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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. 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. 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. 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. 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.

Background Data demand and use culture have a tremendous impact on the proper allocation of scarce resources and evidence-based decision making. However, primary healthcare managers in the majority of Sub-Saharan African countries continue to struggle with using routine health data for decision-making. Purpose/objective This study aimed to assess routine health data use for decision making among primary healthcare managers in Dodoma region. Methods Cross-sectional study design involved 188 primary healthcare managers from Dodoma City Council, Kondoa Town Council and Bahi District Council was conducted. A self-administered questionnaire adapted from the Performance of Routine Information System Management (PRISM) tools was used to collect the data. Data was analysed by using the Statistical Package for Social Science (SPSS) program. Principal Component Analysis was used to find the level of routine health data use, binary logistic regression analysis was used to determine factors associated with routine health data use for decision making among primary healthcare managers. The study was conducted from May to June, 2022. Results The level of adequate routine health data use for decision making among healthcare managers was 63.30%. Factors associated with adequate routine health data use for decision making among healthcare managers were; respondents characteristics: years of working experience (OR = 1.955, 95% CI= [0.892,4.287]), district surveyed (OR = 4.760, 95%CI= [1.412,16.049]), level of health facility (OR = 3.867, 95%CI= [1.354,7.122]) and male gender (OR = 1.901, 95%CI= [1.027,3.521]). Individual factors: comparing data with strategic objectives (OR = 2.986, 95%CI= [1.233–7.229]), decision based on health needs (OR = 7.330, 95%CI= [1.968–27.295]) and decision based on detection of outbreak (OR = 3.769, 95%CI= [1.091–13.019]). Technical factors: ability to check data accuracy (OR = 3.120, 95%CI= [1.682–5.789]), ability to explain findings and its implication (OR = 2.443, 95%CI= [1.278–4.670]) and ability to use information to identity gaps and targets (OR = 2.621, 95%CI= [1.381–4.974]). Organizational factors: organizational support (OR = 3.530, CI= [1.397–8.919]), analyse data regularly (OR = 2.026, 95%CI= [1.075–3.820]) and displays information on key performance indicators (OR = 3.464, 95%CI= [1.525–7.870]). Conclusion and recommendation The level of routine health data use for decision making among primary healthcare managers was found to be modest. The level of data demand and use culture may increase more quickly if capacity building is strengthened and issues that de-motivate primary health care managers from using data are addressed.

Background The Welsh Index of Multiple Deprivation (WIMD) is an area-based deprivation measure comprising eight domains, produced by the Welsh Government to rank Lower Layer Super Output Areas (LSOAs) in Wales. Researchers use the WIMD to account for deprivation, however, as one domain contains health indicators, there is a risk of endogeneity bias when using the WIMD in research on health outcomes. This study evaluated the effect on study results of removing the health domain from the overall WIMD or using only the income domain as deprivation measures. Methods WIMD 2019 scores were linked to 2,760,731 individuals in the SAIL Databank. Original WIMD scores including decile and quintile rankings for each LSOA 2011 were obtained from Welsh Government. The first alternative method removed the health domain from the original WIMD scores. In the second alternative method, WIMD scores were based on only the income domain. Spearman’s correlation and Cohen’s kappa were used to assess the agreement of ranks, deciles, and quintiles between each method. To quantify the change in association between WIMD quintile and diabetes mellitus prevalence for each alternative method, binary logistic regression obtained age-adjusted odds ratios and 95% confidence intervals. Results Removing the health domain from the original WIMD scores resulted in 17.28% of LSOAs changing decile (8.64% to a more deprived group and 8.64% to a less deprived group) and 9.00% changing quintile (4.50% more deprived, 4.50% less deprived). The income-domain-only method caused 50.49% of LSOAs to change decile (26.87% more deprived, 23.62% less deprived) as compared with the original WIMD, and 29.65% changed quintile (15.14% more deprived, 14.51% less deprived). There was a significant association between each of the three methods and diabetes prevalence, with odds ratios increasing with more deprived quintiles, but the 95% confidence intervals for each method showed little or no overlap with each other. Conclusion To avoid biased estimates, researchers using WIMD in studies on health, education, housing, physical environment, income, employment, community safety, and access to services should consider how these domains are related to their outcomes. We describe a methodology for researchers to quantify any bias in their own studies.

