Implementing Opt-Out HIV Testing in the Alameda County Jails.

Incorporating routine opt-out HIV testing in correctional settings is a critical component of an effective strategy to end the HIV/AIDS epidemic. Between 2012 and 2017, Alameda County jails implemented opt-out HIV testing to identify new cases, link those who were newly diagnosed to care, and reengage those who were previously diagnosed but not in care. Over a 6-year period, 15,906 tests were conducted, with a positivity rate of 0.55% for both newly diagnosed cases and previously diagnosed but out of care. Nearly 80% of those who tested positive were linked to care within 90 days. The high positivity and successful linkage and reengagement with care underscores the need to support HIV testing programs in correctional settings.

Standardization and Validation of the Flash Visual Evoked Potential-P2 Conversion Scores in the Diagnosis of Amnestic Mild Cognitive Impairment and Alzheimer's Dementia.

Amnestic mild cognitive impairment (aMCI), which is characterized by normal daily activity, but a significant decline in episodic memory, is now widely accepted as a risk factor for the development of Alzheimer's dementia (AD). Research suggests that many of the same neuropathological changes associated with AD also occur in patients diagnosed with aMCI. A recent review of the literature revealed that the latency of the flash visual-evoked potential-P2 (FVEP-P2) may possess pathognomonic information that may assist in the early detection of aMCI. While standards exist for the recording of FVEP-P2, individual clinics often use recording parameters that may differ, resulting in latencies that may not generalize beyond the clinic that produced them. The present article illustrates the process by which the FVEP-P2 latency can be standardized across clinics using FVEP-P2 Conversion Scores. We then demonstrate the diagnostic accuracy of the newly developed scores. Method: In the present investigation, we used the previously unpublished data containing the FVEP-P2 latencies of 45 AD and 60 controls. Result: We were able to demonstrate the process by which individual clinics may first standardize FVEP-P2 latencies and then examine patient performance using FVEP-P2 Conversion Scores, providing clinicians with a richer context from which to examine the patient performance. Conclusion: Consistent with the findings of previous research, the findings of the present investigation support the use of the FVEP-P2 Conversion Scores in the diagnosis of AD. Future directions, including the modification of recording parameters associated with the FVEP-P2, are also discussed.

The priorities for future clinical trials and large cohort studies addressing health and healthcare for mothers and babies in Aotearoa New Zealand.

AIMS: To identify priorities for clinical trials and large cohort studies addressing maternal and perinatal health and healthcare in Aotearoa New Zealand. METHODS: An Aotearoa New Zealand specific Research Prioritisation Framework was developed. Knowledge gaps were collected from an Audience Group via online questionnaires, video call interviews, and by systematic review. These were formulated into research questions. An Advisory Group reviewed questions suited to a clinical trial or large cohort study. A Ranking Group weighted the ranking criteria and ranked the research questions. RESULTS: A total of 305 online questionnaires, 62 interviews and 62 published prioritisation projects generated 3,347 knowledge gaps. After content analysis, 358 unanswered research questions were ranked. Rating criteria weightings were: effect on equity 26.1%; potential to reduce disease burden 20.5%; effectiveness 20.0%; deliverability 17.9%; and answerability 16.0%. All of the top 20 prioritised research questions directly related to Maori and/or Pacific health and predominantly involved research into healthcare systems and workforce rather than disease conditions. CONCLUSIONS: This project has identified the most important questions for future clinical trials and large cohort studies addressing maternal and perinatal health and healthcare in Aotearoa New Zealand. The Framework and methodology can be adapted for use across all areas of health.

Global holiday datasets for understanding seasonal human mobility and population dynamics.

Public and school holidays have important impacts on population mobility and dynamics across multiple spatial and temporal scales, subsequently affecting the transmission dynamics of infectious diseases and many socioeconomic activities. However, worldwide data on public and school holidays for understanding their changes across regions and years have not been assembled into a single, open-source and multitemporal dataset. To address this gap, an open access archive of data on public and school holidays in 2010-2019 across the globe at daily, weekly, and monthly timescales was constructed. Airline passenger volumes across 90 countries from 2010 to 2018 were also assembled to illustrate the usage of the holiday data for understanding the changing spatiotemporal patterns of population movements.

