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This book focuses on a range of geospatial applications for environmental health research, including environmental justice issues, environmental health disparities, air and water contamination, and infectious diseases. Environmental health research is at an exciting point in its use of geotechnologies, and many researchers are working on innovative approaches. This book is a timely scholarly contribution in updating the key concepts and applications of using GIS and other geospatial methods for environmental health research. Each chapter contains original research which utilizes a geotechnical tool (Geographic Information Systems (GIS), remote sensing, GPS, etc.) to address an environmental health problem. The book is divided into three sections organized around the following themes: issues in GIS and environmental health research; using GIS to assess environmental health impacts; and, geospatial methods for environmental health. Representing diverse case studies and geospatial methods, the book is likely to be of interest to researchers, practitioners and students across the geographic and environmental health sciences.

A routine health information system is one of the essential components of a health system. Interventions to improve routine health information system data quality and use for decision-making in low- and middle-income countries differ in design, methods, and scope. There have been limited efforts to synthesise the knowledge across the currently available intervention studies. Thus, this scoping review synthesised published results from interventions that aimed at improving data quality and use in routine health information systems in low- and middle-income countries. We included articles on intervention studies that aimed to improve data quality and use within routine health information systems in low- and middle-income countries, published in English from January 2008 to February 2020. We searched the literature in the databases Medline/PubMed, Web of Science, Embase, and Global Health. After a meticulous screening, we identified 20 articles on data quality and 16 on data use. We prepared and presented the results as a narrative. Most of the studies were from Sub-Saharan Africa and designed as case studies. Interventions enhancing the quality of data targeted health facilities and staff within districts, and district health managers for improved data use. Combinations of technology enhancement along with capacity building activities, and data quality assessment and feedback system were found useful in improving data quality. Interventions facilitating data availability combined with technology enhancement increased the use of data for planning. The studies in this scoping review showed that a combination of interventions, addressing both behavioural and technical factors, improved data quality and use. Interventions addressing organisational factors were non-existent, but these factors were reported to pose challenges to the implementation and performance of reported interventions.

Strengthening data quality in the routine health information system is vital for the performance of health service outcomes. However, implementation of the routine interventions to improve data quality in the existing health system has been found inadequate two in Ethiopia. This study was aimed to examine the effect of Augmented Capacity Development Interventions (ACDI) on the performance of data quality in the routine health information system. A arm, parallel group, cluster-randomized control trial was implemented from July 1, 2023 to February 29, 2024. Baseline data were collected from April 1–30, 2023, and end-line data from April 1–30, 2024. The cluster design was employed as it allows for minimizing information contamination. The study included 72 health institution clusters and 304 health workers (154 intervention and 150 control arms). The implemented interventions include training, supportive supervision, mentorship, and recognition. General Linear Mixed Model was applied for analysis. The mean score for data quality perception improved from 2.32 at baseline to 3.13 at end-line (95% CI: 3.05, 3.21; P < .001). The data quality practice has significantly improved after the implementation of the ACDI packages (β = .17; 95% CI: 0.05, 0.30; P = .007), ease of data management (β = .14; 95% CI: 0.07, 0.22; P < .001), information use (β = .15; 95% CI: 0.08, 0.23; P < .001), and the combined effects of encouragement and training (β = .44; 95% CI: 0.23, 0.65; P < .001) were significant predictors of the change in the data quality. The ACDI packages implemented in this study effectively influenced data quality improvement. Key predictors of data quality practices included an encouraging system, ease of data management, written guidelines, supportive supervision, and training. Therefore, the interventions are recommended to be adapted and scaled up. Trial registration ID: PACTR202212472091194.

Background Health Information System is the key to making evidence-based decisions. Ethiopia has been implementing the Health Management Information System (HMIS) since 2008 to collect routine health data and revised it in 2017. However, the evidence is meager on the use of routine health information for decision making among department heads in the health facilities. The study aimed to assess the proportion of routine health information systems utilization for evidence-based decisions and factors associated with it. Method A cross-sectional study was carried out among 386 department heads from 83 health facilities in ten selected districts in the Amhara region Northwest of Ethiopia from April to May 2019. The single population proportion formula was applied to estimate the sample size taking into account the proportion of data use 0.69, margin of error 0.05, and the critical value 1.96 at the 95% CI. The final sample size was estimated at 394 by considering 1.5 as a design effect and 5% non-response. The study participants were selected using a simple random sampling technique. Descriptive statistics mean and percentage were calculated. The study employed a generalized linear mixed-effect model. Adjusted Odds Ratio (AOR) and the 95% CI were calculated. Variables with p value < 0.05 were considered as predictors of routine health information system use. Result Proportion of information use among department heads for decision making was estimated at 46%. Displaying demographic (AOR = 12.42, 95% CI [5.52, 27.98]) and performance (AOR = 1.68; 95% CI [1.33, 2.11]) data for monitoring, and providing feedback to HMIS unit (AOR = 2.29; 95% CI [1.05, 5.00]) were individual (level-1) predictors. Maintaining performance monitoring team minute (AOR = 3.53; 95% CI [1.61, 7.75]), receiving senior management directives (AOR = 3.56; 95% CI [1.76, 7.19]), supervision (AOR = 2.84; 95% CI [1.33, 6.07]), using HMIS data for target setting (AOR = 3.43; 95% CI [1.66, 7.09]), and work location (AOR = 0.16; 95% CI [0.07, 0.39]) were organizational (level-2) explanatory variables. Conclusion The proportion of routine health information utilization for decision making was low. Displaying demographic and performance data, providing feedback to HMIS unit, maintaining performance monitoring team minute, conducting supervision, using HMIS data for target setting, and work location were factors associated with the use of routine health information for decision making. Therefore, strengthening the capacity of department heads on data displaying, supervision, feedback mechanisms, and engagement of senior management are highly recommended.

