Leveraging big data for improving the estimation of close to reality travel time to obstetric emergency services in urban low- and middle-income settings
Maternal and perinatal mortality remain huge challenges globally, particularly in low- and middle-income countries (LMICs) where >98% of these deaths occur. Emergency obstetric care (EmOC) provided by skilled health personnel is an evidence-based package of interventions effective in reducing the...
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| Format: | Article |
| Language: | English |
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2022
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| Online Access: | http://hdl.handle.net/11408/5178 |
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| author | Aduragbemi, Banke-Thomas Macharia, Peter M Makanga, Prestige Tatenda Beňová, Lenka Wong, Kerry L M Gwacham-Anisiobi, Uchenna Wang, Jia Olubodun, Tope Ogunyemi, Olakunmi Afolabi, Bosede B Ebenso, Bassey Omolade Abejirinde, Ibukun-Oluwa |
| author_facet | Aduragbemi, Banke-Thomas Macharia, Peter M Makanga, Prestige Tatenda Beňová, Lenka Wong, Kerry L M Gwacham-Anisiobi, Uchenna Wang, Jia Olubodun, Tope Ogunyemi, Olakunmi Afolabi, Bosede B Ebenso, Bassey Omolade Abejirinde, Ibukun-Oluwa |
| author_sort | Aduragbemi, Banke-Thomas |
| collection | DSpace |
| description | Maternal and perinatal mortality remain huge challenges globally, particularly in low- and middle-income countries (LMICs) where >98% of these deaths occur. Emergency obstetric care (EmOC) provided by skilled health personnel is an evidence-based package of interventions effective in reducing these deaths associated with pregnancy and childbirth. Until recently, pregnant women residing in urban areas have been considered to have good access to care, including EmOC. However, emerging evidence shows that due to rapid urbanization, this so called "urban advantage" is shrinking and in some LMIC settings, it is almost non-existent. This poses a complex challenge for structuring an effective health service delivery system, which tend to have poor spatial planning especially in LMIC settings. To optimize access to EmOC and ultimately reduce preventable maternal deaths within the context of urbanization, it is imperative to accurately locate areas and population groups that are geographically marginalized. Underpinning such assessments is accurately estimating travel time to health facilities that provide EmOC. In this perspective, we discuss strengths and weaknesses of approaches commonly used to estimate travel times to EmOC in LMICs, broadly grouped as reported and modeled approaches, while contextualizing our discussion in urban areas. We then introduce the novel OnTIME project, which seeks to address some of the key limitations in these commonly used approaches by leveraging big data. The perspective concludes with a discussion on anticipated outcomes and potential policy applications of the OnTIME project. |
| format | Article |
| id | ir-11408-5178 |
| institution | My University |
| language | English |
| publishDate | 2022 |
| publisher | PubMed Central |
| record_format | dspace |
| spelling | ir-11408-51782022-09-30T10:05:50Z Leveraging big data for improving the estimation of close to reality travel time to obstetric emergency services in urban low- and middle-income settings Aduragbemi, Banke-Thomas Macharia, Peter M Makanga, Prestige Tatenda Beňová, Lenka Wong, Kerry L M Gwacham-Anisiobi, Uchenna Wang, Jia Olubodun, Tope Ogunyemi, Olakunmi Afolabi, Bosede B Ebenso, Bassey Omolade Abejirinde, Ibukun-Oluwa big data digital technology emergency obstetric care equity travel time Maternal and perinatal mortality remain huge challenges globally, particularly in low- and middle-income countries (LMICs) where >98% of these deaths occur. Emergency obstetric care (EmOC) provided by skilled health personnel is an evidence-based package of interventions effective in reducing these deaths associated with pregnancy and childbirth. Until recently, pregnant women residing in urban areas have been considered to have good access to care, including EmOC. However, emerging evidence shows that due to rapid urbanization, this so called "urban advantage" is shrinking and in some LMIC settings, it is almost non-existent. This poses a complex challenge for structuring an effective health service delivery system, which tend to have poor spatial planning especially in LMIC settings. To optimize access to EmOC and ultimately reduce preventable maternal deaths within the context of urbanization, it is imperative to accurately locate areas and population groups that are geographically marginalized. Underpinning such assessments is accurately estimating travel time to health facilities that provide EmOC. In this perspective, we discuss strengths and weaknesses of approaches commonly used to estimate travel times to EmOC in LMICs, broadly grouped as reported and modeled approaches, while contextualizing our discussion in urban areas. We then introduce the novel OnTIME project, which seeks to address some of the key limitations in these commonly used approaches by leveraging big data. The perspective concludes with a discussion on anticipated outcomes and potential policy applications of the OnTIME project. 2022-09-30T10:05:50Z 2022-09-30T10:05:50Z 2022-07-29 Article Banke-Thomas A, Macharia PM, Makanga PT, Beňová L, Wong KLM, Gwacham-Anisiobi U, Wang J, Olubodun T, Ogunyemi O, Afolabi BB, Ebenso B, Omolade Abejirinde IO. Leveraging big data for improving the estimation of close to reality travel time to obstetric emergency services in urban low- and middle-income settings. Front Public Health. 2022 Jul 29;10:931401. doi: 10.3389/fpubh.2022.931401. PMID: 35968464; PMCID: PMC9372297. 2296-2565 doi: 10.3389/fpubh.2022.931401. http://hdl.handle.net/11408/5178 en Frontiers in Public Health;Vol.10 open PubMed Central |
| spellingShingle | big data digital technology emergency obstetric care equity travel time Aduragbemi, Banke-Thomas Macharia, Peter M Makanga, Prestige Tatenda Beňová, Lenka Wong, Kerry L M Gwacham-Anisiobi, Uchenna Wang, Jia Olubodun, Tope Ogunyemi, Olakunmi Afolabi, Bosede B Ebenso, Bassey Omolade Abejirinde, Ibukun-Oluwa Leveraging big data for improving the estimation of close to reality travel time to obstetric emergency services in urban low- and middle-income settings |
| title | Leveraging big data for improving the estimation of close to reality travel time to obstetric emergency services in urban low- and middle-income settings |
| title_full | Leveraging big data for improving the estimation of close to reality travel time to obstetric emergency services in urban low- and middle-income settings |
| title_fullStr | Leveraging big data for improving the estimation of close to reality travel time to obstetric emergency services in urban low- and middle-income settings |
| title_full_unstemmed | Leveraging big data for improving the estimation of close to reality travel time to obstetric emergency services in urban low- and middle-income settings |
| title_short | Leveraging big data for improving the estimation of close to reality travel time to obstetric emergency services in urban low- and middle-income settings |
| title_sort | leveraging big data for improving the estimation of close to reality travel time to obstetric emergency services in urban low- and middle-income settings |
| topic | big data digital technology emergency obstetric care equity travel time |
| url | http://hdl.handle.net/11408/5178 |
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