Practical geospatial and sociodemographic predictors of human mobility.

Understanding seasonal human mobility at subnational scales has important implications across sciences, from urban planning efforts to disease modelling and control. Assessing how, when, and where populations move over the course of the year, however, requires spatially and temporally resolved datasets spanning large periods of time, which can be rare, contain sensitive information, or may be proprietary. Here, we aim to explore how a set of broadly available covariates can describe typical seasonal subnational mobility in Kenya pre-COVID-19, therefore enabling better modelling of seasonal mobility across low- and middle-income country (LMIC) settings in non-pandemic settings. To do this, we used the Google Aggregated Mobility Research Dataset, containing anonymized mobility flows aggregated over users who have turned on the Location History setting, which is off by default. We combined this with socioeconomic and geospatial covariates from 2018 to 2019 to quantify seasonal changes in domestic and international mobility patterns across years. We undertook a spatiotemporal analysis within a Bayesian framework to identify relevant geospatial and socioeconomic covariates explaining human movement patterns, while accounting for spatial and temporal autocorrelations. Typical pre-pandemic mobility patterns in Kenya mostly consisted of shorter, within-county trips, followed by longer domestic travel between counties and international travel, which is important in establishing how mobility patterns changed post-pandemic. Mobility peaked in August and December, closely corresponding to school holiday seasons, which was found to be an important predictor in our model. We further found that socioeconomic variables including urbanicity, poverty, and female education strongly explained mobility patterns, in addition to geospatial covariates such as accessibility to major population centres and temperature. These findings derived from novel data sources elucidate broad spatiotemporal patterns of how populations move within and beyond Kenya, and can be easily generalized to other LMIC settings before the COVID-19 pandemic. Understanding such pre-pandemic mobility patterns provides a crucial baseline to interpret both how these patterns have changed as a result of the pandemic, as well as whether human mobility patterns have been permanently altered once the pandemic subsides. Our findings outline key correlates of mobility using broadly available covariates, alleviating the data bottlenecks of highly sensitive and proprietary mobile phone datasets, which many researchers do not have access to. These results further provide novel insight on monitoring mobility proxies in the context of disease surveillance and control efforts through LMIC settings.

The effect of wavelength on the variability of the flash visual evoked potential P2: A potential biomarker for mild cognitive impairment and Alzheimer's dementia.

As the number of individuals diagnosed with amnestic mild cognitive impairment (aMCI) and Alzheimer's dementia (AD) increases, a need exists for early detection and treatment of the disorders. A recent review of the literature conducted by Arruda et al. (2020) revealed that the latency of the flash visual-evoked potential-P2 (FVEP-P2) may possess pathognomic information that may assist in the early detection and treatment of each disease. Unfortunately, while group differences in latency are robust, the ability to discriminate between individuals remains difficult due to the natural variability associated with the FVEP-P2 latency. In the current investigation, we examine the role of wavelength of light in the production of the FVEP-P2, with the goal of reducing the variability associated with the FVEP-P2 latency and improving the diagnostic accuracy of the FVEP-P2 evaluation. METHOD: Twenty-four healthy individuals (11 males and 13 females), ages 18 to 36 years (M = 25.00, SD = 5.60), participated in this investigation. Each participant experienced five blocks of 100 strobe flashes (or trials) under two different light conditions (blue filtered light and polychromatic white light) with their eyes closed. The FVEP-P2 associated with each trial was identified and the latency and amplitude of each component was calculated. RESULT: The results of several repeated measures analysis of variance revealed no statistically significant differences in intra- and inter-individual variability associated with the P2 latency or amplitude. However, there was a significant difference in the amplitude of the P2 produced by the two lights, with blue filtered light producing significantly lower amplitudes than the polychromatic white light. CONCLUSION: The results of the present investigation suggest that while imperfect, the current practice of employing polychromatic white light in the production of the FVEP-P2 remains the gold standard and that additional methods of reducing the natural variability of the P2 need to be developed if the FVEP-P2 latency is to be used as a biomarker.

