People approaching end of life account disproportionately for health care costs, and the majority of these costs accrue in hospitals. The economic evidence base to improve value of care to this population is thin. Natural experiment methods may be helpful in bridging evidence gaps with credible causal estimates from routine data, but these methods have seldom been applied in this field. This study aimed to evaluate the association between timely palliative care consultation and length of stay for adults with serious illness admitted to acute hospital in Ireland.
In primary analysis we evaluated if timely palliative care receipt following emergency hospital inpatient admission impacted length of stay (LOS); in secondary analysis we verified if palliative medicine service (PMS) implementation co-occurred with any changes in in-hospital mortality, and we estimated cost differences associated with any change in LOS. This was a secondary analysis on routinely collected data for acute admissions to public hospitals in Ireland. We used difference-in-differences analysis to exploit the staggered implementation of PMS teams at acute public hospitals in Ireland between 2010 and 2015. We identified palliative care receipt following PMS implementation using ICD-10 codes, and we matched admissions involving a palliative care interaction to admissions in years prior to PMS implementation using propensity score weights.
Our primary analytic sample included 4,314 observations, of whom 608 (14%) received timely palliative care. We estimated that the intervention reduced LOS by nearly two days, with an estimated associated saving per admission of €1,820. These analyses were robust to multiple sensitivity analyses on regression specification, weighting strategy and site selection. Proportion of admissions ending in death did not change following PMS implementation.
Prompt interaction between suitable patients and palliative care can improve the quality and efficiency of care to this population. Many patients receive palliative care later in the hospital stay, which does not yield cost-savings. Future studies can extend and strengthen our approach with better data, as well as using different methods to understand how to trigger palliative care early in a hospital admission and realise available gains.

Welfare assessment protocols have been developed for dairy cows and veal calves during the past decades. One practical use of such protocols may be conducting welfare assessments by using routinely collected digital data (i.e., data-based assessment). This approach can allow for continuous monitoring of animal welfare in a large number of farms. It recognises changes in the animal welfare status over time and enables comparison between farms. Since no comprehensive data-based assessment for veal calves is currently available, the purposes of this review are (i) to provide an overview of single existing data-based indicators for veal calves and (ii) to work out the necessary requirements for data-based indicators to be used in a comprehensive welfare assessment for veal calves in Switzerland. We used the Welfare Quality Protocol® (WQ) for veal calves and the Terrestrial Animal Health Code from the World Organisation of Animal Health for guidance throughout this process. Subsequently, routinely collected data were evaluated as data sources for welfare assessment in Swiss veal operations. The four WQ principles reflecting animal welfare, i.e., \'good feeding\', \'good housing\', \'good health\' and \'appropriate behaviour\' were scarcely reflected in routinely available data. Animal health, as one element of animal welfare, could be partially assessed using data-based indicators through evaluation of mortality, treatments, and carcass traits. No data-based indicators reflecting feeding, housing and animal behaviour were available. Thus, it is not possible to assess welfare in its multidimensionality using routinely collected digital data in Swiss veal calves to date. A major underlying difficulty is to differentiate between veal calves and other youngstock using routine data, since an identifying category for veal calves is missing in official Swiss databases. In order to infer animal welfare from routine data, adaptations of data collection strategies and animal identification are required. Data-based welfare assessment could then be used to complement on-farm assessments efficiently and, e.g., to attribute financial incentives for specifically high welfare standards accordingly.

Due to nursing staff shortage and growing nursing care demand, resource allocation and optimal task distribution have become primary concerns of nursing management. Grade mix analysis based on nursing interventions and nurse qualifications from routine patient documentation can support this. Case complexity is a key linking factor of nursing interventions, workload, and grade mix. This study determined case complexity predictors based on one year of routine patient documentation (n = 3,373 cases) from a Swiss hospital and predicted the patient clinical complexity level via weighted cumulative logistic regression models. Significant predictors were sex, age, pre-admission residence, admission type, self- care index, pneumonia risk, and number of nursing interventions. The models\' accuracy is limited yet appropriate for applications such as needs- and competence- based staff-planning. After calibration via in-hospital data it could support nursing management in these tasks. The next step is now to test the model in a clinical setting.

