Peralta et al. Population Health Metrics (2019) 17:5 https://doi.org/10.1186/s12963-019-0185-9 CORRECTION Open Access Correction to: Evaluation of the mortality registry in Ecuador (2001–2013) – social and geographical inequalities in completeness and quality Andrés Peralta1,2,3* , Joan Benach2,3,4, Carme Borrell1,5,6,7, Verónica Espinel-Flores1,7, Lucinda Cash-Gibson2,3, Bernardo L. Queiroz8 and Marc Marí-Dell’Olmo1,5,6,7 Correction to: Popul Health Metrics (2019) 17:3 https://doi.org/10.1186/s12963-019-0183-y Following the publication of this article [1], the au- Pampulha, Av. Antônio Carlos, 6627, Pampulha, Belo Horizonte, MG CEP 31270-901, Brazil. thors reported a typesetting error in Table 1 that caused the columns of the table to be ordered incorrectly, and a Received: 29 March 2019 Accepted: 29 March 2019 typographical error in a sentence in the Conclusions section. Reference In the Conclusion section the incorrect sentence was: 1. Peralta A, et al. Evaluation of the mortality registry in Ecuador (2001–2013) – “Galapagos has a low percentage of garbage codes social and geographical inequalities in completeness and quality. Popul Health Metrics. 2019;17:3 https://doi.org/10.1186/s12963-019-0183-y. (high quality) and also a low and also a low complete- ness estimate.” It should say: “Galapagos has a low percentage of garbage codes (high quality) and also a low completeness estimate.” The original article has been corrected. The publisher apologizes to the readers and the authors for any incon- venience caused by these errors. Author details 1 Agència de Salut Pública de Barcelona, Plaça Lesseps 1, 08023 Barcelona, Catalonia, Spain. 2Health Inequalities Research Group, Employment Conditions Knowledge Network (GREDS-EMCONET), Department of Political and Social Sciences, Universitat Pompeu Fabra, Ramon Trias Fargas, 25-27, 08005 Barcelona, Spain. 3Johns Hopkins University, Pompeu Fabra University Public Policy Center, Barcelona, Spain. 4Transdisciplinary Research Group on Socioecological Transitions (GinTRANS2), Universidad Autónoma Madrid, 28049 Madrid, Spain. 5CIBER Epidemiología y Salud Pública (CIBERESP), Madrid, Spain. 6Institut d’Investigació Biomèdica (IIB Sant Pau), Barcelona, Spain. 7Department of Experimental and Health Sciences, Universitat Pompeu Fabra, Barcelona, Catalonia, Spain. 8Department of Demography and Cedeplar, Faculdade de Ciências Econômicas, FACE/UFMG, Campus * Correspondence: aperalta@aspb.cat 1 Agència de Salut Pública de Barcelona, Plaça Lesseps 1, 08023 Barcelona, Catalonia, Spain 2 Health Inequalities Research Group, Employment Conditions Knowledge Network (GREDS-EMCONET), Department of Political and Social Sciences, Universitat Pompeu Fabra, Ramon Trias Fargas, 25-27, 08005 Barcelona, Spain Full list of author information is available at the end of the article © The Author(s). 