[{"id":"1260","desc":"<p>DATASET: version 1.0 estimates representing the probability of, a) receiving four or more antenatal care (ANC) visits at time of delivery, b) skilled birth attendance (SBA) during delivery, and c) postnatal care (PNC) received within 48 hours of delivery.&nbsp;&nbsp;<br><\/p><p>REGION: Africa \r\n<\/p><p>SPATIAL RESOLUTION: 0.0027777778 decimal degrees (approx 300 m at the equator) \r\n<\/p><p>PROJECTION: Geographic, WGS84 \r\n<\/p><p>UNITS: probabilities expressed as decimals values \r\n<\/p><p>MAPPING APPROACH: hierarchical mixed effects logistic regression methods applied to household survey data in order to derive probabilities of receiving different types of maternal newborn health services. Accessibility\/transportation surfaces were used to train the model and derive gridded estimates; see https:\/\/doi.org\/10.1371\/journal.pone.0162006 for more details. \r\n<\/p><p>FORMAT: Geotiff (zipped using 7-zip (open access tool): www.7-zip.org) \r\n<\/p><p>FILENAMES: Example - BDI_MNH_ANC.tif = Burundi (BDI) maternal and newborn health (MNH) data, estimates for antenatal care (ANC). \r\n<\/p><p>DATE OF PRODUCTION: August 2016 \r\n<\/p><p><br><\/p>","doi":"","popyear":null,"date":"2016-05-05","file_img":"344.jpg","continent":"Africa","country":"Burundi","resolution":"300","type":"Maternal and Newborn Health","file_html":null},{"id":"50640","desc":"Proportion of women aged 15 to 49 years in the Democratic Republic of the Congo who have never used modern contraception during the years 2013-14. The estimations along with their associated uncertainties, measured as standard deviations (SD), are calculated following the methodology detailed in Utazi et al. (2021, 2022, 2023).\r\n<p><b>References<\/b>:<\/p>\r\n\r\n<ul>\r\n  <li>Utazi, C.E., Aheto, J.M.K., Chan, H.M.T., Tatem, A.J. and Sahu, S.K., 2022. <i>Conditional probability and ratio-based approaches for mapping the coverage of multi-dose vaccines<\/i>. Statistics in Medicine, 41(29), pp.5662-5678. <a target=\"_blank\"  href=\"https:\/\/doi.org\/10.1002\/sim.9586\">DOI:10.1002\/sim.9586<\/a><\/li>\r\n  <li>Utazi, C.E., Chan, H.M.T., Olowe, I., Wigley, A., Tejedor-Garavito, N., Cunningham, A., Bondarenko, M., Lorin, J., Boyda, D., Hogan, D. and Tatem, A.J., 2023. <i>A zero-dose vulnerability index for equity assessment and spatial prioritization in low-and middle-income countries<\/i>. Spatial Statistics, p.100772. <a target=\"_blank\"  href=\"https:\/\/doi.org\/10.1016\/j.spasta.2023.100772\">DOI:10.1016\/j.spasta.2023.100772<\/a><\/li>\r\n  <li>Utazi, C.E., Nilsen, K., Pannell, O., Dotse-Gborgbortsi, W. and Tatem, A.J., 2021. <i>District-level estimation of vaccination coverage: Discrete vs continuous spatial models<\/i>. Statistics in Medicine, 40(9), pp.2197-2211. <a target=\"_blank\"  href=\"https:\/\/doi.org\/10.1002\/sim.8897\">DOI:10.1002\/sim.8897<\/a><\/li>\r\n<\/ul>\r\n","doi":"10.5258\/SOTON\/WP00767","popyear":"2014","date":"2023-04-07","file_img":"Cover_Contraceptive_use.png","continent":"Africa","country":"DRC","resolution":"1000","type":"Contraceptive Use","file_html":null},{"id":"50641","desc":"Vaccination coverage for diphtheria-tetanus-pertussis dose 1 (DTP1) and dose 3 (DTP3) among children aged 12 to 23 months in the Democratic Republic of the Congo during the years 2013-14. The estimated vaccination coverages, representing the proportions of vaccinated children, along with their associated uncertainties measured as standard deviations (SD), are computed following the methodology detailed in Utazi et al. (2021, 2022, 2023).\r\n<p><b>References<\/b>:<\/p>\r\n\r\n<ul>\r\n  <li>Utazi, C.E., Aheto, J.M.K., Chan, H.M.T., Tatem, A.J. and Sahu, S.K., 2022. <i>Conditional probability and ratio-based approaches for mapping the coverage of multi-dose vaccines<\/i>. Statistics in Medicine, 41(29), pp.5662-5678. <a target=\"_blank\"  href=\"https:\/\/doi.org\/10.1002\/sim.9586\">DOI:10.1002\/sim.9586<\/a><\/li>\r\n  <li>Utazi, C.E., Chan, H.M.T., Olowe, I., Wigley, A., Tejedor-Garavito, N., Cunningham, A., Bondarenko, M., Lorin, J., Boyda, D., Hogan, D. and Tatem, A.J., 2023. <i>A zero-dose vulnerability index for equity assessment and spatial prioritization in low-and middle-income countries<\/i>. Spatial Statistics, p.100772. <a target=\"_blank\"  href=\"https:\/\/doi.org\/10.1016\/j.spasta.2023.100772\">DOI:10.1016\/j.spasta.2023.100772<\/a><\/li>\r\n  <li>Utazi, C.E., Nilsen, K., Pannell, O., Dotse-Gborgbortsi, W. and Tatem, A.J., 2021. <i>District-level estimation of vaccination coverage: Discrete vs continuous spatial models<\/i>. Statistics in Medicine, 40(9), pp.2197-2211. <a target=\"_blank\"  href=\"https:\/\/doi.org\/10.1002\/sim.8897\">DOI:10.1002\/sim.8897<\/a><\/li>\r\n<\/ul>\r\n","doi":"10.5258\/SOTON\/WP00767","popyear":"2014","date":"2023-04-07","file_img":"Cover_DTP_vaccination_coverage.png","continent":"Africa","country":"DRC","resolution":"1000","type":"DTP vaccination coverage","file_html":null},{"id":"50642","desc":"Proportion of children aged 12 to 23 months born to large (&gt;= 9 members) households in the Democratic Republic of the Congo during the years 2013-14. The estimations along with their associated uncertainties, measured as standard deviations (SD), are calculated following the methodology detailed in Utazi et al. (2021, 2022, 2023).\r\n<p><b>References<\/b>:<\/p>\r\n\r\n<ul>\r\n  <li>Utazi, C.E., Aheto, J.M.K., Chan, H.M.T., Tatem, A.J. and Sahu, S.K., 2022. <i>Conditional probability and ratio-based approaches for mapping the coverage of multi-dose vaccines<\/i>. Statistics in Medicine, 41(29), pp.5662-5678. <a target=\"_blank\"  href=\"https:\/\/doi.org\/10.1002\/sim.9586\">DOI:10.1002\/sim.9586<\/a><\/li>\r\n  <li>Utazi, C.E., Chan, H.M.T., Olowe, I., Wigley, A., Tejedor-Garavito, N., Cunningham, A., Bondarenko, M., Lorin, J., Boyda, D., Hogan, D. and Tatem, A.J., 2023. <i>A zero-dose vulnerability index for equity assessment and spatial prioritization in low-and middle-income countries<\/i>. Spatial Statistics, p.100772. <a target=\"_blank\"  href=\"https:\/\/doi.org\/10.1016\/j.spasta.2023.100772\">DOI:10.1016\/j.spasta.2023.100772<\/a><\/li>\r\n  <li>Utazi, C.E., Nilsen, K., Pannell, O., Dotse-Gborgbortsi, W. and Tatem, A.J., 2021. <i>District-level estimation of vaccination coverage: Discrete vs continuous spatial models<\/i>. Statistics in Medicine, 40(9), pp.2197-2211. <a target=\"_blank\"  href=\"https:\/\/doi.org\/10.1002\/sim.8897\">DOI:10.1002\/sim.8897<\/a><\/li>\r\n<\/ul>\r\n","doi":"10.5258\/SOTON\/WP00767","popyear":"2014","date":"2023-04-07","file_img":"Cover_Maternal_and_child_demographic.png","continent":"Africa","country":"DRC","resolution":"1000","type":"Maternal and child demographic","file_html":null},{"id":"50643","desc":"Maternal and child health in the Democratic Republic of the Congo during the years 2013-14. Proportion of children aged 12 to 23 months born to mothers who did not attend antenatal care (ANC), who did not receive (or recall) post-natal care within 2 months after birth (PNC), who had no health insurance (INSURANCE), and who had no tetanus toxoid injections before birth (TT). Proportion of children aged 12 to 23 months who were not delivered by a skilled birth attendant (SBA), and who were underweight (MALNUTRITION). The estimations along with their associated uncertainties, measured as standard deviations (SD), are calculated following the methodology detailed in Utazi et al. (2021, 2022, 2023).\r\n<p><b>References<\/b>:<\/p>\r\n\r\n<ul>\r\n  <li>Utazi, C.E., Aheto, J.M.K., Chan, H.M.T., Tatem, A.J. and Sahu, S.K., 2022. <i>Conditional probability and ratio-based approaches for mapping the coverage of multi-dose vaccines<\/i>. Statistics in Medicine, 41(29), pp.5662-5678. <a target=\"_blank\"  href=\"https:\/\/doi.org\/10.1002\/sim.9586\">DOI:10.1002\/sim.9586<\/a><\/li>\r\n  <li>Utazi, C.E., Chan, H.M.T., Olowe, I., Wigley, A., Tejedor-Garavito, N., Cunningham, A., Bondarenko, M., Lorin, J., Boyda, D., Hogan, D. and Tatem, A.J., 2023. <i>A zero-dose vulnerability index for equity assessment and spatial prioritization in low-and middle-income countries<\/i>. Spatial Statistics, p.100772. <a target=\"_blank\"  href=\"https:\/\/doi.org\/10.1016\/j.spasta.2023.100772\">DOI:10.1016\/j.spasta.2023.100772<\/a><\/li>\r\n  <li>Utazi, C.E., Nilsen, K., Pannell, O., Dotse-Gborgbortsi, W. and Tatem, A.J., 2021. <i>District-level estimation of vaccination coverage: Discrete vs continuous spatial models<\/i>. Statistics in Medicine, 40(9), pp.2197-2211. <a target=\"_blank\"  href=\"https:\/\/doi.org\/10.1002\/sim.8897\">DOI:10.1002\/sim.8897<\/a><\/li>\r\n<\/ul>\r\n","doi":"10.5258\/SOTON\/WP00767","popyear":"2014","date":"2023-04-07","file_img":"Cover_Maternal_and_child_health.png","continent":"Africa","country":"DRC","resolution":"1000","type":"Maternal and child health","file_html":null},{"id":"50644","desc":"Maternal and child socioeconomic in the Democratic Republic of the Congo during the years 2013-14. Proportion of children aged 12 to 23 months born to the poorest \/ poorer households according to DHS \/ MICS-NICS classifications (HWEALTH) and born to mothers who had no formal education (MEDUCATION). Proportion of women aged 15 to 49 years who did not participate in decision-making in their households (DECISION). The estimations along with their associated uncertainties, measured as standard deviations (SD), are calculated following the methodology detailed in Utazi et al. (2021, 2022, 2023).\r\n<p><b>References<\/b>:<\/p>\r\n\r\n<ul>\r\n  <li>Utazi, C.E., Aheto, J.M.K., Chan, H.M.T., Tatem, A.J. and Sahu, S.K., 2022. <i>Conditional probability and ratio-based approaches for mapping the coverage of multi-dose vaccines<\/i>. Statistics in Medicine, 41(29), pp.5662-5678. <a target=\"_blank\"  href=\"https:\/\/doi.org\/10.1002\/sim.9586\">DOI:10.1002\/sim.9586<\/a><\/li>\r\n  <li>Utazi, C.E., Chan, H.M.T., Olowe, I., Wigley, A., Tejedor-Garavito, N., Cunningham, A., Bondarenko, M., Lorin, J., Boyda, D., Hogan, D. and Tatem, A.J., 2023. <i>A zero-dose vulnerability index for equity assessment and spatial prioritization in low-and middle-income countries<\/i>. Spatial Statistics, p.100772. <a target=\"_blank\"  href=\"https:\/\/doi.org\/10.1016\/j.spasta.2023.100772\">DOI:10.1016\/j.spasta.2023.100772<\/a><\/li>\r\n  <li>Utazi, C.E., Nilsen, K., Pannell, O., Dotse-Gborgbortsi, W. and Tatem, A.J., 2021. <i>District-level estimation of vaccination coverage: Discrete vs continuous spatial models<\/i>. Statistics in Medicine, 40(9), pp.2197-2211. <a target=\"_blank\"  href=\"https:\/\/doi.org\/10.1002\/sim.8897\">DOI:10.1002\/sim.8897<\/a><\/li>\r\n<\/ul>\r\n","doi":"10.5258\/SOTON\/WP00767","popyear":"2014","date":"2023-04-07","file_img":"Cover_Maternal_and_child_socioeconomic.png","continent":"Africa","country":"DRC","resolution":"1000","type":"Maternal and child socioeconomic","file_html":null},{"id":"50645","desc":"Vaccination coverage for measles dose 1 (MCV1) among children aged 12 to 23 months in the Democratic Republic of the Congo during the years 2013-14. The estimated vaccination coverages, representing the proportions of vaccinated children, along with their associated uncertainties measured as standard deviations (SD), are computed following the methodology detailed in Utazi et al. (2021, 2022, 2023).\r\n<p><b>References<\/b>:<\/p>\r\n\r\n<ul>\r\n  <li>Utazi, C.E., Aheto, J.M.K., Chan, H.M.T., Tatem, A.J. and Sahu, S.K., 2022. <i>Conditional probability and ratio-based approaches for mapping the coverage of multi-dose vaccines<\/i>. Statistics in Medicine, 41(29), pp.5662-5678. <a target=\"_blank\"  href=\"https:\/\/doi.org\/10.1002\/sim.9586\">DOI:10.1002\/sim.9586<\/a><\/li>\r\n  <li>Utazi, C.E., Chan, H.M.T., Olowe, I., Wigley, A., Tejedor-Garavito, N., Cunningham, A., Bondarenko, M., Lorin, J., Boyda, D., Hogan, D. and Tatem, A.J., 2023. <i>A zero-dose vulnerability index for equity assessment and spatial prioritization in low-and middle-income countries<\/i>. Spatial Statistics, p.100772. <a target=\"_blank\"  href=\"https:\/\/doi.org\/10.1016\/j.spasta.2023.100772\">DOI:10.1016\/j.spasta.2023.100772<\/a><\/li>\r\n  <li>Utazi, C.E., Nilsen, K., Pannell, O., Dotse-Gborgbortsi, W. and Tatem, A.J., 2021. <i>District-level estimation of vaccination coverage: Discrete vs continuous spatial models<\/i>. Statistics in Medicine, 40(9), pp.2197-2211. <a target=\"_blank\"  href=\"https:\/\/doi.org\/10.1002\/sim.8897\">DOI:10.1002\/sim.8897<\/a><\/li>\r\n<\/ul>\r\n","doi":"10.5258\/SOTON\/WP00767","popyear":"2014","date":"2023-04-07","file_img":"Cover_Measles_vaccination_coverage.png","continent":"Africa","country":"DRC","resolution":"1000","type":"Measles vaccination coverage","file_html":null},{"id":"17028","desc":"Diphtheria-tetanus-pertussis (DTP) dose 1, 2 and 3 vaccination coverage among children aged under 5 years in 2011 for Ethiopia.\r\n<p>Estimated vaccination coverage, i.e. proportions of children vaccinated, and associated uncertainties, measured as standard deviations, for Ethiopia in 2011.\r\n<\/p><p>The methodology used for the work is described in Utazi et al. (2017) High resolution age-structure mapping of childhood vaccination coverage in low and middle income countries, Vaccine 36 (12), pp. 1583-1591 and Utazi et al (2019) Mapping vaccination coverage rates at high resolution to explore the effects of delivery mechanisms and inform vaccination strategies, Nature Communications, under review.\r\n<\/p><p><br><\/p><p>Filenames:\r\n<\/p><p>DTP vaccination estimates: \r\n<\/p><p>[COUNTRYCODE]_mean_pred_DTP1_perc.tif = Mean % estimated DTP1 vaccination coverage (0-59 months) \r\n<\/p><p>[COUNTRYCODE]_mean_pred_DTP1_perc.tif = Mean % estimated DTP2 vaccination coverage (0-59 months) \r\n<\/p><p>[COUNTRYCODE]_mean_pred_DTP1_perc.tif = Mean % estimated DTP3 vaccination coverage (0-59 months) \r\n<\/p><p><br><\/p>","doi":"","popyear":"2011","date":"2019-02-03","file_img":"ETH_2011_mean_pred_DTP1_perc.png","continent":"Africa","country":"Ethiopia","resolution":"1000","type":"DTP vaccination coverage","file_html":null},{"id":"17029","desc":"Diphtheria-tetanus-pertussis (DTP) dose 1, 2 and 3 vaccination coverage among children aged under 5 years in 2016 for Ethiopia.\r\n<p>Estimated vaccination coverage, i.e. proportions of children vaccinated, and associated uncertainties, measured as standard deviations, for Ethiopia in 2016.\r\n<\/p><p>The methodology used for the work is described in Utazi et al. (2017) High resolution age-structure mapping of childhood vaccination coverage in low and middle income countries, Vaccine 36 (12), pp. 1583-1591 and Utazi et al (2019) Mapping vaccination coverage rates at high resolution to explore the effects of delivery mechanisms and inform vaccination strategies, Nature Communications, under review.\r\n<\/p><p><br><\/p><p>Filenames:\r\n<\/p><p>DTP vaccination estimates: \r\n<\/p><p>[COUNTRYCODE]_mean_pred_DTP1_perc.tif = Mean % estimated DTP1 vaccination coverage (0-59 months) \r\n<\/p><p>[COUNTRYCODE]_mean_pred_DTP1_perc.tif = Mean % estimated DTP2 vaccination coverage (0-59 months) \r\n<\/p><p>[COUNTRYCODE]_mean_pred_DTP1_perc.tif = Mean % estimated DTP3 vaccination coverage (0-59 months) \r\n<\/p><p><br><\/p>","doi":"","popyear":"2016","date":"2019-02-03","file_img":"ETH_2016_mean_pred_DTP1_perc.png","continent":"Africa","country":"Ethiopia","resolution":"1000","type":"DTP vaccination coverage","file_html":null},{"id":"17038","desc":"Estimated vaccination coverage, i.e. proportions of children vaccinated, and associated uncertainties, measured as standard deviations, broken by age groups (9-11 months, 12-23 months, 24-59 months, 0-59 months and 9-59 months) for Ethiopia in 2011.