Changing US Support for Public Health Data Use Through Pandemic and Political Turmoil.

Policy Points This study examines the impact of several world-changing events in 2020, such as the pandemic and widespread racism protests, on the US population's comfort with the use of identifiable data for public health. Before the 2020 election, there was no significant difference between Democrats and Republicans. However, African Americans exhibited a decrease in comfort that was different from other subgroups. Our findings suggest that the public remained supportive of public health data activities through the pandemic and the turmoil of 2020 election cycle relative to other data use. However, support among African Americans for public health data use experienced a unique decline compared to other demographic groups. CONTEXT: Recent legislative privacy efforts have not included special provisions for public health data use. Although past studies documented support for public health data use, several global events in 2020 have raised awareness and concern about privacy and data use. This study aims to understand whether the events of 2020 affected US privacy preferences on secondary uses of identifiable data, focusing on public health and research uses. METHODS: We deployed two online surveys-in February and November 2020-on data privacy attitudes and preferences using a choice-based-conjoint analysis. Participants received different data-use scenario pairs-varied by the type of data, user, and purpose-and selected scenarios based on their comfort. A hierarchical Bayes regression model simulated population preferences. FINDINGS: There were 1,373 responses. There was no statistically significant difference in the population's data preferences between February and November, each showing the highest comfort with population health and research data activities and the lowest with profit-driven activities. Most subgroups' data preferences were comparable with the population's preferences, except African Americans who showed significant decreases in comfort with population health and research. CONCLUSIONS: Despite world-changing events, including a pandemic, we found bipartisan public support for using identifiable data for public health and research. The decreasing support among African Americans could relate to the increased awareness of systemic racism, its harms, and persistent disparities. The US population's preferences support including legal provisions that permit public health and research data use in US laws, which are currently lacking specific public health use permissions.

Outcomes and Costs of the Transition From a Paper-Based Immunization System to a Digital Immunization System in Vietnam: Mixed Methods Study.

BACKGROUND: The electronic National Immunization Information System (NIIS) was introduced nationwide in Vietnam in 2017. Health workers were expected to use the NIIS alongside the legacy paper-based system. Starting in 2018, Hanoi and Son La provinces transitioned to paperless reporting. Interventions to support this transition included data guidelines and training, internet-based data review meetings, and additional supportive supervision visits. OBJECTIVE: This study aims to assess (1) changes in NIIS data quality and use, (2) changes in immunization program outcomes, and (3) the economic costs of using the NIIS versus the traditional paper system. METHODS: This mixed methods study took place in Hanoi and Son La provinces. It aimed to analyses pre- and postintervention data from various sources including the NIIS; household and health facility surveys; and interviews to measure NIIS data quality, data use, and immunization program outcomes. Financial data were collected at the national, provincial, district, and health facility levels through record review and interviews. An activity-based costing approach was conducted from a health system perspective. RESULTS: NIIS data timeliness significantly improved from pre- to postintervention in both provinces. For example, the mean number of days from birth date to NIIS registration before and after intervention dropped from 18.6 (SD 65.5) to 5.7 (SD 31.4) days in Hanoi (P<.001) and from 36.1 (SD 94.2) to 11.7 (40.1) days in Son La (P<.001). Data from Son La showed that the completeness and accuracy improved, while Hanoi exhibited mixed results, possibly influenced by the COVID-19 pandemic. Data use improved; at postintervention, 100% (667/667) of facilities in both provinces used NIIS data for activities beyond monthly reporting compared with 34.8% (202/580) in Hanoi and 29.4% (55/187) in Son La at preintervention. Across nearly all antigens, the percentage of children who received the vaccine on time was higher in the postintervention cohort compared with the preintervention cohort. Up-front costs associated with developing and deploying the NIIS were estimated at US $0.48 per child in the study provinces. The commune health center level showed cost savings from changing from the paper system to the NIIS, mainly driven by human resource time savings. At the administrative level, incremental costs resulted from changing from the paper system to the NIIS, as some costs increased, such as labor costs for supportive supervision and additional capital costs for equipment associated with the NIIS. CONCLUSIONS: The Hanoi and Son La provinces successfully transitioned to paperless reporting while maintaining or improving NIIS data quality and data use. However, improvements in data quality were not associated with improvements in the immunization program outcomes in both provinces. The COVID-19 pandemic likely had a negative influence on immunization program outcomes, particularly in Hanoi. These improvements entail up-front financial costs.

