Indicators to Measure Adolescent Health at the Country, Regional, and Global Levels: Results of a Five-Year Selection Process by the Global Action for the Measurement of Adolescent Health.

PURPOSE: To improve adolescent health measurement, the Global Action for the Measurement of Adolescent health (GAMA) Advisory Group was formed in 2018 and published a draft list of 52 indicators across six adolescent health domains in 2022. We describe the process and results of selecting the adolescent health indicators recommended by GAMA (hereafter, "GAMA-recommended indicators"). METHODS: Each indicator in the draft list was assessed using the following inputs: (1) availability of data and stakeholders' perceptions on their relevance, acceptability, and feasibility across selected countries; (2) alignment with current measurement recommendations and practices; and (3) data in global databases. Topic-specific working groups comprised of GAMA experts and representatives of United Nations partner agencies reviewed results and provided preliminary recommendations, which were appraised by all GAMA members and finalized. RESULTS: There are 47 GAMA-recommended indicators (36 core and 11 additional) for adolescent health measurement across six domains: policies, programs, and laws (4 indicators); systems performance and interventions (4); health determinants (7); health behaviors and risks (20); subjective well-being (2); and health outcomes and conditions (10). DISCUSSION: These indicators are the result of a robust and structured five-year process to identify a priority set of indicators with relevance to adolescent health globally. This inclusive and participatory approach incorporated inputs from a broad range of stakeholders, including adolescents and young people themselves. The GAMA-recommended indicators are now ready to be used to measure adolescent health at the country, regional, and global levels.

Relevance of the Sustainable Development Goals (SDGs) to Adolescent Health Measurement: A Systematic Mapping of the SDG Framework and Global Adolescent Health Indicators.

PURPOSE: To assess the relevance of the Sustainable Development Goals (SDGs) framework for adolescent health measurement, both in terms of age disaggregation and different health domains captured, and how the adolescent health indicators recommended by the Global Action for Measurement of Adolescent Health (GAMA) can complement the SDG framework. METHODS: We conducted a desk review to systematically map all 248 SDG indicators using the UN metadata repository in three steps: 1) age-related mandates for SDG reporting; 2) linkages between the SDG indicators and priority areas for adolescent health measurement; 3) comparison between the GAMA indicators and the SDG framework. RESULTS: Of the 248 SDG indicators, 35 (14%) targeted an age range overlapping with adolescence (10-19 years) and 33 (13%) called for age disaggregation. Only one indicator (3.7.2 "adolescent birth rate") covered the entire 10-19 age range. Almost half (41%) of the SDG indicators were directly related to adolescent health, but only 33 of those (13% of all SDG indicators) overlapped with the ages 10-19, and 15 (6% of all SDG indicators) explicitly mandated age disaggregation. Among the 47 GAMA indicators, five corresponded to existing SDG indicators, and eight were adolescent-specific age adaptations. Several GAMA indicators shed light on aspects not tracked in the SDG framework, such as obesity, mental health, physical activity, and bullying among 10-19-year-olds. DISCUSSION: Adolescent health cannot be monitored comprehensively with the SDG framework alone. The GAMA indicators complement this framework via age-disaggregated adaptations and by tracking aspects of adolescent health currently absent from the SDGs.

Exploring a Preliminary Set of Indicators to Measure Adolescent Health: Results From a 12-Country Feasibility Study.