Background Sharing of patient electronic health record (EHR) data between healthcare providers can enhance quality and efficiency of healthcare provision, and patient safety. Health information exchange is allowed only with explicit patient consent. In the Netherlands, patient consent and the exchange of information is organized nationally. Consent status is recorded routinely in general practice EHRs. This study examines how various characteristics at the individual and general practice level influence this consent for electronic health information exchange (HIE). Methods Routine EHR data from general practices and out-of-hours primary care services participating in the Nivel Primary Care Database were analysed for the period 2017–2019, just before the coronavirus disease 2019 (COVID-19) pandemic outbreak. Bivariate chi-squared test analysis and multilevel logistic regression analysis, adjusted for practice-level clustering, were conducted for each of the 3 observed years to assess whether consent for electronic HIE (“yes” or “no”) was associated with the individual’s health and healthcare utilization, demographics, neighbourhood characteristics (socioeconomic position, degree of urbanization, area deprivation), and general practice characteristics (practice type, EHR system, practice size). Results Between 2017 and 2019, 38–45% of the individuals provided consent for electronic HIE. Individuals with a higher number of different prescriptions or those with long-lasting health problems or chronic diseases had lower odds of providing consent, in each of the 3 years. This result also applied to female and older individuals (aged ≥ 65 years). In contrast, individuals from below-average socioeconomic position neighbourhoods living in deprived urban or hardly urbanized regions generally had higher odds of providing consent. Conclusions In contrast to what was expected, patient groups with higher healthcare utilization were less, or as likely to provide consent for HIE compared with individuals with no or lower healthcare utilization. This implies that the population most likely to benefit from HIE is, in fact, less likely to profit from it. Further research is needed to determine whether these differences arise from individual trust, privacy concerns, transparency issues or other factors such as physicians’ HIE beliefs. Optimizing the national HIE system in the Netherlands requires considering multiple influencing factors, on both the individual level and practice level.

Background Seasonal malaria chemoprevention (SMC) has been recommended by the World Health Organization since 2012 for eligible children in areas where malaria transmission is highly seasonal and intense. By 2024, SMC had been successfully implemented in all 21 eligible states in Nigeria. Given this widespread implementation, there has been increasing interest in understanding the impact of the intervention under programmatic conditions. This study assessed changes in malaria case incidence and related epidemiological outcomes among the target population of children aged 3–59 months in three SMC implementing states in Nigeria. Methods The study employed a pre-post plausibility evaluation design. Data from routine health management information systems were extracted from selected health facilities to compare the incidence of parasitologically-confirmed uncomplicated malaria cases and secondary outcomes among children aged 3–59 months within the catchment populations of participating health facilities. Mixed-effects, multilevel, negative binomial regression models were used to estimate the impact of SMC on outcomes of interest between the pre-SMC period (2021) and SMC period (2022). Results Data were collected in 36 health facilities: 12 each in Kogi state, Oyo state and the Federal Capital Territory. The mean incidence of uncomplicated malaria was 20 cases per 1000 children aged 3–59 months in 2021, and 9 cases per 1000 children in 2022. After accounting for potential confounders, malaria incidence was 50% (95% confidence interval [CI]: 39–60) lower in the SMC period compared with the pre-SMC period. The level of reduction varied across the three study locations, with the greatest impact in Oyo state and no evidence of impact in Kogi state. Incidence of all-cause fever per 1000 children was 29% (95% CI: 14–41) lower in 2022 compared with 2021. Observed levels of severe malaria and attributable deaths were too low to measure the impact of SMC on these secondary outcomes. Conclusion The study found significantly lower levels of incidence of uncomplicated malaria cases following the introduction of SMC, although the magnitude of impact varied notably across locations. It thus provides evidence on the potential impact of the intervention under programmatic conditions, while underscoring the need to improve both the quality of SMC delivery to maximize impact, and the quality of routine data sources to enhance their utility for evaluating SMC impact in eligible settings.

