Area-specific covid-19 effects on health services utilization in the Democratic Republic of the Congo using routine health information system data.

BACKGROUND: Since March 2020, the COVID-19 pandemic has shocked health systems worldwide. This analysis investigated the effects of the pandemic on basic health services utilization in the Democratic Republic of the Congo (DRC) and examined the variability of COVID effects in the capital city Kinshasa, in other urban areas, and in rural areas. METHODS: We estimated time trends models using national health information system data to replicate pre-COVID-19 (i.e., January 2017-February 2020) trajectories of health service utilization, and then used those models to estimate what the levels would have been in the absence of COVID-19 during the pandemic period, starting in March 2020 through March 2021. We classified the difference between the observed and predicted levels as the effect of COVID-19 on health services. We estimated 95% confidence intervals and p-values to examine if the effect of the pandemic, nationally and within specific geographies, was statistically significant. RESULTS: Our results indicate that COVID-19 negatively impacted health services and subsequent recovery varied by service type and by geographical area. COVID-19 had a lasting impact on overall service utilization as well as on malaria and pneumonia-related visits among young children in the DRC. We also found that the effects of COVID-19 were even more immediate and stronger in the capital city of Kinshasa compared with the national effect. Both nationally and in Kinshasa, most affected services had slow and incomplete recovery to expected levels. Therefore, our analysis indicates that COVID-19 continued to affect health services in the DRC throughout the first year of the pandemic. CONCLUSIONS: The methodology used in this article allows for examining the variability in magnitude, timing, and duration of the COVID effects within geographical areas of the DRC and nationally. This analytical procedure based on national health information system data could be applied to surveil health service disruptions and better inform rapid responses from health service managers and policymakers.

Salus Platform: A Digital Health Solution Tool for Managing Syphilis Cases in Brazil-A Comparative Analysis.

(1) Introduction: Syphilis is a sexually transmitted infection (STI) that constitutes a serious public health problem in Brazil and worldwide; (2) Methods: This was a descriptive and exploratory study that sought to analyze and compare the characteristics of Brazilian health systems with a new platform (Salus) developed by the Laboratory of Technological Innovation in Health in the scope of notification and management of disease data, including syphilis. In addition, this analysis aimed to assess whether Salus fully meets the necessary data management fields and can be indicated as a tool to improve health management in the context of syphilis in Brazil. (3) Results: In this study, the Salus functionalities were demonstrated and compared with the current Brazilian systems by discovering the existing gaps in the evaluated systems. The gaps found may explain the delay in meeting demands, the difficulty of making routine therapeutic follow-ups, in addition to interference with the vital purpose of follow-up in the epidemiological surveillance of diseases. As a result, Salus demonstrates functionalities that surpass all others and meet case management demands in a superior way to the systems currently used in the country. (4) Conclusions: The Brazilian health information systems related to the response to syphilis do not fulfill the purpose for which they were developed. Instead, they contribute to the fragmentation of health data and information, delays in diagnosis, incomplete case management, and loss of data due to inconsistencies and inadequate reporting. In addition, they are systems without interconnection, which do not articulate epidemiological surveillance actions with primary health care. All these factors may be obscuring accurate data on syphilis in Brazil, resulting in high and unnecessary public spending and late care for users of the Unified Health System (SUS).

Status of the health information system in Ireland and its fitness to support health system performance assessment: a multimethod assessment based on stakeholder involvement.

