Underestimating attacks: comparing two sources of publicly-available data about attacks on health care in 2017
Background Attacks on health care represent an area of growing international concern. Publicly available data are important in documenting attacks, and are often the only easily accessible data source. Data collection processes about attacks on health and their implications have received little atte...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Parada, Vanessa [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2023 |
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| Schlagwörter: |
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| Anmerkung: |
© The Author(s) 2023 |
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| Übergeordnetes Werk: |
Enthalten in: Conflict and health - London : BioMed Central, 2007, 17(2023), 1 vom: 30. Jan. |
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| Übergeordnetes Werk: |
volume:17 ; year:2023 ; number:1 ; day:30 ; month:01 |
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| DOI / URN: |
10.1186/s13031-023-00498-w |
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SPR051403455 |
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| 520 | |a Background Attacks on health care represent an area of growing international concern. Publicly available data are important in documenting attacks, and are often the only easily accessible data source. Data collection processes about attacks on health and their implications have received little attention, despite the fact that datasets and their collection processes may result in differing numbers. Comparing two separate datasets compiled using publicly-available data revealed minimal overlap. This article aims to explain the reasons for the lack of overlap, to better understand the gaps and their implications. Methods We compared the data collection processes for datasets comprised of publicly-reported attacks on health care from the World Health Organization (WHO) and Insecurity Insight’s Security in Numbers Database (SiND). We compared each individual event to compile a comparable dataset and identify unique and matched events in order to determine the overlap between them. We report descriptive statistics for this comparison. Results We identified a common dataset of 287 events from 2017, of which only 33 appeared in both datasets, resulting in a mere 12.9% (n = 254) overlap. Events affecting personnel and facilities appeared most often in both, and 22 of 31 countries lacked any overlap between datasets. Conclusions We conclude that the minimal overlap suggests significant underreporting of attacks on health care, and furthermore, that dataset definitions and parameters affect data collection. Source variation appears to best explain the discrepancies and closer comparison of the collection processes reveal weaknesses of both automated and manual data collection that rely on hidden curation processes. To generate more accurate datasets compiled from public sources requires systematic work to translate definitions into effective online search mechanisms to better capture the full range of events, and to increase the diversity of languages and local sources to better capture events across geographies. | ||
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10.1186/s13031-023-00498-w doi (DE-627)SPR051403455 (SPR)s13031-023-00498-w-e DE-627 ger DE-627 rakwb eng Parada, Vanessa verfasserin aut Underestimating attacks: comparing two sources of publicly-available data about attacks on health care in 2017 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2023 Background Attacks on health care represent an area of growing international concern. Publicly available data are important in documenting attacks, and are often the only easily accessible data source. Data collection processes about attacks on health and their implications have received little attention, despite the fact that datasets and their collection processes may result in differing numbers. Comparing two separate datasets compiled using publicly-available data revealed minimal overlap. This article aims to explain the reasons for the lack of overlap, to better understand the gaps and their implications. Methods We compared the data collection processes for datasets comprised of publicly-reported attacks on health care from the World Health Organization (WHO) and Insecurity Insight’s Security in Numbers Database (SiND). We compared each individual event to compile a comparable dataset and identify unique and matched events in order to determine the overlap between them. We report descriptive statistics for this comparison. Results We identified a common dataset of 287 events from 2017, of which only 33 appeared in both datasets, resulting in a mere 12.9% (n = 254) overlap. Events affecting personnel and facilities appeared most often in both, and 22 of 31 countries lacked any overlap between datasets. Conclusions We conclude that the minimal overlap suggests significant underreporting of attacks on health care, and furthermore, that dataset definitions and parameters affect data collection. Source variation appears to best explain the discrepancies and closer comparison of the collection processes reveal weaknesses of both automated and manual data collection that rely on hidden curation processes. To generate more accurate datasets compiled from public sources requires systematic work to translate definitions into effective online search mechanisms to better capture the full range of events, and to increase the diversity of languages and local sources to better capture events across geographies. Attacks on health care (dpeaa)DE-He213 Conflict (dpeaa)DE-He213 Health (dpeaa)DE-He213 Conflict event data (dpeaa)DE-He213 Data quality (dpeaa)DE-He213 Publicly-available data (dpeaa)DE-He213 Media data (dpeaa)DE-He213 Fast, Larissa aut Briody, Carolyn aut Wille, Christina aut Coninx, Rudi aut Enthalten in Conflict and health London : BioMed Central, 2007 17(2023), 1 vom: 30. Jan. (DE-627)52587514X (DE-600)2273783-2 1752-1505 nnns volume:17 year:2023 number:1 day:30 month:01 https://dx.doi.org/10.1186/s13031-023-00498-w kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2111 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4367 GBV_ILN_4700 AR 17 2023 1 30 01 |