\r\n<p>The methodology used for the work is described in Utazi et al. (2017) High resolution age-structure mapping of childhood vaccination coverage in low and middle income countries, Vaccine 36 (12), pp. 1583-1591 and Utazi et al (2019) Mapping vaccination coverage rates at high resolution to explore the effects of delivery mechanisms and inform vaccination strategies, Nature Communications, under review.\r\n<\/p><p><br><\/p><p>Filenames:\r\n<\/p><p>DTP vaccination estimates: \r\n<\/p><p>[COUNTRYCODE]_mean_pred_DTP1_perc.tif = Mean % estimated DTP1 vaccination coverage (0-59 months) \r\n<\/p><p>[COUNTRYCODE]_mean_pred_DTP1_perc.tif = Mean % estimated DTP2 vaccination coverage (0-59 months) \r\n<\/p><p>[COUNTRYCODE]_mean_pred_DTP1_perc.tif = Mean % estimated DTP3 vaccination coverage (0-59 months) \r\n<\/p><p><br><\/p>","doi":"","popyear":"2011","date":"2019-02-03","file_img":"ETH_2011_mean_pred_total_perc.png","continent":"Africa","country":"Ethiopia","resolution":"1000","type":"Measles vaccination coverage","file_html":null},{"id":"17039","desc":"Estimated vaccination coverage, i.e. proportions of children vaccinated, and associated uncertainties, measured as standard deviations, broken by age groups (9-11 months, 12-23 months, 24-59 months, 0-59 months and 9-59 months) for Ethiopia in 2016.\r\n<p>The methodology used for the work is described in Utazi et al. (2017) High resolution age-structure mapping of childhood vaccination coverage in low and middle income countries, Vaccine 36 (12), pp. 1583-1591 and Utazi et al (2019) Mapping vaccination coverage rates at high resolution to explore the effects of delivery mechanisms and inform vaccination strategies, Nature Communications, under review.\r\n<\/p><p><br><\/p><p>Filenames:\r\n<\/p><p>DTP vaccination estimates: \r\n<\/p><p>[COUNTRYCODE]_mean_pred_DTP1_perc.tif = Mean % estimated DTP1 vaccination coverage (0-59 months) \r\n<\/p><p>[COUNTRYCODE]_mean_pred_DTP1_perc.tif = Mean % estimated DTP2 vaccination coverage (0-59 months) \r\n<\/p><p>[COUNTRYCODE]_mean_pred_DTP1_perc.tif = Mean % estimated DTP3 vaccination coverage (0-59 months) \r\n<\/p><p><br><\/p>","doi":"","popyear":"2016","date":"2019-02-03","file_img":"ETH_2016_mean_pred_total_perc.png","continent":"Africa","country":"Ethiopia","resolution":"1000","type":"Measles vaccination coverage","file_html":null},{"id":"50646","desc":"Proportion of women aged 15 to 49 years in Ethiopia who have never used modern contraception during the year 2016. The estimations along with their associated uncertainties, measured as standard deviations (SD), are calculated following the methodology detailed in Utazi et al. (2021, 2022, 2023).\r\n<p><b>References<\/b>:<\/p>\r\n\r\n<ul>\r\n  <li>Utazi, C.E., Aheto, J.M.K., Chan, H.M.T., Tatem, A.J. and Sahu, S.K., 2022. <i>Conditional probability and ratio-based approaches for mapping the coverage of multi-dose vaccines<\/i>. Statistics in Medicine, 41(29), pp.5662-5678. <a target=\"_blank\"  href=\"https:\/\/doi.org\/10.1002\/sim.9586\">DOI:10.1002\/sim.9586<\/a><\/li>\r\n  <li>Utazi, C.E., Chan, H.M.T., Olowe, I., Wigley, A., Tejedor-Garavito, N., Cunningham, A., Bondarenko, M., Lorin, J., Boyda, D., Hogan, D. and Tatem, A.J., 2023. <i>A zero-dose vulnerability index for equity assessment and spatial prioritization in low-and middle-income countries<\/i>. Spatial Statistics, p.100772. <a target=\"_blank\"  href=\"https:\/\/doi.org\/10.1016\/j.spasta.2023.100772\">DOI:10.1016\/j.spasta.2023.100772<\/a><\/li>\r\n  <li>Utazi, C.E., Nilsen, K., Pannell, O., Dotse-Gborgbortsi, W. and Tatem, A.J., 2021. <i>District-level estimation of vaccination coverage: Discrete vs continuous spatial models<\/i>. Statistics in Medicine, 40(9), pp.2197-2211. <a target=\"_blank\"  href=\"https:\/\/doi.org\/10.1002\/sim.8897\">DOI:10.1002\/sim.8897<\/a><\/li>\r\n<\/ul>\r\n","doi":"10.5258\/SOTON\/WP00767","popyear":"2016","date":"2023-04-07","file_img":"ETH_Contraceptive_use.png","continent":"Africa","country":"Ethiopia","resolution":"1000","type":"Contraceptive Use","file_html":null},{"id":"50647","desc":"Vaccination coverage for diphtheria-tetanus-pertussis dose 1 (DTP1) and dose 3 (DTP3) among children aged 12 to 23 months in Ethiopia during the year 2016. The estimated vaccination coverages, representing the proportions of vaccinated children, along with their associated uncertainties measured as standard deviations (SD), are computed following the methodology detailed in Utazi et al. (2021, 2022, 2023).\r\n<p><b>References<\/b>:<\/p>\r\n\r\n<ul>\r\n  <li>Utazi, C.E., Aheto, J.M.K., Chan, H.M.T., Tatem, A.J. and Sahu, S.K., 2022. <i>Conditional probability and ratio-based approaches for mapping the coverage of multi-dose vaccines<\/i>. Statistics in Medicine, 41(29), pp.5662-5678. <a target=\"_blank\"  href=\"https:\/\/doi.org\/10.1002\/sim.9586\">DOI:10.1002\/sim.9586<\/a><\/li>\r\n  <li>Utazi, C.E., Chan, H.M.T., Olowe, I., Wigley, A., Tejedor-Garavito, N., Cunningham, A., Bondarenko, M., Lorin, J., Boyda, D., Hogan, D. and Tatem, A.J., 2023. <i>A zero-dose vulnerability index for equity assessment and spatial prioritization in low-and middle-income countries<\/i>. Spatial Statistics, p.100772. <a target=\"_blank\"  href=\"https:\/\/doi.org\/10.1016\/j.spasta.2023.100772\">DOI:10.1016\/j.spasta.2023.100772<\/a><\/li>\r\n  <li>Utazi, C.E., Nilsen, K., Pannell, O., Dotse-Gborgbortsi, W. and Tatem, A.J., 2021. <i>District-level estimation of vaccination coverage: Discrete vs continuous spatial models<\/i>. Statistics in Medicine, 40(9), pp.2197-2211. <a target=\"_blank\"  href=\"https:\/\/doi.org\/10.1002\/sim.8897\">DOI:10.1002\/sim.8897<\/a><\/li>\r\n<\/ul>\r\n","doi":"10.5258\/SOTON\/WP00767","popyear":"2016","date":"2023-04-07","file_img":"ETH_DTP_vaccination_coverage.png","continent":"Africa","country":"Ethiopia","resolution":"1000","type":"DTP vaccination coverage","file_html":null},{"id":"50648","desc":"Proportion of children aged 12 to 23 months born to large (&gt;= 9 members) households in Ethiopia during the year 2016. The estimations along with their associated uncertainties, measured as standard deviations (SD), are calculated following the methodology detailed in Utazi et al. (2021, 2022, 2023).\r\n<p><b>References<\/b>:<\/p>\r\n\r\n<ul>\r\n  <li>Utazi, C.E., Aheto, J.M.K., Chan, H.M.T., Tatem, A.J. and Sahu, S.K., 2022. <i>Conditional probability and ratio-based approaches for mapping the coverage of multi-dose vaccines<\/i>. Statistics in Medicine, 41(29), pp.5662-5678. <a target=\"_blank\"  href=\"https:\/\/doi.org\/10.1002\/sim.9586\">DOI:10.1002\/sim.9586<\/a><\/li>\r\n  <li>Utazi, C.E., Chan, H.M.T., Olowe, I., Wigley, A., Tejedor-Garavito, N., Cunningham, A., Bondarenko, M., Lorin, J., Boyda, D., Hogan, D. and Tatem, A.J., 2023. <i>A zero-dose vulnerability index for equity assessment and spatial prioritization in low-and middle-income countries<\/i>. Spatial Statistics, p.100772. <a target=\"_blank\"  href=\"https:\/\/doi.org\/10.1016\/j.spasta.2023.100772\">DOI:10.1016\/j.spasta.2023.100772<\/a><\/li>\r\n  <li>Utazi, C.E., Nilsen, K., Pannell, O., Dotse-Gborgbortsi, W. and Tatem, A.J., 2021. <i>District-level estimation of vaccination coverage: Discrete vs continuous spatial models<\/i>. Statistics in Medicine, 40(9), pp.2197-2211. <a target=\"_blank\"  href=\"https:\/\/doi.org\/10.1002\/sim.8897\">DOI:10.1002\/sim.8897<\/a><\/li>\r\n<\/ul>\r\n","doi":"10.5258\/SOTON\/WP00767","popyear":"2016","date":"2023-04-07","file_img":"ETH_Maternal_and_child_demographic.png","continent":"Africa","country":"Ethiopia","resolution":"1000","type":"Maternal and child demographic","file_html":null},{"id":"50649","desc":"Maternal and child health in Ethiopia during the year 2016. Proportion of children aged 12 to 23 months born to mothers who did not attend antenatal care (ANC), who did not receive (or recall) post-natal care within 2 months after birth (PNC), who had no health insurance (INSURANCE), and who had no tetanus toxoid injections before birth (TT). Proportion of children aged 12 to 23 months who were not delivered by a skilled birth attendant (SBA), and who were underweight (MALNUTRITION). The estimations along with their associated uncertainties, measured as standard deviations (SD), are calculated following the methodology detailed in Utazi et al. (2021, 2022, 2023).\r\n<p><b>References<\/b>:<\/p>\r\n\r\n<ul>\r\n  <li>Utazi, C.E., Aheto, J.M.K., Chan, H.M.T., Tatem, A.J. and Sahu, S.K., 2022. <i>Conditional probability and ratio-based approaches for mapping the coverage of multi-dose vaccines<\/i>. Statistics in Medicine, 41(29), pp.5662-5678. <a target=\"_blank\"  href=\"https:\/\/doi.org\/10.1002\/sim.9586\">DOI:10.1002\/sim.9586<\/a><\/li>\r\n  <li>Utazi, C.E., Chan, H.M.T., Olowe, I., Wigley, A., Tejedor-Garavito, N., Cunningham, A., Bondarenko, M., Lorin, J., Boyda, D., Hogan, D. and Tatem, A.J., 2023. <i>A zero-dose vulnerability index for equity assessment and spatial prioritization in low-and middle-income countries<\/i>. Spatial Statistics, p.100772. <a target=\"_blank\"  href=\"https:\/\/doi.org\/10.1016\/j.spasta.2023.100772\">DOI:10.1016\/j.spasta.2023.100772<\/a><\/li>\r\n  <li>Utazi, C.E., Nilsen, K., Pannell, O., Dotse-Gborgbortsi, W. and Tatem, A.J., 2021. <i>District-level estimation of vaccination coverage: Discrete vs continuous spatial models<\/i>. Statistics in Medicine, 40(9), pp.2197-2211. <a target=\"_blank\"  href=\"https:\/\/doi.org\/10.1002\/sim.8897\">DOI:10.1002\/sim.8897<\/a><\/li>\r\n<\/ul>\r\n","doi":"10.5258\/SOTON\/WP00767","popyear":"2016","date":"2023-04-07","file_img":"ETH_Maternal_and_child_health.png","continent":"Africa","country":"Ethiopia","resolution":"1000","type":"Maternal and child health","file_html":null},{"id":"50650","desc":"Maternal and child socioeconomic in Ethiopia during the year 2016. Proportion of children aged 12 to 23 months born to the poorest \/ poorer households according to DHS \/ MICS-NICS classifications (HWEALTH) and born to mothers who had no formal education (MEDUCATION). Proportion of women aged 15 to 49 years who did not participate in decision-making in their households (DECISION). The estimations along with their associated uncertainties, measured as standard deviations (SD), are calculated following the methodology detailed in Utazi et al. (2021, 2022, 2023).\r\n<p><b>References<\/b>:<\/p>\r\n\r\n<ul>\r\n  <li>Utazi, C.E., Aheto, J.M.K., Chan, H.M.T., Tatem, A.J. and Sahu, S.K., 2022. <i>Conditional probability and ratio-based approaches for mapping the coverage of multi-dose vaccines<\/i>. Statistics in Medicine, 41(29), pp.5662-5678. <a target=\"_blank\"  href=\"https:\/\/doi.org\/10.1002\/sim.9586\">DOI:10.1002\/sim.9586<\/a><\/li>\r\n  <li>Utazi, C.E., Chan, H.M.T., Olowe, I., Wigley, A., Tejedor-Garavito, N., Cunningham, A., Bondarenko, M., Lorin, J., Boyda, D., Hogan, D. and Tatem, A.J., 2023. <i>A zero-dose vulnerability index for equity assessment and spatial prioritization in low-and middle-income countries<\/i>. Spatial Statistics, p.100772. <a target=\"_blank\"  href=\"https:\/\/doi.org\/10.1016\/j.spasta.2023.100772\">DOI:10.1016\/j.spasta.2023.100772<\/a><\/li>\r\n  <li>Utazi, C.E., Nilsen, K., Pannell, O., Dotse-Gborgbortsi, W. and Tatem, A.J., 2021. <i>District-level estimation of vaccination coverage: Discrete vs continuous spatial models<\/i>. Statistics in Medicine, 40(9), pp.2197-2211. <a target=\"_blank\"  href=\"https:\/\/doi.org\/10.1002\/sim.8897\">DOI:10.1002\/sim.8897<\/a><\/li>\r\n<\/ul>\r\n","doi":"10.5258\/SOTON\/WP00767","popyear":"2016","date":"2023-04-07","file_img":"ETH_Maternal_and_child_socioeconomic.png","continent":"Africa","country":"Ethiopia","resolution":"1000","type":"Maternal and child socioeconomic","file_html":null},{"id":"50651","desc":"Vaccination coverage for measles dose 1 (MCV1) among children aged 12 to 23 months in Ethiopia during the year 2016. The estimated vaccination coverages, representing the proportions of vaccinated children, along with their associated uncertainties measured as standard deviations (SD), are computed following the methodology detailed in Utazi et al. (2021, 2022, 2023).\r\n<p><b>References<\/b>:<\/p>\r\n\r\n<ul>\r\n  <li>Utazi, C.E., Aheto, J.M.K., Chan, H.M.T., Tatem, A.J. and Sahu, S.K., 2022. <i>Conditional probability and ratio-based approaches for mapping the coverage of multi-dose vaccines<\/i>. Statistics in Medicine, 41(29), pp.5662-5678. <a target=\"_blank\"  href=\"https:\/\/doi.org\/10.1002\/sim.9586\">DOI:10.1002\/sim.9586<\/a><\/li>\r\n  <li>Utazi, C.E., Chan, H.M.T., Olowe, I., Wigley, A., Tejedor-Garavito, N., Cunningham, A., Bondarenko, M., Lorin, J., Boyda, D., Hogan, D. and Tatem, A.J., 2023. <i>A zero-dose vulnerability index for equity assessment and spatial prioritization in low-and middle-income countries<\/i>. Spatial Statistics, p.100772. <a target=\"_blank\"  href=\"https:\/\/doi.org\/10.1016\/j.spasta.2023.100772\">DOI:10.1016\/j.spasta.2023.100772<\/a><\/li>\r\n  <li>Utazi, C.E., Nilsen, K., Pannell, O., Dotse-Gborgbortsi, W. and Tatem, A.J., 2021. <i>District-level estimation of vaccination coverage: Discrete vs continuous spatial models<\/i>. Statistics in Medicine, 40(9), pp.2197-2211. <a target=\"_blank\"  href=\"https:\/\/doi.org\/10.1002\/sim.8897\">DOI:10.1002\/sim.8897<\/a><\/li>\r\n<\/ul>\r\n","doi":"10.5258\/SOTON\/WP00767","popyear":"2016","date":"2023-04-07","file_img":"ETH_Measles_vaccination_coverage.png","continent":"Africa","country":"Ethiopia","resolution":"1000","type":"Measles vaccination coverage","file_html":null},{"id":"1261","desc":"DATASET: version 1.0 estimates of the proportion of women aged 15-49 per grid square that were classed as literate in 2008\/9; the data series is comprised of two datasets, a) predicted proportion of female literacy (KEN_literacy_F.tif), and b) related uncertainty map (KEN_literacy_F_interdecile.tif). \r\n<p>REGION: Africa \r\n<\/p><p>SPATIAL RESOLUTION: 0.00833333 decimal degrees (approx 1km at the equator) \r\n<\/p><p>PROJECTION: Geographic, WGS84 \r\n<\/p><p>UNITS: Dataset a: Proportion of women aged 15-49 classed at literate (median value); dataset b: Interdecile range - the difference between the first and the ninth deciles (uncertainty dataset). \r\n<\/p><p>MAPPING APPROACH: Bayesian geostatistical modelling methods in combination with high resolution gridded spatial covariates applied to GPS-located household survey data on literacy from the DHS program, circa 2008\/9. \r\n<\/p><p>FORMAT: Geotiff (zipped using 7-zip (open access tool): www.7-zip.org) \r\n<\/p><p>FILENAMES: Example - KEN_literacy_F.tif = Kenya (KEN) female (F) literacy values. \r\n<\/p><p>DATE OF PRODUCTION: March 2017<\/p>","doi":"10.5258\/SOTON\/WP00126","popyear":null,"date":"2017-05-05","file_img":"336.jpg","continent":"Africa","country":"Kenya","resolution":"1000","type":"Literacy","file_html":null},{"id":"1262","desc":"DATASET: Alpha version 2008 estimates of proportion of people per grid square living in poverty, as defined by the Multidimensional Poverty Index (http:\/\/www.ophi.org.uk\/policy\/multidimensional-poverty-index\/), and associated uncertainty metrics. \r\n<p>REGION: Africa \r\n<\/p><p>SPATIAL RESOLUTION: 0.00833333 decimal degrees (approx 1km at the