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Article ; Online: Digital technology applications for contact tracing: the new promise for COVID-19 and beyond?

Owusu, Priscilla N

Global health research and policy

2020 Volume 5, Page(s) 36

Abstract: Among the most critical strategies in the fight against the Corona Virus Disease (COVID-19) is the rapid tracing and notification of potentially infected persons. Several nations have implemented mobile software applications ("apps") to alert persons ... ...

Abstract	Among the most critical strategies in the fight against the Corona Virus Disease (COVID-19) is the rapid tracing and notification of potentially infected persons. Several nations have implemented mobile software applications ("apps") to alert persons exposed to the coronavirus. The expected advantages of this new technology over the traditional method of contact tracing include speed, specificity, and mass reach. Beyond its use for mitigating and containing COVID-19, digital technology can complement or even augment the traditional approach to global health program implementation. However, as with any new system, strong regulatory frameworks are necessary to ensure that individual information is not used for surveillance purposes, and user privacy will be maintained. Having safeguarded this, perhaps the global health community will witness the beginning of a new era of implementing mass health programs through the medium of digital technology.
MeSH term(s)	COVID-19/epidemiology ; Communicable Disease Control/instrumentation ; Contact Tracing/instrumentation ; Digital Technology/statistics & numerical data ; Disease Outbreaks/statistics & numerical data ; Humans ; Public Health/methods
Keywords	covid19
Language	English
Publishing date	2020-08-03
Publishing country	England
Document type	Journal Article
ISSN	2397-0642
ISSN (online)	2397-0642
DOI	10.1186/s41256-020-00164-1
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: Digital technology applications for contact tracing

Owusu, Priscilla N.

Global Health Research and Policy

the new promise for COVID-19 and beyond?

2020 Volume 5, Issue 1

Keywords	covid19
Language	English
Publisher	Springer Science and Business Media LLC
Publishing country	us
Document type	Article ; Online
ISSN	2397-0642
DOI	10.1186/s41256-020-00164-1
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Digital technology applications for contact tracing

Priscilla N. Owusu

Global Health Research and Policy, Vol 5, Iss 1, Pp 1-

the new promise for COVID-19 and beyond?

2020 Volume 3

Abstract: Abstract Among the most critical strategies in the fight against the Corona Virus Disease (COVID-19) is the rapid tracing and notification of potentially infected persons. Several nations have implemented mobile software applications (“apps”) to alert ... ...

Abstract	Abstract Among the most critical strategies in the fight against the Corona Virus Disease (COVID-19) is the rapid tracing and notification of potentially infected persons. Several nations have implemented mobile software applications (“apps”) to alert persons exposed to the coronavirus. The expected advantages of this new technology over the traditional method of contact tracing include speed, specificity, and mass reach. Beyond its use for mitigating and containing COVID-19, digital technology can complement or even augment the traditional approach to global health program implementation. However, as with any new system, strong regulatory frameworks are necessary to ensure that individual information is not used for surveillance purposes, and user privacy will be maintained. Having safeguarded this, perhaps the global health community will witness the beginning of a new era of implementing mass health programs through the medium of digital technology.
Keywords	COVID-19 ; Digital health technology ; Population health ; Program implementation ; Health policy ; Public aspects of medicine ; RA1-1270 ; covid19
Subject code	303
Language	English
Publishing date	2020-08-01T00:00:00Z
Publisher	BMC
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article: Digital technology applications for contact tracing: the new promise for COVID-19 and beyond?

Owusu, Priscilla N

Glob Health Res Policy

Abstract	Among the most critical strategies in the fight against the Corona Virus Disease (COVID-19) is the rapid tracing and notification of potentially infected persons. Several nations have implemented mobile software applications ("apps") to alert persons exposed to the coronavirus. The expected advantages of this new technology over the traditional method of contact tracing include speed, specificity, and mass reach. Beyond its use for mitigating and containing COVID-19, digital technology can complement or even augment the traditional approach to global health program implementation. However, as with any new system, strong regulatory frameworks are necessary to ensure that individual information is not used for surveillance purposes, and user privacy will be maintained. Having safeguarded this, perhaps the global health community will witness the beginning of a new era of implementing mass health programs through the medium of digital technology.
Keywords	covid19
Publisher	WHO
Document type	Article
Note	WHO #Covidence: #692453
Database	COVID19

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Article ; Online: Artificial intelligence applications in social media for depression screening: A systematic review protocol for content validity processes.