BACKGROUND: Between 2019 and 2021, the first Irish health system performance assessment (HSPA) framework was developed. As routinely collected health data are necessary to continuously populate indicators of an HSPA framework, a purpose-driven assessment of the health information system (HIS) in Ireland and its fitness to support the implementation of an HSPA framework was conducted. This study reports on the status of the Irish HIS through a multimethod assessment based on continuous broad stakeholder involvement. METHODS: Between May and November 2020, over 50 informants were engaged in individual and group interviews and stakeholder consultation workshops as part of the HIS assessment process. Descriptive themes and high-level data availability heatmaps were derived from interview and workshop data using thematic analysis. Indicator "passports" for the HSPA framework were populated during stakeholder consultation workshops and analysed using univariate descriptive statistics. RESULTS: The HIS in Ireland was able to provide administrative, survey and registry-based data for public sector acute care services, focusing on structure, process and output metrics. Significant data availability gaps, most notably from primary care, private hospitals and community care, were reported, with little availability of electronic health record and people-reported data. Data on outcome metrics were mostly missing, as were linkage possibilities across datasets for care pathway monitoring. The COVID-19 pandemic highlighted the national HIS's shortcomings but also the capacity for rapid development and improvement. CONCLUSIONS: A tailor-made assessment of the HIS in Ireland, involving a broad set of relevant stakeholders, revealed strengths, weaknesses and areas for improvement in the Irish health data landscape. It also contributed to the development of a national HSPA framework and momentum to further strengthen data infrastructure and governance, while working towards a more data-driven and person-centred healthcare system. This work demonstrates the utility of an inclusive HIS assessment process and is applicable beyond Ireland, where this case study was conducted.

Use of routine health information systems to monitor disruptions of coverage of maternal, newborn, and child health services during COVID-19: A scoping review.

Background: The COVID-19 pandemic is a unique global health challenge which disrupted essential health services (EHS). Most early data related to EHS during the COVID-19 pandemic came from country and regional "pulse" surveys conducted by the World Health Organization (WHO) and United Nations Children's Fund (UNICEEF), which relied on respondent perceptions and not necessarily routine health information system (RHIS) data. By conducting a scoping review, we aimed to describe the use of RHIS data for monitoring changes in EHS coverage for maternal, newborn, and child health (MNCH) during the COVID-19 pandemic. Methods: We performed a scoping review using Sample, Phenomenon of Interest, Design, Evaluation, Research type (SPIDER) and Preferred Reporting Items for Systematic Reviews and Meta-Analyses - Scoping Review (PRISMA-SCR) guidelines. We included descriptive or analytic reports on the availability and use of RHIS data published in peer-reviewed, pre-publication, or gray literature on MNCH essential health services coverage during the COVID-19 pandemic. The following databases were searched for studies published between January 2020 and May 2022: PubMed/MEDLINE, Google Scholar, Google, MedRXiv (pre-publication), Embase, CINAHL, Cochrane, Campbell, and OpenGrey. A single reviewer screened the titles, abstracts, and full texts of the retrieved publications, while a second reviewer screened 20% of the total sample. Publications were tabulated by WHO Region, World Bank income group, country, data sources, study topic, and period. We used content analysis to qualitatively describe the trends and use of data for policy or programming in the studies. Results: We included 264 publications after the full-text review. The publications came from 81 countries, covering all WHO regions and World Bank income groups. The most common data sources were hospital information systems (27%) and primary health care management information systems (26%). Most studies examined data trends before COVID-19 compared to periods during COVID-19. Most publications reported a decrease in MNCH services (45%). Reports with follow-up beyond August 2020 (first six months of pandemic) were significantly more likely to report recovery of service coverage (8% vs 30%, P < 0.001). Low- and middle-income countries reported significantly higher morbidity and/or mortality in COVID-19 periods than high-income countries (54% vs 30%, P < 0.001). Less than 10% of reports described RHIS data quality specifically during the COVID-19 period and only 22% reported program mitigation strategies to address reductions noted from routine data. Conclusion: Results suggest awareness and usefulness of RHIS to monitor MNCH service disruptions during the COVID-19 pandemic. However, with only 22% of reports including descriptions of policy or program adaptations, use of RHIS data to monitor MNCH service disruptions was not necessarily followed by data-informed policies or program adaptations. RHIS data on MNCH services should be strengthened to enable its use by program managers and policymakers to respond to direct and indirect effects of future public health emergencies. Registration: Open Science Framework (available at: https://osf.io/usqp3/?view_only=94731785fcba4377adfa1bdf5754998d).