equator) \r\n<\/p><p>PROJECTION: Geographic, WGS84 \r\n<\/p><p>UNITS: Proportion of residents living in MPI-defined poverty (poverty dataset); 95% credible interval (uncertainty dataset) \r\n<\/p><p>MAPPING APPROACH: Bayesian model-based geostatistics in combination with high resolution gridded spatial covariates applied to GPS-located household survey data on poverty from the DHS and\/or LSMS programs. \r\n<\/p><p>FORMAT: Geotiff (zipped using 7-zip (open access tool): www.7-zip.org) \r\n<\/p><p>FILENAMES: Examples - ken08povmpi.tif = Kenya (ken) MPI poverty map for 2008. ken08povmpi-uncert.tif = uncertainty dataset showing 95% credible intervals. \r\n<\/p><p>DATE OF PRODUCTION: January 2013&nbsp;<\/p>","doi":"10.5258\/SOTON\/WP00127","popyear":null,"date":"2013-05-05","file_img":"77.jpg","continent":"Africa","country":"Kenya","resolution":"1000","type":"Poverty","file_html":null},{"id":"1263","desc":"DATASET: version 1.0 estimates representing the probability of, a) receiving four or more antenatal care (ANC) visits at time of delivery, b) skilled birth attendance (SBA) during delivery, and c) postnatal care (PNC) received within 48 hours of delivery. \r\n<p>REGION: Africa \r\n<\/p><p>SPATIAL RESOLUTION: 0.0027777778 decimal degrees (approx 300 m at the equator) \r\n<\/p><p>PROJECTION: Geographic, WGS84 \r\n<\/p><p>UNITS: probabilities expressed as decimals values \r\n<\/p><p>MAPPING APPROACH: hierarchical mixed effects logistic regression methods applied to household survey data in order to derive probabilities of receiving different types of maternal newborn health services. Accessibility\/transportation surfaces were used to train the model and derive gridded estimates; see https:\/\/doi.org\/10.1371\/journal.pone.0162006 for more details. \r\n<\/p><p>FORMAT: Geotiff (zipped using 7-zip (open access tool): www.7-zip.org) \r\n<\/p><p>FILENAMES: Example - KEN_MNH_ANC.tif = Kenya (KEN) maternal and newborn health (MNH) data, estimates for antenatal care (ANC). \r\n<\/p><p>DATE OF PRODUCTION: August 2016&nbsp;<\/p>","doi":"","popyear":null,"date":"2016-05-05","file_img":"345.jpg","continent":"Africa","country":"Kenya","resolution":"300","type":"Maternal and Newborn Health","file_html":null},{"id":"17040","desc":"DATASET: Version 1.0 estimates representing the predicted prevalence of i) skilled birth attendance, ii) 4+ antenatal care visits, iii) postnatal check-up within 48 hours, and iv) absolute change over time among these indicators, using DHS data. Time points available consist , 2003\/2008\/2014<p><br><\/p><p>REGION: Africa\r\n<\/p><p>SPATIAL RESOLUTION: Administrative II\r\n<\/p><p>PROJECTION: Geographic, WGS84\r\n<\/p><p>UNITS: Prevalence expressed as decimal values\r\n<\/p><p>MAPPING APPROACH: Bayesian hierarchical mixed effects logistic regression methods applied to household survey data, in order to derive modelled prevalence of utilising maternal and newborn health services across three time points available. Absolute change over time in prevalence was measured as the difference between the first (t1) and last (t3) time points available per country. \r\n<\/p><p>FORMAT: Shapefile\r\n<\/p><p>FIELDNAMES: Example: SBA_t1 = predicted prevalence of skilled birth attendance at time point 1\r\n<\/p><p><br><\/p><p><\/p>","doi":"10.1186\/s12889-018-6241-8","popyear":null,"date":"2018-12-01","file_img":"KEN_indicators.png","continent":"Africa","country":"Kenya","resolution":null,"type":"Maternal and Newborn Health","file_html":null},{"id":"17026","desc":"Diphtheria-tetanus-pertussis (DTP) dose 1, 2 and 3 vaccination coverage among children aged under 5 years in 2009 for Madagascar.\r\n<p>Estimated vaccination coverage, i.e. proportions of children vaccinated, and associated uncertainties, measured as standard deviations, for Madagascar in 2009.\r\n<\/p><p>The methodology used for the work is described in Utazi et al. (2017) High resolution age-structure mapping of childhood vaccination coverage in low and middle income countries, Vaccine 36 (12), pp. 1583-1591 and Utazi et al (2019) Mapping vaccination coverage rates at high resolution to explore the effects of delivery mechanisms and inform vaccination strategies, Nature Communications, under review.\r\n<\/p><p><br><\/p><p>Filenames:\r\n<\/p><p>DTP vaccination estimates: \r\n<\/p><p>[COUNTRYCODE]_mean_pred_DTP1_perc.tif = Mean % estimated DTP1 vaccination coverage (0-59 months) \r\n<\/p><p>[COUNTRYCODE]_mean_pred_DTP1_perc.tif = Mean % estimated DTP2 vaccination coverage (0-59 months) \r\n<\/p><p>[COUNTRYCODE]_mean_pred_DTP1_perc.tif = Mean % estimated DTP3 vaccination coverage (0-59 months) \r\n<\/p><p><br><\/p>","doi":"","popyear":"2009","date":"2019-02-03","file_img":"MDG_mean_pred_dtp1_perc.png","continent":"Africa","country":"Madagascar","resolution":"1000","type":"DTP vaccination coverage","file_html":null},{"id":"17036","desc":"Estimated vaccination coverage, i.e. proportions of children vaccinated, and associated uncertainties, measured as standard deviations, broken by age groups (9-11 months, 12-23 months, 24-59 months, 0-59 months and 9-59 months) for Madagascar in 2009.\r\n<p>The methodology used for the work is described in Utazi et al. (2017) High resolution age-structure mapping of childhood vaccination coverage in low and middle income countries, Vaccine 36 (12), pp. 1583-1591 and Utazi et al (2019) Mapping vaccination coverage rates at high resolution to explore the effects of delivery mechanisms and inform vaccination strategies, Nature Communications, under review.\r\n<\/p><p><br><\/p><p>Filenames:\r\n<\/p><p>DTP vaccination estimates: \r\n<\/p><p>[COUNTRYCODE]_mean_pred_DTP1_perc.tif = Mean % estimated DTP1 vaccination coverage (0-59 months) \r\n<\/p><p>[COUNTRYCODE]_mean_pred_DTP1_perc.tif = Mean % estimated DTP2 vaccination coverage (0-59 months) \r\n<\/p><p>[COUNTRYCODE]_mean_pred_DTP1_perc.tif = Mean % estimated DTP3 vaccination coverage (0-59 months) \r\n<\/p><p><br><\/p>","doi":"","popyear":"2009","date":"2019-02-03","file_img":"MDG_mean_pred_total_perc.png","continent":"Africa","country":"Madagascar","resolution":"1000","type":"Measles vaccination coverage","file_html":null},{"id":"1264","desc":"DATASET: Alpha version 2010-11 estimates of proportion of people per grid square living in poverty, as defined by $1.25 a day and $2 a day thresholds, and associated uncertainty metrics. \r\n<p>REGION: Africa \r\n<\/p><p>SPATIAL RESOLUTION: 0.00833333 decimal degrees (approx 1km at the equator) \r\n<\/p><p>PROJECTION: Geographic, WGS84 \r\n<\/p><p>UNITS: Proportion of residents living on $1.25 and $2 a day (poverty dataset); 95% credible interval (uncertainty dataset) \r\n<\/p><p>MAPPING APPROACH: Bayesian model-based geostatistics in combination with high resolution gridded spatial covariates applied to GPS-located household survey data on poverty from the LSMS program. \r\n<\/p><p>FORMAT: Geotiff (zipped using 7-zip (open access tool): www.7-zip.org) \r\n<\/p><p>FILENAMES: Examples - nga10povcons125.tif = Nigeria (nga) consumption-based poverty map for 2010 showing proportion of residents living on less than $1.25 a day. nga10povcons125-uncert.tif = uncertainty dataset showing 95% credible intervals. \r\n<\/p><p>DATE OF PRODUCTION: Nov 2013&nbsp;<\/p>","doi":"10.5258\/SOTON\/WP00157","popyear":null,"date":"2013-11-01","file_img":"171.jpg","continent":"Africa","country":"Malawi","resolution":"1000","type":"Poverty","file_html":null},{"id":"17022","desc":"Diphtheria-tetanus-pertussis (DTP) dose 1, 2 and 3 vaccination coverage among children aged under 5 years in 2011 for Mozambique.\r\n<p>Estimated vaccination coverage, i.e. proportions of children vaccinated, and associated uncertainties, measured as standard deviations, for Mozambique in 2011.\r\n<\/p><p>The methodology used for the work is described in Utazi et al. (2017) High resolution age-structure mapping of childhood vaccination coverage in low and middle income countries, Vaccine 36 (12), pp. 1583-1591 and Utazi et al (2019) Mapping vaccination coverage rates at high resolution to explore the effects of delivery mechanisms and inform vaccination strategies, Nature Communications, under review.\r\n<\/p><p><br><\/p><p>Filenames:&nbsp;<br><\/p><p>DTP vaccination estimates: \r\n<\/p><p>[COUNTRYCODE]_mean_pred_DTP1_perc.tif = Mean % estimated DTP1 vaccination coverage (0-59 months) \r\n<\/p><p>[COUNTRYCODE]_mean_pred_DTP1_perc.tif = Mean % estimated DTP2 vaccination coverage (0-59 months) \r\n<\/p><p>[COUNTRYCODE]_mean_pred_DTP1_perc.tif = Mean % estimated DTP3 vaccination coverage (0-59 months) \r\n<\/p><p><br><\/p>","doi":"","popyear":"2011","date":"2019-02-03","file_img":"MOZ_mean_pred_dtp1_perc.png","continent":"Africa","country":"Mozambique","resolution":"1000","type":"DTP vaccination coverage","file_html":null},{"id":"17032","desc":"Estimated vaccination coverage, i.e. proportions of children vaccinated, and associated uncertainties, measured as standard deviations, broken by age groups (9-11 months, 12-23 months, 24-59 months, 0-59 months and 9-59 months) for Mozambique in 2011.\r\n<p>The methodology used for the work is described in Utazi et al. (2017) High resolution age-structure mapping of childhood vaccination coverage in low and middle income countries, Vaccine 36 (12), pp. 1583-1591 and Utazi et al (2019) Mapping vaccination coverage rates at high resolution to explore the effects of delivery mechanisms and inform vaccination strategies, Nature Communications, under review.\r\n<\/p><p><br><\/p><p>Filenames:\r\n<\/p><p>DTP vaccination estimates: \r\n<\/p><p>[COUNTRYCODE]_mean_pred_DTP1_perc.tif = Mean % estimated DTP1 vaccination coverage (0-59 months) \r\n<\/p><p>[COUNTRYCODE]_mean_pred_DTP1_perc.tif = Mean % estimated DTP2 vaccination coverage (0-59 months) \r\n<\/p><p>[COUNTRYCODE]_mean_pred_DTP1_perc.tif = Mean % estimated DTP3 vaccination coverage (0-59 months) \r\n<\/p><p><br><\/p>","doi":"","popyear":"2011","date":"2019-02-03","file_img":"MOZ_mean_pred_total_perc.png","continent":"Africa","country":"Mozambique","resolution":"1000","type":"Measles vaccination coverage","file_html":null},{"id":"1265","desc":"DATASET: version 1.0 estimates of the proportion of women aged 15-49 per grid square that were using modern contraceptive methods in 2013; the data series is comprised of two datasets, a) predicted proportion of female contraceptive use (NGA_contraception_F.tif), and b) related uncertainty map (NGA_contraception_interdecile.tif). \r\n<p>REGION: Africa \r\n<\/p><p>SPATIAL RESOLUTION: 0.00833333 decimal degrees (approx 1km at the equator) \r\n<\/p><p>PROJECTION: Geographic, WGS84 \r\n<\/p><p>UNITS: Dataset a: Proportion of women aged 15-49 who were using modern contraceptive methods (median value); dataset b: Interdecile range - the difference between the first and the ninth deciles (uncertainty dataset). \r\n<\/p><p>MAPPING APPROACH: Bayesian geostatistical modelling methods in combination with high resolution gridded spatial covariates applied to GPS-located household survey data on contraceptive use, from the DHS program, circa 2013. \r\n<\/p><p>FORMAT: Geotiff (zipped using 7-zip (open access tool): www.7-zip.org) \r\n<\/p><p>FILENAMES: Example - NGA_contraception_F.tif = Nigeria (NGA) female (F) contraception estimates. \r\n<\/p><p>DATE OF PRODUCTION: March 2017&nbsp;<\/p>","doi":"10.5258\/SOTON\/WP00198","popyear":null,"date":"2017-04-01","file_img":"333.jpg","continent":"Africa","country":"Nigeria","resolution":"1000","type":"Contraceptive Use","file_html":null},{"id":"1266","desc":"DATASET: version 1.0 estimates of the proportion of men and women aged 15-49 per grid square that were classed as literate in 2013; the data series is comprised of four datasets, a) predicted proportion of (male\/female) literacy (NGA_literacy_*.tif), and b) related uncertainty maps (NGA_literacy_*_interdecile.tif); note * = M or F (male\/female) \r\n<p>REGION: Africa \r\n<\/p><p>SPATIAL RESOLUTION: 0.00833333 decimal degrees (approx 1km at the equator) \r\n<\/p><p>PROJECTION: Geographic, WGS84 \r\n<\/p><p>UNITS: Proportionate datasets: Proportion of men\/women aged 15-49 classed at literate (median value); uncertainty datasets: Interdecile range - the difference between the first and the ninth deciles. \r\n<\/p><p>MAPPING APPROACH: Bayesian geostatistical modelling methods in combination with high resolution gridded spatial covariates applied to GPS-located household survey data on literacy, from the DHS program, circa 2013. \r\n<\/p><p>FORMAT: Geotiff (zipped using 7-zip (open access tool): www.7-zip.org) \r\n<\/p><p>FILENAMES: Example - NGA_literacy_F.tif = Nigeria (NGA) female (F) literacy values. \r\n<\/p><p>DATE OF PRODUCTION: March 2017&nbsp;<\/p>","doi":"10.5258\/SOTON\/WP00199","popyear":null,"date":"2017-03-01","file_img":"344.jpg","continent":"Africa","country":"Nigeria","resolution":"1000","type":"Literacy","file_html":null},{"id":"1267","desc":"DATASET: Alpha version 2010 estimates of proportion of people per grid square living in poverty, as defined by $1.25 a day and $2 a day thresholds, and associated uncertainty metrics. \r\n<p>REGION: Africa \r\n<\/p><p>SPATIAL RESOLUTION: 0.00833333 decimal degrees (approx 1km at the equator) \r\n<\/p><p>PROJECTION: Geographic, WGS84 \r\n<\/p><p>UNITS: Proportion of residents living on $1.25 and $2 a day (poverty dataset); 95% credible interval (uncertainty dataset) \r\n<\/p><p>MAPPING APPROACH: Bayesian model-based geostatistics in combination with high resolution gridded spatial covariates applied to GPS-located household survey data on poverty from the DHS and\/or LSMS programs. \r\n<\/p><p>FORMAT: Geotiff (zipped using 7-zip (open access tool): www.7-zip.org) \r\n<\/p><p>FILENAMES: Examples - nga10povcons125.tif = Nigeria (nga) consumption-based poverty map for 2010 showing proportion of residents living on less than $1.25 a day. nga10povcons125-uncert.tif = uncertainty dataset showing 95% credible intervals. \r\n<\/p><p>DATE OF PRODUCTION: January 2013&nbsp;<\/p>","doi":"10.5258\/SOTON\/WP00200","popyear":null,"date":"2013-01-01","file_img":"78.jpg","continent":"Africa","country":"Nigeria","resolution":"1000","type":"Poverty","file_html":null},{"id":"1268","desc":"DATASET: version 1.0 estimates of the proportion of male and female children aged under 5, per grid square that were classified as stunted in 2013; the data series is comprised of four datasets, a) predicted proportion of (male\/female) children classified as stunted (NGA_stunting_*.tif), and b) related uncertainty maps (NGA_stunting_*_interdecile.tif); note * = M or F (male\/female) \r\n<p>REGION: Africa \r\n<\/p><p>SPATIAL RESOLUTION: 0.00833333 decimal degrees (approx 1km at the equator) \r\n<\/p><p>PROJECTION: Geographic, WGS84 \r\n<\/p><p>UNITS: Proportionate datasets: Proportion of male\/female children aged under 5 classified as stunted (median value); uncertainty datasets: Interdecile range - the difference between the first and the ninth deciles. \r\n<\/p><p>MAPPING APPROACH: Bayesian geostatistical modelling methods in combination with high resolution gridded spatial covariates applied to GPS-located household survey data on stunting, from the DHS program, circa 2013. \r\n<\/p><p>FORMAT: Geotiff (zipped using 7-zip (open access tool): www.7-zip.org) \r\n<\/p><p>FILENAMES: Example - NGA_stunting_F.tif = Nigeria (NGA) female (F) stunting estimates. \r\n<\/p><p>DATE OF PRODUCTION: March 2017&nbsp;<\/p>","doi":"10.5258\/SOTON\/WP00201","popyear":null,"date":"2017-03-01","file_img":"335.jpg","continent":"Africa","country":"Nigeria","resolution":"1000","type":"Stunting","file_html":null},{"id":"17020","desc":"Diphtheria-tetanus-pertussis (DTP) dose 1, 2 and 3 vaccination coverage among children aged under 5 years in 2013 for Nigeria.