+02:00 |
CM_03 | GNB | AFR | 2,002 | SEX | SEX_BTSX | AGEGROUP | AGEGROUP_DAYS0-27 | 2,851 | 2,422 | 3,315 | 2851 [2422-3315] | 2025-04-15T16:15:21.28+02:00 |
CM_03 | LSO | AFR | 1,992 | SEX | SEX_BTSX | AGEGROUP | AGEGROUP_DAYS0-27 | 2,395 | 2,122 | 2,697 | 2395 [2122-2697] | 2025-04-15T16:15:21.28+02:00 |
CM_03 | CIV | AFR | 2,011 | SEX | SEX_BTSX | AGEGROUP | AGEGROUP_DAYS0-27 | 32,725 | 29,117 | 36,671 | 32 725 [29 117-36 671] | 2025-04-15T16:15:21.28+02:00 |
CM_03 | TGO | AFR | 2,019 | SEX | SEX_BTSX | AGEGROUP | AGEGROUP_DAYS0-27 | 6,930 | 5,360 | 8,928 | 6930 [5360-8928] | 2025-04-15T16:15:21.28+02:00 |
CM_03 | MRT | AFR | 1,974 | SEX | SEX_BTSX | AGEGROUP | AGEGROUP_DAYS0-27 | 3,259 | 2,872 | 3,696 | 3259 [2872-3696] | 2025-04-15T16:15:21.28+02:00 |
CM_03 | UGA | AFR | 2,013 | SEX | SEX_BTSX | AGEGROUP | AGEGROUP_DAYS0-27 | 33,449 | 29,026 | 38,717 | 33 449 [29 026-38 717] | 2025-04-15T16:15:21.28+02:00 |
CM_03 | CAF | AFR | 2,000 | SEX | SEX_BTSX | AGEGROUP | AGEGROUP_DAYS0-27 | 6,918 | 5,912 | 8,020 | 6918 [5912-8020] | 2025-04-15T16:15:21.28+02:00 |
CM_03 | MOZ | AFR | 1,990 | SEX | SEX_BTSX | AGEGROUP | AGEGROUP_DAYS0-27 | 37,340 | 32,950 | 42,289 | 37 340 [32 950-42 289] | 2025-04-15T16:15:21.28+02:00 |
CM_03 | BEN | AFR | 1,966 | SEX | SEX_BTSX | AGEGROUP | AGEGROUP_DAYS0-27 | 8,185 | 7,232 | 9,338 | 8185 [7232-9338] | 2025-04-15T16:15:21.28+02:00 |
CM_03 | NAM | AFR | 1,977 | SEX | SEX_BTSX | AGEGROUP | AGEGROUP_DAYS0-27 | 1,317 | 1,090 | 1,579 | 1317 [1090-1579] | 2025-04-15T16:15:21.28+02:00 |
CM_03 | SEN | AFR | 1,977 | SEX | SEX_BTSX | AGEGROUP | AGEGROUP_DAYS0-27 | 13,184 | 11,845 | 14,595 | 13 184 [11 845-14 595] | 2025-04-15T16:15:21.28+02:00 |
CM_03 | COG | AFR | 2,018 | SEX | SEX_BTSX | AGEGROUP | AGEGROUP_DAYS0-27 | 3,507 | 2,127 | 5,728 | 3507 [2127-5728] | 2025-04-15T16:15:21.28+02:00 |
CM_03 | LBR | AFR | 1,992 | SEX | SEX_BTSX | AGEGROUP | AGEGROUP_DAYS0-27 | 5,431 | 4,676 | 6,273 | 5431 [4676-6273] | 2025-04-15T16:15:21.28+02:00 |
CM_03 | LSO | AFR | 2,023 | SEX | SEX_BTSX | AGEGROUP | AGEGROUP_DAYS0-27 | 1,601 | 1,139 | 2,189 | 1601 [1139-2189] | 2025-04-15T16:15:21.28+02:00 |
CM_03 | TGO | AFR | 2,002 | SEX | SEX_BTSX | AGEGROUP | AGEGROUP_DAYS0-27 | 7,159 | 6,369 | 8,013 | 7159 [6369-8013] | 2025-04-15T16:15:21.28+02:00 |
CM_03 | BFA | AFR | 1,998 | SEX | SEX_BTSX | AGEGROUP | AGEGROUP_DAYS0-27 | 21,453 | 18,902 | 24,309 | 21 453 [18 902-24 309] | 2025-04-15T16:15:21.28+02:00 |
