Oral health status and indicators in children of Armenia based on the national surveys.

BACKGROUND: The aim of this study was to determine the dynamics and the structure of dental morbidity of the children population of the Republic of Armenia, in order to improve the system of methods of therapeutic and preventive measures. METHODS: In recommended children's key age groups, 5879 WHO assessment forms (1997) were analyzed. Calculated prevalence and intensity of dental caries, the Significant Index of Caries (SiC) and European dental health indicators, and the condition of periodontal tissues were determined with the help of the community periodontal index (CPI) and oral hygiene by OHI-S. The statistical analysis was performed within the SPSS 19 (SPSS Inc., Chicago, IL, USA). The obtained data were statistically processed in the STATISTICA 6.0 (StataCorp LLC, College Station, TX, USA) for Excel program (Microsoft Corp., Redmond, WA, USA). RESULTS: The prevalence of caries of temporary teeth was 91.7% on the average. In 12-year-old schoolchildren the average prevalence of caries of permanent teeth rate was 87.5%. The prevalence of periodontal lesions in children was 47.8% on the average. CONCLUSIONS: In Armenia there was an increase in dental morbidity during the 2009-2019 period, which presumably would be continued unless the factors affecting the development of diseases will be changed. To improve dental health at the population level not only specialists but also health authorities should make efforts introducing appropriate prevention programs.

Characterizing delayed care among US older adults by self-rated health during the COVID-19 pandemic.

OBJECTIVES: Previous studies showed that older adults with fair or poor self-rated health (SRH) were more likely to experience delayed care during the COVID-19 pandemic. We aim to understand delayed care patterns by SRH during the COVID-19 pandemic among US older adults. METHODS: Using a nationally representative sample of older adults (≥ 70 years old) from the National Health and Aging Trends Study (NHATS), we assessed the patterns of delayed care by good, fair, or poor SRH. RESULTS: Nearly one in five of the survey-weighted population of 9,465,117 older adults who experienced delayed care during the pandemic reported fair or poor SRH. The overall distributions of the numbers of types of delayed care (p = 0.16) and the numbers of reasons for delayed care (p = 0.12) did not differ significantly by SRH status. Older adults with good, fair, or poor SRH shared the four most common types of delayed care and three most common reasons for delayed care but differed in ranking. Older adults with poor SRH mostly delayed seeing a specialist (good vs. fair vs. poor SRH: 40.1%, 46.7%, 73%, p = 0.01). CONCLUSIONS: The results suggest that utilizing SRH as a simple indicator may help researchers and clinicians understand similarities and differences in care needs for older adults during the pandemic. Targeted interventions that address differences in healthcare needs among older adults by SRH during the evolving pandemic may mitigate the negative impacts of delayed care.

Using absolutist word frequency from online searches to measure population mental health dynamics.

The assessment of population mental health relies on survey data from representative samples, which come with considerable costs. Drawing on research which established that absolutist words (e.g. never) are semantic markers for depression, we propose a new measure of population mental health based on the frequency of absolutist words in online search data (absolute thinking index; ATI). Our aims were to first validate the ATI, and to use it to model public mental health dynamics in France and the UK during the current COVID-19 pandemic. To do so, we extracted time series for a validated dictionary of 19 absolutist words, from which the ATI was computed (weekly averages, 2019-2020, n = 208) using Google Trends. We then tested the relationship between ATI and longitudinal survey data of population mental health in the UK (n = 36,520) and France (n = 32,000). After assessing the relationship between ATI and survey measures of depression and anxiety in both populations, and dynamic similarities between ATI and survey measures (France), we tested the ATI's construct validity by showing how it was affected by the pandemic and how it can be predicted by COVID-19-related indicators. A final step consisted in replicating ATI's construct validity tests in Japan, thereby providing evidence for the ATI's cross-cultural generalizability. ATI was linked with survey depression scores in the UK, r = 0.68, 95%CI[0.34,0.86], ß = 0.23, 95%CI[0.09,0.37] in France and displayed similar trends. We finally assessed the pandemic's impact on ATI using Bayesian structural time-series models. These revealed that the pandemic increased ATI by 3.2%, 95%CI[2.1,4.2] in France and 3.7%, 95%CI[2.9,4.4] in the UK. Mixed-effects models showed that ATI was related to COVID-19 new deaths in both countries ß = 0.14, 95%CI[0.14,0.21]. These patterns were replicated in Japan, with a pandemic impact of 4.9%, 95%CI[3.1,6.7] and an influence of COVID-19 death of ß = 0.90, 95%CI[0.36,1.44]. Our results demonstrate the validity of the ATI as a measure of population mental health (depression) in France, the UK and to some extent in Japan. We propose that researchers use it as cost-effective public mental health "thermometer" for applied and research purposes.