Aims/hypothesisType 1 diabetes is an autoimmune disease affecting ~400,000 people across the UK. It is likely that environmental factors trigger the disease process in genetically susceptible individuals. We assessed the associations between a wide range of environmental factors and childhood type 1 diabetes incidence in England, using an agnostic, ecological environment-wide association study (EnWAS) approach, to generate hypotheses about environmental triggers.MethodsWe undertook analyses at the local authority district (LAD) level using a national hospital episode statistics-based incident type 1 diabetes dataset comprising 13,948 individuals with diabetes aged 0–9 years over the period April 2000 to March 2011. We compiled LAD level estimates for a range of potential demographic and environmental risk factors including meteorological, land use and environmental pollution variables. The associations between type 1 diabetes incidence and risk factors were assessed via Poisson regression, disease mapping and ecological regression.ResultsCase counts by LAD varied from 1 to 236 (median 33, interquartile range 24–46). Overall type 1 diabetes incidence was 21.2 (95% CI 20.9, 21.6) per 100,000 individuals. The EnWAS and disease mapping indicated that 15 out of 53 demographic and environmental risk factors were significantly associated with diabetes incidence, after adjusting for multiple testing. These included air pollutants (particulate matter, nitrogen dioxide, nitrogen oxides, carbon monoxide; all inversely associated), as well as lead in soil, radon, outdoor light at night, overcrowding, population density and ethnicity. Disease mapping revealed spatial heterogeneity in type 1 diabetes risk. The ecological regression found an association between type 1 diabetes and the living environment domain of the Index of Multiple Deprivation (RR 0.995; 95% credible interval [CrI] 0.991, 0.998) and radon potential class (RR 1.044; 95% CrI 1.015, 1.074).Conclusions/interpretationOur analysis identifies a range of demographic and environmental factors associated with type 1 diabetes in children in England.

Background Patients are generally willing to contribute to research with routinely collected health data and residual biosamples, but transparency and being able to (to some extent) have control over data are important conditions. A broad consent procedure ensures that patients are informed, without overloading the patients with too many or repeated study-specific consents. In the context of implementation of broad consent, we investigated five aspects: response rates, whether patients felt informed and were able to reach a decision, whether the registered consent was in line with their desired consent, reasons for giving (no) consent or not responding, and suggestions to improve the procedure. Methods We analyzed consent decisions of 31,894 patients, recorded between May 2018 and December 2020 in a specialized cancer hospital. We also interviewed 64 patients selected from first-time visiting patients between October and November 2018 (25 with consent, 16 with no consent and 23 with no response). Results Consent rates were: 85.2% consented, 3.8% did not consent and 11% did not respond. The majority of the interviewees, who recalled that consent was asked, felt sufficiently informed. Those that needed more information, mostly had not (yet) read the information given to them, due to the hectic and emotional period. For the majority of our interviewees the desired consent decision matched with what was registered in the hospitals’ system. Reasons for giving consent were mostly motivational, e.g., altruism and solidarity. Reasons for not giving consent or not responding yet were mostly contextual, e.g., insufficient headspace and needing more time. Privacy concerns, e.g. mistakes resulting in data being publicly accessible, data linkage and hacking, were mentioned as well. Sometimes the reason to not give consent or not respond was based on misunderstanding, e.g. that consenting would require bureaucratic entanglements. Conclusions For high quality research with patient data and samples, broad consent from a large and representative patient population is essential, and patients must feel informed and be able to register their consent decision easily. Our novel consent procedure led to an 85.2% consent rate and desired consent decisions were mostly registered correctly. In addition, patients felt sufficiently informed.

Background Routine health information plays a significant role in managing the healthcare system to make informed decisions, monitor and evaluate, and take action to improve health outcomes. The Eastern Cape Department of Health (ECDoH) officially adopted the online District Health Information System (DHIS2) in 2017. However, evidence suggests that the underutilisation of routinely collected health information for management was often because of poor data quality in routine health information systems. Objectives This study reviewed the level of quality of the routine health data in the DHIS2 using two quality dimensions, namely data completeness and internal consistency. Method A retrospective study design was used to assess the quality of data for April 2017– March 2020 utilising the World Health Organization Data Quality Guidelines. Secondary data were extracted from the DHIS2 using standardised reports and captured in Microsoft Excel Office. A total of 265 health facilities and 77 data elements were included and analysed using descriptive analysis and a score grading system. Results A total of 365 228 data element values were reported, 121 199 missing data values, unaccounted data values were at 248 153, about 6395 data values had outliers, and 5670 data values had validation errors. The rate of data completeness was 74.6%, the internal consistency was 95.1%, and the DHIS2 data quality was 84.9%. Conclusion The study demonstrates the high quality of DHIS2 data in the ECDoH following the implementation of the online-based system. However, the significant number of missing data elements has impacted data completeness. Contribution This study contributes to the body of knowledge on the importance of role of data quality for the utilisation routine health information systems as an essential tool for management in the health system.