[The Medical Informatics Initiative at a glance-establishing a health research data infrastructure in Germany]. / Die Medizininformatik-Initiative im Überblick ­ Aufbau einer Gesundheitsforschungsdateninfrastruktur in Deutschland.

The Medical Informatics Initiative (MII) funded by the Federal Ministry of Education and Research (BMBF) 2016-2027 is successfully laying the foundations for data-based medicine in Germany. As part of this funding, 51 new professorships, 21 junior research groups, and various new degree programs have been established to strengthen teaching, training, and continuing education in the field of medical informatics and to improve expertise in medical data sciences. A joint decentralized federated research data infrastructure encompassing the entire university medical center and its partners was created in the form of data integration centers (DIC) at all locations and the German Portal for Medical Research Data (FDPG) as a central access point. A modular core dataset (KDS) was defined and implemented for the secondary use of patient treatment data with consistent use of international standards (e.g., FHIR, SNOMED CT, and LOINC). An officially approved nationwide broad consent was introduced as the legal basis. The first data exports and data use projects have been carried out, embedded in an overarching usage policy and standardized contractual regulations. The further development of the MII health research data infrastructures within the cooperative framework of the Network of University Medicine (NUM) offers an excellent starting point for a German contribution to the upcoming European Health Data Space (EHDS), which opens opportunities for Germany as a medical research location.

National routine data for low birthweight and preterm births: Systematic data quality assessment for United Nations member states (2000-2020).

OBJECTIVE: Low birthweight (<2500 g) and preterm birth (<37 weeks) are markers of newborn vulnerability. To facilitate informed decisions about investments in prevention and care, it is imperative to enhance data quality and use. Hence, the objective of this study is to systematically assess the quality of data concerning low birthweight and preterm births within routine administrative data sources. DESIGN: Systematic data quality assessment by adopting the WHO Data Quality Framework. SETTING: National routine data system from UN member states. POPULATION: Livebirths. METHODS: National routine administrative data on low birthweight and preterm births for 195 countries from 2000 to 2020 were systematically collated, totalling >700 million live births. The WHO data quality framework was adapted to undertake standardised data quality assessments. MAIN OUTCOME MEASURES: Availability, reporting quality, internal and external consistency of low birthweight and preterm data. RESULTS: Most United States Member States (64%: 124/195) had national data on low birthweight and (40%: 82/195) had data on preterm birth. Routine data system reporting was highest in North America, Australasia and Europe, where more than 95% live births had data on low birthweight and over 75% had data preterm births. In contrast, data reporting was lowest in sub-Saharan Africa (13% for low birthweight, 8% for preterm births) and Southern Asia (16% for low birthweight, 5% for preterm births). Most countries collect individual-level data; but, aggregate data reporting from hospital-based systems remain common in sub-Saharan Africa and Southern Asia. While data quality was generally high in North America, Australasia and Europe, gaps remain in the availability of gestational age metadata. Consistency between low birthweight and preterm rates were poor in Southern Asia and sub-Saharan Africa regions across time. There was high external consistency between low birthweight rates obtained from routine administrative data compared with low birthweight rates obtained from survey data for countries with high data quality. CONCLUSIONS: Sub-Saharan Africa and South Asia countries have data gaps but also opportunities for rapid progress. Most births occure in facilities, electronic health information systems already include low birthweight, and adding accurate gestational age including with ultrasound assessment is becoming increasingly attainable. Moving toward the collection of individual level data would enable monitoring of quality of care and longer-term outcomes. This is crucial for every child and family and essential for measuring progress towards relevant sustainable development goals. The assessment will inform countries' actions for data quality improvement at national level and use of data for impact.

Public Preferences for Digital Health Data Sharing: Discrete Choice Experiment Study in 12 European Countries.