Impact of COVID-19 pandemic on notifiable diseases in Northern Brazil / Impacto da COVID-19 em doenças de notificação compulsória no Norte do Brasil / Impacto de la COVID-19 en las enfermedades de notificación compulsoria del Norte de Brasil

Objetivo: Avaliar o impacto da pandemia COVID-19 em doenças de notificação compulsória no Norte do Brasil. Métodos: Estudo descritivo e retrospectivo realizado com dados das bases Sinan (Sistema de Informação de Agravos de Notificação) e SIH/SUS (Sistema de Informações Hospitalares do SUS) sobre meningite bacteriana e viral, dengue, febre hemorrágica da dengue, arboviroses, sífilis, tuberculose, hanseníase e hepatites virais. Utilizou-se estatística descritiva para avaliação da variação nos números absolutos das notificações e internações do período de 2015 a 2020 e suas variações médias. Resultados: As notificações e internações de meningite, arboviroses não-dengue, hanseníase, leptospirose e hepatites virais, em geral, demonstraram redução de 50 até 80% em relação a períodos anteriores. Houve variabilidade regional com dengue e febre hemorrágica da dengue, com aumentos e reduções independentes. As sífilis adquirida, gestacional e congênita demonstraram queda de até 60% nas notificações e nas internações, exceto sífilis congênita, que se manteve em estabilidade. As notificações e as internações de tuberculose se mantiveram estáveis em toda a região. Conclusão: Houve redução geral de mais da metade das notificações e das internações hospitalares, apresentando um impacto variável, dependendo do Estado e do processo de endemia de cada sub-região.

Objective: To assess the impact of the COVID-19 pandemic on notifiable diseases in Northern Brazil. Methods: A descriptive retrospective study was conducted using data from the Notifiable Disease Information System (Sistema de Informação de Agravos de Notificação ­ Sinan) and SUS Hospital Information System (Sistema de Informações Hospitalares do SUS ­ SIH/SUS) on bacterial and viral meningitis, dengue, dengue hemorrhagic fever, arboviruses, syphilis, tuberculosis, leprosy, and viral hepatitis. Descriptive statistics was used to assess the variation in absolute numbers of notifications and hospitalizations from 2015 to 2020 and their average variations. Results: Notifications and hospitalizations for meningitis, non-dengue arboviruses, leprosy, leptospirosis, and viral hepatitis exhibited a general decrease of 50 to 80% compared to previous years. There was regional variation in dengue and dengue hemorrhagic fever, with independent increases and decreases. Acquired, gestational and congenital syphilis presented a 60% decrease in notifications and hospitalizations, except for congenital syphilis, which remained stable. Tuberculosis notifications and hospitalizations remained stable in the entire region. Conclusion: There was an overall decrease of more than a half of notifications and hospitalizations, representing a variable impact depending on the state and the endemic process of each subregion.

Objetivo: Evaluar el impacto de la pandemia de la COVID-19 en las enfermedades de notificación compulsoria del Norte de Brasil. Métodos: Estudio descriptivo y retrospectivo realizado con datos de las bases SINAN (Sistema de Información de Agravios de Notificación) y SIH/SUS (Sistema de Informaciones Hospitalarias del SUS) sobre la meningitis bacteriana y viral, el dengue, la fiebre hemorrágica del dengue, las arbovirosis, la sífilis, la tuberculosis, la lepra y las hepatitis virales. Se utilizó de la estadística descriptiva para la evaluación de la variación de los números absolutos de las notificaciones y los ingresos del periodo entre 2015 y 2020 y sus variaciones medias. Resultados: Las notificaciones y los ingresos de meningitis, arbovirosis no-dengue,la lepra, la leptospirosis y las hepatitis virales, en general, han demostrado una disminución del 50 hasta el 80% respecto los periodos anteriores. Hubo variabilidad por región respecto el dengue y fiebre hemorrágica del dengue con subidas y bajadas independientes. Las sífilis adquirida, gestacional y congénita han demostrado caída hasta el 60% de las notificaciones e ingresos excepto la sífilis congénita que se mantuvo estable. Las notificaciones y los ingresos por tuberculosis se mantuvieron estables em toda la región. Conclusión: Hubo una disminución general de más de la mitad de las notificaciones y los ingresos hospitalarios, presentando un impacto variable, a depender del Estado y del proceso de endemia de cada sub-región.

Leveraging PEPFAR-Supported Health Information Systems for COVID-19 Pandemic Response.