\r\n<p>Estimated vaccination coverage, i.e. proportions of children vaccinated, and associated uncertainties, measured as standard deviations, for Nigeria in 2013.\r\n<\/p><p>The methodology used for the work is described in Utazi et al. (2017) High resolution age-structure mapping of childhood vaccination coverage in low and middle income countries, Vaccine 36 (12), pp. 1583-1591 and Utazi et al (2019) Mapping vaccination coverage rates at high resolution to explore the effects of delivery mechanisms and inform vaccination strategies, Nature Communications, under review.\r\n<\/p><p><br><\/p><p>Filenames:\r\n<\/p><p>DTP vaccination estimates: \r\n<\/p><p>[COUNTRYCODE]_mean_pred_DTP1_perc.tif = Mean % estimated DTP1 vaccination coverage (0-59 months) \r\n<\/p><p>[COUNTRYCODE]_mean_pred_DTP1_perc.tif = Mean % estimated DTP2 vaccination coverage (0-59 months) \r\n<\/p><p>[COUNTRYCODE]_mean_pred_DTP1_perc.tif = Mean % estimated DTP3 vaccination coverage (0-59 months) \r\n<\/p><p><br><\/p><p><br><\/p>","doi":"","popyear":"2013","date":"2019-02-03","file_img":"NGA_mean_pred_dtp1_perc.png","continent":"Africa","country":"Nigeria","resolution":"1000","type":"DTP vaccination coverage","file_html":null},{"id":"17031","desc":"Estimated vaccination coverage, i.e. proportions of children vaccinated, and associated uncertainties, measured as standard deviations, broken by age groups (9-11 months, 12-23 months, 24-59 months, 0-59 months and 9-59 months) for Nigeria in 2013.\r\n<p>The methodology used for the work is described in Utazi et al. (2017) High resolution age-structure mapping of childhood vaccination coverage in low and middle income countries, Vaccine 36 (12), pp. 1583-1591 and Utazi et al (2019) Mapping vaccination coverage rates at high resolution to explore the effects of delivery mechanisms and inform vaccination strategies, Nature Communications, under review.\r\n<\/p><p>Filenames:\r\n<\/p><p>DTP vaccination estimates: \r\n<\/p><p>[COUNTRYCODE]_mean_pred_DTP1_perc.tif = Mean % estimated DTP1 vaccination coverage (0-59 months) \r\n<\/p><p>[COUNTRYCODE]_mean_pred_DTP1_perc.tif = Mean % estimated DTP2 vaccination coverage (0-59 months) \r\n<\/p><p>[COUNTRYCODE]_mean_pred_DTP1_perc.tif = Mean % estimated DTP3 vaccination coverage (0-59 months) \r\n<\/p><p><br><\/p>","doi":"","popyear":"2013","date":"2019-02-03","file_img":"NGA_mean_pred_total_perc.png","continent":"Africa","country":"Nigeria","resolution":"1000","type":"Measles vaccination coverage","file_html":null},{"id":"50658","desc":"Proportion of women aged 15 to 49 years in Nigeria who have never used modern contraception during the year 2021. The estimations along with their associated uncertainties, measured as standard deviations (SD), are calculated following the methodology detailed in Utazi et al. (2021, 2022, 2023).\r\n<p><b>References<\/b>:<\/p>\r\n\r\n<ul>\r\n  <li>Utazi, C.E., Aheto, J.M.K., Chan, H.M.T., Tatem, A.J. and Sahu, S.K., 2022. <i>Conditional probability and ratio-based approaches for mapping the coverage of multi-dose vaccines<\/i>. Statistics in Medicine, 41(29), pp.5662-5678. <a target=\"_blank\"  href=\"https:\/\/doi.org\/10.1002\/sim.9586\">DOI:10.1002\/sim.9586<\/a><\/li>\r\n  <li>Utazi, C.E., Chan, H.M.T., Olowe, I., Wigley, A., Tejedor-Garavito, N., Cunningham, A., Bondarenko, M., Lorin, J., Boyda, D., Hogan, D. and Tatem, A.J., 2023. <i>A zero-dose vulnerability index for equity assessment and spatial prioritization in low-and middle-income countries<\/i>. Spatial Statistics, p.100772. <a target=\"_blank\"  href=\"https:\/\/doi.org\/10.1016\/j.spasta.2023.100772\">DOI:10.1016\/j.spasta.2023.100772<\/a><\/li>\r\n  <li>Utazi, C.E., Nilsen, K., Pannell, O., Dotse-Gborgbortsi, W. and Tatem, A.J., 2021. <i>District-level estimation of vaccination coverage: Discrete vs continuous spatial models<\/i>. Statistics in Medicine, 40(9), pp.2197-2211. <a target=\"_blank\"  href=\"https:\/\/doi.org\/10.1002\/sim.8897\">DOI:10.1002\/sim.8897<\/a><\/li>\r\n<\/ul>\r\n","doi":"10.5258\/SOTON\/WP00767","popyear":"2021","date":"2023-04-07","file_img":"NGA_Contraceptive_use.png","continent":"Africa","country":"Nigeria","resolution":"1000","type":"Contraceptive Use","file_html":null},{"id":"50659","desc":"Vaccination coverage for diphtheria-tetanus-pertussis dose 1 (DTP1) and dose 3 (DTP3) among children aged 12 to 23 months in India during the year 2021. The estimated vaccination coverages, representing the proportions of vaccinated children, along with their associated uncertainties measured as standard deviations (SD), are computed following the methodology detailed in Utazi et al. (2021, 2022, 2023).\r\n<p><b>References<\/b>:<\/p>\r\n\r\n<ul>\r\n  <li>Utazi, C.E., Aheto, J.M.K., Chan, H.M.T., Tatem, A.J. and Sahu, S.K., 2022. <i>Conditional probability and ratio-based approaches for mapping the coverage of multi-dose vaccines<\/i>. Statistics in Medicine, 41(29), pp.5662-5678. <a target=\"_blank\"  href=\"https:\/\/doi.org\/10.1002\/sim.9586\">DOI:10.1002\/sim.9586<\/a><\/li>\r\n  <li>Utazi, C.E., Chan, H.M.T., Olowe, I., Wigley, A., Tejedor-Garavito, N., Cunningham, A., Bondarenko, M., Lorin, J., Boyda, D., Hogan, D. and Tatem, A.J., 2023. <i>A zero-dose vulnerability index for equity assessment and spatial prioritization in low-and middle-income countries<\/i>. Spatial Statistics, p.100772. <a target=\"_blank\"  href=\"https:\/\/doi.org\/10.1016\/j.spasta.2023.100772\">DOI:10.1016\/j.spasta.2023.100772<\/a><\/li>\r\n  <li>Utazi, C.E., Nilsen, K., Pannell, O., Dotse-Gborgbortsi, W. and Tatem, A.J., 2021. <i>District-level estimation of vaccination coverage: Discrete vs continuous spatial models<\/i>. Statistics in Medicine, 40(9), pp.2197-2211. <a target=\"_blank\"  href=\"https:\/\/doi.org\/10.1002\/sim.8897\">DOI:10.1002\/sim.8897<\/a><\/li>\r\n<\/ul>\r\n","doi":"10.5258\/SOTON\/WP00767","popyear":"2021","date":"2023-04-07","file_img":"NGA_DTP_vaccination_coverage.png","continent":"Africa","country":"Nigeria","resolution":"1000","type":"DTP vaccination coverage","file_html":null},{"id":"50660","desc":"Proportion of children aged 12 to 23 months born to large (&gt;= 9 members) households in Nigeria during the year 2021. The estimations along with their associated uncertainties, measured as standard deviations (SD), are calculated following the methodology detailed in Utazi et al. (2021, 2022, 2023).\r\n<p><b>References<\/b>:<\/p>\r\n\r\n<ul>\r\n  <li>Utazi, C.E., Aheto, J.M.K., Chan, H.M.T., Tatem, A.J. and Sahu, S.K., 2022. <i>Conditional probability and ratio-based approaches for mapping the coverage of multi-dose vaccines<\/i>. Statistics in Medicine, 41(29), pp.5662-5678. <a target=\"_blank\"  href=\"https:\/\/doi.org\/10.1002\/sim.9586\">DOI:10.1002\/sim.9586<\/a><\/li>\r\n  <li>Utazi, C.E., Chan, H.M.T., Olowe, I., Wigley, A., Tejedor-Garavito, N., Cunningham, A., Bondarenko, M., Lorin, J., Boyda, D., Hogan, D. and Tatem, A.J., 2023. <i>A zero-dose vulnerability index for equity assessment and spatial prioritization in low-and middle-income countries<\/i>. Spatial Statistics, p.100772. <a target=\"_blank\"  href=\"https:\/\/doi.org\/10.1016\/j.spasta.2023.100772\">DOI:10.1016\/j.spasta.2023.100772<\/a><\/li>\r\n  <li>Utazi, C.E., Nilsen, K., Pannell, O., Dotse-Gborgbortsi, W. and Tatem, A.J., 2021. <i>District-level estimation of vaccination coverage: Discrete vs continuous spatial models<\/i>. Statistics in Medicine, 40(9), pp.2197-2211. <a target=\"_blank\"  href=\"https:\/\/doi.org\/10.1002\/sim.8897\">DOI:10.1002\/sim.8897<\/a><\/li>\r\n<\/ul>\r\n","doi":"10.5258\/SOTON\/WP00767","popyear":"2021","date":"2023-04-07","file_img":"NGA_Maternal_and_child_demographic.png","continent":"Africa","country":"Nigeria","resolution":"1000","type":"Maternal and child demographic","file_html":null},{"id":"50661","desc":"Maternal and child health in Nigeria during the year 2021. Proportion of children aged 12 to 23 months born to mothers who did not attend antenatal care (ANC), who did not receive (or recall) post-natal care within 2 months after birth (PNC), who had no health insurance (INSURANCE), and who had no tetanus toxoid injections before birth (TT). Proportion of children aged 12 to 23 months who were not delivered by a skilled birth attendant (SBA). The estimations along with their associated uncertainties, measured as standard deviations (SD), are calculated following the methodology detailed in Utazi et al. (2021, 2022, 2023).\r\n<p><b>References<\/b>:<\/p>\r\n\r\n<ul>\r\n  <li>Utazi, C.E., Aheto, J.M.K., Chan, H.M.T., Tatem, A.J. and Sahu, S.K., 2022. <i>Conditional probability and ratio-based approaches for mapping the coverage of multi-dose vaccines<\/i>. Statistics in Medicine, 41(29), pp.5662-5678. <a target=\"_blank\"  href=\"https:\/\/doi.org\/10.1002\/sim.9586\">DOI:10.1002\/sim.9586<\/a><\/li>\r\n  <li>Utazi, C.E., Chan, H.M.T., Olowe, I., Wigley, A., Tejedor-Garavito, N., Cunningham, A., Bondarenko, M., Lorin, J., Boyda, D., Hogan, D. and Tatem, A.J., 2023. <i>A zero-dose vulnerability index for equity assessment and spatial prioritization in low-and middle-income countries<\/i>. Spatial Statistics, p.100772. <a target=\"_blank\"  href=\"https:\/\/doi.org\/10.1016\/j.spasta.2023.100772\">DOI:10.1016\/j.spasta.2023.100772<\/a><\/li>\r\n  <li>Utazi, C.E., Nilsen, K., Pannell, O., Dotse-Gborgbortsi, W. and Tatem, A.J., 2021. <i>District-level estimation of vaccination coverage: Discrete vs continuous spatial models<\/i>. Statistics in Medicine, 40(9), pp.2197-2211. <a target=\"_blank\"  href=\"https:\/\/doi.org\/10.1002\/sim.8897\">DOI:10.1002\/sim.8897<\/a><\/li>\r\n<\/ul>\r\n","doi":"10.5258\/SOTON\/WP00767","popyear":"2021","date":"2023-04-07","file_img":"NGA_Maternal_and_child_health.png","continent":"Africa","country":"Nigeria","resolution":"1000","type":"Maternal and child health","file_html":null},{"id":"50662","desc":"Maternal and child socioeconomic in Nigeria during the year 2021. Proportion of children aged 12 to 23 months born to the poorest \/ poorer households according to DHS \/ MICS-NICS classifications (HWEALTH) and born to mothers who had no formal education (MEDUCATION). The estimations along with their associated uncertainties, measured as standard deviations (SD), are calculated following the methodology detailed in Utazi et al. (2021, 2022, 2023).\r\n<p><b>References<\/b>:<\/p>\r\n\r\n<ul>\r\n  <li>Utazi, C.E., Aheto, J.M.K., Chan, H.M.T., Tatem, A.J. and Sahu, S.K., 2022. <i>Conditional probability and ratio-based approaches for mapping the coverage of multi-dose vaccines<\/i>. Statistics in Medicine, 41(29), pp.5662-5678. <a target=\"_blank\"  href=\"https:\/\/doi.org\/10.1002\/sim.9586\">DOI:10.1002\/sim.9586<\/a><\/li>\r\n  <li>Utazi, C.E., Chan, H.M.T., Olowe, I., Wigley, A., Tejedor-Garavito, N., Cunningham, A., Bondarenko, M., Lorin, J., Boyda, D., Hogan, D. and Tatem, A.J., 2023. <i>A zero-dose vulnerability index for equity assessment and spatial prioritization in low-and middle-income countries<\/i>. Spatial Statistics, p.100772. <a target=\"_blank\"  href=\"https:\/\/doi.org\/10.1016\/j.spasta.2023.100772\">DOI:10.1016\/j.spasta.2023.100772<\/a><\/li>\r\n  <li>Utazi, C.E., Nilsen, K., Pannell, O., Dotse-Gborgbortsi, W. and Tatem, A.J., 2021. <i>District-level estimation of vaccination coverage: Discrete vs continuous spatial models<\/i>. Statistics in Medicine, 40(9), pp.2197-2211. <a target=\"_blank\"  href=\"https:\/\/doi.org\/10.1002\/sim.8897\">DOI:10.1002\/sim.8897<\/a><\/li>\r\n<\/ul>\r\n","doi":"10.5258\/SOTON\/WP00767","popyear":"2021","date":"2023-04-07","file_img":"NGA_Maternal_and_child_socioeconomic.png","continent":"Africa","country":"Nigeria","resolution":"1000","type":"Maternal and child socioeconomic","file_html":null},{"id":"50663","desc":"Vaccination coverage for measles dose 1 (MCV1) among children aged 12 to 23 months in Nigeria during the year 2021. The estimated vaccination coverages, representing the proportions of vaccinated children, along with their associated uncertainties measured as standard deviations (SD), are computed following the methodology detailed in Utazi et al. (2021, 2022, 2023).\r\n<p><b>References<\/b>:<\/p>\r\n\r\n<ul>\r\n  <li>Utazi, C.E., Aheto, J.M.K., Chan, H.M.T., Tatem, A.J. and Sahu, S.K., 2022. <i>Conditional probability and ratio-based approaches for mapping the coverage of multi-dose vaccines<\/i>. Statistics in Medicine, 41(29), pp.5662-5678. <a target=\"_blank\"  href=\"https:\/\/doi.org\/10.1002\/sim.9586\">DOI:10.1002\/sim.9586<\/a><\/li>\r\n  <li>Utazi, C.E., Chan, H.M.T., Olowe, I., Wigley, A., Tejedor-Garavito, N., Cunningham, A., Bondarenko, M., Lorin, J., Boyda, D., Hogan, D. and Tatem, A.J., 2023. <i>A zero-dose vulnerability index for equity assessment and spatial prioritization in low-and middle-income countries<\/i>. Spatial Statistics, p.100772. <a target=\"_blank\"  href=\"https:\/\/doi.org\/10.1016\/j.spasta.2023.100772\">DOI:10.1016\/j.spasta.2023.100772<\/a><\/li>\r\n  <li>Utazi, C.E., Nilsen, K., Pannell, O., Dotse-Gborgbortsi, W. and Tatem, A.J., 2021. <i>District-level estimation of vaccination coverage: Discrete vs continuous spatial models<\/i>. Statistics in Medicine, 40(9), pp.2197-2211. <a target=\"_blank\"  href=\"https:\/\/doi.org\/10.1002\/sim.8897\">DOI:10.1002\/sim.8897<\/a><\/li>\r\n<\/ul>\r\n","doi":"10.5258\/SOTON\/WP00767","popyear":"2021","date":"2023-04-07","file_img":"NGA_Measles_vaccination_coverage.png","continent":"Africa","country":"Nigeria","resolution":"1000","type":"Measles vaccination coverage","file_html":null},{"id":"1269","desc":"DATASET: version 1.0 estimates representing the probability of, a) receiving four or more antenatal care (ANC) visits at time of delivery, b) skilled birth attendance (SBA) during delivery, and c) postnatal care (PNC) received within 48 hours of delivery. \r\n<p>REGION: Africa \r\n<\/p><p>SPATIAL RESOLUTION: 0.0027777778 decimal degrees (approx 300 m at the equator) \r\n<\/p><p>PROJECTION: Geographic, WGS84 \r\n<\/p><p>UNITS: probabilities expressed as decimals values \r\n<\/p><p>MAPPING APPROACH: hierarchical mixed effects logistic regression methods applied to household survey data in order to derive probabilities of receiving different types of maternal newborn health services. Accessibility\/transportation surfaces were used to train the model and derive gridded estimates; see https:\/\/doi.org\/10.1371\/journal.pone.0162006 for more details. \r\n<\/p><p>FORMAT: Geotiff (zipped using 7-zip (open access tool): www.7-zip.org) \r\n<\/p><p>FILENAMES: Example - RWA_MNH_ANC.tif = Rwanda (RWA) maternal and newborn health (MNH) data, estimates for antenatal care (ANC). \r\n<\/p><p>DATE OF PRODUCTION: August 2016&nbsp;<\/p>","doi":"","popyear":null,"date":"2016-08-01","file_img":"346.jpg","continent":"Africa","country":"Rwanda","resolution":"300","type":"Maternal and Newborn Health","file_html":null},{"id":"17041","desc":"DATASET: Version 1.0 estimates representing the predicted prevalence of i) skilled birth attendance, ii) 4+ antenatal care visits, iii) postnatal check-up within 48 hours, and iv) absolute change over time among these indicators, using DHS data. Time points available consist of&nbsp; Rwanda, 2005\/2010 and 2014&nbsp;<p>REGION: Africa\r\n<\/p><p>SPATIAL