The US COVID-19 Trends and Impact Survey: Continuous real-time measurement of COVID-19 symptoms, risks, protective behaviors, testing, and vaccination.

The US COVID-19 Trends and Impact Survey (CTIS) is a large, cross-sectional, internet-based survey that has operated continuously since April 6, 2020. By inviting a random sample of Facebook active users each day, CTIS collects information about COVID-19 symptoms, risks, mitigating behaviors, mental health, testing, vaccination, and other key priorities. The large scale of the survey-over 20 million responses in its first year of operation-allows tracking of trends over short timescales and allows comparisons at fine demographic and geographic detail. The survey has been repeatedly revised to respond to emerging public health priorities. In this paper, we describe the survey methods and content and give examples of CTIS results that illuminate key patterns and trends and help answer high-priority policy questions relevant to the COVID-19 epidemic and response. These results demonstrate how large online surveys can provide continuous, real-time indicators of important outcomes that are not subject to public health reporting delays and backlogs. The CTIS offers high value as a supplement to official reporting data by supplying essential information about behaviors, attitudes toward policy and preventive measures, economic impacts, and other topics not reported in public health surveillance systems.

Can auxiliary indicators improve COVID-19 forecasting and hotspot prediction?

Short-term forecasts of traditional streams from public health reporting (such as cases, hospitalizations, and deaths) are a key input to public health decision-making during a pandemic. Since early 2020, our research group has worked with data partners to collect, curate, and make publicly available numerous real-time COVID-19 indicators, providing multiple views of pandemic activity in the United States. This paper studies the utility of five such indicators-derived from deidentified medical insurance claims, self-reported symptoms from online surveys, and COVID-related Google search activity-from a forecasting perspective. For each indicator, we ask whether its inclusion in an autoregressive (AR) model leads to improved predictive accuracy relative to the same model excluding it. Such an AR model, without external features, is already competitive with many top COVID-19 forecasting models in use today. Our analysis reveals that 1) inclusion of each of these five indicators improves on the overall predictive accuracy of the AR model; 2) predictive gains are in general most pronounced during times in which COVID cases are trending in "flat" or "down" directions; and 3) one indicator, based on Google searches, seems to be particularly helpful during "up" trends.

An open repository of real-time COVID-19 indicators.

The COVID-19 pandemic presented enormous data challenges in the United States. Policy makers, epidemiological modelers, and health researchers all require up-to-date data on the pandemic and relevant public behavior, ideally at fine spatial and temporal resolution. The COVIDcast API is our attempt to fill this need: Operational since April 2020, it provides open access to both traditional public health surveillance signals (cases, deaths, and hospitalizations) and many auxiliary indicators of COVID-19 activity, such as signals extracted from deidentified medical claims data, massive online surveys, cell phone mobility data, and internet search trends. These are available at a fine geographic resolution (mostly at the county level) and are updated daily. The COVIDcast API also tracks all revisions to historical data, allowing modelers to account for the frequent revisions and backfill that are common for many public health data sources. All of the data are available in a common format through the API and accompanying R and Python software packages. This paper describes the data sources and signals, and provides examples demonstrating that the auxiliary signals in the COVIDcast API present information relevant to tracking COVID activity, augmenting traditional public health reporting and empowering research and decision-making.