PURPOSE: To explore data availability, perceived relevance, acceptability and feasibility of implementing 52 draft indicators for adolescent health measurement in different countries globally. METHODS: A mixed-methods, sequential explanatory study was conducted in 12 countries. An online spreadsheet was used to assess data availability and a stakeholder survey to assess perceived relevance, acceptability, and feasibility of implementing each draft indicator proposed by the Global Action for Measurement of Adolescent health (GAMA). The assessments were discussed in virtual meetings with all countries and in deep dives with three countries. Findings were synthesized using descriptive statistics and qualitative thematic analysis. RESULTS: Data availability varied across the 52 draft GAMA indicators and across countries. Nine countries reported measuring over half of the indicators. Most indicators were rated relevant by stakeholders, while some were considered less acceptable and feasible. The ten lowest-ranking indicators were related to mental health, sexual health and substance use; the highest-ranking indicators centered on broader adolescent health issues, like use of health services. Indicators with higher data availability and alignment with national priorities were generally considered most relevant, acceptable and feasible. Barriers to measurement included legal, ethical and sensitivity issues, challenges with multi-sectoral coordination and data systems flexibility. DISCUSSION: Most of the draft GAMA indicators were deemed relevant and feasible, but contextual priorities and perceived acceptability influenced their implementation in countries. To increase their use for a more comprehensive understanding of adolescent health, better multi-sectoral coordination and tailored capacity building to accommodate the diverse data systems in countries will be required.

Towards Harmonized Adolescent Health Measurement: Assessing Alignment Between Current Recommendations and the Global Action for Measurement of Adolescent Health-Recommended Indicators.

PURPOSE: This study identified alignment of indicators across different initiatives and data collection instruments as a foundation for future harmonization of adolescent health measurement. METHODS: Using the Global Action for Measurement of Adolescent health (GAMA) recommended indicators as the basis for comparison, we conducted a desk review of 14 global-level initiatives, such as the Sustainable Development Goals and the Global Strategy for Women's, Children's and Adolescents' Health, and five multicountry survey programs, such as the Multiple Indicator Cluster Surveys and the Global school-based Student Health Survey. We identified initiative and survey indicators similar to a GAMA indicator, deconstructed indicators into standard elements to facilitate comparison, and assessed alignment to the corresponding GAMA indicator across each of the elements. RESULTS: A total of 144 initiative indicators and 90 survey indicators were identified. Twenty-four initiative indicators (17%) and 14 survey indicators (16%) matched the corresponding GAMA indicators across all elements. Population of interest was the most commonly discrepant element; whereas GAMA indicators mostly refer to ages 10-19, many survey and initiative indicators encompass only part of this age range, for example, 15-19-year-olds as a subset of adults ages 15-49 years. An additional 53 initiative indicators (39%) and 44 survey indicators (49%) matched on all elements except the population of interest. DISCUSSION: The current adolescent measurement landscape is inconsistent, with differing recommendations on what and how to measure. Findings from this study support efforts to promote indicator alignment and harmonization across adolescent health measurement stakeholders at the global, regional, and country levels.

Imaging focused laser differential interferometry.

Focused laser differential interferometry (FLDI) is an important diagnostic for measuring density fluctuations in high-speed flows. Currently, however, high dynamic range FLDI is limited to photodiode measurements. In order to spatially resolve multiple locations within complex flows, we present a novel, to the best of our knowledge, refractive-optic imaging FLDI concept that not only produces two-dimensional images without scanning but also reduces the measurement noise floor of those images. To demonstrate this concept, a 33 × 33 grid of FLDI points is first generated using a microlens array. Then, the beams are split and recombined using two polarized Mach-Zehnder interferometers to maximize flexibility in beam separation and optimize signal sensitivity. Next, the FLDI points are collected slightly out of focus on a high-speed camera in order to increase the number of pixels n per FLDI point, thereby reducing noise floor by n. Finally, an under-expanded jet with a characteristic screech at 14.1 kHz is tested with the imaging FLDI setup, showing clear barrel and reflected shock features as well as spatially varying turbulence densities. Overall, this unique concept enables the creation of reduced-noise-floor, two-dimensional FLDI datasets for the study of supersonic and hypersonic flows.

Priority Indicators for Adolescent Health Measurement - Recommendations From the Global Action for Measurement of Adolescent Health (GAMA) Advisory Group.

