Pattern of Oral and Maxillofacial New Referrals During COVID-19 Lockdown

The study aimed to quantify the impact of lockdown during the COVID-19 pandemic on new case referrals to the Oral and Maxillofacial Surgery (OMS) service. The researchers retrospectively reviewed all new referrals received during a government-imposed 47-day lockdown period and a similar period pre-lockdown as a control group. The main outcome was the differences in the number of new case referrals between the two periods. The contributing clinical and demographic factors were also explored. Appropriate bivariate statistics were computed and the level of significance was set at 0.05 for all tests. A total of 309 referrals were received during the study period. There was a reduction of new referrals due to the lockdown from five to two cases per day. There was a statistically significant reduction of cases referred from outpatient and emergency departments. There was also a statistically significant difference with regard to home address distance to the centre. Medically compromised and orofacial infection referrals were not affected by lockdown. The lockdown imposed due to the pandemic has significantly impacted the pattern of new OMS referrals. Referrals for orofacial infections, the medically compromised and inpatients were minimally affected by lockdown.

Access to advanced-level hospital care: differences in prehospital times calculated using incident locations compared with patients’ usual residence

Studies estimate that 84% of the USA and New Zealand’s (NZ) resident populations have timely access (within 60 min) to advanced-level hospital care. Our aim was to assess whether usual residence (ie, home address) is a suitable proxy for location of injury incidence. In this observational study, injury fatalities registered in NZ’s Mortality Collection during 2008–2012 were linked to Coronial files. Estimated access times via emergency medical services were calculated using locations of incident and home. Using incident locations, 73% (n=4445/6104) had timely access to care compared with 77% when using home location. Access calculations using patients’ home locations overestimated timely access, especially for those injured in industrial/construction areas (18%; 95% CI 6% to 29%) and from drowning (14%; 95% CI 7% to 22%). When considering timely access to definitive care, using the location of the injury as the origin provides important information for health system planning.

The Effect of Early Neighborhood Contexts on Cognitive Function in Midlife

We evaluated associations between objective and subjective early-life neighborhood contexts and cognitive function at midlife. Study participants grew up in different addresses but resided in the same urban zip code at the time of data collection thus controlling for concurrent neighborhood contexts. Participants provided their home address when they were five-years-old and recalled their age-five neighborhood conditions (Mage= 40.59 (7.91); n = 130). Age-five addresses were geocoded and linked with harmonized longitudinal Census tract boundaries and variables. Predictive models with a self-reported neighborhood conditions score, an objective neighborhood deprivation indicator, and other sociodemographic covariates indicated that poorer age-five self-reported neighborhood conditions were significantly associated with lower baseline (Cohen’s d = -.24) and average daily (d = -.21) working memory performance. There were no associations with objective age-five neighborhoods. Results contribute to a growing literature on the role of psychosocial neighborhood contexts on cognition that may extend back to childhood neighborhoods.

Identified micro-organisms in hospitalized community-acquired pneumonia patients living near goat and poultry farms

Background In the Netherlands, an increased risk of community-acquired pneumonia (CAP) has been reported for adults living near goat and poultry farms. Previous results of respiratory microbiome studies in hospitalized CAP patients near poultry farms suggested a higher relative abundance of Streptococcus pneumoniae. This retrospective study, using routine laboratory data from hospitalized CAP patients, aims to explore possible aetiologic micro-organisms of CAP in relation to livestock exposure. Methods Patient characteristics and PCR and urinary antigen test results were retrieved retrospectively from electronic medical records of CAP patients admitted to the Jeroen Bosch Hospital or Gelre Hospital in the Netherlands during 2016–2017. Distances between the patients’ home address and the nearest poultry and goat farm were calculated. Differences in laboratory test results between CAP patients with and without goat or poultry farms within 2 km of their home address were analyzed using Fisher’s exact test. Results In total, 2230 CAP episodes with diagnostic results were included. In only 25% of the CAP episodes, a micro-organism was detected. A positive urinary antigen test for S. pneumoniae was found more often in patients living within two kilometers of goat (15.2% vs. 11.3%) and poultry farms (14.4% vs. 11.3%), however these differences were not statistically significant (p = 0.1047 and p = 0.1376). Conclusion Our retrospective analysis did not show statistically significant differences in the identified micro-organisms in hospitalized CAP patients related to livestock farming. The study was hampered by limited statistical power and limited laboratory results. Therefore, the potential increased CAP risk around goat and poultry farms will be further explored in a prospective study among CAP patients in primary care.