Modeling SF-6D Health Utilities: Is Bayesian Approach Appropriate?

BACKGROUND: Valuation studies of preference-based health measures like SF6D have been conducted in many countries. However, the cost of conducting such studies in countries with small populations or low- and middle-income countries (LMICs) can be prohibitive. There is potential to use results from readily available countries' valuations to produce better valuation estimates. METHODS: Data from Lebanon and UK SF-6D value sets were analyzed, where values for 49 and 249 health states were extracted from samples of Lebanon and UK populations, respectively, using standard gamble techniques. A nonparametric Bayesian model was used to estimate a Lebanon value set using the UK data as informative priors. The resulting estimates were then compared to a Lebanon value set obtained using Lebanon data by itself via various prediction criterions. RESULTS: The findings permit the UK evidence to contribute potential prior information to the Lebanon analysis by producing more precise valuation estimates than analyzing Lebanon data only under all criterions used. CONCLUSIONS: The positive findings suggest that existing valuation studies can be merged with a small valuation set in another country to produce value sets, thereby making own country value sets more attainable for LMICs.

Identifying Communities at Risk for COVID-19-Related Burden Across 500 US Cities and Within New York City: Unsupervised Learning of the Coprevalence of Health Indicators.

BACKGROUND: Although it is well-known that older individuals with certain comorbidities are at the highest risk for complications related to COVID-19 including hospitalization and death, we lack tools to identify communities at the highest risk with fine-grained spatial resolution. Information collected at a county level obscures local risk and complex interactions between clinical comorbidities, the built environment, population factors, and other social determinants of health. OBJECTIVE: This study aims to develop a COVID-19 community risk score that summarizes complex disease prevalence together with age and sex, and compares the score to different social determinants of health indicators and built environment measures derived from satellite images using deep learning. METHODS: We developed a robust COVID-19 community risk score (COVID-19 risk score) that summarizes the complex disease co-occurrences (using data for 2019) for individual census tracts with unsupervised learning, selected on the basis of their association with risk for COVID-19 complications such as death. We mapped the COVID-19 risk score to corresponding zip codes in New York City and associated the score with COVID-19-related death. We further modeled the variance of the COVID-19 risk score using satellite imagery and social determinants of health. RESULTS: Using 2019 chronic disease data, the COVID-19 risk score described 85% of the variation in the co-occurrence of 15 diseases and health behaviors that are risk factors for COVID-19 complications among ~28,000 census tract neighborhoods (median population size of tracts 4091). The COVID-19 risk score was associated with a 40% greater risk for COVID-19-related death across New York City (April and September 2020) for a 1 SD change in the score (risk ratio for 1 SD change in COVID-19 risk score 1.4; P<.001) at the zip code level. Satellite imagery coupled with social determinants of health explain nearly 90% of the variance in the COVID-19 risk score in the United States in census tracts (r2=0.87). CONCLUSIONS: The COVID-19 risk score localizes risk at the census tract level and was able to predict COVID-19-related mortality in New York City. The built environment explained significant variations in the score, suggesting risk models could be enhanced with satellite imagery.

Disparities in Native Hawaiian and Pacific Islander COVID-19 Mortality: A Community-Driven Data Response.

As of March 2021, Native Hawaiians and Pacific Islanders (NHPIs) in the United States have lost more than 800 lives to COVID-19-the highest per capita death rate in 18 of 20 US states reporting NHPI deaths. However, NHPI risks are overlooked in policy discussions. We discuss the NHPI COVID-19 Data Policy Lab and dashboard, featuring the disproportionate COVID-19 mortality burden for NHPIs. The Lab democratized NHPI data, developed community infrastructure and resources, and informed testing site and outreach policies related to health equity.