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10.1186/s13031-023-00498-w doi (DE-627)SPR051403455 (SPR)s13031-023-00498-w-e DE-627 ger DE-627 rakwb eng Parada, Vanessa verfasserin aut Underestimating attacks: comparing two sources of publicly-available data about attacks on health care in 2017 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2023 Background Attacks on health care represent an area of growing international concern. Publicly available data are important in documenting attacks, and are often the only easily accessible data source. Data collection processes about attacks on health and their implications have received little attention, despite the fact that datasets and their collection processes may result in differing numbers. Comparing two separate datasets compiled using publicly-available data revealed minimal overlap. This article aims to explain the reasons for the lack of overlap, to better understand the gaps and their implications. Methods We compared the data collection processes for datasets comprised of publicly-reported attacks on health care from the World Health Organization (WHO) and Insecurity Insight’s Security in Numbers Database (SiND). We compared each individual event to compile a comparable dataset and identify unique and matched events in order to determine the overlap between them. We report descriptive statistics for this comparison. Results We identified a common dataset of 287 events from 2017, of which only 33 appeared in both datasets, resulting in a mere 12.9% (n = 254) overlap. Events affecting personnel and facilities appeared most often in both, and 22 of 31 countries lacked any overlap between datasets. Conclusions We conclude that the minimal overlap suggests significant underreporting of attacks on health care, and furthermore, that dataset definitions and parameters affect data collection. Source variation appears to best explain the discrepancies and closer comparison of the collection processes reveal weaknesses of both automated and manual data collection that rely on hidden curation processes. To generate more accurate datasets compiled from public sources requires systematic work to translate definitions into effective online search mechanisms to better capture the full range of events, and to increase the diversity of languages and local sources to better capture events across geographies. Attacks on health care (dpeaa)DE-He213 Conflict (dpeaa)DE-He213 Health (dpeaa)DE-He213 Conflict event data (dpeaa)DE-He213 Data quality (dpeaa)DE-He213 Publicly-available data (dpeaa)DE-He213 Media data (dpeaa)DE-He213 Fast, Larissa aut Briody, Carolyn aut Wille, Christina aut Coninx, Rudi aut Enthalten in Conflict and health London : BioMed Central, 2007 17(2023), 1 vom: 30. Jan. (DE-627)52587514X (DE-600)2273783-2 1752-1505 nnns volume:17 year:2023 number:1 day:30 month:01 https://dx.doi.org/10.1186/s13031-023-00498-w kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2111 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4367 GBV_ILN_4700 AR 17 2023 1 30 01 |
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10.1186/s13031-023-00498-w doi (DE-627)SPR051403455 (SPR)s13031-023-00498-w-e DE-627 ger DE-627 rakwb eng Parada, Vanessa verfasserin aut Underestimating attacks: comparing two sources of publicly-available data about attacks on health care in 2017 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2023 Background Attacks on health care represent an area of growing international concern. Publicly available data are important in documenting attacks, and are often the only easily accessible data source. Data collection processes about attacks on health and their implications have received little attention, despite the fact that datasets and their collection processes may result in differing numbers. Comparing two separate datasets compiled using publicly-available data revealed minimal overlap. This article aims to explain the reasons for the lack of overlap, to better understand the gaps and their implications. Methods We compared the data collection processes for datasets comprised of publicly-reported attacks on health care from the World Health Organization (WHO) and Insecurity Insight’s Security in Numbers Database (SiND). We compared each individual event to compile a comparable dataset and identify unique and matched events in order to determine the overlap between them. We report descriptive statistics for this comparison. Results We identified a common dataset of 287 events from 2017, of which only 33 appeared in both datasets, resulting in a mere 12.9% (n = 254) overlap. Events affecting personnel and facilities appeared most often in both, and 22 of 31 countries lacked any overlap between datasets. Conclusions We conclude that the minimal overlap suggests significant underreporting of attacks on health care, and furthermore, that dataset definitions and parameters affect data