PURPOSE: This article describes the selection of priority indicators for adolescent (10-19 years) health measurement proposed by the Global Action for Measurement of Adolescent health advisory group and partners, building on previous work identifying 33 core measurement areas and mapping 413 indicators across these areas. METHODS: The indicator selection process considered inputs from a broad range of stakeholders through a structured four-step approach: (1) definition of selection criteria and indicator scoring; (2) development of a draft list of indicators with metadata; (3) collection of public feedback through a survey; and (4) review of the feedback and finalization of the indicator list. As a part of the process, measurement gaps were also identified. RESULTS: Fifty-two priority indicators were identified, including 36 core indicators considered to be most important for measuring the health of all adolescents, one alternative indicator for settings where measuring the core indicator is not feasible, and 15 additional indicators for settings where further detail on a topic would add value. Of these indicators, 17 (33%) measure health behaviors and risks, 16 (31%) health outcomes and conditions, eight (15%) health determinants, five (10%) systems performance and interventions, four (8%) policies, programmes, laws, and two (4%) subjective well-being. DISCUSSION: A consensus list of priority indicators with metadata covering the most important health issues for adolescents was developed with structured inputs from a broad range of stakeholders. This list will now be pilot tested to assess the feasibility of indicator data collection to inform global, regional, national, and sub-national monitoring.

The influence of heatwave temperatures on fracture patient presentation to hospital.

INTRODUCTION: As global warming continues at its current rate, heatwaves are likely to become an increasing phenomenon. At present, knowledge of the influence of heatwave temperatures on fracture patient presentation to hospital remains limited. METHODS: This was a retrospective descriptive epidemiology study performed through hospital database review, linked to meteorological data. Emergency Department and Fracture Patient Presentation Data was obtained for the adult (16+) South Glasgow population (population count - 525,839) and the adult (16+) population covered by the West of Scotland Major Trauma Centre (population count - 2,218,326) from May 2021 to August 2021. This was combined with maximum temperature data, along with humidity and humidex data. Humidex is a measure which quantifies the temperature experienced by the patient, through a combined score incorporating both maximum temperature and humidity RESULTS: During the study period, there was one temperature heatwave (19th to 25th July), and four humidex heatwaves (27th June to 3rd July, 15th to 17th July, 19th to 27th July, 22nd to 26th August). During the temperature heatwave, there was a significantly higher incidence of orthopaedic polytrauma patient presentation (IRR 2.37: p < 0.027), as well as ED patient presentation (IRR 1.07: p < 0.036). The humidex heatwaves were associated with a significantly higher incidence of orthopaedic polytrauma patient presentation (IRR 2.31: p < 0.002) and overall fracture patient presentation (IRR 1.18: p < 0.002). Positive correlations were found between orthopaedic polytrauma patient presentation vs temperature (R=0.217: p < 0.016), ED patient presentation vs temperature (R=0.427: p < 0.001), fracture patient presentation vs temperature (R=0.394: p < 0.001), and distal radius fracture patient presentation vs temperature (R=0.246: p < 0.006). CONCLUSION: This study finds that heatwave temperatures result in a significantly increased number of orthopaedic polytrauma patients presenting to a Major Trauma Centre. Given the significant resources these patients require for care, Major Trauma Centres should be aware of such findings, and consider staff and resources profiles in response.

Driving reaction time following periacetabular osteotomy.

INTRODUCTION: Periacetabular osteotomy (PAO) is increasingly being used to treat young adults with symptomatic hip dysplasia. Currently there is a lack of evidence to guide return to driving after this procedure. This study aimed to identify the length of time required after a Periacetabular Osteotomy procedure before a patient can safely return to driving. METHODS: All patients undergoing PAO were assessed for suitability for the study. Inclusion criteria were: currently driving with a valid licence; and being able to attend follow-up assessment. Baseline driving reaction time was assessed using a driving simulator preoperatively. The simulation was repeated 5 times for each patient and reaction times recorded (Thinking time, Action time and Total reaction time for braking at 30 mph). The driving simulation was repeated using the same methods at 6 weeks and 12 weeks postoperatively. Pre- and postoperative times were compared. RESULTS: 26 patients were included (24 females, 2 males) with a mean age of 32 (range 19-50) years. The mean preoperative times were: Thinking time 0.48, Action time 0.21, Total time 0.69 seconds. At 6 weeks postoperatively, mean Action time increased to 0.26 seconds (p = 0.012) and mean Total time increased to 0.78 seconds (p = 0.013). By 12 weeks post procedure, there was no significant difference in reaction times compared to baseline (mean Thinking time 0.47 seconds, Action time 0.23, Total time 0.72; p > 0.05). CONCLUSIONS: Most patients may not be safe to drive at 6 weeks following PAO procedures but should be safe to drive at 12 weeks postoperatively. Individual patient factors should also be taken into consideration.