Troponin and Other Biomarker Levels and Outcomes Among Patients Hospitalized With COVID-19: Derivation and Validation of the HA2T2 COVID-19 Mortality Risk Score.

Background The independent prognostic value of troponin and other biomarker elevation among patients with coronavirus disease 2019 (COVID-19) are unclear. We sought to characterize biomarker levels in patients hospitalized with COVID-19 and develop and validate a mortality risk score. Methods and Results An observational cohort study of 1053 patients with COVID-19 was conducted. Patients with all of the following biomarkers measured-troponin-I, B-type natriuretic peptide, C-reactive protein, ferritin, and d-dimer (n=446) -were identified. Maximum levels for each biomarker were recorded. The primary end point was 30-day in-hospital mortality. Multivariable logistic regression was used to construct a mortality risk score. Validation of the risk score was performed using an independent patient cohort (n=440). Mean age of patients was 65.0±15.2 years and 65.3% were men. Overall, 444 (99.6%) had elevation of any biomarker. Among tested biomarkers, troponin-I ≥0.34 ng/mL was the only independent predictor of 30-day mortality (adjusted odds ratio, 4.38; P<0.001). Patients with a mortality score using hypoxia on presentation, age, and troponin-I elevation, age (HA2T2) ≥3 had a 30-day mortality of 43.7% while those with a score <3 had mortality of 5.9%. Area under the receiver operating characteristic curve of the HA2T2 score was 0.834 for the derivation cohort and 0.784 for the validation cohort. Conclusions Elevated troponin and other biomarker levels are commonly seen in patients hospitalized with COVID-19. High troponin levels are a potent predictor of 30-day in-hospital mortality. A simple risk score can stratify patients at risk for COVID-19-associated mortality.

Metodología de la Encuesta Nacional de Salud y Nutrición 2020 sobre Covid-19.

Objetivo. Describir el diseño y los resultados de campo de la Encuesta Nacional de Salud y Nutrición (Ensanut) 2020 so-bre Covid-19. Material y métodos. La Ensanut Covid-19 es una encuesta probabilística de hogares. En este artículo se describen los siguientes elementos del diseño: alcance, muestreo, medición, inferencia y logística. Resultados. Se obtuvieron 10 216 entrevistas de hogar completas y 9 464 resultados sobre seropositividad a SARS-CoV-2. La tasa de respuesta de hogar fue 80% y la de prueba de seropositividad de 44%. Conclusiones. El diseño probabilístico de la Ensa-nut Covid-19 permite hacer inferencias estadísticas válidas sobre parámetros de interés para la salud pública a nivel nacional y regional; en particular, permitirá hacer inferencias de utilidad práctica sobre la prevalencia de seropositividad a SARS-CoV-2 en México. Además, la Ensanut Covid-19 podrá ser comparada con Ensanut previas para identificar potenciales cambios en los estados de salud y nutrición de la población mexicana.

Strengthening Culturally Competent Health Communication.

Vulnerable refugee communities are disproportionately affected by the ongoing COVID-19 pandemic; existing longstanding health inequity in these communities is exacerbated by ineffective risk communication practices about COVID-19. Culturally and linguistically appropriate health communication following health literacy guidelines is needed to dispel cultural myths, social stigma, misinformation, and disinformation. For refugee communities, the physical, mental, and social-related consequences of displacement further complicate understanding of risk communication practices grounded in a Western cultural ethos. We present a case study of Clarkston, Georgia, the "most diverse square mile in America," where half the population is foreign born and majority refugee. Supporting marginalized communities in times of risk will require a multipronged, systemic approach to health communication including: (1) creating a task force of local leaders and community members to deal with emergent issues; (2) expanding English-language education and support for refugees; (3) including refugee perspectives on risk, health, and wellness into risk communication messaging; (4) improving cultural competence and health literacy training for community leaders and healthcare providers; and (5) supporting community health workers. Finally, better prepared public health programs, including partnerships with trusted community organizations and leadership, can ensure that appropriate and supportive risk communication and health education and promotion are in place long before the next emergency.