BACKGROUND: With new technologies, health data can be collected in a variety of different clinical, research, and public health contexts, and then can be used for a range of new purposes. Establishing the public's views about digital health data sharing is essential for policy makers to develop effective harmonization initiatives for digital health data governance at the European level. OBJECTIVE: This study investigated public preferences for digital health data sharing. METHODS: A discrete choice experiment survey was administered to a sample of European residents in 12 European countries (Austria, Denmark, France, Germany, Iceland, Ireland, Italy, the Netherlands, Norway, Spain, Sweden, and the United Kingdom) from August 2020 to August 2021. Respondents answered whether hypothetical situations of data sharing were acceptable for them. Each hypothetical scenario was defined by 5 attributes ("data collector," "data user," "reason for data use," "information on data sharing and consent," and "availability of review process"), which had 3 to 4 attribute levels each. A latent class model was run across the whole data set and separately for different European regions (Northern, Central, and Southern Europe). Attribute relative importance was calculated for each latent class's pooled and regional data sets. RESULTS: A total of 5015 completed surveys were analyzed. In general, the most important attribute for respondents was the availability of information and consent during health data sharing. In the latent class model, 4 classes of preference patterns were identified. While respondents in 2 classes strongly expressed their preferences for data sharing with opposing positions, respondents in the other 2 classes preferred not to share their data, but attribute levels of the situation could have had an impact on their preferences. Respondents generally found the following to be the most acceptable: a national authority or academic research project as the data user; being informed and asked to consent; and a review process for data transfer and use, or transfer only. On the other hand, collection of their data by a technological company and data use for commercial communication were the least acceptable. There was preference heterogeneity across Europe and within European regions. CONCLUSIONS: This study showed the importance of transparency in data use and oversight of health-related data sharing for European respondents. Regional and intraregional preference heterogeneity for "data collector," "data user," "reason," "type of consent," and "review" calls for governance solutions that would grant data subjects the ability to control their digital health data being shared within different contexts. These results suggest that the use of data without consent will demand weighty and exceptional reasons. An interactive and dynamic informed consent model combined with oversight mechanisms may be a solution for policy initiatives aiming to harmonize health data use across Europe.

Opt-In and Opt-Out Consent Procedures for the Reuse of Routinely Recorded Health Data in Scientific Research and Their Consequences for Consent Rate and Consent Bias: Systematic Review.

BACKGROUND: Scientific researchers who wish to reuse health data pertaining to individuals can obtain consent through an opt-in procedure or opt-out procedure. The choice of procedure may have consequences for the consent rate and representativeness of the study sample and the quality of the research, but these consequences are not well known. OBJECTIVE: This review aimed to provide insight into the consequences for the consent rate and consent bias of the study sample of opt-in procedures versus opt-out procedures for the reuse of routinely recorded health data for scientific research purposes. METHODS: A systematic review was performed based on searches in PubMed, Embase, CINAHL, PsycINFO, Web of Science Core Collection, and the Cochrane Library. Two reviewers independently included studies based on predefined eligibility criteria and assessed whether the statistical methods used in the reviewed literature were appropriate for describing the differences between consenters and nonconsenters. Statistical pooling was conducted, and a description of the results was provided. RESULTS: A total of 15 studies were included in this meta-analysis. Of the 15 studies, 13 (87%) implemented an opt-in procedure, 1 (7%) implemented an opt-out procedure, and 1 (7%) implemented both the procedures. The average weighted consent rate was 84% (60,800/72,418 among the studies that used an opt-in procedure and 96.8% (2384/2463) in the single study that used an opt-out procedure. In the single study that described both procedures, the consent rate was 21% in the opt-in group and 95.6% in the opt-out group. Opt-in procedures resulted in more consent bias compared with opt-out procedures. In studies with an opt-in procedure, consenting individuals were more likely to be males, had a higher level of education, higher income, and higher socioeconomic status. CONCLUSIONS: Consent rates are generally lower when using an opt-in procedure compared with using an opt-out procedure. Furthermore, in studies with an opt-in procedure, participants are less representative of the study population. However, both the study populations and the way in which opt-in or opt-out procedures were organized varied widely between the studies, which makes it difficult to draw general conclusions regarding the desired balance between patient control over data and learning from health data. The reuse of routinely recorded health data for scientific research purposes may be hampered by administrative burdens and the risk of bias.

Assessment of data demand for informed-decisions among health facility and department heads in public health facilities of Amhara Region, northwest Ethiopia.

BACKGROUND: Evidence-based decision-making is a foundation of health information systems; however, routine health information is not mostly utilized by decision makers in the Amhara region. Therefore, this study aimed to explore the facility and department heads' perceptions towards the demand for and use of routine health information for decision making. METHODS: A phenomenological qualitative study was done in eight districts of the Amhara region from June 10/2019 to July 30/2019. We obtained written informed consent and recruited 22 key informants purposively. The research team prepared a codebook, assigned codes to ideas, identified salient patterns, grouped similar ideas, and developed themes from the data. Thus, data were analyzed thematically using OpenCode software. RESULTS: The study revealed that health workers collected many data, but little was demanded and utilized to inform decisions. The majority of respondents perceived that data were collected merely for reporting. Lack of skills in data management, analysis, interpretation, and use were the technical attributes. Individual attributes included low staff motivation, carelessness, and lack of value for data. Poor access to data, low support for Health Information System, limited space for archiving, and inadequate finance were related to organizational attributes. The contextual (social-political) factors also influenced the use of eHealth applications for improved data demand and use among health care providers. CONCLUSION: In this study, health workers collect routine health data merely for reporting, and they did not demand and use it mostly to inform decisions and solve problems. Technical, individual, organizational, and contextual attributes were contributors to low demand and use of routine health data. Thus, we recommend building the technical capacity of health workers, introducing motivation mechanisms and ensuring accountability systems for better data use.