collection. Source variation appears to best explain the discrepancies and closer comparison of the collection processes reveal weaknesses of both automated and manual data collection that rely on hidden curation processes. To generate more accurate datasets compiled from public sources requires systematic work to translate definitions into effective online search mechanisms to better capture the full range of events, and to increase the diversity of languages and local sources to better capture events across geographies. Attacks on health care (dpeaa)DE-He213 Conflict (dpeaa)DE-He213 Health (dpeaa)DE-He213 Conflict event data (dpeaa)DE-He213 Data quality (dpeaa)DE-He213 Publicly-available data (dpeaa)DE-He213 Media data (dpeaa)DE-He213 Fast, Larissa aut Briody, Carolyn aut Wille, Christina aut Coninx, Rudi aut Enthalten in Conflict and health London : BioMed Central, 2007 17(2023), 1 vom: 30. Jan. (DE-627)52587514X (DE-600)2273783-2 1752-1505 nnns volume:17 year:2023 number:1 day:30 month:01 https://dx.doi.org/10.1186/s13031-023-00498-w kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2111 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4367 GBV_ILN_4700 AR 17 2023 1 30 01 |
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10.1186/s13031-023-00498-w doi (DE-627)SPR051403455 (SPR)s13031-023-00498-w-e DE-627 ger DE-627 rakwb eng Parada, Vanessa verfasserin aut Underestimating attacks: comparing two sources of publicly-available data about attacks on health care in 2017 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2023 Background Attacks on health care represent an area of growing international concern. Publicly available data are important in documenting attacks, and are often the only easily accessible data source. Data collection processes about attacks on health and their implications have received little attention, despite the fact that datasets and their collection processes may result in differing numbers. Comparing two separate datasets compiled using publicly-available data revealed minimal overlap. This article aims to explain the reasons for the lack of overlap, to better understand the gaps and their implications. Methods We compared the data collection processes for datasets comprised of publicly-reported attacks on health care from the World Health Organization (WHO) and Insecurity Insight’s Security in Numbers Database (SiND). We compared each individual event to compile a comparable dataset and identify unique and matched events in order to determine the overlap between them. We report descriptive statistics for this comparison. Results We identified a common dataset of 287 events from 2017, of which only 33 appeared in both datasets, resulting in a mere 12.9% (n = 254) overlap. Events affecting personnel and facilities appeared most often in both, and 22 of 31 countries lacked any overlap between datasets. Conclusions We conclude that the minimal overlap suggests significant underreporting of attacks on health care, and furthermore, that dataset definitions and parameters affect data collection. Source variation appears to best explain the discrepancies and closer comparison of the collection processes reveal weaknesses of both automated and manual data collection that rely on hidden curation processes. To generate more accurate datasets compiled from public sources requires systematic work to translate definitions into effective online search mechanisms to better capture the full range of events, and to increase the diversity of languages and local sources to better capture events across geographies. Attacks on health care (dpeaa)DE-He213 Conflict (dpeaa)DE-He213 Health (dpeaa)DE-He213 Conflict event data (dpeaa)DE-He213 Data quality (dpeaa)DE-He213 Publicly-available data (dpeaa)DE-He213 Media data (dpeaa)DE-He213 Fast, Larissa aut Briody, Carolyn aut Wille, Christina aut Coninx, Rudi aut Enthalten in Conflict and health London : BioMed Central, 2007 17(2023), 1 vom: 30. Jan. (DE-627)52587514X (DE-600)2273783-2 1752-1505 nnns volume:17 year:2023 number:1 day:30 month:01 https://dx.doi.org/10.1186/s13031-023-00498-w kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2111 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4367 GBV_ILN_4700 AR 17 2023 1 30 01 |
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10.1186/s13031-023-00498-w doi (DE-627)SPR051403455 (SPR)s13031-023-00498-w-e DE-627 ger DE-627 rakwb eng Parada, Vanessa verfasserin aut Underestimating attacks: comparing two sources of publicly-available data about attacks on health care in 2017 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2023 Background Attacks on health care represent an area of growing international concern. Publicly available data are important in documenting attacks, and are often the only easily accessible data source. Data collection processes about attacks on health and their implications have received little attention, despite the fact that datasets and their collection processes may result in differing numbers. Comparing two separate datasets compiled using publicly-available data revealed minimal overlap. This article aims to explain the reasons for the lack of overlap, to better understand the gaps and their implications. Methods We compared the data collection processes for datasets comprised of publicly-reported attacks on health care from the World Health Organization (WHO) and Insecurity Insight’s Security in Numbers Database (SiND). We compared each individual event to compile a comparable dataset and identify unique and matched events in order to determine the overlap between them. We report descriptive statistics