'Standing together - at a distance': Documenting changes in mental-health indicators in Denmark during the COVID-19 pandemic.

Aims: There is a need to document the mental-health effects of the COVID-19 pandemic and its associated societal lockdowns. We initiated a large mixed-methods data collection, focusing on crisis-specific worries and mental-health indicators during the lockdown in Denmark. Methods: The study incorporated five data sources, including quantitative surveys and qualitative interviews. The surveys included a time series of cross-sectional online questionnaires starting on 20 March 2020, in which 300 (3×100) Danish residents were drawn every three days from three population groups: the general population (N=1046), families with children (N=1032) and older people (N=1059). These data were analysed by trend analysis. Semi-structured interviews were conducted with 32 people aged 24-83 throughout Denmark to provide context to the survey results and to gain insight into people's experiences of the lockdown. Results: Absolute level of worries, quality of life and social isolation were relatively stable across all population groups during the lockdown, although there was a slight deterioration in older people's overall mental health. Many respondents were worried about their loved ones' health (74-76%) and the potential long-term economic consequences of the pandemic (61-66%). The qualitative interviews documented significant variation in people's experiences, suggesting that the lockdown's effect on everyday life had not been altogether negative. Conclusions: People in Denmark seem to have managed the lockdown without alarming changes in their mental health. However, it is important to continue investigating the effects of the pandemic and various public-health measures on mental health over time and across national contexts.

Leaving no one behind in prison: improving the health of people in prison as a key contributor to meeting the Sustainable Development Goals 2030.

Worldwide, approximately 11 million people are currently being held in prison, a number that has steadily grown since the turn of the 21st century. The prison population is more likely to suffer from physical and mental ailments both during and prior to their imprisonment due to poverty, social exclusion and chaotic lifestyles. Recognition of people in prison is noticeably absent from the Sustainable Development Goals (SDGs), despite the goals' ethos of 'leaving no one behind'.We present the first analysis of how improving the health of people in prison can contribute to achieving 15 SDGs. Relevant indicators are proposed to fulfil these goals while meeting the existing international prison health standards. We also assess the political, economic and social challenges, alongside the unparalleled COVID-19 pandemic that can thwart the realisation of the SDGs. To reach the 'furthest behind first', prison health must be at the forefront of the SDGs.

Immunogenicity of the BNT162b2 vaccine in frail or disabled nursing home residents: COVID-A study.

BACKGROUND/OBJECTIVES: The safety and immunogenicity of the BNT162b2 coronavirus disease 2019 (COVID-19) vaccine in older adults with different frailty and disability profiles have not been well determined. Our objective was to analyze immunogenicity of the BNT162b2 mRNA COVID-19 vaccine in older adults across frailty and disability profiles. DESIGN: Multicenter longitudinal cohort study. SETTING AND PARTICIPANTS: A total of 134 residents aged ≥65 years with different frailty and disability profiles in five long-term care facilities (LTCFs) in Albacete, Spain. INTERVENTION AND MEASUREMENTS: Residents were administered two vaccine doses as per the label, and antibody levels were determined 21.9 days (SD 9.3) after both the first and second dose. Functional variables were assessed using activities of daily living (Barthel Index), and frailty status was determined with the FRAIL instrument. Cognitive status and comorbidity were also evaluated. RESULTS: Mean age was 82.9 years (range 65-99), and 71.6% were female. The mean antibody titers in residents with and without previous COVID-19 infection were 49,878 AU/ml and 15,274 AU/ml, respectively (mean difference 34,604; 95% confidence interval [CI]: 27,699-41,509). No severe adverse reactions were observed, after either vaccine dose. Those with prevaccination COVID-19 had an increased antibody level after the vaccine (B = 31,337; 95% CI: 22,725-39,950; p < 0.001). Frailty, disability, older age, sex, cognitive impairment, or comorbidities were not associated with different antibody titers. CONCLUSIONS: The BNT162b2 mRNA COVID-19 vaccine in older adults is safe and produces immunogenicity, independently of the frailty and disability profiles. Older adults in LTCFs should receive a COVID-19 vaccine.