The DASH model: Data for addressing social determinants of health in local health departments.

Recent frameworks, models, and reports highlight the critical need to address social determinants of health for achieving health equity in the United States and around the globe. In the United States, data play an important role in better understanding community-level and population-level disparities particularly for local health departments. However, data-driven decision-making-the use of data for public health activities such as program implementation, policy development, and resource allocation-is often presented theoretically or through case studies in the literature. We sought to develop a preliminary model that identifies the factors that contribute to data-driven decision-making in US local health departments and describe relationships between them. Guided by implementation science literature, we examined organizational-level capacity and individual-level factors contributing to using data for decision-making related to social determinants of health and the reduction of county-level disparities. This model has the potential to improve implementation of public health interventions and programs aimed at upstream structural factors, by elucidating the factors critical to incorporating data in decision-making.

Post-high school outcomes of students with autism spectrum disorder and students with intellectual disability: Utilizing predictive analytics and state data for decision making.

This study analyzed the post-high school outcomes of exited high-school students with intellectual disability and autism spectrum disorder from a southwestern U.S. state. A predictive analytics approach was used to analyze these students' post-high school outcomes data, which every state is required to collect each year under U.S. special-education law. Data modeling was conducted with machine learning and logistic regression, which produced two main findings. One, the strongest significant predictors were (a) students spending at least 80% of their instructional days in general education settings and (b) graduating from high school. Two, machine learning models were consistently more accurate in predicting post-high school education or employment than were multilevel logistic regression models. This study concluded with the limitations of the data and predictive-analytic models, and the implications for researchers and state and local education professionals to utilize predictive analytics and state-level post-high school outcomes data for decision making.

The spectrum of data sharing policies in neuroimaging data repositories.

Sharing data is a scientific imperative that accelerates scientific discoveries, reinforces open science inquiry, and allows for efficient use of public investment and research resources. Considering these benefits, data sharing has been widely promoted in diverse fields and neuroscience has been no exception to this movement. For all its promise, however, the sharing of human neuroimaging data raises critical ethical and legal issues, such as data privacy. Recently, the heightened risks to data privacy posed by the rapid advances in artificial intelligence and machine learning techniques have made data sharing more challenging; the regulatory landscape around data sharing has also been evolving rapidly. Here we present an in-depth ethical and regulatory analysis that examines how neuroimaging data are currently shared against the backdrop of the relevant regulations and policies in the United States and how advanced software tools and algorithms might undermine subjects' privacy in neuroimaging data sharing. The implications of these novel technological threats to privacy in neuroimaging data sharing practices and policies will also be discussed. We then conclude with a proposal for a legal prohibition against malicious use of neuroscience data as a regulatory mechanism to address privacy risks associated with the data while maximizing the benefits of data sharing and open science practice in the field of neuroscience.

Video education about genetic privacy and patient perspectives about sharing prenatal genetic data: a randomized trial.