CM_03 | MDG | AFR | 2,012 | SEX | SEX_BTSX | AGEGROUP | AGEGROUP_DAYS0-27 | 20,170 | 17,820 | 22,657 | 20 170 [17 820-22 657] | 2025-04-15T16:15:21.28+02:00 |
CM_03 | MRT | AFR | 1,980 | SEX | SEX_BTSX | AGEGROUP | AGEGROUP_DAYS0-27 | 3,618 | 3,230 | 4,073 | 3618 [3230-4073] | 2025-04-15T16:15:21.28+02:00 |
CM_03 | MDG | AFR | 1,996 | SEX | SEX_BTSX | AGEGROUP | AGEGROUP_DAYS0-27 | 22,217 | 20,258 | 24,572 | 22 217 [20 258-24 572] | 2025-04-15T16:15:21.28+02:00 |
CM_03 | SYC | AFR | 2,008 | SEX | SEX_BTSX | AGEGROUP | AGEGROUP_DAYS0-27 | 15 | 13 | 17 | 15 [13-17] | 2025-04-15T16:15:21.28+02:00 |
CM_03 | COG | AFR | 2,000 | SEX | SEX_BTSX | AGEGROUP | AGEGROUP_DAYS0-27 | 3,491 | 3,020 | 4,030 | 3491 [3020-4030] | 2025-04-15T16:15:21.28+02:00 |
CM_03 | KEN | AFR | 2,005 | SEX | SEX_BTSX | AGEGROUP | AGEGROUP_DAYS0-27 | 34,455 | 31,206 | 38,031 | 34 455 [31 206-38 031] | 2025-04-15T16:15:21.28+02:00 |
CM_03 | KEN | AFR | 1,964 | SEX | SEX_BTSX | AGEGROUP | AGEGROUP_DAYS0-27 | 21,090 | 18,579 | 23,764 | 21 090 [18 579-23 764] | 2025-04-15T16:15:21.28+02:00 |
CM_03 | MLI | AFR | 1,994 | SEX | SEX_BTSX | AGEGROUP | AGEGROUP_DAYS0-27 | 28,205 | 25,343 | 31,363 | 28 205 [25 343-31 363] | 2025-04-15T16:15:21.28+02:00 |
CM_03 | DZA | AFR | 2,015 | SEX | SEX_BTSX | AGEGROUP | AGEGROUP_DAYS0-27 | 15,659 | 14,626 | 16,767 | 15 659 [14 626-16 767] | 2025-04-15T16:15:21.28+02:00 |
CM_03 | MRT | AFR | 1,982 | SEX | SEX_BTSX | AGEGROUP | AGEGROUP_DAYS0-27 | 3,731 | 3,317 | 4,243 | 3731 [3317-4243] | 2025-04-15T16:15:21.28+02:00 |
CM_03 | NAM | AFR | 1,981 | SEX | SEX_BTSX | AGEGROUP | AGEGROUP_DAYS0-27 | 1,299 | 1,100 | 1,525 | 1299 [1100-1525] | 2025-04-15T16:15:21.28+02:00 |
CM_03 | ERI | AFR | 1,998 | SEX | SEX_BTSX | AGEGROUP | AGEGROUP_DAYS0-27 | 2,453 | 2,153 | 2,771 | 2453 [2153-2771] | 2025-04-15T16:15:21.28+02:00 |
CM_03 | MLI | AFR | 1,973 | SEX | SEX_BTSX | AGEGROUP | AGEGROUP_DAYS0-27 | 31,918 | 28,121 | 36,263 | 31 918 [28 121-36 263] | 2025-04-15T16:15:21.28+02:00 |
CM_03 | NER | AFR | 2,012 | SEX | SEX_BTSX | AGEGROUP | AGEGROUP_DAYS0-27 | 29,746 | 25,651 | 34,371 | 29 746 [25 651-34 371] | 2025-04-15T16:15:21.28+02:00 |
CM_03 | TZA | AFR | 2,022 | SEX | SEX_BTSX | AGEGROUP | AGEGROUP_DAYS0-27 | 48,609 | 37,985 | 62,324 | 48 609 [37 985-62 324] | 2025-04-15T16:15:21.28+02:00 |
CM_03 | AGO | AFR | 2,013 | SEX | SEX_BTSX | AGEGROUP | AGEGROUP_DAYS0-27 | 36,780 | 20,481 | 59,433 | 36 780 [20 481-59 433] | 2025-04-15T16:15:21.28+02:00 |