Owusu, Priscilla N / Reininghaus, Ulrich / Koppe, Georgia / Dankwa-Mullan, Irene / Bärnighausen, Till

PloS one

2021 Volume 16, Issue 11, Page(s) e0259499

Abstract: Background: The popularization of social media has led to the coalescing of user groups around mental health conditions; in particular, depression. Social media offers a rich environment for contextualizing and predicting users' self-reported burden of ... ...

Abstract	Background: The popularization of social media has led to the coalescing of user groups around mental health conditions; in particular, depression. Social media offers a rich environment for contextualizing and predicting users' self-reported burden of depression. Modern artificial intelligence (AI) methods are commonly employed in analyzing user-generated sentiment on social media. In the forthcoming systematic review, we will examine the content validity of these computer-based health surveillance models with respect to standard diagnostic frameworks. Drawing from a clinical perspective, we will attempt to establish a normative judgment about the strengths of these modern AI applications in the detection of depression. Methods: We will perform a systematic review of English and German language publications from 2010 to 2020 in PubMed, APA PsychInfo, Science Direct, EMBASE Psych, Google Scholar, and Web of Science. The inclusion criteria span cohort, case-control, cross-sectional studies, randomized controlled studies, in addition to reports on conference proceedings. The systematic review will exclude some gray source materials, specifically editorials, newspaper articles, and blog posts. Our primary outcome is self-reported depression, as expressed on social media. Secondary outcomes will be the types of AI methods used for social media depression screen, and the clinical validation procedures accompanying these methods. In a second step, we will utilize the evidence-strengthening Population, Intervention, Comparison, Outcomes, Study type (PICOS) tool to refine our inclusion and exclusion criteria. Following the independent assessment of the evidence sources by two authors for the risk of bias, the data extraction process will culminate in a thematic synthesis of reviewed studies. Discussion: We present the protocol for a systematic review which will consider all existing literature from peer reviewed publication sources relevant to the primary and secondary outcomes. The completed review will discuss depression as a self-reported health outcome in social media material. We will examine the computational methods, including AI and machine learning techniques which are commonly used for online depression surveillance. Furthermore, we will focus on standard clinical assessments, as indicating content validity, in the design of the algorithms. The methodological quality of the clinical construct of the algorithms will be evaluated with the COnsensus-based Standards for the selection of health status Measurement Instruments (COSMIN) framework. We conclude the study with a normative judgment about the current application of AI to screen for depression on social media. Systematic review registration: International Prospective Register of Systematic Reviews PROSPERO (registration number CRD42020187874).
MeSH term(s)	Artificial Intelligence ; Cross-Sectional Studies ; Depression ; Social Media ; Systematic Reviews as Topic
Language	English
Publishing date	2021-11-08
Publishing country	United States
Document type	Journal Article
ZDB-ID	2267670-3
ISSN	1932-6203 ; 1932-6203
ISSN (online)	1932-6203
ISSN	1932-6203
DOI	10.1371/journal.pone.0259499
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: Cross-national examination of adolescent suicidal behavior: a pooled and multi-level analysis of 193,484 students from 53 LMIC countries.

Abio, Anne / Owusu, Priscilla N / Posti, Jussi P / Bärnighausen, Till / Shaikh, Masood Ali / Shankar, Viswanathan / Lowery Wilson, Michael

Social psychiatry and psychiatric epidemiology

2022 Volume 57, Issue 8, Page(s) 1603–1613

Abstract: Introduction: Suicide is a leading cause of adolescent mortality worldwide. We aimed to estimate the prevalence and identify individual-level and country-level factors which might explain the variability in suicidal behavior among students in 53 low to ... ...