A Scoping Review of Adolescent Health Indicators.

PURPOSE: A host of recent initiatives relating to adolescent health have been accompanied by varying indicator recommendations, with little stakeholder coordination. We assessed currently included adolescent health-related indicators for their measurement focus, identified overlap across initiatives, and determined measurement gaps. METHODS: We conducted a scoping review to map the existing indicator landscape as depicted by major measurement initiatives. We classified indicators as per 33 previously identified core adolescent health measurement areas across five domains and by age groups. We also identified indicators common across measurement initiatives even if differing in details. RESULTS: We identified 413 indicators across 16 measurement initiatives, with most measuring health outcomes and conditions (162 [39%]) and health behaviors and risks (136 [33%]); followed by policies, programs, and laws (49 [12%]); health determinants (44 [11%]); and system performance and interventions (22 [5%]). Age specification was available for 221 (54%) indicators, with 51 (23%) focusing on the full adolescent age range (10-19 years), 1 (<1%) on 10-14 years, 27 (12%) on 15-19 years, and 142 (64%) on a broader age range including adolescents. No definitional information, such as numerator and denominator, was available for 138 indicators. We identified 236 distinct indicators after accounting for overlap. CONCLUSION: The adolescent health measurement landscape is vast and includes substantial variation among indicators purportedly assessing the same concept. Gaps persist in measuring systems performance and interventions; policies, programs, and laws; and younger adolescents' health. Addressing these gaps and harmonizing measurement is fundamental to improve program implementation and accountability for adolescent health globally.

A call for standardised age-disaggregated health data.

The 2030 Sustainable Development Goals agenda calls for health data to be disaggregated by age. However, age groupings used to record and report health data vary greatly, hindering the harmonisation, comparability, and usefulness of these data, within and across countries. This variability has become especially evident during the COVID-19 pandemic, when there was an urgent need for rapid cross-country analyses of epidemiological patterns by age to direct public health action, but such analyses were limited by the lack of standard age categories. In this Personal View, we propose a recommended set of age groupings to address this issue. These groupings are informed by age-specific patterns of morbidity, mortality, and health risks, and by opportunities for prevention and disease intervention. We recommend age groupings of 5 years for all health data, except for those younger than 5 years, during which time there are rapid biological and physiological changes that justify a finer disaggregation. Although the focus of this Personal View is on the standardisation of the analysis and display of age groups, we also outline the challenges faced in collecting data on exact age, especially for health facilities and surveillance data. The proposed age disaggregation should facilitate targeted, age-specific policies and actions for health care and disease management.

Cement-in-cement femoral component revision : a comparison of two different taper-slip designs with medium-term follow up.

AIMS: Cement-in-cement revision of the femoral component represents a widely practised technique for a variety of indications in revision total hip arthroplasty. In this study, we compare the clinical and radiological outcomes of two polished tapered femoral components. METHODS: From our prospectively collated database, we identified all patients undergoing cement-in-cement revision from January 2005 to January 2013 who had a minimum of two years' follow-up. All cases were performed by the senior author using either an Exeter short revision stem or the C-Stem AMT high offset No. 1 prosthesis. Patients were followed-up annually with clinical and radiological assessment. RESULTS: A total of 97 patients matched the inclusion criteria (50 Exeter and 47 C-Stem AMT components). There were no significant differences between the patient demographic data in either group. Mean follow-up was 9.7 years. A significant improvement in Oxford Hip Score (OHS), Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC), and 12-item Short-Form Survey (SF-12) scores was observed in both cohorts. Leg lengths were significantly shorter in the Exeter group, with a mean of -4 mm in this cohort compared with 0 mm in the C-Stem AMT group. One patient in the Exeter group had early evidence of radiological loosening. In total, 16 patients (15%) underwent further revision of the femoral component (seven in the C-Stem AMT group and nine in the Exeter group). No femoral components were revised for aseptic loosening. There were two cases of femoral component fracture in the Exeter group. CONCLUSION: Our series shows promising mid-term outcomes for the cement-in-cement revision technique using either the Exeter or C-Stem AMT components. These results demonstrate that cement-in-cement revision using a double or triple taper-slip design is a safe and reliable technique when used for the correct indications. Cite this article: Bone Joint J 2021;103-B(7):1215-1221.