CM_03 | SWZ | AFR | 1,990 | SEX | SEX_BTSX | AGEGROUP | AGEGROUP_DAYS0-27 | 776 | 651 | 919 | 776 [651-919] | 2025-04-15T16:15:21.28+02:00 |
CM_03 | GHA | AFR | 2,002 | SEX | SEX_BTSX | AGEGROUP | AGEGROUP_DAYS0-27 | 24,955 | 22,882 | 27,059 | 24 955 [22 882-27 059] | 2025-04-15T16:15:21.28+02:00 |
CM_03 | LBR | AFR | 1,967 | SEX | SEX_BTSX | AGEGROUP | AGEGROUP_DAYS0-27 | 6,516 | 5,611 | 7,597 | 6516 [5611-7597] | 2025-04-15T16:15:21.28+02:00 |
CM_03 | ZMB | AFR | 1,972 | SEX | SEX_BTSX | AGEGROUP | AGEGROUP_DAYS0-27 | 8,002 | 6,981 | 9,089 | 8002 [6981-9089] | 2025-04-15T16:15:21.28+02:00 |
CM_03 | NAM | AFR | 1,986 | SEX | SEX_BTSX | AGEGROUP | AGEGROUP_DAYS0-27 | 1,326 | 1,136 | 1,536 | 1326 [1136-1536] | 2025-04-15T16:15:21.28+02:00 |
CM_03 | TGO | AFR | 1,984 | SEX | SEX_BTSX | AGEGROUP | AGEGROUP_DAYS0-27 | 6,903 | 6,129 | 7,799 | 6903 [6129-7799] | 2025-04-15T16:15:21.28+02:00 |
CM_03 | SWZ | AFR | 1,999 | SEX | SEX_BTSX | AGEGROUP | AGEGROUP_DAYS0-27 | 863 | 754 | 991 | 863 [754-991] | 2025-04-15T16:15:21.28+02:00 |
CM_03 | LBR | AFR | 1,999 | SEX | SEX_BTSX | AGEGROUP | AGEGROUP_DAYS0-27 | 6,196 | 5,480 | 6,981 | 6196 [5480-6981] | 2025-04-15T16:15:21.28+02:00 |
CM_03 | CAF | AFR | 2,014 | SEX | SEX_BTSX | AGEGROUP | AGEGROUP_DAYS0-27 | 7,355 | 5,988 | 8,874 | 7355 [5988-8874] | 2025-04-15T16:15:21.28+02:00 |
CM_03 | CIV | AFR | 1,988 | SEX | SEX_BTSX | AGEGROUP | AGEGROUP_DAYS0-27 | 25,903 | 23,338 | 28,686 | 25 903 [23 338-28 686] | 2025-04-15T16:15:21.28+02:00 |
CM_03 | COM | AFR | 1,987 | SEX | SEX_BTSX | AGEGROUP | AGEGROUP_DAYS0-27 | 1,045 | 872 | 1,249 | 1045 [872-1249] | 2025-04-15T16:15:21.28+02:00 |
CM_03 | CAF | AFR | 2,009 | SEX | SEX_BTSX | AGEGROUP | AGEGROUP_DAYS0-27 | 8,435 | 7,211 | 9,723 | 8435 [7211-9723] | 2025-04-15T16:15:21.28+02:00 |
CM_03 | CAF | AFR | 1,990 | SEX | SEX_BTSX | AGEGROUP | AGEGROUP_DAYS0-27 | 6,799 | 5,850 | 7,982 | 6799 [5850-7982] | 2025-04-15T16:15:21.28+02:00 |
CM_03 | MLI | AFR | 1,976 | SEX | SEX_BTSX | AGEGROUP | AGEGROUP_DAYS0-27 | 32,515 | 28,778 | 36,805 | 32 515 [28 778-36 805] | 2025-04-15T16:15:21.28+02:00 |
Africa — WHO GHO: Number of neonatal deaths
Indicator code: CM_03
HuggingFace slug: electricsheepafrica/africa-who-number-of-neonatal-deaths
Source: WHO Global Health Observatory
License: CC BY 4.0 — WHO Open Data
Dataset Description
This dataset contains country-level observations for the WHO GHO indicator "Number of neonatal deaths" (CM_03) across African nations, spanning 1955–2023. It is part of the Electric Sheep Africa collection — a unified, ML-ready repository of African data.