Abstract	Introduction: Suicide is a leading cause of adolescent mortality worldwide. We aimed to estimate the prevalence and identify individual-level and country-level factors which might explain the variability in suicidal behavior among students in 53 low to middle income countries. Methods: We used data on adolescents aged 12-16 years from the Global School-based Student Health Surveys from 2009-2016. The suicidal behaviors investigated included suicide ideation, suicidal planning and suicide attempt. The prevalence was estimated for 53 countries, while a multilevel logistic regression analysis (33 countries) was used to investigate the associations of these behaviors with individual and country-level contextual risk factors. The contextual variables included the Gini Coefficient, Gross Domestic Product per capita, pupil-to-teacher ratios, population density, homicide rates, law criminalizing suicide and the night light index. Results: The overall prevalence of suicide ideation, making a plan and suicide attempt were 10.4%, 10.3% and 11.0%, respectively. The highest prevalence rates reported were from the Americas. The strongest risk factors associated with suicidal behavior included anxiety, loneliness, no close friends and the substance abuse. Among the country level variables, the night light index was associated with making a suicide plan and attempting suicide. Conclusion: The non-significant country level findings were not entirely surprising given the mixed results from prior studies. Additional knowledge is thus achieved with regard to country level factors associated with suicidal behavior across adolescent populations.
MeSH term(s)	Adolescent ; Adolescent Behavior ; Developing Countries ; Health Surveys ; Humans ; Prevalence ; Risk Factors ; Students ; Suicidal Ideation ; Suicide, Attempted
Language	English
Publishing date	2022-04-21
Publishing country	Germany
Document type	Journal Article
ZDB-ID	623071-4
ISSN	1433-9285 ; 0037-7813 ; 0933-7954
ISSN (online)	1433-9285
ISSN	0037-7813 ; 0933-7954
DOI	10.1007/s00127-022-02287-x
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: Artificial intelligence applications in social media for depression screening

Priscilla N. Owusu / Ulrich Reininghaus / Georgia Koppe / Irene Dankwa-Mullan / Till Bärnighausen

PLoS ONE, Vol 16, Iss

A systematic review protocol for content validity processes

2021 Volume 11

Abstract: Background The popularization of social media has led to the coalescing of user groups around mental health conditions; in particular, depression. Social media offers a rich environment for contextualizing and predicting users’ self-reported burden of ... ...

Abstract	Background The popularization of social media has led to the coalescing of user groups around mental health conditions; in particular, depression. Social media offers a rich environment for contextualizing and predicting users’ self-reported burden of depression. Modern artificial intelligence (AI) methods are commonly employed in analyzing user-generated sentiment on social media. In the forthcoming systematic review, we will examine the content validity of these computer-based health surveillance models with respect to standard diagnostic frameworks. Drawing from a clinical perspective, we will attempt to establish a normative judgment about the strengths of these modern AI applications in the detection of depression. Methods We will perform a systematic review of English and German language publications from 2010 to 2020 in PubMed, APA PsychInfo, Science Direct, EMBASE Psych, Google Scholar, and Web of Science. The inclusion criteria span cohort, case-control, cross-sectional studies, randomized controlled studies, in addition to reports on conference proceedings. The systematic review will exclude some gray source materials, specifically editorials, newspaper articles, and blog posts. Our primary outcome is self-reported depression, as expressed on social media. Secondary outcomes will be the types of AI methods used for social media depression screen, and the clinical validation procedures accompanying these methods. In a second step, we will utilize the evidence-strengthening Population, Intervention, Comparison, Outcomes, Study type (PICOS) tool to refine our inclusion and exclusion criteria. Following the independent assessment of the evidence sources by two authors for the risk of bias, the data extraction process will culminate in a thematic synthesis of reviewed studies. Discussion We present the protocol for a systematic review which will consider all existing literature from peer reviewed publication sources relevant to the primary and secondary outcomes. The completed review will discuss depression ...
Keywords	Medicine ; R ; Science ; Q
Subject code	302
Language	English
Publishing date	2021-01-01T00:00:00Z
Publisher	Public Library of Science (PLoS)
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Artificial intelligence applications in social media for depression screening

Priscilla N Owusu / Ulrich Reininghaus / Georgia Koppe / Irene Dankwa-Mullan / Till Bärnighausen

PLoS ONE, Vol 16, Iss 11, p e

A systematic review protocol for content validity processes.

