IntroductionFoundational to a well-functioning health system is a strong routine health information system (RHIS) that informs decisions and actions at all levels of the health system. In the context of decentralization across low- and middle-income countries, RHIS has the promise of supporting sub-national health staff to take data-informed actions to improve health system performance. However, there is wide variation in how “RHIS data use” is defined and measured in the literature, impeding the development and evaluation of interventions that effectively promote RHIS data use.MethodsAn integrative review methodology was used to: (1) synthesize the state of the literature on how RHIS data use in low- and middle-income countries is conceptualized and measured; (2) propose a refined RHIS data use framework and develop a common definition for RHIS data use; and (3) propose improved approaches to measure RHIS data use. Four electronic databases were searched for peer-reviewed articles published between 2009 and 2021 investigating RHIS data use.ResultsA total of 45 articles, including 24 articles measuring RHIS data use, met the inclusion criteria. Less than half of included articles (42%) explicitly defined RHIS data use. There were differences across the literature whether RHIS data tasks such as data analysis preceded or were a part of RHIS data use; there was broad consensus that data-informed decisions and actions were essential steps within the RHIS data use process. Based on the synthesis, the Performance of Routine Information System Management (PRISM) framework was refined to specify the steps of the RHIS data use process.ConclusionConceptualizing RHIS data use as a process that includes data-informed actions emphasizes the importance of actions in improving health system performance. Future studies and implementation strategies should be designed with consideration for the different support needs for each step of the RHIS data use process.

IntroductionFoundational to a well-functioning health system is a strong routine health information system (RHIS) that informs decisions and actions at all levels of the health system. In the context of decentralization across low- and middle-income countries, RHIS has the promise of supporting sub-national health staff to take data-informed actions to improve health system performance. However, there is wide variation in how “RHIS data use” is defined and measured in the literature, impeding the development and evaluation of interventions that effectively promote RHIS data use.MethodsAn integrative review methodology was used to: (1) synthesize the state of the literature on how RHIS data use in low- and middle-income countries is conceptualized and measured; (2) propose a refined RHIS data use framework and develop a common definition for RHIS data use; and (3) propose improved approaches to measure RHIS data use. Four electronic databases were searched for peer-reviewed articles published between 2009 and 2021 investigating RHIS data use.ResultsA total of 45 articles, including 24 articles measuring RHIS data use, met the inclusion criteria. Less than half of included articles (42%) explicitly defined RHIS data use. There were differences across the literature whether RHIS data tasks such as data analysis preceded or were a part of RHIS data use; there was broad consensus that data-informed decisions and actions were essential steps within the RHIS data use process. Based on the synthesis, the Performance of Routine Information System Management (PRISM) framework was refined to specify the steps of the RHIS data use process.ConclusionConceptualizing RHIS data use as a process that includes data-informed actions emphasizes the importance of actions in improving health system performance. Future studies and implementation strategies should be designed with consideration for the different support needs for each step of the RHIS data use process.

Supplemental material to the 2025 article "Demonstration of dispersion gas barometry" by Y. Yang, J.A. Stone, and P.F. Egan. The archive file contains two-color data for the gases helium, neon, argon, and nitrogen. Two-color data means measured quadruplets of pressure and temperature together with refractivity at two wavelengths 1542.3912 nm (194.368624 THz) and 632.9919 nm (473.611873 THz). Two analysis scripts are included: 1. plotHelium.py: performs the helium analysis to deduce the cavity distortion coefficient kappa and the conversion factor epsilon_p needed to realize the optical pressure scale. The script reproduces Fig. 2 from the article. 2. plotGases.py: analyzes the gases neon, argon, and nitrogen and deduces the two parameters describing dispersion polarizability A_epsilon and A_2. The script reproduces Fig 3 from the article. Additionally, the script "pgtProp.py" is a library function, which offers best knowledge (as of January 2025) of gas properties to be used in Polarizing Gas Thermometry. The library functions synthesize the optical and thermophysical properties of helium, neon, argon, and nitrogen. The synthesis combines literature sources plus the measurement results from the main text.

1. Extensive research has demonstrated that urbanization strongly alters ecological processes, often perniciously. However, quantifying the magnitude of urban effects and determining how generalized they can be across systems depends on the ways in which urbanization is measured and modelled. 2. We coupled a formal literature survey with a novel conceptual framework to document and synthesize the myriad of metrics used to quantify urbanization. The framework enables clear cataloguing of urban metrics by identifying) the urban component measured, ii) the method of measurement, iii) the metric’s spatial scale, and iv) the metric’s temporal nature. Thus, the framework comprehensively captures the what, how, where, and when of urban metrics.3. We documented striking variability in urban metrics with respect to which urban components were measured as well as how, where, and when they were quantified. Overall, our survey revealed that they tended to be: i) be structurally-focused, ii) methodologically simplistic, iii) spatially variable, and iv) temporally static. 4. Synthesis and applications. Many metrics are used to quantify urbanization or “urban-ness”. The variation in urban metrics complicates the development of theory, comparisons of findings across studies, and the implementation of management and conservation actions. To pave a clear path forward for more efficient and policy-relevant urban research, we systematically organized urban metrics using a simple, flexible, and comprehensive framework. The framework clarifies what urbanization actually means in empirical practice and identifies several crucial areas for future research, including: i) systematic assessments of urban metrics across multiple scales, ii) an increased and judicious use of more complex urban metrics aimed at evaluating both mechanistic and broad-scale correlative ecological hypotheses, and iii) an increased emphasis on the socio-economic aspects of urban effects.