Data is sourced directly from the WHO Global Health Observatory OData API and repackaged as Parquet files with a consistent schema. All values are drawn from NumericValue (the float-precision field), not the display string. Confidence interval bounds (value_low, value_high) are included where available.
Coverage
| Countries | 47 African nations |
| Years | 1955 – 2023 |
| Total rows | 2,414 |
| Region filter | WHO AFRO (ParentLocationCode = 'AFR') |
Countries included: AGO, BDI, BEN, BFA, BWA, CAF, CIV, CMR, COD, COG, COM, CPV, DZA, ERI, ETH, GAB, GHA, GIN, GMB, GNB … and 27 more
Sub-dimensions
- SEX: SEX_BTSX
When an indicator is stratified (e.g., by sex or age group), each unique combination of country × year × dimension produces a separate row. Filter on dim1 / dim2 for the stratum you need, or aggregate across strata.
Schema
| Column | Type | Description |
|---|---|---|
indicator_code |
string | GHO indicator code (e.g., CM_03) |
country_iso3 |
string | ISO 3166-1 alpha-3 country code |
who_region |
string | WHO region code (always AFR here) |
year |
int | Observation year |
value_numeric |
float | Point estimate (primary ML target) |
value_low |
float | Lower confidence bound (if available) |
value_high |
float | Upper confidence bound (if available) |
value_display |
string | Formatted display string, e.g. "58.3 [57.7–59.0]" |
dim1_type |
string | Dimension 1 type, e.g. SEX, RESIDENCEAREATYPE |
dim1 |
string | Dimension 1 value, e.g. SEX_BTSX, RURAL |
dim2_type |
string | Dimension 2 type (if present) |
dim2 |
string | Dimension 2 value (if present) |
last_updated |
string | WHO data last-updated timestamp |
Usage
from datasets import load_dataset
ds = load_dataset("electricsheepafrica/africa-who-number-of-neonatal-deaths")
df = ds["train"].to_pandas()
# Both-sexes, national level only
national = df[df.get("dim1", "").str.endswith("_BTSX") | df.get("dim1", pd.Series()).isna()]
# Time series for one country
kenya = df[df["country_iso3"] == "KEN"].sort_values("year")
Citation
@misc{who_gho_cm_03,
title = {WHO Global Health Observatory: Number of neonatal deaths},
author = {World Health Organization},
year = {2023},
url = {https://www.who.int/data/gho/data/indicators/indicator-details/GHO/CM_03},
note = {Repackaged by Electric Sheep Africa}
}
Repackaged by Electric Sheep Africa from WHO GHO open data. Original data © World Health Organization, licensed CC BY 4.0.
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