BACKGROUND: Frailty is increasingly present in patients with acute myocardial infarction. The electronic Frailty Index (eFI) is a validated method of identifying vulnerable older patients in the community from routine primary care data. Our aim was to assess the relationship between the eFI and outcomes in older patients hospitalised with acute myocardial infarction.
METHODS: Retrospective cohort study using the DataLoch Heart Disease Registry comprising consecutive patients aged 65 years or over hospitalised with a myocardial infarction between October 2013 and March 2021.
METHODS: Patients were classified as fit, mild, moderate, or severely frail based on their eFI score. Cox-regression analysis was used to determine the association between frailty category and all-cause mortality.
RESULTS: In 4670 patients (median age 77 years [71-84], 43% female), 1865 (40%) were classified as fit, with 1699 (36%), 798 (17%) and 308 (7%) classified as mild, moderate and severely frail, respectively. In total, 1142 patients died within 12 months of which 248 (13%) and 147 (48%) were classified as fit and severely frail, respectively. After adjustment, any degree of frailty was associated with an increased risk of all-cause death with the risk greatest in the severely frail (reference = fit, adjusted hazard ratio 2.87 [95% confidence intervals 2.24 to 3.66]).
CONCLUSIONS: The eFI identified patients at high risk of death following myocardial infarction. Automatic calculation within administrative data is feasible and could provide a low-cost method of identifying vulnerable older patients on hospital presentation.

BackgroundEffective pandemic preparedness requires robust severe acute respiratory infection (SARI) surveillance. However, identifying SARI patients based on symptoms is time-consuming. Using the number of reverse transcription (RT)-PCR tests or contact and droplet precaution labels as a proxy for SARI could accurately reflect the epidemiology of patients presenting with SARI.AimWe aimed to compare the number of RT-PCR tests, contact and droplet precaution labels and SARI-related International Classification of Disease (ICD)-10 codes and evaluate their use as surveillance indicators.MethodsPatients from all age groups hospitalised at Leiden University Medical Center between 1 January 2017 up to and including 30 April 2023 were eligible for inclusion. We used a clinical data collection tool to extract data from electronic medical records. For each surveillance indicator, we plotted the absolute count for each week, the incidence proportion per week and the correlation between the three surveillance indicators.ResultsWe included 117,404 hospital admissions. The three surveillance indicators generally followed a similar pattern before and during the COVID-19 pandemic. The correlation was highest between contact and droplet precaution labels and ICD-10 diagnostic codes (Pearson correlation coefficient: 0.84). There was a strong increase in the number of RT-PCR tests after the start of the COVID-19 pandemic.DiscussionAll three surveillance indicators have advantages and disadvantages. ICD-10 diagnostic codes are suitable but are subject to reporting delays. Contact and droplet precaution labels are a feasible option for automated SARI surveillance, since these reflect trends in SARI incidence and may be available real-time.

[This corrects the article DOI: 10.3389/fpsyt.2023.1002526.].

BACKGROUND: Although many individuals with alcohol dependence (AD) are recognized in the German healthcare system, only a few utilize addiction-specific treatment services. Those who enter treatment are not well characterized regarding their prospective pathways through the highly fragmented German healthcare system. This paper aims to (1) identify typical care pathways of patients with AD and their adherence to treatment guidelines and (2) explore the characteristics of these patients using routine data from different healthcare sectors.
METHODS: We linked routinely collected register data of individuals with a documented alcohol-related diagnosis in the federal state of Bremen, Germany, in 2016/2017 and their addiction-specific health care: two statutory health insurance funds (outpatient pharmacotherapy for relapse prevention and inpatient episodes due to AD with and without qualified withdrawal treatment (QWT)), the German Pension Insurance (rehabilitation treatment) and a group of communal hospitals (outpatient addiction care). Individual care pathways of five different daily states of utilized addiction-specific treatment following an index inpatient admission due to AD were analyzed using state sequence analysis and cluster analysis. The follow-up time was 307 days (10 months). Individuals of the clustered pathways were compared concerning current treatment recommendations (1: QWT followed by postacute treatment; 2: time between QWT and rehabilitation). Patients\' characteristics not considered during the cluster analysis (sex, age, nationality, comorbidity, and outpatient addiction care) were then compared using a multinomial logistic regression.
RESULTS: The analysis of 518 individual sequences resulted in the identification of four pathway clusters differing in their utilization of acute and postacute treatment. Most did not utilize subsequent addiction-specific treatment after their index inpatient episode (n = 276) or had several inpatient episodes or QWT without postacute treatment (n = 205). Two small clusters contained pathways either starting rehabilitation (n = 26) or pharmacotherapy after the index episode (n = 11). Overall, only 9.3% utilized postacute treatment as recommended.
CONCLUSIONS: A concern besides the generally low utilization of addiction-specific treatment is the implementation of postacute treatments for individuals after QWT.