2021 Volume 0259499

Abstract	Background The popularization of social media has led to the coalescing of user groups around mental health conditions; in particular, depression. Social media offers a rich environment for contextualizing and predicting users' self-reported burden of depression. Modern artificial intelligence (AI) methods are commonly employed in analyzing user-generated sentiment on social media. In the forthcoming systematic review, we will examine the content validity of these computer-based health surveillance models with respect to standard diagnostic frameworks. Drawing from a clinical perspective, we will attempt to establish a normative judgment about the strengths of these modern AI applications in the detection of depression. Methods We will perform a systematic review of English and German language publications from 2010 to 2020 in PubMed, APA PsychInfo, Science Direct, EMBASE Psych, Google Scholar, and Web of Science. The inclusion criteria span cohort, case-control, cross-sectional studies, randomized controlled studies, in addition to reports on conference proceedings. The systematic review will exclude some gray source materials, specifically editorials, newspaper articles, and blog posts. Our primary outcome is self-reported depression, as expressed on social media. Secondary outcomes will be the types of AI methods used for social media depression screen, and the clinical validation procedures accompanying these methods. In a second step, we will utilize the evidence-strengthening Population, Intervention, Comparison, Outcomes, Study type (PICOS) tool to refine our inclusion and exclusion criteria. Following the independent assessment of the evidence sources by two authors for the risk of bias, the data extraction process will culminate in a thematic synthesis of reviewed studies. Discussion We present the protocol for a systematic review which will consider all existing literature from peer reviewed publication sources relevant to the primary and secondary outcomes. The completed review will discuss depression as a self-reported health outcome in social media material. We will examine the computational methods, including AI and machine learning techniques which are commonly used for online depression surveillance. Furthermore, we will focus on standard clinical assessments, as indicating content validity, in the design of the algorithms. The methodological quality of the clinical construct of the algorithms will be evaluated with the COnsensus-based Standards for the selection of health status Measurement Instruments (COSMIN) framework. We conclude the study with a normative judgment about the current application of AI to screen for depression on social media. Systematic review registration International Prospective Register of Systematic Reviews PROSPERO (registration number CRD42020187874).
Keywords	Medicine ; R ; Science ; Q
Subject code	302
Language	English
Publishing date	2021-01-01T00:00:00Z
Publisher	Public Library of Science (PLoS)
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article: Artificial intelligence applications in social media for depression screening

Owusu, Priscilla N. / Reininghaus, Ulrich / Koppe, Georgia / Dankwa-Mullan, Irene / Bärnighausen, Till