Since 2003, the US President's Emergency Plan for AIDS Relief (PEPFAR) has supported implementation and maintenance of health information systems for HIV/AIDS and related diseases, such as tuberculosis, in numerous countries. As the COVID-19 pandemic emerged, several countries conducted rapid assessments and enhanced existing PEPFAR-funded HIV and national health information systems to support COVID-19 surveillance data collection, analysis, visualization, and reporting needs. We describe efforts at the US Centers for Disease Control and Prevention (CDC) headquarters in Atlanta, Georgia, USA, and CDC country offices that enhanced existing health information systems in support COVID-19 pandemic response. We describe CDC activities in Haiti as an illustration of efforts in PEPFAR countries. We also describe how investments used to establish and maintain standards-based health information systems in resource-constrained settings can have positive effects on health systems beyond their original scope.

The Interplay of Work, Digital Health Usage, and the Perceived Effects of Digitalization on Physicians' Work: Network Analysis Approach.

BACKGROUND: In health care, the benefits of digitalization need to outweigh the risks, but there is limited knowledge about the factors affecting this balance in the work environment of physicians. To achieve the benefits of digitalization, a more comprehensive understanding of this complex phenomenon related to the digitalization of physicians' work is needed. OBJECTIVE: The aim of this study was to examine physicians' perceptions of the effects of health care digitalization on their work and to analyze how these perceptions are associated with multiple factors related to work and digital health usage. METHODS: A representative sample of 4630 (response rate 24.46%) Finnish physicians (2960/4617, 64.11% women) was used. Statements measuring the perceived effects of digitalization on work included the patients' active role, preventive work, interprofessional cooperation, decision support, access to patient information, and faster consultations. Network analysis of the perceived effects of digitalization and factors related to work and digital health usage was conducted using mixed graphical modeling. Adjusted and standardized regression coefficients are denoted by b. Centrality statistics were examined to evaluate the relative influence of each variable in terms of node strength. RESULTS: Nearly half of physicians considered that digitalization has promoted an active role for patients in their own care (2104/4537, 46.37%) and easier access to patient information (1986/4551, 43.64%), but only 1 in 10 (445/4529, 9.82%) felt that the impact has been positive on consultation times with patients. Almost half of the respondents estimated that digitalization has neither increased nor decreased the possibilities for preventive work (2036/4506, 45.18%) and supportiveness of clinical decision support systems (1941/4458, 43.54%). When all variables were integrated into the network, the most influential variables were purpose of using health information systems, employment sector, and specialization status. However, the grade given to the electronic health record (EHR) system that was primarily used had the strongest direct links to faster consultations (b=0.32) and facilitated access to patient information (b=0.28). At least 6 months of use of the main EHR was associated with facilitated access to patient information (b=0.18). CONCLUSIONS: The results highlight the complex interdependence of multiple factors associated with the perceived effects of digitalization on physicians' work. It seems that a high-quality EHR system is critical for promoting smooth clinical practice. In addition, work-related factors may influence other factors that affect digital health success. These factors should be considered when developing and implementing new digital health technologies or services for physicians' work. The adoption of digital health is not just a technological project but a project that changes existing work practices.

Health Information Systems Adaptation and Flexibility in Extreme Situations: The COVID Effect.

In 2020, a pandemic forced the entire world to adapt to a new scenario. The objective of this study was to know how Health Information Systems were adapted driven by the pandemic of COVID. 12 CIOS of healthcare organizations were interviewed and the interviews were classified according to the dimensions of a sociotechnical model: Infrastructure, Clinical Content, Human Computer Interface, People, Workflow and Communication, Organizational Characteristics and Internal Policies, Regulations, and Measurement and Monitoring. Adaptation to the Pandemic involved social, organizational and cultural rather than merely technical aspects in private organizations with mature and stable Health Information Systems.

Fit for Purpose-Re-Designing Australia's Mental Health Information System.