Clinical decision making in the provision of audiovisual care for upper limb trauma: a survey of UK experiences.

Introduction: For many patients, audio-visual appointments have provided a timely and efficient way of seeking advice, assessment and treatment for their hand injuries during the NHS response to COVID-19. This study aimed to explore the experience of hand units across the UK in determining the safe and judicious use of audio-visual outpatient care for the management of acute upper limb trauma. Methods: An online cross-sectional survey was sent to the therapy leads of hand units across the UK. Questions focused on the experience of using audio-visual technology in the management of upper limb trauma, and the relevant factors in determining its appropriate use. A deductive mixed methods analysis was used to identify both common themes and capture community experience and characteristics. Results: A total of 51 out of 76 hand therapy units completed the survey; a response rate of 67%. Of these, 82% (42/51) reported using audio-visual technology to manage upper limb trauma during the UK COVID-19 lockdown. When determining patient suitability for audio-visual consultations, 73% (37/51) of respondents reported the use of COVID-19 guidelines, but only 35% (18/51) reported the use of a clinical decision-making tool. In agreement with our experience at Salisbury Hospital Foundation Trust, 92% (47/51) had concerns relating to the use of audio-visual care. Conclusion: The choice of safely managed remote care or in-person consultation has, to date, largely relied on the discretion of the clinician. A carefully designed clinical decision-making tool for the management of upper limb trauma is needed for use both in clinical practice and in future service planning.

The flash visual evoked potential-P2 and the detection of amnestic mild cognitive impairment: A review of empirical literature.

Amnestic Mild Cognitive Impairment (aMCI) is now recognized as an early risk state for the development of Alzheimer's dementia (AD). Biomarkers, including those that are cerebrospinal fluid or brain imaging based, have yet to provide the ultimate marker variable. A need currently exists for a non-invasive, easy to administer biomarker that contains aMCI/AD specific pathognomic information. OBJECTIVE: The objective of the present investigation was to provide an updated review of the Flash Visual Evoked Potential-P2 (FVEP-P2) as a biomarker for aMCI and AD. The FVEP-P2 has been shown to possess AD specific pathognomic information. METHOD: A review was conducted of all articles published between the years 1976 and 2019 that examined the clinical utility of the FVEP-P2 in the diagnosis of aMCI or AD. Only 17 published investigations met the criteria of the review. RESULT: The weighted average effect size, as measured by Cohen's d, was 1.07, with patients diagnosed with either aMCI or AD exhibiting a significant delay in the FVEP-P2 latency. The weighted mean latency for the controls was 143.92 ms (SD = 17.13). The weighted mean latency for the aMCI/AD was 164.02 ms (SD = 21.33). Estimates of sensitivity, specificity, and accuracy were based on the weighted means and standard deviations and were equal to 0.73. The area under the curve was equal to 0.78. CONCLUSION: The results of the current review suggest that the FVEP-P2 latency possesses AD specific pathognomic information and that it should be included as part of a much larger assessment process that includes neuropsychological, cerebrospinal fluid, and brain imaging findings.

Trauma transformed: A positive review of change during the COVID-19 pandemic.

Charles Moore in The Telegraph recently described the NHS as 'lumbering'.1 Far from this description, it has been our experience that the NHS has rapidly transformed across specialties in order to respond to the unprecedented global crisis of COVID-19. We describe here the multiple ways in which the plastic surgery trauma service at Salisbury District Hospital swiftly adapted over a two-week period in March 2020. Our aim is to deliver a tailored trauma service whilst adhering to the same high standards of patient care established prior to the COVID-19 pandemic. It is our view that many of these changes will be positive enduring practices for the future.

Annually modelling built-settlements between remotely-sensed observations using relative changes in subnational populations and lights at night.