Background The Health Information System (HIS) in public healthcare services in Serbia was introduced in 2008, with the first comprehensive evaluation of its maturity conducted in 2021. Since then, several improvement initiatives have been implemented. This study aimed to assess the extent of HIS advancement between 2021 and 2024 and to identify both the desirable and realistic future maturity status. Methods The maturity assessment of the Serbian HIS in 2024 was conducted using the same tool as in 2021: The Health Information Systems Stages of Continuous Improvement (SOCI), enabling direct comparison between the two periods. Progress was measured across five domains: Leadership and Governance, Management and Workforce, Information and Communication Technologies (ICT), Standards and Interoperability, and Data Quality and Use. These domains covered 13 components and 39 single subcomponents, with their maturity stages being assessed on a 5-point Likert scale on the basis of the opinions of key informants and documented through a desk review. Higher scores indicate a higher level of development. Along with a current assessment of maturity, key informants identified desired maturity levels for the future, using the same scale. Data were presented as comparisons in total scores per domain in 2024 versus 2021, for both current and projected statuses. Results Between 2021 and 2024, the overall maturity of the Serbian HIS improved by nearly 1 point (from 1.6/5 to 2.5/5). The same difference of 0.9 was observed between the current 2024 status and the future desired status (2.5 versus 3.4). The most notable improvements were observed in the HIS Strategic Plan under Leadership and Governance (2.5-point increase) and Business Continuity under ICT Infrastructure (2-point increase). The primary driver of progress over the past 3 years was the adoption of the national Program for Digitalization in the Health System of Serbia (eHealth Strategy) and its corresponding Action Plan, which served as a development blueprint. Conclusions Substantial progress in HIS maturity was achieved between 2021 and 2024, driven by strong governmental commitment, international donor support, and the engagement of dedicated national professionals. If current momentum and resourcing are sustained, the projected maturity levels are likely to be attainable in the near future.

Background Accurate and high-quality data are important for improving program effectiveness and informing policy. In 2009 Bangladesh’s health management information system (HMIS) adopted the District Health Information Software, Version 2 (DHIS2) to capture real-time health service utilization data. However, routinely collected data are being underused because of poor data quality and reporting. W e aimed to understand the facilitators and barriers to implementing DHIS2 as a way to retrieve meaningful and accurate data for reproductive, maternal, newborn, child, and adolescent health (RMNCAH) services. Methods This qualitative study was conducted in two districts of Bangladesh from September 2017 to 2018. Data collection included key informant interviews ( n  = 11), in-depth interviews ( n  = 23), and focus group discussions ( n  = 2). The study participants were involved with DHIS2 implementation from the community level to the national level. The data were analyzed thematically. Results DHIS2 could improve the timeliness and completeness of data reporting over time. The reported facilitating factors were strong government commitment, extensive donor support, and positive attitudes toward technology among staff. Quality checks and feedback loops at multiple levels of data gathering points are helpful for minimizing data errors. Introducing a dashboard makes DHIS2 compatible to use as a monitoring tool. Barriers to effective DHIS2 implementation were lack of human resources, slow Internet connectivity, frequent changes to DHIS2 versions, and maintaining both manual and electronic system side-by-side. The data in DHIS2 remains incomplete because it does not capture data from private health facilities. Having two parallel HMIS reporting the same RMNCAH indicators threatens data quality and increases the reporting workload. Conclusion The overall insights from this study are expected to contribute to the development of effective strategies for successful DHIS2 implementation and building a responsive HMIS. Focused strategic direction is needed to sustain the achievements of digital data culture. Periodic refresher trainings, incentives for increased performance, and an automated single reporting system for multiple stakeholders could make the system more user-friendly. A national electronic health strategy and implementation framework can facilitate creating a culture of DHIS2 use for planning, setting priorities, and decision making among stakeholder groups.