Achieving kinetic control to synthesize metastable compounds is a challenging task, especially in solid-state reactions where the diffusion is slow. Another challenge is the unambiguous crystal structure determination for metastable compounds when high-quality single crystals suitable for single-crystal X-ray diffraction are inaccessible. In this work, we report an unconventional means of synthesis and an effective strategy to solve the crystal structure of an unprecedented metastable compound LiNi12B8. This compound can only be produced upon heating a metastable layered boride, HT-Li0.4NiB (HT: high temperature), in a sealed niobium container. A conventional heating and annealing of elements do not yield the title compound, which is consistent with the metastable nature of LiNi12B8. The process to crystallize this compound is sensitive to the annealing temperature and dwelling time, a testament to the complex kinetics involved in the formation of the product. The unavailability of crystals suitable for single-crystal X-ray diffraction experiments prompted solving the crystal structure from high-resolution synchrotron powder X-ray diffraction data. This compound crystallizes in a new structure type with space group I4/mmm (a = 10.55673(9) Å, c = 10.00982(8) Å, V = 1115.54(3) Å3, Z = 6). The resulting complex crystal structure of LiNi12B8 is confirmed by scanning transmission electron microscopy and solid-state 11B and 7Li NMR spectroscopy analyses. The extended Ni framework with Li/Ni disorder in its crystal structure resulted in the spin-glass or cluster glass type magnetic ordering below 24 K. This report illustrates a “contemporary twist” to traditional methodologies toward synthesizing a metastable compound and provides a recipe for solving structures by combining the complementary characterization techniques in the cases where the traditionally used single-crystal X-ray diffraction method is nonapplicable.

BackgroundPersons with dementia have twice the acute hospital use as older persons without dementia. In addition to straining overburdened healthcare systems, acute hospital use impacts patient and caregiver quality of life and is associated with increased risk of adverse outcomes including death. Reducing avoidable acute hospital use in persons with dementia is thus a global healthcare priority. However, evidence regarding the impact of health service interventions as defined by the Effective Practice and Organization of Care Cochrane Group on acute hospital use is scant and inconclusive. The aim of this systematic review and meta-analysis was to synthesize available evidence on the impact of health service interventions on acute hospital use in community-dwelling persons with dementia compared to usual care.MethodsData Sources: MEDLINE, EMBASE, CINAHL and Cochrane CENTRAL (from 01/1995 to 08/2017). Study eligibility criteria: Randomised controlled trials measuring the impact of health service interventions on acute hospital use (proportion and mean number of emergency department visits and hospitalisations, mean number of hospital days, measured at 12 months, and at longest follow-up) in community-dwelling persons with dementia, compared to usual care. Study selection, appraisal and synthesis methods: Reviewers independently identified studies, extracted data, and assessed the risk of bias, with the Cochrane risk of bias tool. Authors of relevant trials were queried about unpublished data. Random effects model was used for meta-analyses. Effect heterogeneity was assessed through prediction intervals, and explored using sub-group analyses.FindingsSeventeen trials provided data on 4,549 persons. Unpublished data were obtained for 13 trials, representing 65% of synthesized data. Most interventions included a case management or a self-management component. None of the outcome comparisons provided conclusive evidence supporting the hypothesis that these interventions would lead to a decrease in acute hospital use. Furthermore, prediction intervals indicated possible and important increased service use associated with these interventions, such as emergency department visits, hospital admissions, and hospital days. Subgroup analyses did not favour any type of intervention. A limitation of this study is the inclusion of any type of health service intervention, which may have increased the observed heterogeneity.ConclusionDespite a comprehensive systematic review and meta-analysis, including predominantly unpublished data, no health service intervention beyond usual care was found to reduce acute hospital use in community-dwelling persons with dementia. An important increase in service use may be associated with these interventions. Further research is urgently needed to identify effective interventions for this vulnerable population to limit rising acute hospital use, associated costs and adverse outcomes. Systematic review registration PROSPERO CRD42016046444.

The outcome data extracted from the primary research.

Overview of meta-analysis results according to outcome and endpoint.

Comparison of posterior means: Original model vs. sensitivity analysis.