Mapping urban features/human built-settlement extents at the annual time step has a wide variety of applications in demography, public health, sustainable development, and many other fields. Recently, while more multitemporal urban features/human built-settlement datasets have become available, issues still exist in remotely-sensed imagery due to spatial and temporal coverage, adverse atmospheric conditions, and expenses involved in producing such datasets. Remotely-sensed annual time-series of urban/built-settlement extents therefore do not yet exist and cover more than specific local areas or city-based regions. Moreover, while a few high-resolution global datasets of urban/built-settlement extents exist for key years, the observed date often deviates many years from the assigned one. These challenges make it difficult to increase temporal coverage while maintaining high fidelity in the spatial resolution. Here we describe an interpolative and flexible modelling framework for producing annual built-settlement extents. We use a combined technique of random forest and spatio-temporal dasymetric modelling with open source subnational data to produce annual 100 m × 100 m resolution binary built-settlement datasets in four test countries located in varying environmental and developmental contexts for test periods of five-year gaps. We find that in the majority of years, across all study areas, the model correctly identified between 85 and 99% of pixels that transition to built-settlement. Additionally, with few exceptions, the model substantially out performed a model that gave every pixel equal chance of transitioning to built-settlement in each year. This modelling framework shows strong promise for filling gaps in cross-sectional urban features/built-settlement datasets derived from remotely-sensed imagery, provides a base upon which to create urban future/built-settlement extent projections, and enables further exploration of the relationships between urban/built-settlement area and population dynamics.

Gustilo-Anderson type III tibial fractures have poor functional outcomes in patients over 75 years.

BACKGROUND: Previous outcome studies in open tibial fractures have commonly assessed young patients and there is a paucity of data regarding outcomes in the elderly. The aim of this study is to assess functional outcomes for patients over 75 years with Gustilo-Anderson Grade III open tibial fractures, including mobility and residential status. METHODS: Outcomes for all patients over 75 years admitted with grade III open tibial fractures to a UK level 1 trauma centre during a 5-year period (January 2010-May 2015) were analysed. Long-term follow up of surviving patients (median 44 months post injury) was undertaken in February 2016. RESULTS: Twenty-nine patients (24 female, 5 male - median 85 years) were included. Prior to injury 48% (n = 14) patients were independently mobile and 45% (n = 13) were living at home without care. Two-thirds of injuries were low energy; all patients sustained a grade III open tibial fracture. The 12-month mortality rate was 28% (n = 8) and mortality at long-term follow-up 48% (n = 14). From pre-injury to long-term 8% (n = 1) patients did not change mobility status, 75% (n = 9) reduced by one grade (e.g. independent to walking aid) and 16% (n = 2) by two grades. Fifty eight percent (n = 7) of patients retained residential status, 17% (n = 2) reduced by one grade and 25% (n = 3) by two grades. CONCLUSION: Grade III open tibial fractures are a significant injury in the elderly associated with poor outcomes with respect to return to mobility and pre-injury residential status. Our results suggest that a greater emphasis on intensive rehabilitation should be considered in this patient group.

Exploring the use of mobile phone data for national migration statistics.

Statistics on internal migration are important for keeping estimates of subnational population numbers up-to-date as well as urban planning, infrastructure development and impact assessment, among other applications. However, migration flow statistics typically remain constrained by the logistics of infrequent censuses or surveys. The penetration rate of mobile phones is now high across the globe with rapid recent increases in ownership in low-income countries. Analysing the changing spatiotemporal distribution of mobile phone users through anonymized call detail records (CDRs) offers the possibility to measure migration at multiple temporal and spatial scales. Based on a dataset of 72 billion anonymized CDRs in Namibia from October 2010 to April 2014, we explore how internal migration estimates can be derived and modelled from CDRs at subnational and annual scales, and how precision and accuracy of these estimates compare to census-derived migration statistics. We also demonstrate the use of CDRs to assess how migration patterns change over time, with a finer temporal resolution compared to censuses. Moreover, we show how gravity-type spatial interaction models built using CDRs can accurately capture migration flows. Results highlight that estimates of migration flows made using mobile phone data is a promising avenue for complementing more traditional national migration statistics and obtaining more timely and local data.