Novel Method for Identifying Care Home Residents in England: A Validation Study

IntroductionThe ability to identify residents of care homes in routinely collected health care data is key to informing healthcare planning decisions and delivery initiatives targeting the older and frail population. Health-care planning and delivery implications at national level concerning this population subgroup have considerably and suddenly grown in urgency following the onset of the COVID-19 pandemic, which has especially hit care homes. The range of applicability of this information has widened with the increased availability in England of retrospectively collected administrative databases, holding rich patient-level details on health and prognostic status who have made or are in contact with the National Health Service. In practice lack of a national registry of care homes residents in England complicates assessing an individual's care home residency status, which has been typically identified via manual address matching from pseudonymised patient-level healthcare databases linked with publicly availably care home address information. ObjectivesTo examine a novel methodology based on linking unique care home address identifiers with primary care patient registration data, enabling routine identification of care home residents in health-care data. MethodsThis study benchmarks the proposed strategy against the manual address matching standard approach through a diagnostic assessment of a stratified random sample of care home post codes in England. ResultsDerived estimates of diagnostic performance, albeit showing a non-insignificant false negative rate (21.98%), highlight a remarkable true negative rate (99.69%) and positive predictive value (99.35%) as well as a satisfactory negative predictive value (88.25%). ConclusionsThe validation exercise lends confidence to the reliability of the novel address matching method as a viable and general alternative to manual address matching.

Classify Refugee Status Using Common Features in EMR

ABSTRACTObjectiveAutomated and accurate identification of refugees in healthcare databases is a critical first step to investigate healthcare needs of this vulnerable population and improve health disparities. This study developed a machine-learning method, named refugee identification system (RIS) that uses features commonly collected in healthcare databases to classify refugees and non-refugees.Materials and MethodsWe compiled a curated data set consisting of 103 refugees and 930 non-refugees in Arizona. For each person in the curated data set, we collected age, primary language, and home address. We supplemented individual-level data with state-level refugee resettlement statistics and world language statistics, then performed feature engineering to convert primary language and home address into quantitative features. Finally, we built a random forest model to classify refugee status.ResultsEvaluated on holdout testing data, RIS achieved a high classification accuracy of 0.97, specificity of 0.98, sensitivity of 0.88, positive predictive value of 0.83, and negative predictive value of 0.99. The receiver operating characteristic curve had an area under the curve value of 0.96.Discussion and ConclusionRIS is an automated, accurate, generalizable, and scalable method that can be used to identify refugees in healthcare databases. It enables large-scale investigation of refugee healthcare needs and improvement of health disparities.

Assessing the Quality of Reporting to China’s National TB Surveillance Systems

(1) Background: The reliability of disease surveillance may be restricted by sensitivity or ability to capture all disease. Objective: To quantify under-reporting and concordance of recording persons with tuberculosis (TB) in national TB surveillance systems: the Infectious Disease Reporting System (IDRS) and Tuberculosis Information Management System (TBIMS). (2) Methods: This retrospective review includes 4698 patients identified in 2016 in China. County staff linked TB patients identified from facility-specific health and laboratory information systems with records in IDRS and TBIMS. Under-reporting was calculated, and timeliness, concordance, accuracy, and completeness were analyzed. Multivariable logistic regression was used to examine factors associated with under-reporting. (3) Results: We found that 505 (10.7%) patients were missing within IDRS and 1451 (30.9%) patients were missing within TBIMS. Of 171 patient records reviewed in IDRS and 170 patient records in TBIMS, 12.3% and 6.5% were found to be untimely, and 10.7% and 7.1% were found to have an inconsistent home address. The risk of under-reporting to both IDRS and TBIMS was greatest at tertiary health facilities and among non-residents; the risk of under-reporting to TBIMS was greatest with patients aged 65 or older and with extrapulmonary TB (EPTB). (4) Conclusions: It is important to improve the reporting and recording of TB patients. Local TB programs that focus on training, and mentoring high-burden hospitals, facilities that cater to EPTB, and migrant patients may improve reporting and recording.