The use of routinely recorded electronic health record (EHR) data is increasingly common, especially in epidemiological research. However, data must be processed and prepared for secondary use, and decisions made during this process could significantly impact research outcomes. A demonstration of the extent of these consequences is necessary. The aim of this study was to investigate the influence of data processing steps on research outcomes derived from the secondary use of EHR data. EHR data from 8 Dutch general practices from 2019 were used. These practices contributed data to 2 research databases: the Academic General Practitioner Development Network registry and the Nivel Primary Care Database. Data were extracted and processed through distinct extraction, transformation, and loading (ETL) pipelines, allowing the evaluation of the impact of different ETL methods by comparing the 2 datasets in three steps: (1) patient demographics, (2) epidemiology of concordant patients, and (3) health service use of patients with 3 diagnoses. A number of similarity indicators, including the number of contacts, regular consultations and visits, prescriptions, and episodes, were compared between the 2 databases. The outcomes were compared by performing paired samples t tests using 99% CIs. Prevalence, number of prescriptions, and number of regular consultations and visits per 1000 patient years were calculated and compared for 3 diagnoses (diabetes mellitus, urinary tract infection, and cough). These outcomes were compared using the SD. Differences were observed between the datasets in the number of enrolled patients (Academic General Practitioner Development Network registry: n=47,517; Nivel Primary Care Database: n=44,247). Despite this, patient demographics were similar. All indicator outcomes of the concordant patients showed significant differences between the databases, that is, the number of contacts, prescriptions, and episodes per patient, and the number of regular consultations and visits. Differences in the indicator outcomes for the 3 diagnosis groups varied greatly in SD, however, none of the differences were deemed significant. The findings highlight the importance of routine health data users' awareness of different ETL steps involved. Transparency and shared knowledge about these processes are critical, and making them available for research is necessary. Data processors should share their knowledge regarding their choices, and researchers and policy makers should invest in their knowledge of this type of metadata. Transparency and shared knowledge are particularly important in light of the European Health Data Space and the ever-increasing secondary use of routinely recorded health data. Future research should focus on the role of transparency, joint decision-making, and the minimization of effects of ETL steps, and on the insight into the individual influence of ETL steps on research outcomes. This could stimulate standardized approaches among data processors and researchers, resulting in increased data interoperability.

Background Electronic health record (EHR) data are available for research in all UK nations and cross-nation comparative studies are becoming more common. All UK inpatient EHRs are based around episodes, but episode-based analysis may not sufficiently capture the patient journey. There is no UK-wide method for aggregating episodes into standardised person-based spells. This study identifies two data quality issues affecting the creation of person-based spells, and tests four methods to create these spells, for implementation across all UK nations. Methods Welsh inpatient EHRs from 2013 to 2017 were analysed. Phase one described two data quality issues; transfers of care and episode sequencing. Phase two compared four methods for creating person spells. Measures were mean length of stay (LOS, expressed in days) and number of episodes per person spell for each method. Results 3.5% of total admissions were transfers-in and 3.1% of total discharges were transfers-out. 68.7% of total transfers-in and 48.7% of psychiatric transfers-in had an identifiable preceding transfer-out, and 78.2% of total transfers-out and 59.0% of psychiatric transfers-out had an identifiable subsequent transfer-in. 0.2% of total episodes and 4.0% of psychiatric episodes overlapped with at least one other episode of any specialty. Method one (no evidence of transfer required; overlapping episodes grouped together) resulted in the longest mean LOS (4.0 days for all specialties; 48.5 days for psychiatric specialties) and the fewest single episode person spells (82.4% of all specialties; 69.7% for psychiatric specialties). Method three (evidence of transfer required; overlapping episodes separated) resulted in the shortest mean LOS (3.7 days for all specialties; 45.8 days for psychiatric specialties) and the most single episode person spells; (86.9% for all specialties; 86.3% for psychiatric specialties). Conclusions Transfers-in appear better recorded than transfers-out. Transfer coding is incomplete, particularly for psychiatric specialties. The proportion of episodes that overlap is small but psychiatric episodes are disproportionately affected. The most successful method for grouping episodes into person spells aggregated overlapping episodes and required no evidence of transfer from admission source/method or discharge destination codes. The least successful method treated overlapping episodes as distinct and required transfer coding. The impact of all four methods was greater for psychiatric specialties.