Correction to: Extending Data for Urban Health Decision-Making: a Menu of New and Potential Neighborhood-Level Health Determinants Datasets in LMICs.

Readers should note an additional Acknowledgment for this article: Dana Thomson is funded by the Economic and Social Research Council grant number ES/5500161/1.

Extending Data for Urban Health Decision-Making: a Menu of New and Potential Neighborhood-Level Health Determinants Datasets in LMICs.

Area-level indicators of the determinants of health are vital to plan and monitor progress toward targets such as the Sustainable Development Goals (SDGs). Tools such as the Urban Health Equity Assessment and Response Tool (Urban HEART) and UN-Habitat Urban Inequities Surveys identify dozens of area-level health determinant indicators that decision-makers can use to track and attempt to address population health burdens and inequalities. However, questions remain as to how such indicators can be measured in a cost-effective way. Area-level health determinants reflect the physical, ecological, and social environments that influence health outcomes at community and societal levels, and include, among others, access to quality health facilities, safe parks, and other urban services, traffic density, level of informality, level of air pollution, degree of social exclusion, and extent of social networks. The identification and disaggregation of indicators is necessarily constrained by which datasets are available. Typically, these include household- and individual-level survey, census, administrative, and health system data. However, continued advancements in earth observation (EO), geographical information system (GIS), and mobile technologies mean that new sources of area-level health determinant indicators derived from satellite imagery, aggregated anonymized mobile phone data, and other sources are also becoming available at granular geographic scale. Not only can these data be used to directly calculate neighborhood- and city-level indicators, they can be combined with survey, census, administrative and health system data to model household- and individual-level outcomes (e.g., population density, household wealth) with tremendous detail and accuracy. WorldPop and the Demographic and Health Surveys (DHS) have already modeled dozens of household survey indicators at country or continental scales at resolutions of 1 × 1 km or even smaller. This paper aims to broaden perceptions about which types of datasets are available for health and development decision-making. For data scientists, we flag area-level indicators at city and sub-city scales identified by health decision-makers in the SDGs, Urban HEART, and other initiatives. For local health decision-makers, we summarize a menu of new datasets that can be feasibly generated from EO, mobile phone, and other spatial data-ideally to be made free and publicly available-and offer lay descriptions of some of the difficulties in generating such data products.

Estimating uncertainty in geospatial modelling at multiple spatial resolutions: the pattern of delivery via caesarean section in Tanzania.

Visualising maternal and newborn health (MNH) outcomes at fine spatial resolutions is crucial to ensuring the most vulnerable women and children are not left behind in improving health. Disaggregated data on life-saving MNH interventions remain difficult to obtain, however, necessitating the use of Bayesian geostatistical models to map outcomes at small geographical areas. While these methods have improved model parameter estimates and precision among spatially correlated health outcomes and allowed for the quantification of uncertainty, few studies have examined the trade-off between higher spatial resolution modelling and how associated uncertainty propagates. Here, we explored the trade-off between model outcomes and associated uncertainty at increasing spatial resolutions by quantifying the posterior distribution of delivery via caesarean section (c-section) in Tanzania. Overall, in modelling delivery via c-section at multiple spatial resolutions, we demonstrated poverty to be negatively correlated across spatial resolutions, suggesting important disparities in obtaining life-saving obstetric surgery persist across sociodemographic factors. Lastly, we found that while uncertainty increased with higher spatial resolution input, model precision was best approximated at the highest spatial resolution, suggesting an important policy trade-off between identifying concealed spatial heterogeneities in health indicators.

Cross-Modal Priming Effect of Rhythm on Visual Word Recognition and Its Relationships to Music Aptitude and Reading Achievement.