for this comparison. Results We identified a common dataset of 287 events from 2017, of which only 33 appeared in both datasets, resulting in a mere 12.9% (n = 254) overlap. Events affecting personnel and facilities appeared most often in both, and 22 of 31 countries lacked any overlap between datasets. Conclusions We conclude that the minimal overlap suggests significant underreporting of attacks on health care, and furthermore, that dataset definitions and parameters affect data collection. Source variation appears to best explain the discrepancies and closer comparison of the collection processes reveal weaknesses of both automated and manual data collection that rely on hidden curation processes. To generate more accurate datasets compiled from public sources requires systematic work to translate definitions into effective online search mechanisms to better capture the full range of events, and to increase the diversity of languages and local sources to better capture events across geographies. Attacks on health care (dpeaa)DE-He213 Conflict (dpeaa)DE-He213 Health (dpeaa)DE-He213 Conflict event data (dpeaa)DE-He213 Data quality (dpeaa)DE-He213 Publicly-available data (dpeaa)DE-He213 Media data (dpeaa)DE-He213 Fast, Larissa aut Briody, Carolyn aut Wille, Christina aut Coninx, Rudi aut Enthalten in Conflict and health London : BioMed Central, 2007 17(2023), 1 vom: 30. Jan. (DE-627)52587514X (DE-600)2273783-2 1752-1505 nnns volume:17 year:2023 number:1 day:30 month:01 https://dx.doi.org/10.1186/s13031-023-00498-w kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2111 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4367 GBV_ILN_4700 AR 17 2023 1 30 01 |
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underestimating attacks: comparing two sources of publicly-available data about attacks on health care in 2017 |
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Underestimating attacks: comparing two sources of publicly-available data about attacks on health care in 2017 |
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Background Attacks on health care represent an area of growing international concern. Publicly available data are important in documenting attacks, and are often the only easily accessible data source. Data collection processes about attacks on health and their implications have received little attention, despite the fact that datasets and their collection processes may result in differing numbers. Comparing two separate datasets compiled using publicly-available data revealed minimal overlap. This article aims to explain the reasons for the lack of overlap, to better understand the gaps and their implications. Methods We compared the data collection processes for datasets comprised of publicly-reported attacks on health care from the World Health Organization (WHO) and Insecurity Insight’s Security in Numbers Database (SiND). We compared each individual event to compile a comparable dataset and identify unique and matched events in order to determine the overlap between them. We report descriptive statistics for this comparison. Results We identified a common dataset of 287 events from 2017, of which only 33 appeared in both datasets, resulting in a mere 12.9% (n = 254) overlap. Events affecting personnel and facilities appeared most often in both, and 22 of 31 countries lacked any overlap between datasets. Conclusions We conclude that the minimal overlap suggests significant underreporting of attacks on health care, and furthermore, that dataset definitions and parameters affect data collection. Source variation appears to best explain the discrepancies and closer comparison of the collection processes reveal weaknesses of both automated and manual data collection that rely on hidden curation processes. To generate more accurate datasets compiled from public sources requires systematic work to translate definitions into effective online search mechanisms to better capture the full range of events, and to increase the diversity of languages and local sources to better capture events across geographies. © The Author(s) 2023 |
| abstractGer |
Background Attacks on health care represent an area of growing international concern. Publicly available data are important in documenting attacks, and are often the only easily accessible data source. Data collection processes about attacks on health and their implications have received little attention, despite the fact that datasets and their collection processes may result in differing numbers. Comparing two separate datasets compiled using publicly-available data revealed minimal overlap. This article aims to explain the reasons for the lack of overlap, to better understand the gaps and their implications. Methods We compared the data collection processes for datasets comprised of publicly-reported attacks on health care from the World Health Organization (WHO) and Insecurity Insight’s Security in Numbers Database (SiND). We compared each individual event to compile a comparable dataset and identify unique and matched events in order to determine the overlap between them. We report descriptive statistics for this comparison. Results We identified a common dataset of 287 events from 2017, of which only 33 appeared in both datasets, resulting in a mere 12.9% (n = 254) overlap. Events affecting personnel and facilities appeared most often in both, and 22 of 31 countries lacked any overlap between datasets. Conclusions We conclude that the minimal overlap suggests significant underreporting of attacks on health care, and furthermore, that dataset definitions and parameters affect data collection. Source variation appears to best explain the discrepancies and closer comparison of the collection processes reveal weaknesses of both automated and manual data collection that rely on hidden curation processes. To generate more accurate datasets compiled from public sources requires systematic work to translate definitions into effective online search mechanisms to better capture the full range of events, and to increase the diversity of languages and local sources to better capture events across geographies. © The Author(s) 2023 |