BACKGROUND: Laboratories offering cell-free DNA often reserve the right to share prenatal genetic data for research or even commercial purposes, and obtain this permission on the patient consent form. Although it is known that nonpregnant patients are often reluctant to share their genetic data for research, pregnant patients' knowledge of, and opinions about, genetic data privacy are unknown. OBJECTIVE: We investigated whether pregnant patients who had already undergone cell-free DNA screening were aware that genetic data derived from cell-free DNA may be shared for research. Furthermore, we examined whether pregnant patients exposed to video education about the Genetic Information Nondiscrimination Act-a federal law that mandates workplace and health insurance protections against genetic discrimination-were more willing to share cell-free DNA-related genetic data for research than pregnant patients who were unexposed. STUDY DESIGN: In this randomized controlled trial (ClinicalTrials.gov Identifier: NCT04420858), English-speaking patients with singleton pregnancies who underwent cell-free DNA and subsequently presented at 17 0/7 to 23 6/7 weeks of gestation for a detailed anatomy scan were randomized 1:1 to a control or intervention group. Both groups viewed an infographic about cell-free DNA. In addition, the intervention group viewed an educational video about the Genetic Information Nondiscrimination Act. The primary outcomes were knowledge about, and willingness to share, prenatal genetic data from cell-free DNA by commercial laboratories for nonclinical purposes, such as research. The secondary outcomes included knowledge about existing genetic privacy laws, knowledge about the potential for reidentification of anonymized genetic data, and acceptability of various use and sharing scenarios for prenatal genetic data. Eighty-one participants per group were required for 80% power to detect an increase in willingness to share data from 60% to 80% (α=0.05). RESULTS: A total of 747 pregnant patients were screened, and 213 patients were deemed eligible and approached for potential study participation. Of these patients, 163 (76.5%) consented and were randomized; one participant discontinued the intervention, and two participants were excluded from analysis after the intervention when it was discovered that they did not fulfill all eligibility criteria. Overall, 160 (75.1%) of those approached were included in the final analysis. Most patients in the control group (72 [90.0%]) and intervention (76 [97.4%]) group were either unsure about or incorrectly thought that cell-free DNA companies could not share prenatal genetic data for research. Participants in the intervention group were more likely to incorrectly believe that their prenatal genetic data would not be shared for nonclinical purposes than participants in the control group (28.8% in the control group vs 46.2% in the intervention; P=.03). However, video education did not increase participant willingness to share genetic data in multiple scenarios. Non-White participants were less willing than White participants to allow sharing of genetic data specifically for academic research (P<.001). CONCLUSION: Most participants were unaware that their prenatal genetic data may be used for nonclinical purposes. Pregnant patients who were educated about the Genetic Information Nondiscrimination Act were not more willing to share genetic data than those who did not receive this education. Surprisingly, video education about the Genetic Information Nondiscrimination Act led patients to falsely believe that their data would not be shared for research, and participants who identified as racial minorities were less willing to share genetic data. New strategies are needed to improve pregnant patients' understanding of genetic privacy.

Routine use of DHIS2 data: a scoping review.

BACKGROUND: In regard to health service planning and delivery, the use of information at different levels in the health system is vital, ranging from the influencing of policy to the programming of action to the ensuring of evidence-informed practices. However, neither ownership of, nor access to, good quality data guarantees actual use of these data. For information to be used, relevant data need to be collected, processed and analysed in an accessible format. This problem of underused data, and indeed the absence of data use entirely, is widespread and has been evident for decades. The DHIS2 software platform supports routine health management for an estimated 2.4 billion people, in over 70 countries worldwide. It is by far the largest and most widespread software for this purpose and adopts a holistic, socio-technical approach to development and implementation. Given this approach, and the rapid and extensive scaling of DHIS2, we questioned whether or not there has been a parallel increase in the scaling of improved information use. To date, there has been no rigorous review of the documentation on how exactly DHIS2 data is routinely being used for decision-making and subsequent programming of action. This scoping review addresses this review gap. METHODS: The five-stage approach of Arksey and O'Malley progressed by Levac et al. and Peters was followed. Three databases (PubMed, Web of Science and Embase) were searched, along with relevant conference proceedings and postgraduate theses. In total, over 500 documents were reviewed and data from 19 documents were extracted. RESULTS: Overall, DHIS2 data are being used but there are few detailed descriptions of this usage in peer reviewed or grey literature. We find that, commonly, there exists a centralised versus decentralised pattern of use in terms of access to data and the reporting of data 'up' in the system. We also find that the different conceptualisations of data use and how data use is conceptualised are not made explicit. CONCLUSIONS: We conclude with some suggestions for a way forward, namely: i) the need to document in more detail and share how data are being used, ii) the need to investigate how data were created and who uses such data, iii) the need to design systems based on work practices, and in tandem develop and promote forums in which 'conversations' around data can take place.

Enhancing and promoting data management and systematic monitoring for an improved HIV/AIDS programs in Addis Ababa, Ethiopia.