Recent evidence suggests the existence of shared neural resources for rhythm processing in language and music. Such overlaps could be the basis of the facilitating effect of regular musical rhythm on spoken word processing previously reported for typical children and adults, as well as adults with Parkinson's disease and children with developmental language disorders. The present study builds upon these previous findings by examining whether non-linguistic rhythmic priming also influences visual word processing, and the extent to which such cross-modal priming effect of rhythm is related to individual differences in musical aptitude and reading skills. An electroencephalogram (EEG) was recorded while participants listened to a rhythmic tone prime, followed by a visual target word with a stress pattern that either matched or mismatched the rhythmic structure of the auditory prime. Participants were also administered standardized assessments of musical aptitude and reading achievement. Event-related potentials (ERPs) elicited by target words with a mismatching stress pattern showed an increased fronto-central negativity. Additionally, the size of the negative effect correlated with individual differences in musical rhythm aptitude and reading comprehension skills. Results support the existence of shared neurocognitive resources for linguistic and musical rhythm processing, and have important implications for the use of rhythm-based activities for reading interventions.

Carpal Tunnel Syndrome in Aberrant Muscle Syndrome: A Case Report and Review of the Literature.

Aberrant Muscle Syndrome (AMS) is a rare congenital hand difference that is characterised by unilateral non-progressive muscular hyperplasia. The aetiology of aberrant muscle syndrome is not known, but a recently published case has shown a somatic PIK3CA activating mutation in a patient with AMS. Carpal tunnel syndrome (CTS) in children is rare. The most common causes are the mucopolysaccaridoses but space-occupying lesions have also been reported to cause CTS in children. We report the first case of CTS in a child with AMS successfully treated with open carpal tunnel release and excision of aberrant muscles.

Investigation of the activation of the temporalis and masseter muscles in voluntary and spontaneous smile production.

INTRODUCTION: Masticatory muscles or their nerve supply are options for facial reanimation surgery, but their ability to create spontaneous smile has been questioned. This study assessed the percentage of healthy adults who activate the temporalis and masseter muscles during voluntary and spontaneous smile. METHODS: Healthy volunteer adults underwent electromyography (EMG) studies of the temporalis and masseter muscles during voluntary and spontaneous smile. Responses were repeated three times and recorded as negative, weakly positive, or strongly positive according to the activity observed. The best response was used for analysis. RESULTS: Thirty healthy adults (median age: 34 years, range: 25-69 years) participated. Overall, 92% of the masseter muscles were activated during voluntary smile (22% strong, 70% weak). Seventy-seven percent of the masseter muscles were activated in spontaneous smile (12% strong, 65% weak). The temporalis muscle was activated in 62% of responses in voluntary smile (15% strong, 47% weak) and in 45% of responses in spontaneous smile (13% strong, 32% weak). No significant difference was found for males vs females or closed vs open mouth smiles. There was no significant difference in responses between voluntary and spontaneous smiles for the temporalis and masseter muscles, and their use in voluntary smile did not predict activity in spontaneous smile. CONCLUSIONS: Our study has shown that masseter and temporalis are active in a high proportion of healthy adults during voluntary and spontaneous smiles. Further work is required to determine the relationship between preoperative donor muscle activation and postoperative spontaneous smile, and whether masticatory muscle activity can be upregulated with appropriate training.

Mapping poverty using mobile phone and satellite data.

Poverty is one of the most important determinants of adverse health outcomes globally, a major cause of societal instability and one of the largest causes of lost human potential. Traditional approaches to measuring and targeting poverty rely heavily on census data, which in most low- and middle-income countries (LMICs) are unavailable or out-of-date. Alternate measures are needed to complement and update estimates between censuses. This study demonstrates how public and private data sources that are commonly available for LMICs can be used to provide novel insight into the spatial distribution of poverty. We evaluate the relative value of modelling three traditional poverty measures using aggregate data from mobile operators and widely available geospatial data. Taken together, models combining these data sources provide the best predictive power (highest r2 = 0.78) and lowest error, but generally models employing mobile data only yield comparable results, offering the potential to measure poverty more frequently and at finer granularity. Stratifying models into urban and rural areas highlights the advantage of using mobile data in urban areas and different data in different contexts. The findings indicate the possibility to estimate and continually monitor poverty rates at high spatial resolution in countries with limited capacity to support traditional methods of data collection.