RESOLUTION: Administrative II\r\n<\/p><p>PROJECTION: Geographic, WGS84\r\n<\/p><p>UNITS: Prevalence expressed as decimal values\r\n<\/p><p>MAPPING APPROACH: Bayesian hierarchical mixed effects logistic regression methods applied to household survey data, in order to derive modelled prevalence of utilising maternal and newborn health services across three time points available. Absolute change over time in prevalence was measured as the difference between the first (t1) and last (t3) time points available per country. \r\n<\/p><p>FORMAT: Shapefile\r\n<\/p><p>FIELDNAMES: Example: SBA_t1 = predicted prevalence of skilled birth attendance at time point 1\r\n<\/p><p><br><\/p>","doi":"10.1186\/s12889-018-6241-8","popyear":null,"date":"2018-12-01","file_img":"RWA_indicators.png","continent":"Africa","country":"Rwanda","resolution":null,"type":"Maternal and Newborn Health","file_html":null},{"id":"1270","desc":"DATASET: version 1.0 estimates representing the probability of, a) receiving four or more antenatal care (ANC) visits at time of delivery, b) skilled birth attendance (SBA) during delivery, and c) postnatal care (PNC) received within 48 hours of delivery. \r\n<p>REGION: Africa \r\n<\/p><p>SPATIAL RESOLUTION: 0.0027777778 decimal degrees (approx 300 m at the equator) \r\n<\/p><p>PROJECTION: Geographic, WGS84 \r\n<\/p><p>UNITS: probabilities expressed as decimals values \r\n<\/p><p>MAPPING APPROACH: hierarchical mixed effects logistic regression methods applied to household survey data in order to derive probabilities of receiving different types of maternal newborn health services. Accessibility\/transportation surfaces were used to train the model and derive gridded estimates; see https:\/\/doi.org\/10.1371\/journal.pone.0162006 for more details. \r\n<\/p><p>FORMAT: Geotiff (zipped using 7-zip (open access tool): www.7-zip.org) \r\n<\/p><p>FILENAMES: Example - TZA_MNH_ANC.tif = Tanzania (TZA) maternal and newborn health (MNH) data, estimates for antenatal care (ANC). \r\n<\/p><p>DATE OF PRODUCTION: August 2016&nbsp;<\/p>","doi":"","popyear":null,"date":"2016-08-01","file_img":"347.jpg","continent":"Africa","country":"Tanzania","resolution":"300","type":"Maternal and Newborn Health","file_html":null},{"id":"1273","desc":"DATASET: Alpha version 2010 estimates of proportion of people per grid square living in poverty, as defined by the Multidimensional Poverty Index (http:\/\/www.ophi.org.uk\/policy\/multidimensional-poverty-index\/), and $1.25 a day and $2 a day thresholds, and associated uncertainty metrics. \r\n<p>REGION: Africa \r\n<\/p><p>SPATIAL RESOLUTION: 0.00833333 decimal degrees (approx 1km at the equator) \r\n<\/p><p>PROJECTION: Geographic, WGS84 \r\n<\/p><p>UNITS: Proportion of residents living in MPI-defined poverty (poverty dataset), and on $1.25 and $2 a day (poverty dataset); 95% credible interval (uncertainty dataset) \r\n<\/p><p>MAPPING APPROACH: Bayesian model-based geostatistics in combination with high resolution gridded spatial covariates applied to GPS-located household survey data on poverty from the DHS and\/or LSMS programs. \r\n<\/p><p>FORMAT: Geotiff (zipped using 7-zip (open access tool): www.7-zip.org) \r\n<\/p><p>FILENAMES: Examples - nga10povcons125.tif = Nigeria (nga) consumption-based poverty map for 2010 showing proportion of residents living on less than $1.25 a day. nga10povcons125-uncert.tif = uncertainty dataset showing 95% credible intervals. \r\n<\/p><p>DATE OF PRODUCTION: January 2013<\/p>","doi":"10.5258\/SOTON\/WP00290","popyear":null,"date":"2013-01-01","file_img":"80.jpg","continent":"Africa","country":"Tanzania","resolution":"1000","type":"Poverty","file_html":null},{"id":"17042","desc":"Version 1.0 estimates representing the predicted prevalence of i) skilled birth attendance, ii) 4+ antenatal care visits, iii) postnatal check-up within 48 hours, and iv) absolute change over time among these indicators, using DHS data. Time points available consist of Tanzania, 1999\/2010 and 2015.<p>REGION: Africa\r\n<\/p><p>SPATIAL RESOLUTION: Administrative II\r\n<\/p><p>PROJECTION: Geographic, WGS84\r\n<\/p><p>UNITS: Prevalence expressed as decimal values\r\n<\/p><p>MAPPING APPROACH: Bayesian hierarchical mixed effects logistic regression methods applied to household survey data, in order to derive modelled prevalence of utilising maternal and newborn health services across three time points available. Absolute change over time in prevalence was measured as the difference between the first (t1) and last (t3) time points available per country. \r\n<\/p><p>FORMAT: Shapefile\r\n<\/p><p>FIELDNAMES: Example: SBA_t1 = predicted prevalence of skilled birth attendance at time point 1\r\n<\/p><p><br><\/p>","doi":"10.1186\/s12889-018-6241-8","popyear":null,"date":"2018-12-01","file_img":"TZA_indicators.png","continent":"Africa","country":"Tanzania","resolution":null,"type":"Maternal and Newborn Health","file_html":null},{"id":"1271","desc":"DATASET: Alpha version 2011 estimates of proportion of people per grid square living in poverty, as defined by the Multidimensional Poverty Index (http:\/\/www.ophi.org.uk\/policy\/multidimensional-poverty-index\/), and $1.25 a day and $2 a day thresholds, and associated uncertainty metrics. \r\n<p>REGION: Africa \r\n<\/p><p>SPATIAL RESOLUTION: 0.00833333 decimal degrees (approx 1km at the equator) \r\n<\/p><p>PROJECTION: Geographic, WGS84 \r\n<\/p><p>UNITS: Proportion of residents living in MPI-defined poverty (poverty dataset), and on $1.25 and $2 a day (poverty dataset); 95% credible interval (uncertainty dataset) \r\n<\/p><p>MAPPING APPROACH: Bayesian model-based geostatistics in combination with high resolution gridded spatial covariates applied to GPS-located household survey data on poverty from the DHS and\/or LSMS programs. \r\n<\/p><p>FORMAT: Geotiff (zipped using 7-zip (open access tool): www.7-zip.org) \r\n<\/p><p>FILENAMES: Examples - nga10povcons125.tif = Nigeria (nga) consumption-based poverty map for 2010 showing proportion of residents living on less than $1.25 a day. nga10povcons125-uncert.tif = uncertainty dataset showing 95% credible intervals.&nbsp;<\/p>","doi":"10.5258\/SOTON\/WP00285","popyear":null,"date":"2013-01-01","file_img":"79.jpg","continent":"Africa","country":"Uganda","resolution":"1000","type":"Poverty","file_html":null},{"id":"1272","desc":"DATASET: version 1.0 estimates representing the probability of, a) receiving four or more antenatal care (ANC) visits at time of delivery, b) skilled birth attendance (SBA) during delivery, and c) postnatal care (PNC) received within 48 hours of delivery. \r\n<p>REGION: Africa \r\n<\/p><p>SPATIAL RESOLUTION: 0.0027777778 decimal degrees (approx 300 m at the equator) \r\n<\/p><p>PROJECTION: Geographic, WGS84 \r\n<\/p><p>UNITS: probabilities expressed as decimals values \r\n<\/p><p>MAPPING APPROACH: hierarchical mixed effects logistic regression methods applied to household survey data in order to derive probabilities of receiving different types of maternal newborn health services. Accessibility\/transportation surfaces were used to train the model and derive gridded estimates; see https:\/\/doi.org\/10.1371\/journal.pone.0162006 for more details. \r\n<\/p><p>FORMAT: Geotiff (zipped using 7-zip (open access tool): www.7-zip.org) \r\n<\/p><p>FILENAMES: Example - UGA_MNH_ANC.tif = Uganda (UGA) maternal and newborn health (MNH) data, estimates for antenatal care (ANC). \r\n<\/p><p>DATE OF PRODUCTION: August 2016&nbsp;<\/p>","doi":"","popyear":null,"date":"2016-08-01","file_img":"348.jpg","continent":"Africa","country":"Uganda","resolution":"300","type":"Maternal and Newborn Health","file_html":null},{"id":"17043","desc":"Version 1.0 estimates representing the predicted prevalence of i) skilled birth attendance, ii) 4+ antenatal care visits, iii) postnatal check-up within 48 hours, and iv) absolute change over time among these indicators, using DHS data. Time points available consist of Uganda, 2000\/2006 amd 2011. \r\n<p>REGION: Africa\r\n<\/p><p>SPATIAL RESOLUTION: Administrative II\r\n<\/p><p>PROJECTION: Geographic, WGS84\r\n<\/p><p>UNITS: Prevalence expressed as decimal values\r\n<\/p><p>MAPPING APPROACH: Bayesian hierarchical mixed effects logistic regression methods applied to household survey data, in order to derive modelled prevalence of utilising maternal and newborn health services across three time points available. Absolute change over time in prevalence was measured as the difference between the first (t1) and last (t3) time points available per country. \r\n<\/p><p>FORMAT: Shapefile\r\n<\/p><p>FIELDNAMES: Example: SBA_t1 = predicted prevalence of skilled birth attendance at time point 1\r\n<\/p><p><br><\/p>","doi":"10.1186\/s12889-018-6241-8","popyear":null,"date":"2018-12-01","file_img":"UGA_indicators.png","continent":"Africa","country":"Uganda","resolution":null,"type":"Maternal and Newborn Health","file_html":null},{"id":"50670","desc":"Proportion of women aged 15 to 49 years in Uganda who have never used modern contraception during the year 2016. The estimations along with their associated uncertainties, measured as standard deviations (SD), are calculated following the methodology detailed in Utazi et al. (2021, 2022, 2023).\r\n<p><b>References<\/b>:<\/p>\r\n\r\n<ul>\r\n  <li>Utazi, C.E., Aheto, J.M.K., Chan, H.M.T., Tatem, A.J. and Sahu, S.K., 2022. <i>Conditional probability and ratio-based approaches for mapping the coverage of multi-dose vaccines<\/i>. Statistics in Medicine, 41(29), pp.5662-5678. <a target=\"_blank\"  href=\"https:\/\/doi.org\/10.1002\/sim.9586\">DOI:10.1002\/sim.9586<\/a><\/li>\r\n  <li>Utazi, C.E., Chan, H.M.T., Olowe, I., Wigley, A., Tejedor-Garavito, N., Cunningham, A., Bondarenko, M., Lorin, J., Boyda, D., Hogan, D. and Tatem, A.J., 2023. <i>A zero-dose vulnerability index for equity assessment and spatial prioritization in low-and middle-income countries<\/i>. Spatial Statistics, p.100772. <a target=\"_blank\"  href=\"https:\/\/doi.org\/10.1016\/j.spasta.2023.100772\">DOI:10.1016\/j.spasta.2023.100772<\/a><\/li>\r\n  <li>Utazi, C.E., Nilsen, K., Pannell, O., Dotse-Gborgbortsi, W. and Tatem, A.J., 2021. <i>District-level estimation of vaccination coverage: Discrete vs continuous spatial models<\/i>. Statistics in Medicine, 40(9), pp.2197-2211. <a target=\"_blank\"  href=\"https:\/\/doi.org\/10.1002\/sim.8897\">DOI:10.1002\/sim.8897<\/a><\/li>\r\n<\/ul>\r\n","doi":"10.5258\/SOTON\/WP00767","popyear":"2016","date":"2023-04-07","file_img":"UGA_Contraceptive_use.png","continent":"Africa","country":"Uganda","resolution":"1000","type":"Contraceptive Use","file_html":null},{"id":"50671","desc":"Vaccination coverage for diphtheria-tetanus-pertussis dose 1 (DTP1) and dose 3 (DTP3) among children aged 12 to 23 months in India during the year 2016. The estimated vaccination coverages, representing the proportions of vaccinated children, along with their associated uncertainties measured as standard deviations (SD), are computed following the methodology detailed in Utazi et al. (2021, 2022, 2023).\r\n<p><b>References<\/b>:<\/p>\r\n\r\n<ul>\r\n  <li>Utazi, C.E., Aheto, J.M.K., Chan, H.M.T., Tatem, A.J. and Sahu, S.K., 2022. <i>Conditional probability and ratio-based approaches for mapping the coverage of multi-dose vaccines<\/i>. Statistics in Medicine, 41(29), pp.5662-5678. <a target=\"_blank\"  href=\"https:\/\/doi.org\/10.1002\/sim.9586\">DOI:10.1002\/sim.9586<\/a><\/li>\r\n  <li>Utazi, C.E., Chan, H.M.T., Olowe, I., Wigley, A., Tejedor-Garavito, N., Cunningham, A., Bondarenko, M., Lorin, J., Boyda, D., Hogan, D. and Tatem, A.J., 2023. <i>A zero-dose vulnerability index for equity assessment and spatial prioritization in low-and middle-income countries<\/i>. Spatial Statistics, p.100772. <a target=\"_blank\"  href=\"https:\/\/doi.org\/10.1016\/j.spasta.2023.100772\">DOI:10.1016\/j.spasta.2023.100772<\/a><\/li>\r\n  <li>Utazi, C.E., Nilsen, K., Pannell, O., Dotse-Gborgbortsi, W. and Tatem, A.J., 2021. <i>District-level estimation of vaccination coverage: Discrete vs continuous spatial models<\/i>. Statistics in Medicine, 40(9), pp.2197-2211. <a target=\"_blank\"  href=\"https:\/\/doi.org\/10.1002\/sim.8897\">DOI:10.1002\/sim.8897<\/a><\/li>\r\n<\/ul>\r\n","doi":"10.5258\/SOTON\/WP00767","popyear":"2016","date":"2023-04-07","file_img":"UGA_DTP_vaccination_coverage.png","continent":"Africa","country":"Uganda","resolution":"1000","type":"DTP vaccination coverage","file_html":null},{"id":"50672","desc":"Proportion of children aged 12 to 23 months born to large (&gt;= 9 members) households in Uganda during the year 2016. The estimations along with their associated uncertainties, measured as standard deviations (SD), are calculated following the methodology detailed in Utazi et al. (2021, 2022, 2023).\r\n<p><b>References<\/b>:<\/p>\r\n\r\n<ul>\r\n  <li>Utazi, C.E., Aheto, J.M.K., Chan, H.M.T., Tatem, A.J. and Sahu, S.K., 2022. <i>Conditional probability and ratio-based approaches for mapping the coverage of multi-dose vaccines<\/i>. Statistics in Medicine, 41(29), pp.5662-5678. <a target=\"_blank\"  href=\"https:\/\/doi.org\/10.1002\/sim.9586\">DOI:10.1002\/sim.9586<\/a><\/li>\r\n  <li>Utazi, C.E., Chan, H.M.T., Olowe, I., Wigley, A., Tejedor-Garavito, N., Cunningham, A., Bondarenko, M., Lorin, J., Boyda, D., Hogan, D. and Tatem, A.J., 2023. <i>A zero-dose vulnerability index for equity assessment and spatial prioritization in low-and middle-income countries<\/i>. Spatial Statistics, p.100772. <a target=\"_blank\"  href=\"https:\/\/doi.org\/10.1016\/j.spasta.2023.100772\">DOI:10.1016\/j.spasta.2023.100772<\/a><\/li>\r\n  <li>Utazi, C.E., Nilsen, K., Pannell, O., Dotse-Gborgbortsi, W. and Tatem, A.J., 2021. <i>District-level estimation of vaccination coverage: Discrete vs continuous spatial models<\/i>. Statistics in Medicine, 40(9), pp.2197-2211. <a target=\"_blank\"  href=\"https:\/\/doi.org\/10.1002\/sim.8897\">DOI:10.1002\/sim.8897<\/a><\/li>\r\n<\/ul>\r\n","doi":"10.5258\/SOTON\/WP00767","popyear":"2016","date":"2023-04-07","file_img":"UGA_Maternal_and_child_demographic.png","continent":"Africa","country":"Uganda","resolution":"1000","type":"Maternal and child demographic","file_html":null},{"id":"50673","desc":"Maternal and child health in Uganda during the year 2016. Proportion of children aged 12 to 23 months born to mothers who did not attend antenatal care (ANC), who did not receive (or recall) post-natal care within 2 months after birth (PNC), who had no health insurance (INSURANCE), and who had no tetanus toxoid injections before birth (TT). Proportion of children aged 12 to 23 months who were not delivered by a skilled birth attendant (SBA), and who were underweight (MALNUTRITION). The estimations along with their associated uncertainties, measured as standard deviations (SD), are calculated following the methodology detailed in Utazi et al. (2021, 2022, 2023).