The collection of sexual orientation in routine data, generated either from contacts with health services or in infrastructure data resources designed and collected for policy and research, has improved substantially in the United Kingdom in the last decade. Inclusive measures of gender and transgender status are now also beginning to be collected. This viewpoint considers current data collections, and their strengths and limitations, including accessing data, sample size, measures of sexual orientation and gender, measures of health outcomes, and longitudinal follow-up. The available data are considered within both sociopolitical and biomedical models of health for individuals who are lesbian, gay, bisexual, transgender, queer, or of other identities including nonbinary (LGBTQ+). Although most individual data sets have some methodological limitations, when put together, there is now a real depth of routine data for LGBTQ+ health research. This paper aims to provide a framework for how these data can be used to improve health and health care outcomes. Four practical analysis approaches are introduced-descriptive epidemiology, risk prediction, intervention development, and impact evaluation-and are discussed as frameworks for translating data into research with the potential to improve health.

Data and indicator estimates are considered vital to document persisting challenges in maternal and newborn health and track progress towards global goals. However, prioritization of standardised, comparable quantitative data can preclude the collection of locally relevant information and pose overwhelming burdens in low-resource settings, with negative effects on the provision of quality of care. A growing body of qualitative studies aims to provide a place-based understanding of the complex processes and human experiences behind the generation and use of maternal and neonatal health data. We conducted a qualitative systematic review exploring how national or international requirements to collect and report data on maternal and neonatal health indicators are perceived and experienced at the sub-national and country level in low-income and lower-middle income countries. We systematically searched six electronic databases for qualitative and mixed-methods studies published between January 2000 and March 2023. Following screening of 4084 records by four reviewers, 47 publications were included in the review. Data were analysed thematically and synthesised from a Complex Adaptive Systems (CAS) theoretical perspective. Our findings show maternal and neonatal health data and indicators are not fixed, neutral entities, but rather outcomes of complex processes. Their collection and uptake is influenced by a multitude of system hardware elements (human resources, relevancy and adequacy of tools, infrastructure, and interoperability) and software elements (incentive systems, supervision and feedback, power and social relations, and accountability). When these components are aligned and sufficiently supportive, data and indicators can be used for positive system adaptivity through performance evaluation, prioritization, learning, and advocacy. Yet shortcomings and broken loops between system components can lead to unforeseen emergent behaviors such as blame, fear, and data manipulation. This review highlights the importance of measurement approaches that prioritize local relevance and feasibility, necessitating participatory approaches to define context-specific measurement objectives and strategies.

UNASSIGNED: In the Western Cape, South Africa, public-sector individual-level routine data are consolidated from multiple sources through the Provincial Health Data Centre (PHDC). This enables the description of temporal changes in population-wide antenatal HIV seroprevalence. We evaluated the validity of these data compared to aggregated program data and population-wide sentinel antenatal HIV seroprevalence surveys for the Western Cape province.
UNASSIGNED: We conducted a retrospective cohort analysis of all pregnancies identified in the PHDC from January 2011 to December 2020. Evidence of antenatal and HIV care from electronic platforms were linked using a unique patient identifier. HIV prevalence estimates were triangulated and compared with available survey estimates and aggregated programmatic data from registers as recorded in the District Health Information System. Provincial, district-level and age-group HIV prevalence estimates were compared between data systems using correlation coefficients, absolute differences and trend analysis.
UNASSIGNED: Of the 977800 pregnancies ascertained, PHDC HIV prevalence estimates from 2011-2013 were widely disparate from aggregate and survey data (due to incomplete electronic data), whereas from 2014 onwards, estimates were within the 95% confidence interval of survey estimates, and closely correlated to aggregate data estimates (r = 0.8; p = 0.01), with an average prevalence difference of 0.4%. PHDC data show a slow but steady increase in provincial HIV prevalence from 16.7% in 2015 to 18.6% in 2020. The highest HIV prevalence was in the Cape Metro district (20.3%) Prevalence estimates by age group were comparable between sentinel surveys and PHDC from 2015 onwards, with prevalence estimates stable over time among younger age-groups (15-24 years) but increased among older age-groups (> 34 years).
UNASSIGNED: This study compares sentinel seroprevalence surveys with both register-based aggregate data and consolidated individuated administrative data. We show that in this setting linked individuated data may be reliably used for HIV surveillance and provide more granular estimates with greater efficiency than seroprevalence surveys and register-based aggregate data.