BACKGROUND: Ethiopia Population-based HIV Impact Assessment findings showed that in Addis Ababa, only 65.2% of people living with HIV (PLHIV) know their status. We present the enhanced HIV/AIDS data management and systematic monitoring experience in Addis Ababa City Administration Health Bureau (AACAHB). METHODS: AACAHB established a command-post with leadership and technical team members from the health bureau, 10 sub-city health offices, and non-governmental stakeholders. The command-post improved governance, standardized HIV program implementation, and established accountability mechanism. A web-based database was established at each health facility, sub-city, and AACAHB level. Performance was scored (green, ≥75%; yellow, 50-74%; red, < 50%). The command-post reviewed performance on weekly basis. A mentorship team provided a weekly site-level support at underperforming public and private health facilities. At facility level, quality of data on recording tools such as registers, and individual medical records were maintained through continued review, feedback mechanisms and regular consistency check of data. Percentage and 95% confidence interval were computed to compare the improvement in program performance over time. RESULTS: After 6 months of intervention period, the monthly New HIV case finding in 47 health facilities increased from 422 to 734 (1.7 times) and treatment initiation increased from 302 to 616 (2 times). After 6 months, the aggregate scoring for HIV testing at city level improved from yellow to green, HIV case finding improved from red to green, and treatment initiation improved from red to yellow. An increasing trend was noted in HIV positive case finding with statistically significant improvement from 43.4% [95% Confidence Interval: 40.23-46.59%] in May 2019 to 74.9% [95% Confidence Interval: 72.03-77.6%] in September 2019. Similarly, significant improvement was recorded for new HIV treatment from 30.9% [95% Confidence Interval: 28.01-33.94%] in May 2019 to 62.5% [95% Confidence Interval: 59.38-65.6%] in September 2019. CONCLUSIONS: Regular data driven HIV program review was institutionalized at city, sub-city and health facility levels which further improved HIV program monitoring and performance. The performance of HIV case finding and treatment initiation improved significantly via using intensified monitoring, data driven performance review, targeted site-level support based on the gap, and standardized approaches.

A realist systematic review of evidence from low- and middle-income countries of interventions to improve immunization data use.

BACKGROUND: The use of routine immunization data by health care professionals in low- and middle-income countries remains an underutilized resource in decision-making. Despite the significant resources invested in developing national health information systems, systematic reviews of the effectiveness of data use interventions are lacking. Applying a realist review methodology, this study synthesized evidence of effective interventions for improving data use in decision-making. METHODS: We searched PubMed, POPLINE, Centre for Agriculture and Biosciences International Global Health, and African Journals Online for published literature. Grey literature was obtained from conference, implementer, and technical agency websites and requested from implementing organizations. Articles were included if they reported on an intervention designed to improve routine data use or reported outcomes related to data use, and targeted health care professionals as the principal data users. We developed a theory of change a priori for how we expect data use interventions to influence data use. Evidence was then synthesized according to data use intervention type and level of the health system targeted by the intervention. RESULTS: The searches yielded 549 articles, of which 102 met our inclusion criteria, including 49 from peer-reviewed journals and 53 from grey literature. A total of 66 articles reported on immunization data use interventions and 36 articles reported on data use interventions for other health sectors. We categorized 68 articles as research evidence and 34 articles as promising strategies. We identified ten primary intervention categories, including electronic immunization registries, which were the most reported intervention type (n = 14). Among the research evidence from the immunization sector, 32 articles reported intermediate outcomes related to data quality and availability, data analysis, synthesis, interpretation, and review. Seventeen articles reported data-informed decision-making as an intervention outcome, which could be explained by the lack of consensus around how to define and measure data use. CONCLUSIONS: Few immunization data use interventions have been rigorously studied or evaluated. The review highlights gaps in the evidence base, which future research and better measures for assessing data use should attempt to address.

Key informant perspectives on the challenges and opportunities for using routine health data for decision-making in Senegal.

BACKGROUND: Increasing the performance of routine health information systems (RHIS) is an important policy priority both globally and in Senegal. As RHIS data become increasingly important in driving decision-making in Senegal, it is imperative to understand the factors that determine their use. METHODS: Semi-structured interviews were conducted with 18 high- and mid-level key informants active in the malaria, tuberculosis and HIV programmatic areas in Senegal. Key informants were employed in the relevant divisions of the Senegal Ministry of Health or nongovernmental / civil society organizations. We asked respondents questions related to the flow, quality and use of RHIS data in their organizations. A framework approach was used to analyze the qualitative data. RESULTS: Although the respondents worked at the strategic levels of their respective organizations, they consistently indicated that data quality and data use issues began at the operational level of the health system before the data made its way to the central level. We classify the main identified barriers and facilitators to the use of routine data into six categories and attempt to describe their interrelated nature. We find that data quality is a central and direct determinant of RHIS data use. We report that a number of upstream factors in the Senegal context interact to influence the quality of routine data produced. We identify the sociopolitical, financial and system design determinants of RHIS data collection, dissemination and use. We also discuss the organizational and infrastructural factors that influence the use of RHIS data. CONCLUSIONS: We recommend specific prescriptive actions with potential to improve RHIS performance in Senegal, the quality of the data produced and their use. These actions include addressing sociopolitical factors that often interrupt RHIS functioning in Senegal, supporting and motivating staff that maintain RHIS data systems as well as ensuring RHIS data completeness and representativeness. We argue for improved coordination between the various stakeholders in order to streamline RHIS data processes and improve transparency. Finally, we recommend the promotion of a sustained culture of data quality assessment and use.