Effective coverage measurement in maternal, newborn, child, and adolescent health and nutrition: progress, future prospects, and implications for quality health systems.

Intervention coverage-the proportion of the population with a health-care need who receive care-does not account for intervention quality and potentially overestimates health benefits of services provided to populations. Effective coverage introduces the dimension of quality of care to the measurement of intervention coverage. Many definitions and methodological approaches to measuring effective coverage have been developed, resulting in confusion over definition, calculation, interpretation, and monitoring of these measures. To develop a consensus on the definition and measurement of effective coverage for maternal, newborn, child, and adolescent health and nutrition (MNCAHN), WHO and UNICEF convened a group of experts, the Effective Coverage Think Tank Group, to make recommendations for standardising the definition of effective coverage, measurement approaches for effective coverage, indicators of effective coverage in MNCAHN, and to develop future effective coverage research priorities. Via a series of consultations, the group recommended that effective coverage be defined as the proportion of a population in need of a service that resulted in a positive health outcome from the service. The proposed effective coverage measures and care cascade steps can be applied to further develop effective coverage measures across a broad range of MNCAHN services. Furthermore, advances in measurement of effective coverage could improve monitoring efforts towards the achievement of universal health coverage.

Validating a GPS-based approach to detect health facility visits against maternal response to prompted recall survey.

INTRODUCTION: Common approaches to measure health behaviors rely on participant responses and are subject to bias. Technology-based alternatives, particularly using GPS, address these biases while opening new channels for research. This study describes the development and implementation of a GPS-based approach to detect health facility visits in rural Pune district, India. METHODS: Participants were mothers of under-five year old children within the Vadu Demographic Surveillance area. Participants received GPS-enabled smartphones pre-installed with a location-aware application to continuously record and transmit participant location data to a central server. Data were analyzed to identify health facility visits according to a parameter-based approach, optimal thresholds of which were calibrated through a simulation exercise. Lists of GPS-detected health facility visits were generated at each of six follow-up home visits and reviewed with participants through prompted recall survey, confirming visits which were correctly identified. Detected visits were analyzed using logistic regression to explore factors associated with the identification of false positive GPS-detected visits. RESULTS: We enrolled 200 participants and completed 1098 follow-up visits over the six-month study period. Prompted recall surveys were completed for 694 follow-up visits with one or more GPS-detected health facility visits. While the approach performed well during calibration (positive predictive value (PPV) 78%), performance was poor when applied to participant data. Only 440 of 22 251 detected visits were confirmed (PPV 2%). False positives increased as participants spent more time in areas of high health facility density (odds ratio (OR) = 2.29, 95% confidence interval (CI) = 1.62-3.25). Visits detected at facilities other than hospitals and clinics were also more likely to be false positives (OR = 2.78, 95% CI = 1.65-4.67) as were visits detected to facilities nearby participant homes, with the likelihood decreasing as distance increased (OR = 0.89, 95% CI = 0.82-0.97). Visit duration was not associated with confirmation status. CONCLUSIONS: The optimal parameter combination for health facility visits simulated by field workers substantially overestimated health visits from participant GPS data. This study provides useful insights into the challenges in detecting health facility visits where providers are numerous, highly clustered within urban centers and located near residential areas of the population which they serve.