BACKGROUND: Monitoring and reporting mental health is complex. Australia's first National Mental Health Strategy in 1992 included a new national commitment to accountability and data collection in mental health. This article provides a narrative review of thirty years of experience. MATERIALS AND METHODS: This review considers key documents, policies, plans and strategies in relation to the evolution of mental health data and reporting. Documents produced by the Federal and the eight state and territory governments are considered, as well as publications produced by key information agencies, statutory authorities and others. A review of this literature demonstrates both its abundance and limitations. RESULTS: Australia's approach to mental health reporting is characterised by duplication and a lack of clarity. The data available fail to do justice to the mental health services provided in Australia. Mental health data collection and reporting processes are centrally driven, top-down and activity-focused, largely eschewing actual health outcomes, the social determinants of mental health. There is little, if any, link to clearly identifiable service user or carer priorities. Consequently, it is difficult to link this process longitudinally to clinical or systemic quality improvement. Initial links between the focus of national reform efforts and mental health data collection were evident, but these links have weakened over time. Changes to governance and reporting, including under COVID, have made the task of delivering accountability for mental health more difficult. CONCLUSION: Australia's current approach is not fit for purpose. It is at a pivotal point in mental health reform, with new capacity to use modelled data to simulate prospective mental health reform options. By drawing on these new techniques and learning the lessons of the past, Australia (and other nations) can design and implement more effective systems of planning, reporting and accountability for mental health.

[Uniformity and comparability of national Health Information Systems for home care, nursing homes, and hospices in Italy]. / Uniformità e comparabilità dei flussi informativi nazionali per le cure domiciliari (flusso SIAD), l'assistenza residenziale (flusso FAR) e gli hospice (flusso HOS).

BACKGROUND: Health Information Systems (HIS) play a strategic role in the development of community healthcare services, a field still underdeveloped in Italy as shown by the COVID-19 pandemic, and their use for epidemiological purposes is increasing over the years. However, some general critical issues have been reported concerning national community health information systems, but no detailed study was found after a non-systematic review in Medline database and institutional websites. OBJECTIVES: to assess uniformity and comparability of health information collected by the national health information Systems for Home Care (SIAD), Nursing Homes (FAR), and Hospices (HOS) in Italy, three healthcare settings sharing patients with common conditions and healthcare needs. METHODS: information was gathered from current Technical Regulations Papers of the Italian Health Ministry. All variables of the three Information Systems were ordered on the basis of the characteristics provided by the ministerial documents into a single grouping model created for the purpose and their distribution compared among and between the Systems. According to the grouping model, the variables were divided in two main groups: System Variables for administrative, bill, and identification-personal data and the Pathway Variables for patient's conditions and provided healthcare data. Common information content variables among all systems and between two of them were then identified, highlighting those with also identical terminology and allowed values. On the basis of the percentages of common and identical variables, uniformity in content and terminology was then calculated among all systems and between two of them. Besides, levels of content and terminology agreement have been calculated with Cohen's K matching the three Systems in all possible combinations of pairs. RESULTS: there are 70 variables in SIAD, 45 variables in FAR, and 34 in HOS. System variables are nearly the half in FAR and HOS, 22 (48.9%) and 17 (50.0%) variables, respectively. Pathway variables are prevalent only in SIAD, with 55 variables (78.6%). Only SIAD and HOS use ICD-9-CM, with 2 (2.8%) and 9 (26.5%) variables, respectively. The three Systems share 18 common variables, with other 16 common between just two of them. Considering the common variables, the total number of variables used by all the Systems is 97, with 23 System variables (23.7%) and 74 Pathway variables (76.3%). Overall, content uniformity among the three Systems is 18.5%, but becomes 60.9% considering only the System variables and 5.4% for the Pathway variables, with respectively 14 and 4 common variables. Among the common variables, 11 have the same denomination and allowed values, with an overall terminological uniformity of 11.3%. Being all of them System variables, no terminological uniformity has been found among the three Systems.The level of content agreement was fair for the couple FAR-HOS (K Cohen 0.26), but null for HOS-SIAD and SIAD-FAR (K Cohen -0.20 and <0.01). Null was also the level of terminological agreement in all the possible pairs of matched systems (K Cohen all negative). CONCLUSIONS: content and terminology uniformity of National Health Information Systems in Italy for Home Care, Nursing Homes, and Hospices has been found to be poor, with little reference to standardised classification systems and a scarce level of comparability of the information gathered in the three healthcare settings, although similarity in patients' characteristics and provided health services. Data comparability among them is mainly limited to administrative and identification-personal information, with little possibility of comparing information on patients' conditions and provided healthcare in the three settings. This scarce uniformity might undermine the contribution of national Health Information Systems in the development of community healthcare services in Italy and limit the potential of epidemiological research in this area. Also in the light of the establishment of new national Health Information Systems for Primary Care and Community Hospitals, a methodological reassessment of languages, codes, and evaluation tools used by community health services and information systems is needed.