Secondary research use of personal medical data: patient attitudes towards data donation.

BACKGROUND: The SARS-CoV-2 pandemic has highlighted once more the great need for comprehensive access to, and uncomplicated use of, pre-existing patient data for medical research. Enabling secondary research-use of patient-data is a prerequisite for the efficient and sustainable promotion of translation and personalisation in medicine, and for the advancement of public-health. However, balancing the legitimate interests of scientists in broad and unrestricted data-access and the demand for individual autonomy, privacy and social justice is a great challenge for patient-based medical research. METHODS: We therefore conducted two questionnaire-based surveys among North-German outpatients (n = 650) to determine their attitude towards data-donation for medical research, implemented as an opt-out-process. RESULTS: We observed a high level of acceptance (75.0%), the most powerful predictor of a positive attitude towards data-donation was the conviction that every citizen has a duty to contribute to the improvement of medical research (> 80% of participants approving data-donation). Interestingly, patients distinguished sharply between research inside and outside the EU, despite a general awareness that universities and public research institutions cooperate with commercial companies, willingness to allow use of donated data by the latter was very low (7.1% to 29.1%, depending upon location of company). The most popular measures among interviewees to counteract reservations against commercial data-use were regulation by law (61.4%), stipulating in the process that data are not sold or resold (84.6%). A majority requested control of both the use (46.8%) and the protection (41.5%) of the data by independent bodies. CONCLUSIONS: In conclusion, data-donation for medical research, implemented as a combination of legal entitlement and easy-to-exercise-right to opt-out, was found to be widely supported by German patients and therefore warrants further consideration for a transposition into national law.

Level and contributing factors of health data quality and information use in two districts in Northwest Ethiopia: social-ecological perspective.

BACKGROUND: The health management information system has been implemented at all levels of healthcare delivery to ensure quality data production and information use in Ethiopia. Including the capacity-building activities and provision of infrastructure, various efforts have been made to improve the production and use of quality health data though the result is still unsatisfactory. This study aimed to examine the quality of health data and use in Wogera and Tach-Armacheho districts and understand its barriers and facilitators. METHODS: The study utilized a mixed-method; for the quantitative approach, institution-based cross-sectional study was conducted to determine the quality of health data and use by 95 departments in the two districts. The qualitative approach involved 16 in-depth interviewees from Wogera district. A descriptive Phenomenological design was used to explore factors influencing the quality and use of health data. The quantitative data were expressed descriptively with tables, graphs, and percent whereas the qualitative data were analyzed with content analysis guided by the social-ecological model framework. RESULT: The average levels of information use for Wogera and Tach-Armacheho districts were estimated at 29 and 35.9, respectively. The overall average level of accuracy of reports for six different health services in the HCs of Wogera and Tach Armacheho districts were 0.95 and 0.86, respectively. The qualitatively identified factors that influence the production and use of quality health data include valuing data, getting staff training, being a patriotic staff, and getting supportive supervision, were identified from individual-level characteristics; similarly, coaching, supportive supervision, and peer-to-peer learning from relational/interpersonal level characteristics, and organizational culture, incentive, infrastructure establishing accountability, and staff turnover, were identified from organizational level characteristics. CONCLUSION: The quality of data and routine information utilization was low and were influenced by a number of actors presented in and around the health system including individual, interpersonal, and organizational characteristics. Incentive affects data quality and information use directly or indirectly after modifying factors at all levels of the social-ecological model. Therefore, interventions should gear towards addressing multiple social-ecological factors of the health system concomitantly or intervene on incentive which has a multifaceted effect on the outcome.

Use of a quantitative data report in a hypothetical decision scenario for health policymaking: a computer-assisted laboratory study.