2019 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated. The incorrect Table 1 was published as: Table 1 Provincial and national completeness estimates (2001 – 2010) Area Women Men a b Population Population Deaths IC Age Range Population Population Deaths IC GGB Age Range SEG GGB - Mean GGB a SEG b GGB - Mean 2001 2010 Period Used 2001 2010 Period Used SEGc SEG c Azuay 319,974 375,027 12,730 98.82 80.50 66.91 80.03 15–50 279,339 335,739 14,465 107.10 91.49 72.69 88.17 15–50 Bolivar 86,727 93,767 4280 98.81 71.91 71.29 78.83 20–60 83,969 89,975 5100 95.56 79.52 74.28 82.18 25–60 Cañar 111,707 119,621 4187 99.87 57.49 61.89 68.86 20–55 94,639 104,812 5137 95.54 79.06 70.87 80.59 15–50 Peralta et al. Population Health Metrics Carchi 76,891 83,257 3147 80.20 56.76 55.98 62.56 15–50 75,106 80,905 3800 85.18 60.80 62.58 67.92 15–50 Cotopaxi 180,050 209,723 8369 99.92 83.67 72.23 83.79 20–55 169,507 197,990 10,264 101.66 84.40 65.66 81.27 15–50 Chimborazo 213,297 239,339 10,896 96.01 78.20 74.17 81.78 20–55 191,150 219,221 12,414 95.61 93.55 78.81 88.66 20–55 El Oro 258,108 295,256 8020 76.86 55.03 50.83 58.99 15–50 265,561 302,735 12,037 68.33 62.60 55.16 61.55 15–50 (2019) 17:5 Esmeraldas 188,213 262,143 5758 43.07 86.01 63.62 59.33 15–50 197,331 270,912 9675 43.94 106.46 72.29 65.24 15–50 Guayas / Santa 1,654,813 1,975,927 57,031 57.04 57.70 46.62 53.27 15–50 1,643,493 1,968,672 79,288 54.37 67.21 51.12 56.79 15–50 Elena Imbabura 176,064 204,094 8243 101.18 86.67 73.53 85.66 15–55 167,689 193,105 9597 100.32 86.91 74.17 85.82 20–60 Loja 207,913 228,704 8141 100.81 62.41 59.31 70.09 15–50 198,316 221,638 9962 101.55 79.32 71.79 82.45 15–50 Los Rios 315,609 379,883 12,055 75.27 68.77 52.78 64.14 15–50 335,909 398,252 19,140 70.13 77.06 57.89 67.40 15–50 Manabi 592,524 681,575 20,872 82.71 63.62 55.61 65.51 15–50 599,521 689,525 29,639 85.19 74.86 61.20 72.40 15–50 Morona Santiago 57,891 73,126 1327 46.48 39.80 33.43 39.19 15–50 56,904 74,529 1806 54.03 63.17 49.05 54.82 15–50 Napo 38,776 50,641 1029 62.22 67.23 53.19 60.31 15–50 39,965 52,220 1577 71.98 88.74 64.05 73.57 15–50 Pastaza 29,686 41,394 718 40.59 73.71 56.18 53.57 15–50 31,808 42,084 1053 45.81 61.87 48.02 51.01 15–50 Pichincha / Santo 1,218,916 1,504,023 42,824 72.07 81.98 65.03 72.37 20–55 1,167,281 1,441,529 52,093 64.46 81.18 63.62 68.89 15–50 Domingo Tungurahua 227,308 258,907 10,747 98.69 83.24 82.22 87.44 15–55 213,470 244,014 12,569 99.48 87.86 78.87 87.94 15–55 Zamora Chinchipe 37,017 43,780 976 53.53 45.88 39.20 45.46 15–50 39,462 46,627 1391 47.16 52.06 41.78 46.62 15–50 Galapagos 8137 11,392 123 12.58 35.10 30.43 21.30 15–50 9618 12,238 187 25.46 45.66 38.83 34.51 15–50 Sucumbíos 58,774 83,431 1279 22.00 49.52 41.57 33.45 15–50 67,901 91,050 2589 28.53 58.70 46.24 40.70 15–50 Orellana 39,478 64,034 1073 28.85 105.65 74.30 52.10 15–50 45,147 70,655 1766 36.30 126.62 79.87 62.54 15–50 Ecuador 6,132,183 7,293,907 224,300 71.97 67.67 57.21 65.00 15–50 6,010,246 7,165,170 296,474 71.76 74.68 60.09 68.23 15–50 a Generalized growth balance method b Synthetic extinct generations method c Hybrid generalized growth balance and synthetic extinct generations method Page 2 of 3 The correct Table 1 should be: Table 1 Provincial and national completeness estimates (2001 – 2010) Area Women Men Population Population Deaths Completeness (%) Age