\r\n<p><b>References<\/b>:<\/p>\r\n\r\n<ul>\r\n  <li>Utazi, C.E., Aheto, J.M.K., Chan, H.M.T., Tatem, A.J. and Sahu, S.K., 2022. <i>Conditional probability and ratio-based approaches for mapping the coverage of multi-dose vaccines<\/i>. Statistics in Medicine, 41(29), pp.5662-5678. <a target=\"_blank\"  href=\"https:\/\/doi.org\/10.1002\/sim.9586\">DOI:10.1002\/sim.9586<\/a><\/li>\r\n  <li>Utazi, C.E., Chan, H.M.T., Olowe, I., Wigley, A., Tejedor-Garavito, N., Cunningham, A., Bondarenko, M., Lorin, J., Boyda, D., Hogan, D. and Tatem, A.J., 2023. <i>A zero-dose vulnerability index for equity assessment and spatial prioritization in low-and middle-income countries<\/i>. Spatial Statistics, p.100772. <a target=\"_blank\"  href=\"https:\/\/doi.org\/10.1016\/j.spasta.2023.100772\">DOI:10.1016\/j.spasta.2023.100772<\/a><\/li>\r\n  <li>Utazi, C.E., Nilsen, K., Pannell, O., Dotse-Gborgbortsi, W. and Tatem, A.J., 2021. <i>District-level estimation of vaccination coverage: Discrete vs continuous spatial models<\/i>. Statistics in Medicine, 40(9), pp.2197-2211. <a target=\"_blank\"  href=\"https:\/\/doi.org\/10.1002\/sim.8897\">DOI:10.1002\/sim.8897<\/a><\/li>\r\n<\/ul>\r\n","doi":"10.5258\/SOTON\/WP00767","popyear":"2016","date":"2023-04-07","file_img":"UGA_Maternal_and_child_health.png","continent":"Africa","country":"Uganda","resolution":"1000","type":"Maternal and child health","file_html":null},{"id":"50674","desc":"Maternal and child socioeconomic in Uganda during the year 2016. Proportion of children aged 12 to 23 months born to the poorest \/ poorer households according to DHS \/ MICS-NICS classifications (HWEALTH) and born to mothers who had no formal education (MEDUCATION). Proportion of women aged 15 to 49 years who did not participate in decision-making in their households (DECISION). The estimations along with their associated uncertainties, measured as standard deviations (SD), are calculated following the methodology detailed in Utazi et al. (2021, 2022, 2023).\r\n<p><b>References<\/b>:<\/p>\r\n\r\n<ul>\r\n  <li>Utazi, C.E., Aheto, J.M.K., Chan, H.M.T., Tatem, A.J. and Sahu, S.K., 2022. <i>Conditional probability and ratio-based approaches for mapping the coverage of multi-dose vaccines<\/i>. Statistics in Medicine, 41(29), pp.5662-5678. <a target=\"_blank\"  href=\"https:\/\/doi.org\/10.1002\/sim.9586\">DOI:10.1002\/sim.9586<\/a><\/li>\r\n  <li>Utazi, C.E., Chan, H.M.T., Olowe, I., Wigley, A., Tejedor-Garavito, N., Cunningham, A., Bondarenko, M., Lorin, J., Boyda, D., Hogan, D. and Tatem, A.J., 2023. <i>A zero-dose vulnerability index for equity assessment and spatial prioritization in low-and middle-income countries<\/i>. Spatial Statistics, p.100772. <a target=\"_blank\"  href=\"https:\/\/doi.org\/10.1016\/j.spasta.2023.100772\">DOI:10.1016\/j.spasta.2023.100772<\/a><\/li>\r\n  <li>Utazi, C.E., Nilsen, K., Pannell, O., Dotse-Gborgbortsi, W. and Tatem, A.J., 2021. <i>District-level estimation of vaccination coverage: Discrete vs continuous spatial models<\/i>. Statistics in Medicine, 40(9), pp.2197-2211. <a target=\"_blank\"  href=\"https:\/\/doi.org\/10.1002\/sim.8897\">DOI:10.1002\/sim.8897<\/a><\/li>\r\n<\/ul>\r\n","doi":"10.5258\/SOTON\/WP00767","popyear":"2016","date":"2023-04-07","file_img":"UGA_Maternal_and_child_socioeconomic.png","continent":"Africa","country":"Uganda","resolution":"1000","type":"Maternal and child socioeconomic","file_html":null},{"id":"50675","desc":"Vaccination coverage for measles dose 1 (MCV1) among children aged 12 to 23 months in Uganda during the year 2016. The estimated vaccination coverages, representing the proportions of vaccinated children, along with their associated uncertainties measured as standard deviations (SD), are computed following the methodology detailed in Utazi et al. (2021, 2022, 2023).\r\n<p><b>References<\/b>:<\/p>\r\n\r\n<ul>\r\n  <li>Utazi, C.E., Aheto, J.M.K., Chan, H.M.T., Tatem, A.J. and Sahu, S.K., 2022. <i>Conditional probability and ratio-based approaches for mapping the coverage of multi-dose vaccines<\/i>. Statistics in Medicine, 41(29), pp.5662-5678. <a target=\"_blank\"  href=\"https:\/\/doi.org\/10.1002\/sim.9586\">DOI:10.1002\/sim.9586<\/a><\/li>\r\n  <li>Utazi, C.E., Chan, H.M.T., Olowe, I., Wigley, A., Tejedor-Garavito, N., Cunningham, A., Bondarenko, M., Lorin, J., Boyda, D., Hogan, D. and Tatem, A.J., 2023. <i>A zero-dose vulnerability index for equity assessment and spatial prioritization in low-and middle-income countries<\/i>. Spatial Statistics, p.100772. <a target=\"_blank\"  href=\"https:\/\/doi.org\/10.1016\/j.spasta.2023.100772\">DOI:10.1016\/j.spasta.2023.100772<\/a><\/li>\r\n  <li>Utazi, C.E., Nilsen, K., Pannell, O., Dotse-Gborgbortsi, W. and Tatem, A.J., 2021. <i>District-level estimation of vaccination coverage: Discrete vs continuous spatial models<\/i>. Statistics in Medicine, 40(9), pp.2197-2211. <a target=\"_blank\"  href=\"https:\/\/doi.org\/10.1002\/sim.8897\">DOI:10.1002\/sim.8897<\/a><\/li>\r\n<\/ul>\r\n","doi":"10.5258\/SOTON\/WP00767","popyear":"2016","date":"2023-04-07","file_img":"UGA_Measles_vaccination_coverage.png","continent":"Africa","country":"Uganda","resolution":"1000","type":"Measles vaccination coverage","file_html":null},{"id":"17025","desc":"Diphtheria-tetanus-pertussis (DTP) dose 1, 2 and 3 vaccination coverage among children aged under 5 years in 2014 for Zambia.\r\n<p>Estimated vaccination coverage, i.e. proportions of children vaccinated, and associated uncertainties, measured as standard deviations, for Zambia in 2014.\r\n<\/p><p>The methodology used for the work is described in Utazi et al. (2017) High resolution age-structure mapping of childhood vaccination coverage in low and middle income countries, Vaccine 36 (12), pp. 1583-1591 and Utazi et al (2019) Mapping vaccination coverage rates at high resolution to explore the effects of delivery mechanisms and inform vaccination strategies, Nature Communications, under review.\r\n<\/p><p><br><\/p><p>Filenames:\r\n<\/p><p>DTP vaccination estimates: \r\n<\/p><p>[COUNTRYCODE]_mean_pred_DTP1_perc.tif = Mean % estimated DTP1 vaccination coverage (0-59 months) \r\n<\/p><p>[COUNTRYCODE]_mean_pred_DTP1_perc.tif = Mean % estimated DTP2 vaccination coverage (0-59 months) \r\n<\/p><p>[COUNTRYCODE]_mean_pred_DTP1_perc.tif = Mean % estimated DTP3 vaccination coverage (0-59 months) \r\n<\/p><p>\r\n<\/p><p><br><\/p>","doi":"","popyear":"2014","date":"2019-02-03","file_img":"ZMB_mean_pred_dtp1_perc.png","continent":"Africa","country":"Zambia","resolution":"1000","type":"DTP vaccination coverage","file_html":null},{"id":"17035","desc":"Estimated vaccination coverage, i.e. proportions of children vaccinated, and associated uncertainties, measured as standard deviations, broken by age groups (9-11 months, 12-23 months, 24-59 months, 0-59 months and 9-59 months) for Zambia in 2014.\r\n<p>The methodology used for the work is described in Utazi et al. (2017) High resolution age-structure mapping of childhood vaccination coverage in low and middle income countries, Vaccine 36 (12), pp. 1583-1591 and Utazi et al (2019) Mapping vaccination coverage rates at high resolution to explore the effects of delivery mechanisms and inform vaccination strategies, Nature Communications, under review.\r\n<\/p><p><br><\/p><p>Filenames:\r\n<\/p><p>DTP vaccination estimates: \r\n<\/p><p>[COUNTRYCODE]_mean_pred_DTP1_perc.tif = Mean % estimated DTP1 vaccination coverage (0-59 months) \r\n<\/p><p>[COUNTRYCODE]_mean_pred_DTP1_perc.tif = Mean % estimated DTP2 vaccination coverage (0-59 months) \r\n<\/p><p>[COUNTRYCODE]_mean_pred_DTP1_perc.tif = Mean % estimated DTP3 vaccination coverage (0-59 months) \r\n<\/p><p><br><\/p>","doi":"","popyear":"2014","date":"2019-02-03","file_img":"ZMB_mean_pred_total_perc.png","continent":"Africa","country":"Zambia","resolution":"1000","type":"Measles vaccination coverage","file_html":null},{"id":"17023","desc":"Diphtheria-tetanus-pertussis (DTP) dose 1, 2 and 3 vaccination coverage among children aged under 5 years in 2015 for Afghanistan.\r\n<p>Estimated vaccination coverage, i.e. proportions of children vaccinated, and associated uncertainties, measured as standard deviations, for Mozambique in 2015.\r\n<\/p><p>\r\n<\/p><p>Utazi et al (2018) A spatial regression model for the disaggregation of areal unit based data to high-resolution grids with application to vaccination coverage mapping. Statistical Methods in Medical Research. https:\/\/doi.org\/10.1177\/0962280218797362)<\/p><p><br><\/p><p>Filenames:<br><\/p><p>DTP vaccination estimates: \r\n<\/p><p>[COUNTRYCODE]_mean_pred_DTP1_perc.tif = Mean % estimated DTP1 vaccination coverage (0-59 months) \r\n<\/p><p>[COUNTRYCODE]_mean_pred_DTP1_perc.tif = Mean % estimated DTP2 vaccination coverage (0-59 months) \r\n<\/p><p>[COUNTRYCODE]_mean_pred_DTP1_perc.tif = Mean % estimated DTP3 vaccination coverage (0-59 months) \r\n<\/p><p><br><\/p>","doi":"","popyear":"2015","date":"2019-02-03","file_img":"AFG_mean_pred_DTP1_per.png","continent":"Asia","country":"Afghanistan","resolution":"5000","type":"DTP vaccination coverage","file_html":null},{"id":"17033","desc":"Estimated vaccination coverage, i.e. proportions of children vaccinated, and associated uncertainties, measured as standard deviations, broken by age groups (9-11 months, 12-23 months, 24-59 months, 0-59 months and 9-59 months) for Afghanistan in 2015.\r\n<p>The methodology used for the work is described in Utazi et al. (2017) High resolution age-structure mapping of childhood vaccination coverage in low and middle income countries, Vaccine 36 (12), pp. 1583-1591 and Utazi et al (2019) Mapping vaccination coverage rates at high resolution to explore the effects of delivery mechanisms and inform vaccination strategies, Nature Communications, under review.\r\n<\/p><p>Filenames:\r\n<\/p><p>DTP vaccination estimates: \r\n<\/p><p>[COUNTRYCODE]_mean_pred_DTP1_perc.tif = Mean % estimated DTP1 vaccination coverage (0-59 months) \r\n<\/p><p>[COUNTRYCODE]_mean_pred_DTP1_perc.tif = Mean % estimated DTP2 vaccination coverage (0-59 months) \r\n<\/p><p>[COUNTRYCODE]_mean_pred_DTP1_perc.tif = Mean % estimated DTP3 vaccination coverage (0-59 months) \r\n<\/p><p><br><\/p>","doi":"","popyear":"2015","date":"2019-02-03","file_img":"AFG_mean_pred_total_perc.png","continent":"Asia","country":"Afghanistan","resolution":"5000","type":"Measles vaccination coverage","file_html":null},{"id":"1274","desc":"DATASET: 2011 estimates of mean DHS wealth index score per grid square, and associated uncertainty metrics. \r\n<p>REGION: Asia \r\n<\/p><p>SPATIAL RESOLUTION: 0.00833333 decimal degrees (approx. 1km at the equator) \r\n<\/p><p>PROJECTION: Geographic, WGS84 \r\n<\/p><p>UNITS: DHS wealth index score (poverty dataset); standard deviation (uncertainty dataset) \r\n<\/p><p>MAPPING APPROACH: Bayesian model-based geostatistics in combination with high resolution gridded spatial covariates and aggregated mobile phone data applied to GPS-located household survey data on poverty from the DHS Program. \r\n<\/p><p>FORMAT: Geotiff (zipped using 7-zip (open access tool): www.7-zip.org) \r\n<\/p><p>FILENAMES: bgd2011wipov.tif = Bangladesh (bgd) asset-based poverty map for 2011 showing estimates of mean DHS wealth index score per grid square. bgd2011wipovsd.tif = uncertainty dataset showing standard deviation per grid square. \r\n<\/p><p>DATE OF PRODUCTION: January 2017 \r\n<\/p><p>CITATION: Steele, J. E. et al. Mapping poverty using mobile phone and satellite data. J. R. Soc. Interface 14, 20160690 (2017). Online here: http:\/\/rsif.royalsocietypublishing.org\/content\/14\/127\/20160690\r\n<\/p><p>\r\n<\/p><p>----------------------- \r\n<\/p><p>\r\n<\/p><p>DATASET: 2013 estimates of mean likelihood of living in poverty per grid square, as defined by $2.50 a day poverty line, and associated uncertainty metrics. \r\n<\/p><p>REGION: Asia \r\n<\/p><p>SPATIAL RESOLUTION: 0.00833333 decimal degrees (approx. 1km at the equator) \r\n<\/p><p>PROJECTION: Geographic, WGS84 \r\n<\/p><p>UNITS: Mean likelihood of living on less than $2.50 a day (poverty dataset); standard deviation (uncertainty dataset) \r\n<\/p><p>MAPPING APPROACH: Bayesian model-based geostatistics in combination with high resolution gridded spatial covariates applied to GPS-located household survey data on poverty from the Progress out of Poverty Index. \r\n<\/p><p>FORMAT: Geotiff (zipped using 7-zip (open access tool): www.7-zip.org) \r\n<\/p><p>FILENAMES: bgd2013ppipov.tif = Bangladesh (bgd) Poverty Index-based poverty map for 2013 showing estimates of mean likelihood of living in poverty per grid square. bgd2013ppipovsd.tif = uncertainty dataset showing standard deviation per grid square. \r\n<\/p><p>DATE OF PRODUCTION: January 2017 \r\n<\/p><p>CITATION: Steele, J. E. et al. Mapping poverty using mobile phone and satellite data. J. R. Soc. Interface 14, 20160690 (2017). Online here: http:\/\/rsif.royalsocietypublishing.org\/content\/14\/127\/20160690\r\n<\/p><p>\r\n<\/p><p>----------------------- \r\n<\/p><p>\r\n<\/p><p>DATASET: 2013 estimates of income in USD per grid square, and associated uncertainty metrics. \r\n<\/p><p>REGION: Asia \r\n<\/p><p>SPATIAL RESOLUTION: 0.00833333 decimal degrees (approx. 1km at the equator) \r\n<\/p><p>PROJECTION: Geographic, WGS84 \r\n<\/p><p>UNITS: USD (poverty dataset); standard deviation (uncertainty dataset) \r\n<\/p><p>MAPPING APPROACH: Bayesian model-based geostatistics in combination with high resolution gridded spatial covariates and aggregated mobile phone data applied to mobile tower-located household survey data on income from Grameenphone Ltd. \r\n<\/p><p>FORMAT: Geotiff (zipped using 7-zip (open access tool): www.7-zip.org) \r\n<\/p><p>FILENAMES: bgd2013incpov.tif = Bangladesh (bgd) income-based poverty map for 2013 showing estimates of mean household income in USD per grid square. bgd2013incpovsd.tif = uncertainty dataset showing standard deviation per grid square. \r\n<\/p><p>DATE OF PRODUCTION: January 2017 \r\n<\/p><p>CITATION: Steele, J. E. et al. Mapping poverty using mobile phone and satellite data. J. R. Soc. Interface 14, 20160690 (2017). Online here: http:\/\/rsif.royalsocietypublishing.org\/content\/14\/127\/20160690<\/p>","doi":"10.5258\/SOTON\/WP00020","popyear":null,"date":"2017-01-01","file_img":"330.jpg","continent":"Asia","country":"Bangladesh","resolution":"1000","type":"Poverty","file_html":null},{"id":"17021","desc":"Diphtheria-tetanus-pertussis (DTP) dose 1, 2 and 3 vaccination coverage among children aged under 5 years in 2014 for Cambodia.\r\n<p>Estimated vaccination coverage, i.e. proportions of children vaccinated, and associated uncertainties, measured as standard deviations, for Cambodia in 2014.\r\n<\/p><p>The methodology used for the work is described in Utazi et al. (2017) High resolution age-structure mapping of childhood vaccination coverage in low and middle income countries, Vaccine 36 (12), pp. 1583-1591 and Utazi et al (2019) Mapping vaccination coverage rates at high resolution to explore the effects of delivery mechanisms and inform vaccination strategies, Nature Communications, under review.\r\n<\/p><p><br><\/p><p>Filenames:\r\n<\/p><p>DTP vaccination estimates: \r\n<\/p><p>[COUNTRYCODE]_mean_pred_DTP1_perc.tif = Mean % estimated DTP1 vaccination coverage (0-59 months) \r\n<\/p><p>[COUNTRYCODE]_mean_pred_DTP1_perc.tif = Mean % estimated DTP2 vaccination coverage (0-59 months) \r\n<\/p><p>[COUNTRYCODE]_mean_pred_DTP1_perc.tif = Mean % estimated DTP3 vaccination coverage (0-59 months) \r\n<\/p><p><br><\/p>","doi":"","popyear":"2014","date":"2019-02-03","file_img":"KHM_mean_pred_dtp1_perc.png","continent":"Asia","country":"Cambodia","resolution":"1000","type":"DTP vaccination coverage","file_html":null},{"id":"17030","desc":"Estimated vaccination coverage, i.e. proportions of children vaccinated, and associated uncertainties, measured as standard deviations, broken by age groups (9-11 months, 12-23 months, 24-59 months, 0-59 months and 9-59 months) for Cambodia in 2014.