BACKGROUND: Quantitative data reports are widely produced to inform health policy decisions. Policymakers are expected to critically assess provided information in order to incorporate the best available evidence into the decision-making process. Many other factors are known to influence this process, but little is known about how quantitative data reports are actually read. We explored the reading behavior of (future) health policy decision-makers, using innovative methods. METHODS: We conducted a computer-assisted laboratory study, involving starting and advanced students in medicine and health sciences, and professionals as participants. They read a quantitative data report to inform a decision on the use of resources for long-term care in dementia in a hypothetical decision scenario. Data were collected through eye-tracking, questionnaires, and a brief interview. Eye-tracking data were used to generate 'heatmaps' and five measures of reading behavior. The questionnaires provided participants' perceptions of understandability and helpfulness as well as individual characteristics. Interviews documented reasons for attention to specific report sections. The quantitative analysis was largely descriptive, complemented by Pearson correlations. Interviews were analyzed by qualitative content analysis. RESULTS: In total, 46 individuals participated [students (85%), professionals (15%)]. Eye-tracking observations showed that the participants spent equal time and attention for most parts of the presented report, but were less focused when reading the methods section. The qualitative content analysis identified 29 reasons for attention to a report section related to four topics. Eye-tracking measures were largely unrelated to participants' perceptions of understandability and helpfulness of the report. CONCLUSIONS: Eye-tracking data added information on reading behaviors that were not captured by questionnaires or interviews with health decision-makers.

Using health data for decision-making at each level of the health system to achieve universal health coverage in Ethiopia: the case of an immunization programme in a low-resource setting.

BACKGROUND: For evidence-based decision-making, there is a need for quality, timely, relevant and accessible information at each level of the health system. Limited use of local data at each level of the health system is reported to be a main challenge for evidence-based decision-making in low- and middle-income countries. Although evidence is available on the timeliness and quality of local data, we know little about how it is used for decision-making at different levels of the health system. Therefore, this study aimed to assess the level of data use and its effect on data quality and shared accountability at different levels of the health system. METHODS: An implementation science study was conducted using key informants and document reviews between January and September 2017. A total of 21 key informants were selected from community representatives, data producers, data users and decision-makers from the community to the regional level. Reviewed documents include facility reports, district reports, zonal reports and feedback in supervision from the district. Thematic content analysis was performed for the qualitative data. RESULTS: Respondents reported that routine data use for routine decision-making was low. All health facilities and health offices have a performance monitoring team, but these were not always functional. Awareness gaps, lack of motivating incentives, irregularity of supportive supervision, lack of community engagement in health report verification as well as poor technical capacity of health professionals were found to be the major barriers to data use. The study also revealed that there are no institutional or national-level regulations or policies on the accountability mechanisms related to health data. The community-level Health Development Army programme was found to be a strong community engagement approach that can be leveraged for data verification at the source of community data. CONCLUSION: The culture of using routine data for decision-making at the local level was found to be low. Strengthening the capacity of health workers and performance monitoring teams, introducing incentive mechanisms for data use, engaging the community in data verification and introducing accountability mechanisms for health data are essential to improve data use and quality.

Strengthening the national health information system through a capacity-building and mentorship partnership (CBMP) programme: a health system and university partnership initiative in Ethiopia.

BACKGROUND: A strong health information system (HIS) is one of the essential building blocks for a resilient health system. The Ministry of Health (MOH) of Ethiopia is working on different initiatives to strengthen the national HIS. Among these is the Capacity-Building and Mentorship Partnership (CBMP) Programme in collaboration with public universities in Ethiopia since November 2017. This study aims to evaluate the outcomes and share experiences of the country in working with universities to strengthen the national HIS. METHODS: The study employed a mixed-methods approach that included 247 health organizations (health offices and facilities) of CBMP-implementing woredas (districts) and 23 key informant interviews. The programme focused on capacity-building and mentoring facilities and woreda health offices. The status of HIS was measured using a connected woreda checklist before and after the intervention. The checklist consists of items related to HIS infrastructure, data quality and administrative use. The organizations were classified as emerging, candidate or model based on the score. The findings were triangulated with qualitative data collected through key informant interviews. RESULTS: The results showed that the overall score of the HIS implementation was 46.3 before and 74.2 after implementation of the programme. The proportion of model organizations increased from 1.2% before to 31.8% after the programme implementation. The health system-university partnership has provided an opportunity for higher education institutions to understand the health system and tune their curricula to address real-world challenges. The partnership brought opportunities to conduct and produce local- and national-level evidence to improve the HIS. Weak ownership, poor responsiveness and poor perceptions of the programme were mentioned as major challenges in programme implementation. CONCLUSION: The overall HIS has shown substantial progress in CBMP implementation woredas. A number of facilities became models in a short period of time after the implementation of the programme. The health system-university partnership was found to be a promising approach to improve the national HIS and to share the on-the-ground experiences with the university academicians. However, weak ownership and poor responsiveness to feedback were the major challenges identified as needing more attention in future programme implementation.