PLoS ONE

A systematic review protocol for content validity processes

2021 Volume 16, Issue 11, Page(s) No

Title translation	Anwendungen der künstlichen Intelligenz in sozialen Medien für das Depressionsscreening: Ein systematisches Reviewprotokoll für Prozesse der Inhaltsvalidität
Abstract	Background: The popularization of social media has led to the coalescing of user groups around mental health conditions; in particular, depression. Social media offers a rich environment for contextualizing and predicting users' self-reported burden of depression. Modern artificial intelligence (AI) methods are commonly employed in analyzing user-generated sentiment on social media. In the forthcoming systematic review, we will examine the content validity of these computer-based health surveillance models with respect to standard diagnostic frameworks. Drawing from a clinical perspective, we will attempt to establish a normative judgment about the strengths of these modern AI applications in the detection of depression. Methods: We will perform a systematic review of English and German language publications from 2010 to 2020 in PubMed, APA PsychInfo, Science Direct, EMBASE Psych, Google Scholar, and Web of Science. The inclusion criteria span cohort, case-control, cross-sectional studies, randomized controlled studies, in addition to reports on conference proceedings. The systematic review will exclude some gray source materials, specifically editorials, newspaper articles, and blog posts. Our primary outcome is self-reported depression, as expressed on social media. Secondary outcomes will be the types of AI methods used for social media depression screen, and the clinical validation procedures accompanying these methods. In a second step, we will utilize the evidence-strengthening Population, Intervention, Comparison, Outcomes, Study type (PICOS) tool to refine our inclusion and exclusion criteria. Following the independent assessment of the evidence sources by two authors for the risk of bias, the data extraction process will culminate in a thematic synthesis of reviewed studies. Discussion: We present the protocol for a systematic review which will consider all existing literature from peer reviewed publication sources relevant to the primary and secondary outcomes. The completed review will discuss depression as a self-reported health outcome in social media material. We will examine the computational methods, including AI and machine learning techniques which are commonly used for online depression surveillance. Furthermore, we will focus on standard clinical assessments, as indicating content validity, in the design of the algorithms. The methodological quality of the clinical construct of the algorithms will be evaluated with the COnsensus-based Standards for the selection of health status Measurement Instruments (COSMIN) framework. We conclude the study with a normative judgment about the current application of AI to screen for depression on social media.
Keywords	Artificial Intelligence ; Content Validity ; Inhaltsvalidität ; Internet ; Künstliche Intelligenz ; Machine Learning ; Major Depression ; Maschinelles Lernen ; Measurement ; Messung ; Screening ; Selbstbericht ; Self-Report ; Social Media ; Soziale Medien
Language	English
Document type	Article
DOI	10.1371/journal.pone.0259499
Database	PSYNDEX

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Article ; Online: Genetic analysis and heterotic grouping of quality protein maize (Zea mays L.) inbred lines and derived hybrids under conditions of low soil nitrogen and drought stress

Owusu, Godfred Afrifa / Abe, Ayodeji / Ribeiro, Priscilla Francisco

Euphytica. 2023 Feb., v. 219, no. 2 p.29-29

2023

Abstract: ... yield (GY) in West and Central Africa (WCA) due to low soil nitrogen (low-N) and intermittent ... low-N and DS conditions. Seventy-eight single cross hybrids were generated through half-diallel mating ... under the low-N and DS conditions. Significant general combining ability (GCA) and specific ...

Abstract	Quality Protein Maize (QPM) varieties are rich in lysine and tryptophan, but suffer reduced grain yield (GY) in West and Central Africa (WCA) due to low soil nitrogen (low-N) and intermittent drought stress (DS). Development of stress-tolerant QPM hybrids will enhance sustainable maize production and improve nutritional health in WCA. Knowledge of combining ability, gene action and heterotic grouping of QPM inbred lines are crucial to successful breeding strategies for the development of superior hybrids with enhanced nutritional values. The objectives of this study were to: (i) determine the combining ability for GY and yield-related traits among 13 newly developed QPM inbred lines, and (ii) assign the QPM inbred lines to distinct heterotic groups based on general combining ability effects of multiple traits under low-N and DS conditions. Seventy-eight single cross hybrids were generated through half-diallel mating of 13 QPM inbred lines and evaluated along with three commercial checks for GY and yield-related traits under the low-N and DS conditions. Significant general combining ability (GCA) and specific combining ability effects were obtained for GY and yield-related traits. Both additive and non-additive gene effects were involved in the inheritance of GY and other traits under low-N and DS conditions. However, the additive gene effect for GY was twice as large as non-additive gene effect. Three heterotic groups were each delineated under low-N and DS. Inbred lines, CRIZEQ-44 and CRIZEQ-77 belonging to different heterotic groups were identified as testers for the development of superior hybrids for low-N and DS environments.
Keywords	Zea mays ; additive gene effects ; corn ; genes ; genetic analysis ; grain yield ; heterosis ; lysine ; nitrogen ; soil ; tryptophan ; water stress ; Central Africa
Language	English
Dates of publication	2023-02
Size	p. 29.
Publishing place	Springer Netherlands
Document type	Article ; Online
ZDB-ID	216568-5
ISSN	1573-5060 ; 0014-2336
ISSN (online)	1573-5060
ISSN	0014-2336
DOI	10.1007/s10681-023-03159-4
Database	NAL-Catalogue (AGRICOLA)

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