\r\n<p>The methodology used for the work is described in Utazi et al. (2017) High resolution age-structure mapping of childhood vaccination coverage in low and middle income countries, Vaccine 36 (12), pp. 1583-1591 and Utazi et al (2019) Mapping vaccination coverage rates at high resolution to explore the effects of delivery mechanisms and inform vaccination strategies, Nature Communications, under review.\r\n<\/p><p><br><\/p><p>Filenames:\r\n<\/p><p>DTP vaccination estimates: \r\n<\/p><p>[COUNTRYCODE]_mean_pred_DTP1_perc.tif = Mean % estimated DTP1 vaccination coverage (0-59 months) \r\n<\/p><p>[COUNTRYCODE]_mean_pred_DTP1_perc.tif = Mean % estimated DTP2 vaccination coverage (0-59 months) \r\n<\/p><p>[COUNTRYCODE]_mean_pred_DTP1_perc.tif = Mean % estimated DTP3 vaccination coverage (0-59 months) \r\n<\/p><p><br><\/p>","doi":"","popyear":"2014","date":"2019-02-03","file_img":"KHM_mean_pred_total_perc.png","continent":"Asia","country":"Cambodia","resolution":"1000","type":"Measles vaccination coverage","file_html":null},{"id":"17027","desc":"Diphtheria-tetanus-pertussis (DTP) dose 1, 2 and 3 vaccination coverage among children aged under 5 years in 2016 for India.\r\n<p>Estimated vaccination coverage, i.e. proportions of children vaccinated, and associated uncertainties, measured as standard deviations, for India in 2016.\r\n<\/p><p>The methodology used for the work is described in Utazi et al. (2017) High resolution age-structure mapping of childhood vaccination coverage in low and middle income countries, Vaccine 36 (12), pp. 1583-1591 and Utazi et al (2019) Mapping vaccination coverage rates at high resolution to explore the effects of delivery mechanisms and inform vaccination strategies, Nature Communications, under review.\r\n<\/p><p><br><\/p><p>Filenames:\r\n<\/p><p>DTP vaccination estimates: \r\n<\/p><p>[COUNTRYCODE]_mean_pred_DTP1_perc.tif = Mean % estimated DTP1 vaccination coverage (0-59 months) \r\n<\/p><p>[COUNTRYCODE]_mean_pred_DTP1_perc.tif = Mean % estimated DTP2 vaccination coverage (0-59 months) \r\n<\/p><p>[COUNTRYCODE]_mean_pred_DTP1_perc.tif = Mean % estimated DTP3 vaccination coverage (0-59 months) \r\n<\/p><p><br><\/p>","doi":"","popyear":"2016","date":"2019-02-03","file_img":"IND_mean_pred_DTP1_perc.png","continent":"Asia","country":"India","resolution":"1000","type":"DTP vaccination coverage","file_html":null},{"id":"17037","desc":"Estimated vaccination coverage, i.e. proportions of children vaccinated, and associated uncertainties, measured as standard deviations, broken by age groups (9-11 months, 12-23 months, 24-59 months, 0-59 months and 9-59 months) for India in 2016.\r\n<p>The methodology used for the work is described in Utazi et al. (2017) High resolution age-structure mapping of childhood vaccination coverage in low and middle income countries, Vaccine 36 (12), pp. 1583-1591 and Utazi et al (2019) Mapping vaccination coverage rates at high resolution to explore the effects of delivery mechanisms and inform vaccination strategies, Nature Communications, under review.\r\n<\/p><p><br><\/p><p>Filenames:\r\n<\/p><p>DTP vaccination estimates: \r\n<\/p><p>[COUNTRYCODE]_mean_pred_DTP1_perc.tif = Mean % estimated DTP1 vaccination coverage (0-59 months) \r\n<\/p><p>[COUNTRYCODE]_mean_pred_DTP1_perc.tif = Mean % estimated DTP2 vaccination coverage (0-59 months) \r\n<\/p><p>[COUNTRYCODE]_mean_pred_DTP1_perc.tif = Mean % estimated DTP3 vaccination coverage (0-59 months) \r\n<\/p><p><br><\/p>","doi":"","popyear":"2016","date":"2019-02-03","file_img":"IND_mean_pred_total_perc.png","continent":"Asia","country":"India","resolution":"1000","type":"Measles vaccination coverage","file_html":null},{"id":"50652","desc":"Proportion of women aged 15 to 49 years in India who have never used modern contraception during the years 2015-16. The estimations along with their associated uncertainties, measured as standard deviations (SD), are calculated following the methodology detailed in Utazi et al. (2021, 2022, 2023).\r\n<p><b>References<\/b>:<\/p>\r\n\r\n<ul>\r\n  <li>Utazi, C.E., Aheto, J.M.K., Chan, H.M.T., Tatem, A.J. and Sahu, S.K., 2022. <i>Conditional probability and ratio-based approaches for mapping the coverage of multi-dose vaccines<\/i>. Statistics in Medicine, 41(29), pp.5662-5678. <a target=\"_blank\"  href=\"https:\/\/doi.org\/10.1002\/sim.9586\">DOI:10.1002\/sim.9586<\/a><\/li>\r\n  <li>Utazi, C.E., Chan, H.M.T., Olowe, I., Wigley, A., Tejedor-Garavito, N., Cunningham, A., Bondarenko, M., Lorin, J., Boyda, D., Hogan, D. and Tatem, A.J., 2023. <i>A zero-dose vulnerability index for equity assessment and spatial prioritization in low-and middle-income countries<\/i>. Spatial Statistics, p.100772. <a target=\"_blank\"  href=\"https:\/\/doi.org\/10.1016\/j.spasta.2023.100772\">DOI:10.1016\/j.spasta.2023.100772<\/a><\/li>\r\n  <li>Utazi, C.E., Nilsen, K., Pannell, O., Dotse-Gborgbortsi, W. and Tatem, A.J., 2021. <i>District-level estimation of vaccination coverage: Discrete vs continuous spatial models<\/i>. Statistics in Medicine, 40(9), pp.2197-2211. <a target=\"_blank\"  href=\"https:\/\/doi.org\/10.1002\/sim.8897\">DOI:10.1002\/sim.8897<\/a><\/li>\r\n<\/ul>\r\n","doi":"10.5258\/SOTON\/WP00767","popyear":"2016","date":"2023-04-07","file_img":"IND_Contraceptive_use.png","continent":"Asia","country":"India","resolution":"1000","type":"Contraceptive Use","file_html":null},{"id":"50653","desc":"Vaccination coverage for diphtheria-tetanus-pertussis dose 1 (DTP1) and dose 3 (DTP3) among children aged 12 to 23 months in India during the years 2015-16. The estimated vaccination coverages, representing the proportions of vaccinated children, along with their associated uncertainties measured as standard deviations (SD), are computed following the methodology detailed in Utazi et al. (2021, 2022, 2023).\r\n<p><b>References<\/b>:<\/p>\r\n\r\n<ul>\r\n  <li>Utazi, C.E., Aheto, J.M.K., Chan, H.M.T., Tatem, A.J. and Sahu, S.K., 2022. <i>Conditional probability and ratio-based approaches for mapping the coverage of multi-dose vaccines<\/i>. Statistics in Medicine, 41(29), pp.5662-5678. <a target=\"_blank\"  href=\"https:\/\/doi.org\/10.1002\/sim.9586\">DOI:10.1002\/sim.9586<\/a><\/li>\r\n  <li>Utazi, C.E., Chan, H.M.T., Olowe, I., Wigley, A., Tejedor-Garavito, N., Cunningham, A., Bondarenko, M., Lorin, J., Boyda, D., Hogan, D. and Tatem, A.J., 2023. <i>A zero-dose vulnerability index for equity assessment and spatial prioritization in low-and middle-income countries<\/i>. Spatial Statistics, p.100772. <a target=\"_blank\"  href=\"https:\/\/doi.org\/10.1016\/j.spasta.2023.100772\">DOI:10.1016\/j.spasta.2023.100772<\/a><\/li>\r\n  <li>Utazi, C.E., Nilsen, K., Pannell, O., Dotse-Gborgbortsi, W. and Tatem, A.J., 2021. <i>District-level estimation of vaccination coverage: Discrete vs continuous spatial models<\/i>. Statistics in Medicine, 40(9), pp.2197-2211. <a target=\"_blank\"  href=\"https:\/\/doi.org\/10.1002\/sim.8897\">DOI:10.1002\/sim.8897<\/a><\/li>\r\n<\/ul>\r\n","doi":"10.5258\/SOTON\/WP00767","popyear":"2016","date":"2023-04-07","file_img":"IND_DTP_vaccination_coverage.png","continent":"Asia","country":"India","resolution":"1000","type":"DTP vaccination coverage","file_html":null},{"id":"50654","desc":"Proportion of children aged 12 to 23 months born to large (&gt;= 9 members) households in India during the years 2015-16. The estimations along with their associated uncertainties, measured as standard deviations (SD), are calculated following the methodology detailed in Utazi et al. (2021, 2022, 2023).\r\n<p><b>References<\/b>:<\/p>\r\n\r\n<ul>\r\n  <li>Utazi, C.E., Aheto, J.M.K., Chan, H.M.T., Tatem, A.J. and Sahu, S.K., 2022. <i>Conditional probability and ratio-based approaches for mapping the coverage of multi-dose vaccines<\/i>. Statistics in Medicine, 41(29), pp.5662-5678. <a target=\"_blank\"  href=\"https:\/\/doi.org\/10.1002\/sim.9586\">DOI:10.1002\/sim.9586<\/a><\/li>\r\n  <li>Utazi, C.E., Chan, H.M.T., Olowe, I., Wigley, A., Tejedor-Garavito, N., Cunningham, A., Bondarenko, M., Lorin, J., Boyda, D., Hogan, D. and Tatem, A.J., 2023. <i>A zero-dose vulnerability index for equity assessment and spatial prioritization in low-and middle-income countries<\/i>. Spatial Statistics, p.100772. <a target=\"_blank\"  href=\"https:\/\/doi.org\/10.1016\/j.spasta.2023.100772\">DOI:10.1016\/j.spasta.2023.100772<\/a><\/li>\r\n  <li>Utazi, C.E., Nilsen, K., Pannell, O., Dotse-Gborgbortsi, W. and Tatem, A.J., 2021. <i>District-level estimation of vaccination coverage: Discrete vs continuous spatial models<\/i>. Statistics in Medicine, 40(9), pp.2197-2211. <a target=\"_blank\"  href=\"https:\/\/doi.org\/10.1002\/sim.8897\">DOI:10.1002\/sim.8897<\/a><\/li>\r\n<\/ul>\r\n","doi":"10.5258\/SOTON\/WP00767","popyear":"2016","date":"2023-04-07","file_img":"IND_Maternal_and_child_demographic.png","continent":"Asia","country":"India","resolution":"1000","type":"Maternal and child demographic","file_html":null},{"id":"50655","desc":"Maternal and child health in India during the years 2015-16. Proportion of children aged 12 to 23 months born to mothers who did not attend antenatal care (ANC), who did not receive (or recall) post-natal care within 2 months after birth (PNC), who had no health insurance (INSURANCE), and who had no tetanus toxoid injections before birth (TT). Proportion of children aged 12 to 23 months who were not delivered by a skilled birth attendant (SBA), and who were underweight (MALNUTRITION). The estimations along with their associated uncertainties, measured as standard deviations (SD), are calculated following the methodology detailed in Utazi et al. (2021, 2022, 2023).\r\n<p><b>References<\/b>:<\/p>\r\n\r\n<ul>\r\n  <li>Utazi, C.E., Aheto, J.M.K., Chan, H.M.T., Tatem, A.J. and Sahu, S.K., 2022. <i>Conditional probability and ratio-based approaches for mapping the coverage of multi-dose vaccines<\/i>. Statistics in Medicine, 41(29), pp.5662-5678. <a target=\"_blank\"  href=\"https:\/\/doi.org\/10.1002\/sim.9586\">DOI:10.1002\/sim.9586<\/a><\/li>\r\n  <li>Utazi, C.E., Chan, H.M.T., Olowe, I., Wigley, A., Tejedor-Garavito, N., Cunningham, A., Bondarenko, M., Lorin, J., Boyda, D., Hogan, D. and Tatem, A.J., 2023. <i>A zero-dose vulnerability index for equity assessment and spatial prioritization in low-and middle-income countries<\/i>. Spatial Statistics, p.100772. <a target=\"_blank\"  href=\"https:\/\/doi.org\/10.1016\/j.spasta.2023.100772\">DOI:10.1016\/j.spasta.2023.100772<\/a><\/li>\r\n  <li>Utazi, C.E., Nilsen, K., Pannell, O., Dotse-Gborgbortsi, W. and Tatem, A.J., 2021. <i>District-level estimation of vaccination coverage: Discrete vs continuous spatial models<\/i>. Statistics in Medicine, 40(9), pp.2197-2211. <a target=\"_blank\"  href=\"https:\/\/doi.org\/10.1002\/sim.8897\">DOI:10.1002\/sim.8897<\/a><\/li>\r\n<\/ul>\r\n","doi":"10.5258\/SOTON\/WP00767","popyear":"2016","date":"2023-04-07","file_img":"IND_Maternal_and_child_health.png","continent":"Asia","country":"India","resolution":"1000","type":"Maternal and child health","file_html":null},{"id":"50656","desc":"Maternal and child socioeconomic in India during the years 2015-16. Proportion of children aged 12 to 23 months born to the poorest \/ poorer households according to DHS \/ MICS-NICS classifications (HWEALTH) and born to mothers who had no formal education (MEDUCATION). Proportion of women aged 15 to 49 years who did not participate in decision-making in their households (DECISION). The estimations along with their associated uncertainties, measured as standard deviations (SD), are calculated following the methodology detailed in Utazi et al. (2021, 2022, 2023).\r\n<p><b>References<\/b>:<\/p>\r\n\r\n<ul>\r\n  <li>Utazi, C.E., Aheto, J.M.K., Chan, H.M.T., Tatem, A.J. and Sahu, S.K., 2022. <i>Conditional probability and ratio-based approaches for mapping the coverage of multi-dose vaccines<\/i>. Statistics in Medicine, 41(29), pp.5662-5678. <a target=\"_blank\"  href=\"https:\/\/doi.org\/10.1002\/sim.9586\">DOI:10.1002\/sim.9586<\/a><\/li>\r\n  <li>Utazi, C.E., Chan, H.M.T., Olowe, I., Wigley, A., Tejedor-Garavito, N., Cunningham, A., Bondarenko, M., Lorin, J., Boyda, D., Hogan, D. and Tatem, A.J., 2023. <i>A zero-dose vulnerability index for equity assessment and spatial prioritization in low-and middle-income countries<\/i>. Spatial Statistics, p.100772. <a target=\"_blank\"  href=\"https:\/\/doi.org\/10.1016\/j.spasta.2023.100772\">DOI:10.1016\/j.spasta.2023.100772<\/a><\/li>\r\n  <li>Utazi, C.E., Nilsen, K., Pannell, O., Dotse-Gborgbortsi, W. and Tatem, A.J., 2021. <i>District-level estimation of vaccination coverage: Discrete vs continuous spatial models<\/i>. Statistics in Medicine, 40(9), pp.2197-2211. <a target=\"_blank\"  href=\"https:\/\/doi.org\/10.1002\/sim.8897\">DOI:10.1002\/sim.8897<\/a><\/li>\r\n<\/ul>\r\n","doi":"10.5258\/SOTON\/WP00767","popyear":"2016","date":"2023-04-07","file_img":"IND_Maternal_and_child_socioeconomic.png","continent":"Asia","country":"India","resolution":"1000","type":"Maternal and child socioeconomic","file_html":null},{"id":"50657","desc":"Vaccination coverage for measles dose 1 (MCV1) among children aged 12 to 23 months in India during the years 2015-16. The estimated vaccination coverages, representing the proportions of vaccinated children, along with their associated uncertainties measured as standard deviations (SD), are computed following the methodology detailed in Utazi et al. (2021, 2022, 2023).\r\n<p><b>References<\/b>:<\/p>\r\n\r\n<ul>\r\n  <li>Utazi, C.E., Aheto, J.M.K., Chan, H.M.T., Tatem, A.J. and Sahu, S.K., 2022. <i>Conditional probability and ratio-based approaches for mapping the coverage of multi-dose vaccines<\/i>. Statistics in Medicine, 41(29), pp.5662-5678. <a target=\"_blank\"  href=\"https:\/\/doi.org\/10.1002\/sim.9586\">DOI:10.1002\/sim.9586<\/a><\/li>\r\n  <li>Utazi, C.E., Chan, H.M.T., Olowe, I., Wigley, A., Tejedor-Garavito, N., Cunningham, A., Bondarenko, M., Lorin, J., Boyda, D., Hogan, D. and Tatem, A.J., 2023. <i>A zero-dose vulnerability index for equity assessment and spatial prioritization in low-and middle-income countries<\/i>. Spatial Statistics, p.100772. <a target=\"_blank\"  href=\"https:\/\/doi.org\/10.1016\/j.spasta.2023.100772\">DOI:10.1016\/j.spasta.2023.100772<\/a><\/li>\r\n  <li>Utazi, C.E., Nilsen, K., Pannell, O., Dotse-Gborgbortsi, W. and Tatem, A.J., 2021. <i>District-level estimation of vaccination coverage: Discrete vs continuous spatial models<\/i>. Statistics in Medicine, 40(9), pp.2197-2211. <a target=\"_blank\"  href=\"https:\/\/doi.org\/10.1002\/sim.8897\">DOI:10.1002\/sim.8897<\/a><\/li>\r\n<\/ul>\r\n","doi":"10.5258\/SOTON\/WP00767","popyear":"2016","date":"2023-04-07","file_img":"IND_Measles_vaccination_coverage.png","continent":"Asia","country":"India","resolution":"1000","type":"Measles vaccination coverage","file_html":null},{"id":"17045","desc":"DATASET:&nbsp; Wealth Index and International Wealth Index for Nepal 2006 <br>REGION: Nepal <br>PATIAL RESOLUTION: 0.00833333 decimal degrees (approx 1km at the equator) <br>PROJECTION: Geographic, WGS84 <br>&nbsp;<br>FILENAMES: Nepal_2006_IWI_HR.tif - International Wealth Index (IWI), Nepal_2006_IWI_interdecile_HR.tif is an uncertainty raster, Nepal_2006_WI_HR.tif - Wealth Index (WI), Nepal_2006_WI_interdecile_HR.tif is an uncertainty raster.","doi":"","popyear":"2006","date":"2018-05-18","file_img":"WI_IWI_2006.jpg","continent":"Asia","country":"Nepal","resolution":"1000","type":"Poverty","file_html":null},{"id":"17046","desc":"DATASET: Wealth Index and International Wealth Index for Nepal 2011 <br>REGION: Nepal <br>PATIAL RESOLUTION: 0.00833333 decimal degrees (approx 1km at the equator) <br>PROJECTION: Geographic, WGS84 <br>&nbsp;<br>FILENAMES: Nepal_2011_IWI_HR.tif - International Wealth Index (IWI), Nepal_2011_IWI_interdecile_HR.tif is an uncertainty raster, Nepal_2011_WI_HR.tif - Wealth Index (WI), Nepal_2011_WI_interdecile_HR.tif is an uncertainty raster","doi":"","popyear":"2011","date":"2018-05-18","file_img":"WI_IWI_2011.jpg","continent":"Asia","country":"Nepal","resolution":"1000","type":"Poverty","file_html":null},{"id":"1275","desc":"DATASET: Alpha version 2007 estimates of proportion of people per grid square living in poverty, as defined by the Multidimensional Poverty Index (http:\/\/www.ophi.org.uk\/policy\/multidimensional-poverty-index\/), and associated uncertainty metrics. \r\n<p>REGION: Asia \r\n<\/p><p>SPATIAL RESOLUTION: 0.00833333 decimal degrees (approx 1km at the equator) \r\n<\/p><p>PROJECTION: Geographic, WGS84 \r\n<\/p><p>UNITS: Proportion of residents living in MPI-defined poverty (poverty dataset); 95% credible interval (uncertainty dataset) \r\n<\/p><p>MAPPING APPROACH: Bayesian model-based geostatistics in combination with high resolution gridded spatial covariates applied to GPS-located household survey data on poverty from the DHS and\/or LSMS programs. \r\n<\/p><p>FORMAT: Geotiff (zipped using 7-zip (open access tool): www.7-zip.org) \r\n<\/p><p>FILENAMES: Examples - ken08povmpi.tif = Kenya (ken) MPI poverty map for 2008. ken08povmpi-uncert.tif = uncertainty dataset showing 95% credible intervals. \r\n<\/p><p>DATE OF PRODUCTION: January 2013 \r\n<\/p><p><br><\/p>","doi":"10.5258\/SOTON\/WP00208","popyear":null,"date":"2013-01-01","file_img":null,"continent":"Asia","country":"Pakistan","resolution":"1000","type":"Poverty","file_html":null},{"id":"17024","desc":"Diphtheria-tetanus-pertussis (DTP) dose 1, 2 and 3 vaccination coverage among children aged under 5 years in 2013 for Pakistan.