Security Engineering of Patient-Centered Health Care Information Systems in Peer-to-Peer Environments: Systematic Review.

BACKGROUND: Patient-centered health care information systems (PHSs) enable patients to take control and become knowledgeable about their own health, preferably in a secure environment. Current and emerging PHSs use either a centralized database, peer-to-peer (P2P) technology, or distributed ledger technology for PHS deployment. The evolving COVID-19 decentralized Bluetooth-based tracing systems are examples of disease-centric P2P PHSs. Although using P2P technology for the provision of PHSs can be flexible, scalable, resilient to a single point of failure, and inexpensive for patients, the use of health information on P2P networks poses major security issues as users must manage information security largely by themselves. OBJECTIVE: This study aims to identify the inherent security issues for PHS deployment in P2P networks and how they can be overcome. In addition, this study reviews different P2P architectures and proposes a suitable architecture for P2P PHS deployment. METHODS: A systematic literature review was conducted following PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) reporting guidelines. Thematic analysis was used for data analysis. We searched the following databases: IEEE Digital Library, PubMed, Science Direct, ACM Digital Library, Scopus, and Semantic Scholar. The search was conducted on articles published between 2008 and 2020. The Common Vulnerability Scoring System was used as a guide for rating security issues. RESULTS: Our findings are consolidated into 8 key security issues associated with PHS implementation and deployment on P2P networks and 7 factors promoting them. Moreover, we propose a suitable architecture for P2P PHSs and guidelines for the provision of PHSs while maintaining information security. CONCLUSIONS: Despite the clear advantages of P2P PHSs, the absence of centralized controls and inconsistent views of the network on some P2P systems have profound adverse impacts in terms of security. The security issues identified in this study need to be addressed to increase patients' intention to use PHSs on P2P networks by making them safe to use.

Addressing missing values in routine health information system data: an evaluation of imputation methods using data from the Democratic Republic of the Congo during the COVID-19 pandemic.

BACKGROUND: Poor data quality is limiting the use of data sourced from routine health information systems (RHIS), especially in low- and middle-income countries. An important component of this data quality issue comes from missing values, where health facilities, for a variety of reasons, fail to report to the central system. METHODS: Using data from the health management information system in the Democratic Republic of the Congo and the advent of COVID-19 pandemic as an illustrative case study, we implemented seven commonly used imputation methods and evaluated their performance in terms of minimizing bias in imputed values and parameter estimates generated through subsequent analytical techniques, namely segmented regression, which is widely used in interrupted time series studies, and pre-post-comparisons through paired Wilcoxon rank-sum tests. We also examined the performance of these imputation methods under different missing mechanisms and tested their stability to changes in the data. RESULTS: For regression analyses, there were no substantial differences found in the coefficient estimates generated from all methods except mean imputation and exclusion and interpolation when the data contained less than 20% missing values. However, as the missing proportion grew, k-NN started to produce biased estimates. Machine learning algorithms, i.e. missForest and k-NN, were also found to lack robustness to small changes in the data or consecutive missingness. On the other hand, multiple imputation methods generated the overall most unbiased estimates and were the most robust to all changes in data. They also produced smaller standard errors than single imputations. For pre-post-comparisons, all methods produced p values less than 0.01, regardless of the amount of missingness introduced, suggesting low sensitivity of Wilcoxon rank-sum tests to the imputation method used. CONCLUSIONS: We recommend the use of multiple imputation in addressing missing values in RHIS datasets and appropriate handling of data structure to minimize imputation standard errors. In cases where necessary computing resources are unavailable for multiple imputation, one may consider seasonal decomposition as the next best method. Mean imputation and exclusion and interpolation, however, always produced biased and misleading results in the subsequent analyses, and thus, their use in the handling of missing values should be discouraged.