Population Population Deaths Completeness (%) Age 2001 2010 IC a b c Range 2001 2010 IC a b c Range GGB SEG GGB - SEG Mean GGB SEG GGB - SEG Mean Period Used Period Used Azuay 319,974 375,027 12,730 98.82 80.50 66.91 80.03 15 - 50 279,339 335,739 14,465 107.10 91.49 72.69 88.17 15 - 50 Bolivar 86,727 93,767 4,280 98.81 71.91 71.29 78.83 20 - 60 83,969 89,975 5,100 95.56 79.52 74.28 82.18 25 - 60 Cañar 111,707 119,621 4,187 99.87 57.49 61.89 68.86 20 - 55 94,639 104,812 5,137 95.54 79.06 70.87 80.59 15 - 50 Peralta et al. Population Health Metrics Carchi 76,891 83,257 3,147 80.20 56.76 55.98 62.56 15 - 50 75,106 80,905 3,800 85.18 60.80 62.58 67.92 15 - 50 Cotopaxi 180,050 209,723 8,369 99.92 83.67 72.23 83.79 20 - 55 169,507 197,990 10,264 101.66 84.40 65.66 81.27 15 - 50 Chimborazo 213,297 239,339 10,896 96.01 78.20 74.17 81.78 20 - 55 191,150 219,221 12,414 95.61 93.55 78.81 88.66 20 - 55 El Oro 258,108 295,256 8,020 76.86 55.03 50.83 58.99 15 - 50 265,561 302,735 12,037 68.33 62.60 55.16 61.55 15 - 50 (2019) 17:5 Esmeraldas 188,213 262,143 5,758 43.07 86.01 63.62 59.33 15 - 50 197,331 270,912 9,675 43.94 106.46 72.29 65.24 15 - 50 Guayas / Santa Elena 1,654,813 1,975,927 57,031 57.04 57.70 46.62 53.27 15 - 50 1,643,493 1,968,672 79,288 54.37 67.21 51.12 56.79 15 - 50 Imbabura 176,064 204,094 8,243 101.18 86.67 73.53 85.66 15 - 55 167,689 193,105 9,597 100.32 86.91 74.17 85.82 20 - 60 Loja 207,913 228,704 8,141 100.81 62.41 59.31 70.09 15 - 50 198,316 221,638 9,962 101.55 79.32 71.79 82.45 15 - 50 Los Rios 315,609 379,883 12,055 75.27 68.77 52.78 64.14 15 - 50 335,909 398,252 19,140 70.13 77.06 57.89 67.40 15 - 50 Manabi 592,524 681,575 20,872 82.71 63.62 55.61 65.51 15 - 50 599,521 689,525 29,639 85.19 74.86 61.20 72.40 15 - 50 Morona Santiago 57,891 73,126 1,327 46.48 39.80 33.43 39.19 15 - 50 56,904 74,529 1,806 54.03 63.17 49.05 54.82 15 - 50 Napo 38,776 50,641 1,029 62.22 67.23 53.19 60.31 15 - 50 39,965 52,220 1,577 71.98 88.74 64.05 73.57 15 - 50 Pastaza 29,686 41,394 718 40.59 73.71 56.18 53.57 15 - 50 31,808 42,084 1,053 45.81 61.87 48.02 51.01 15 - 50 Pichincha / Santo Domingo 1,218,916 1,504,023 42,824 72.07 81.98 65.03 72.37 20 - 55 1,167,281 1,441,529 52,093 64.46 81.18 63.62 68.89 15 - 50 Tungurahua 227,308 258,907 10,747 98.69 83.24 82.22 87.44 15 - 55 213,470 244,014 12,569 99.48 87.86 78.87 87.94 15 - 55 Zamora Chinchipe 37,017 43,780 976 53.53 45.88 39.20 45.46 15 - 50 39,462 46,627 1,391 47.16 52.06 41.78 46.62 15 - 50 Galapagos 8,137 11,392 123 12.58 35.10 30.43 21.30 15 - 50 9,618 12,238 187 25.46 45.66 38.83 34.51 15 - 50 Sucumbíos 58,774 83,431 1,279 22.00 49.52 41.57 33.45 15 - 50 67,901 91,050 2,589 28.53 58.70 46.24 40.70 15 - 50 Orellana 39,478 64,034 1,073 28.85 105.65 74.30 52.10 15 - 50 45,147 70,655 1,766 36.30 126.62 79.87 62.54 15 - 50 Ecuador 6,132,183 7,293,907 224,300 71.97 67.67 57.21 65.00 15 - 50 6,010,246 7,165,170 296,474 71.76 74.68 60.09 68.23 15 - 50 a Generalized growth balance method b Synthetic extinct generations method c Hybrid generalized growth balance and synthetic extinct generations method Page 3 of 3