| abstract_unstemmed |
Background Attacks on health care represent an area of growing international concern. Publicly available data are important in documenting attacks, and are often the only easily accessible data source. Data collection processes about attacks on health and their implications have received little attention, despite the fact that datasets and their collection processes may result in differing numbers. Comparing two separate datasets compiled using publicly-available data revealed minimal overlap. This article aims to explain the reasons for the lack of overlap, to better understand the gaps and their implications. Methods We compared the data collection processes for datasets comprised of publicly-reported attacks on health care from the World Health Organization (WHO) and Insecurity Insight’s Security in Numbers Database (SiND). We compared each individual event to compile a comparable dataset and identify unique and matched events in order to determine the overlap between them. We report descriptive statistics for this comparison. Results We identified a common dataset of 287 events from 2017, of which only 33 appeared in both datasets, resulting in a mere 12.9% (n = 254) overlap. Events affecting personnel and facilities appeared most often in both, and 22 of 31 countries lacked any overlap between datasets. Conclusions We conclude that the minimal overlap suggests significant underreporting of attacks on health care, and furthermore, that dataset definitions and parameters affect data collection. Source variation appears to best explain the discrepancies and closer comparison of the collection processes reveal weaknesses of both automated and manual data collection that rely on hidden curation processes. To generate more accurate datasets compiled from public sources requires systematic work to translate definitions into effective online search mechanisms to better capture the full range of events, and to increase the diversity of languages and local sources to better capture events across geographies. © The Author(s) 2023 |
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Publicly available data are important in documenting attacks, and are often the only easily accessible data source. Data collection processes about attacks on health and their implications have received little attention, despite the fact that datasets and their collection processes may result in differing numbers. Comparing two separate datasets compiled using publicly-available data revealed minimal overlap. This article aims to explain the reasons for the lack of overlap, to better understand the gaps and their implications. Methods We compared the data collection processes for datasets comprised of publicly-reported attacks on health care from the World Health Organization (WHO) and Insecurity Insight’s Security in Numbers Database (SiND). We compared each individual event to compile a comparable dataset and identify unique and matched events in order to determine the overlap between them. We report descriptive statistics for this comparison. Results We identified a common dataset of 287 events from 2017, of which only 33 appeared in both datasets, resulting in a mere 12.9% (n = 254) overlap. Events affecting personnel and facilities appeared most often in both, and 22 of 31 countries lacked any overlap between datasets. Conclusions We conclude that the minimal overlap suggests significant underreporting of attacks on health care, and furthermore, that dataset definitions and parameters affect data collection. Source variation appears to best explain the discrepancies and closer comparison of the collection processes reveal weaknesses of both automated and manual data collection that rely on hidden curation processes. To generate more accurate datasets compiled from public sources requires systematic work to translate definitions into effective online search mechanisms to better capture the full range of events, and to increase the diversity of languages and local sources to better capture events across geographies.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Attacks on health care</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Conflict</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Health</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Conflict event data</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Data quality</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Publicly-available data</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Media data</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Fast, Larissa</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Briody, Carolyn</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Wille, Christina</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Coninx, Rudi</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="t">Conflict and health</subfield><subfield code="d">London : BioMed Central, 2007</subfield><subfield code="g">17(2023), 1 vom: 30. 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