Applying a Home-Based Approach to the Understanding Distribution of Economic Impacts of Traffic Crashes

The current practice of road safety attributes traffic crash costs to the location of traffic crashes. Therefore it is challenging to estimate the economic cost of traffic crashes and individuals who are more prone to the burden of traffic crashes. To address this limitation, this study used the home address of individuals who were involved in traffic crashes in the Knoxville Regional Travel Model (KRTM) region between 2015 and 2016. After geocoding the home addresses, 110,312 individuals were assigned to the Traffic Analysis Zone (TAZ) corresponding to their home address and the economic cost of traffic crashes per capita (ECCPC) was calculated for each TAZ. The average ECCPC in the study area was $1,250. The KRTM output was used for extracting travel behavior data elements for modeling ECCPC at the zonal level. This study also established an index to measure average zonal activity in the transportation system for each TAZ. Analysis indicates that the burden of traffic crashes was more tangible in the TAZs with lower-income households and higher average zonal activities. To account for spatial autocorrelation, a Spatial Autoregressive model (SAR) and a spatial error model (SEM) were used. The SAR model was more suitable compared with SEM and ordinary least squares regression. Findings indicate that average zonal activity and traffic exposure have a significant positive association with ECCPC. The ECCPC could be used as an index for allocating proper countermeasures and interventions to groups and areas where the burden of traffic crashes is more tangible.

Genetic risk of obesity as a modifier of associations between neighbourhood environment and body mass index: an observational study of 335 046 UK Biobank participants

BackgroundThere is growing recognition that recent global increases in obesity are the product of a complex interplay between genetic and environmental factors. However, in gene-environment studies of obesity, ‘environment’ usually refers to individual behavioural factors that influence energy balance, whereas more upstream environmental factors are overlooked. We examined gene-environment interactions between genetic risk of obesity and two neighbourhood characteristics likely to be associated with obesity (proximity to takeaway/fast-food outlets and availability of physical activity facilities).MethodsWe used data from 335 046 adults aged 40–70 in the UK Biobank cohort to conduct a population-based cross-sectional study of interactions between neighbourhood characteristics and genetic risk of obesity, in relation to body mass index (BMI). Proximity to a fast-food outlet was defined as distance from home address to nearest takeaway/fast-food outlet, and availability of physical activity facilities as the number of formal physical activity facilities within 1 km of home address. Genetic risk of obesity was operationalised by weighted Genetic Risk Scores of 91 or 69 single nucleotide polymorphisms (SNP), and by six individual SNPs considered separately. Multivariable, mixed-effects models with product terms for the gene-environment interactions were estimated.ResultsAfter accounting for likely confounding, the association between proximity to takeaway/fast-food outlets and BMI was stronger among those at increased genetic risk of obesity, with evidence of an interaction with polygenic risk scores (p=0.018 and p=0.028 for 69-SNP and 91-SNP scores, respectively) and in particular with a SNP linked to MC4R (p=0.009), a gene known to regulate food intake. We found very little evidence of gene-environment interaction for the availability of physical activity facilities.ConclusionsIndividuals at an increased genetic risk of obesity may be more sensitive to exposure to the local fast-food environment. Ensuring that neighbourhood residential environments are designed to promote a healthy weight may be particularly important for those with greater genetic susceptibility to obesity.