\r\n<p>Estimated vaccination coverage, i.e. proportions of children vaccinated, and associated uncertainties, measured as standard deviations, for Mozambique in 2013.\r\n<\/p><p>\r\n<\/p><p>Utazi et al (2018) A spatial regression model for the disaggregation of areal unit based data to high-resolution grids with application to vaccination coverage mapping. Statistical Methods in Medical Research. https:\/\/doi.org\/10.1177\/0962280218797362)<\/p><p><br><\/p><p>Filenames:\r\n<\/p><p>DTP vaccination estimates: \r\n<\/p><p>[COUNTRYCODE]_mean_pred_DTP1_perc.tif = Mean % estimated DTP1 vaccination coverage (0-59 months) \r\n<\/p><p>[COUNTRYCODE]_mean_pred_DTP1_perc.tif = Mean % estimated DTP2 vaccination coverage (0-59 months) \r\n<\/p><p>[COUNTRYCODE]_mean_pred_DTP1_perc.tif = Mean % estimated DTP3 vaccination coverage (0-59 months) \r\n<\/p><p><br><\/p>","doi":"","popyear":"2013","date":"2019-02-03","file_img":"PKS_mean_pred_DTP1_perc.png","continent":"Asia","country":"Pakistan","resolution":"5000","type":"DTP vaccination coverage","file_html":null},{"id":"17034","desc":"Estimated vaccination coverage, i.e. proportions of children vaccinated, and associated uncertainties, measured as standard deviations, broken by age groups (9-11 months, 12-23 months, 24-59 months, 0-59 months and 9-59 months) for Pakistan in 2013.\r\n<p>The methodology used for the work is described in Utazi et al. (2017) High resolution age-structure mapping of childhood vaccination coverage in low and middle income countries, Vaccine 36 (12), pp. 1583-1591 and Utazi et al (2019) Mapping vaccination coverage rates at high resolution to explore the effects of delivery mechanisms and inform vaccination strategies, Nature Communications, under review.\r\n<\/p><p>Filenames:\r\n<\/p><p>DTP vaccination estimates: \r\n<\/p><p>[COUNTRYCODE]_mean_pred_DTP1_perc.tif = Mean % estimated DTP1 vaccination coverage (0-59 months) \r\n<\/p><p>[COUNTRYCODE]_mean_pred_DTP1_perc.tif = Mean % estimated DTP2 vaccination coverage (0-59 months) \r\n<\/p><p>[COUNTRYCODE]_mean_pred_DTP1_perc.tif = Mean % estimated DTP3 vaccination coverage (0-59 months) \r\n<\/p><p><br><\/p>","doi":"","popyear":"2013","date":"2019-02-03","file_img":"PKS_mean_pred_total_perc.png","continent":"Asia","country":"Pakistan","resolution":"5000","type":"Measles vaccination coverage","file_html":null},{"id":"50664","desc":"Proportion of women aged 15 to 49 years in Pakistan who have never used modern contraception during the years 2017-18. The estimations along with their associated uncertainties, measured as standard deviations (SD), are calculated following the methodology detailed in Utazi et al. (2021, 2022, 2023).\r\n<p><b>References<\/b>:<\/p>\r\n\r\n<ul>\r\n  <li>Utazi, C.E., Aheto, J.M.K., Chan, H.M.T., Tatem, A.J. and Sahu, S.K., 2022. <i>Conditional probability and ratio-based approaches for mapping the coverage of multi-dose vaccines<\/i>. Statistics in Medicine, 41(29), pp.5662-5678. <a target=\"_blank\"  href=\"https:\/\/doi.org\/10.1002\/sim.9586\">DOI:10.1002\/sim.9586<\/a><\/li>\r\n  <li>Utazi, C.E., Chan, H.M.T., Olowe, I., Wigley, A., Tejedor-Garavito, N., Cunningham, A., Bondarenko, M., Lorin, J., Boyda, D., Hogan, D. and Tatem, A.J., 2023. <i>A zero-dose vulnerability index for equity assessment and spatial prioritization in low-and middle-income countries<\/i>. Spatial Statistics, p.100772. <a target=\"_blank\"  href=\"https:\/\/doi.org\/10.1016\/j.spasta.2023.100772\">DOI:10.1016\/j.spasta.2023.100772<\/a><\/li>\r\n  <li>Utazi, C.E., Nilsen, K., Pannell, O., Dotse-Gborgbortsi, W. and Tatem, A.J., 2021. <i>District-level estimation of vaccination coverage: Discrete vs continuous spatial models<\/i>. Statistics in Medicine, 40(9), pp.2197-2211. <a target=\"_blank\"  href=\"https:\/\/doi.org\/10.1002\/sim.8897\">DOI:10.1002\/sim.8897<\/a><\/li>\r\n<\/ul>\r\n","doi":"10.5258\/SOTON\/WP00767","popyear":"2018","date":"2023-04-07","file_img":"PAK_Contraceptive_use.png","continent":"Asia","country":"Pakistan","resolution":"1000","type":"Contraceptive Use","file_html":null},{"id":"50665","desc":"Vaccination coverage for diphtheria-tetanus-pertussis dose 1 (DTP1) and dose 3 (DTP3) among children aged 12 to 23 months in India during the years 2017-18. The estimated vaccination coverages, representing the proportions of vaccinated children, along with their associated uncertainties measured as standard deviations (SD), are computed following the methodology detailed in Utazi et al. (2021, 2022, 2023).\r\n<p><b>References<\/b>:<\/p>\r\n\r\n<ul>\r\n  <li>Utazi, C.E., Aheto, J.M.K., Chan, H.M.T., Tatem, A.J. and Sahu, S.K., 2022. <i>Conditional probability and ratio-based approaches for mapping the coverage of multi-dose vaccines<\/i>. Statistics in Medicine, 41(29), pp.5662-5678. <a target=\"_blank\"  href=\"https:\/\/doi.org\/10.1002\/sim.9586\">DOI:10.1002\/sim.9586<\/a><\/li>\r\n  <li>Utazi, C.E., Chan, H.M.T., Olowe, I., Wigley, A., Tejedor-Garavito, N., Cunningham, A., Bondarenko, M., Lorin, J., Boyda, D., Hogan, D. and Tatem, A.J., 2023. <i>A zero-dose vulnerability index for equity assessment and spatial prioritization in low-and middle-income countries<\/i>. Spatial Statistics, p.100772. <a target=\"_blank\"  href=\"https:\/\/doi.org\/10.1016\/j.spasta.2023.100772\">DOI:10.1016\/j.spasta.2023.100772<\/a><\/li>\r\n  <li>Utazi, C.E., Nilsen, K., Pannell, O., Dotse-Gborgbortsi, W. and Tatem, A.J., 2021. <i>District-level estimation of vaccination coverage: Discrete vs continuous spatial models<\/i>. Statistics in Medicine, 40(9), pp.2197-2211. <a target=\"_blank\"  href=\"https:\/\/doi.org\/10.1002\/sim.8897\">DOI:10.1002\/sim.8897<\/a><\/li>\r\n<\/ul>\r\n","doi":"10.5258\/SOTON\/WP00767","popyear":"2018","date":"2023-04-07","file_img":"PAK_DTP_vaccination_coverage.png","continent":"Asia","country":"Pakistan","resolution":"1000","type":"DTP vaccination coverage","file_html":null},{"id":"50666","desc":"Proportion of children aged 12 to 23 months born to large (&gt;= 9 members) households in Pakistan during the years 2017-18. The estimations along with their associated uncertainties, measured as standard deviations (SD), are calculated following the methodology detailed in Utazi et al. (2021, 2022, 2023).\r\n<p><b>References<\/b>:<\/p>\r\n\r\n<ul>\r\n  <li>Utazi, C.E., Aheto, J.M.K., Chan, H.M.T., Tatem, A.J. and Sahu, S.K., 2022. <i>Conditional probability and ratio-based approaches for mapping the coverage of multi-dose vaccines<\/i>. Statistics in Medicine, 41(29), pp.5662-5678. <a target=\"_blank\"  href=\"https:\/\/doi.org\/10.1002\/sim.9586\">DOI:10.1002\/sim.9586<\/a><\/li>\r\n  <li>Utazi, C.E., Chan, H.M.T., Olowe, I., Wigley, A., Tejedor-Garavito, N., Cunningham, A., Bondarenko, M., Lorin, J., Boyda, D., Hogan, D. and Tatem, A.J., 2023. <i>A zero-dose vulnerability index for equity assessment and spatial prioritization in low-and middle-income countries<\/i>. Spatial Statistics, p.100772. <a target=\"_blank\"  href=\"https:\/\/doi.org\/10.1016\/j.spasta.2023.100772\">DOI:10.1016\/j.spasta.2023.100772<\/a><\/li>\r\n  <li>Utazi, C.E., Nilsen, K., Pannell, O., Dotse-Gborgbortsi, W. and Tatem, A.J., 2021. <i>District-level estimation of vaccination coverage: Discrete vs continuous spatial models<\/i>. Statistics in Medicine, 40(9), pp.2197-2211. <a target=\"_blank\"  href=\"https:\/\/doi.org\/10.1002\/sim.8897\">DOI:10.1002\/sim.8897<\/a><\/li>\r\n<\/ul>\r\n","doi":"10.5258\/SOTON\/WP00767","popyear":"2018","date":"2023-04-07","file_img":"PAK_Maternal_and_child_demographic.png","continent":"Asia","country":"Pakistan","resolution":"1000","type":"Maternal and child demographic","file_html":null},{"id":"50667","desc":"Maternal and child health in Pakistan during the years 2017-18. Proportion of children aged 12 to 23 months born to mothers who did not attend antenatal care (ANC), who did not receive (or recall) post-natal care within 2 months after birth (PNC), who had no health insurance (INSURANCE), and who had no tetanus toxoid injections before birth (TT). Proportion of children aged 12 to 23 months who were not delivered by a skilled birth attendant (SBA), and who were underweight (MALNUTRITION). The estimations along with their associated uncertainties, measured as standard deviations (SD), are calculated following the methodology detailed in Utazi et al. (2021, 2022, 2023).\r\n<p><b>References<\/b>:<\/p>\r\n\r\n<ul>\r\n  <li>Utazi, C.E., Aheto, J.M.K., Chan, H.M.T., Tatem, A.J. and Sahu, S.K., 2022. <i>Conditional probability and ratio-based approaches for mapping the coverage of multi-dose vaccines<\/i>. Statistics in Medicine, 41(29), pp.5662-5678. <a target=\"_blank\"  href=\"https:\/\/doi.org\/10.1002\/sim.9586\">DOI:10.1002\/sim.9586<\/a><\/li>\r\n  <li>Utazi, C.E., Chan, H.M.T., Olowe, I., Wigley, A., Tejedor-Garavito, N., Cunningham, A., Bondarenko, M., Lorin, J., Boyda, D., Hogan, D. and Tatem, A.J., 2023. <i>A zero-dose vulnerability index for equity assessment and spatial prioritization in low-and middle-income countries<\/i>. Spatial Statistics, p.100772. <a target=\"_blank\"  href=\"https:\/\/doi.org\/10.1016\/j.spasta.2023.100772\">DOI:10.1016\/j.spasta.2023.100772<\/a><\/li>\r\n  <li>Utazi, C.E., Nilsen, K., Pannell, O., Dotse-Gborgbortsi, W. and Tatem, A.J., 2021. <i>District-level estimation of vaccination coverage: Discrete vs continuous spatial models<\/i>. Statistics in Medicine, 40(9), pp.2197-2211. <a target=\"_blank\"  href=\"https:\/\/doi.org\/10.1002\/sim.8897\">DOI:10.1002\/sim.8897<\/a><\/li>\r\n<\/ul>\r\n","doi":"10.5258\/SOTON\/WP00767","popyear":"2018","date":"2023-04-07","file_img":"PAK_Maternal_and_child_health.png","continent":"Asia","country":"Pakistan","resolution":"1000","type":"Maternal and child health","file_html":null},{"id":"50668","desc":"Maternal and child socioeconomic in Pakistan during the years 2017-18. Proportion of children aged 12 to 23 months born to the poorest \/ poorer households according to DHS \/ MICS-NICS classifications (HWEALTH) and born to mothers who had no formal education (MEDUCATION). Proportion of women aged 15 to 49 years who did not participate in decision-making in their households (DECISION). The estimations along with their associated uncertainties, measured as standard deviations (SD), are calculated following the methodology detailed in Utazi et al. (2021, 2022, 2023).\r\n<p><b>References<\/b>:<\/p>\r\n\r\n<ul>\r\n  <li>Utazi, C.E., Aheto, J.M.K., Chan, H.M.T., Tatem, A.J. and Sahu, S.K., 2022. <i>Conditional probability and ratio-based approaches for mapping the coverage of multi-dose vaccines<\/i>. Statistics in Medicine, 41(29), pp.5662-5678. <a target=\"_blank\"  href=\"https:\/\/doi.org\/10.1002\/sim.9586\">DOI:10.1002\/sim.9586<\/a><\/li>\r\n  <li>Utazi, C.E., Chan, H.M.T., Olowe, I., Wigley, A., Tejedor-Garavito, N., Cunningham, A., Bondarenko, M., Lorin, J., Boyda, D., Hogan, D. and Tatem, A.J., 2023. <i>A zero-dose vulnerability index for equity assessment and spatial prioritization in low-and middle-income countries<\/i>. Spatial Statistics, p.100772. <a target=\"_blank\"  href=\"https:\/\/doi.org\/10.1016\/j.spasta.2023.100772\">DOI:10.1016\/j.spasta.2023.100772<\/a><\/li>\r\n  <li>Utazi, C.E., Nilsen, K., Pannell, O., Dotse-Gborgbortsi, W. and Tatem, A.J., 2021. <i>District-level estimation of vaccination coverage: Discrete vs continuous spatial models<\/i>. Statistics in Medicine, 40(9), pp.2197-2211. <a target=\"_blank\"  href=\"https:\/\/doi.org\/10.1002\/sim.8897\">DOI:10.1002\/sim.8897<\/a><\/li>\r\n<\/ul>\r\n","doi":"10.5258\/SOTON\/WP00767","popyear":"2018","date":"2023-04-07","file_img":"PAK_Maternal_and_child_socioeconomic.png","continent":"Asia","country":"Pakistan","resolution":"1000","type":"Maternal and child socioeconomic","file_html":null},{"id":"50669","desc":"Vaccination coverage for measles dose 1 (MCV1) among children aged 12 to 23 months in Pakistan during the years 2017-18. The estimated vaccination coverages, representing the proportions of vaccinated children, along with their associated uncertainties measured as standard deviations (SD), are computed following the methodology detailed in Utazi et al. (2021, 2022, 2023).\r\n<p><b>References<\/b>:<\/p>\r\n\r\n<ul>\r\n  <li>Utazi, C.E., Aheto, J.M.K., Chan, H.M.T., Tatem, A.J. and Sahu, S.K., 2022. <i>Conditional probability and ratio-based approaches for mapping the coverage of multi-dose vaccines<\/i>. Statistics in Medicine, 41(29), pp.5662-5678. <a target=\"_blank\"  href=\"https:\/\/doi.org\/10.1002\/sim.9586\">DOI:10.1002\/sim.9586<\/a><\/li>\r\n  <li>Utazi, C.E., Chan, H.M.T., Olowe, I., Wigley, A., Tejedor-Garavito, N., Cunningham, A., Bondarenko, M., Lorin, J., Boyda, D., Hogan, D. and Tatem, A.J., 2023. <i>A zero-dose vulnerability index for equity assessment and spatial prioritization in low-and middle-income countries<\/i>. Spatial Statistics, p.100772. <a target=\"_blank\"  href=\"https:\/\/doi.org\/10.1016\/j.spasta.2023.100772\">DOI:10.1016\/j.spasta.2023.100772<\/a><\/li>\r\n  <li>Utazi, C.E., Nilsen, K., Pannell, O., Dotse-Gborgbortsi, W. and Tatem, A.J., 2021. <i>District-level estimation of vaccination coverage: Discrete vs continuous spatial models<\/i>. Statistics in Medicine, 40(9), pp.2197-2211. <a target=\"_blank\"  href=\"https:\/\/doi.org\/10.1002\/sim.8897\">DOI:10.1002\/sim.8897<\/a><\/li>\r\n<\/ul>\r\n","doi":"10.5258\/SOTON\/WP00767","popyear":"2018","date":"2023-04-07","file_img":"PAK_Measles_vaccination_coverage.png","continent":"Asia","country":"Pakistan","resolution":"1000","type":"Measles vaccination coverage","file_html":null}]