The number of patients "worse than death" while waiting for a hip or knee arthroplasty has nearly doubled during the COVID-19 pandemic.

AIMS: The aim of this study was to assess the quality of life of patients on the waiting list for a total hip (THA) or knee arthroplasty (KA) during the COVID-19 pandemic. Secondary aims were to assess whether length of time on the waiting list influenced quality of life and rate of deferral of surgery. METHODS: During the study period (August and September 2020) 843 patients (THA n = 394, KA n = 449) from ten centres in the UK reported their EuroQol five dimension (EQ-5D) scores and completed a waiting list questionnaire (2020 group). Patient demographic details, procedure, and date when listed were recorded. Patients scoring less than zero for their EQ-5D score were defined to be in a health state "worse than death" (WTD). Data from a retrospective cohort (January 2014 to September 2017) were used as the control group. RESULTS: The 2020 group had a significantly worse EQ-5D score compared to the control group for both THA (p < 0.001) and KA (p < 0.001). Over one-third (35.0%, n = 138/394) of patients waiting for a THA and nearly a quarter (22.3%, n = 100/449) for KA were in a health state WTD, which was significantly greater than the control group (odds ratio 2.30 (95% confidence interval (CI) 1.83 to 2.93) and 2.08 (95% CI 1.61 to 2.70), respectively; p < 0.001). Over 80% (n = 680/843) of the 2020 group felt that their quality of life had deteriorated while waiting. Each additional month spent on the waiting list was independently associated with a decrease in quality of life (EQ-5D: -0.0135, p = 0.004). There were 117 (13.9%) patients who wished to defer their surgery and the main reason for this was health concerns for themselves and or their family (99.1%, n = 116/117). CONCLUSION: Over one-third of patients waiting for THA and nearly one-quarter waiting for a KA were in a state WTD, which was approaching double that observed prior to the pandemic. Increasing length of time on the waiting list was associated with decreasing quality of life. Level of evidence: Level III retrospective case control study Cite this article: Bone Joint J 2021;103-B(4):672-680.

Spotlight on Cardiovascular Scoring Systems in Covid-19: Severity Correlations in Real-world Setting.

OBJECTIVES AND METHODS: the current understanding of the interplay between cardiovascular (CV) risk and Covid-19 is grossly inadequate. CV risk-prediction models are used to identify and treat high risk populations and to communicate risk effectively. These tools are unexplored in Covid-19. The main objective is to evaluate the association between CV scoring systems and chest X ray (CXR) examination (in terms of severity of lung involvement) in 50 Italian Covid-19 patients. Results only the Framingham Risk Score (FRS) was applicable to all patients. The Atherosclerotic Cardiovascular Disease Score (ASCVD) was applicable to half. 62% of patients were classified as high risk according to FRS and 41% according to ASCVD. Patients who died had all a higher FRS compared to survivors. They were all hypertensive. FRS≥30 patients had a 9.7 higher probability of dying compared to patients with a lower FRS. We found a strong correlation between CXR severity and FRS and ASCVD (P < 0.001). High CV risk patients had consolidations more frequently. CXR severity was significantly associated with hypertension and diabetes. 71% of hypertensive patients' CXR and 88% of diabetic patients' CXR had consolidations. Patients with diabetes or hypertension had 8 times greater risk of having consolidations. CONCLUSIONS: High CV risk correlates with more severe CXR pattern and death. Diabetes and hypertension are associated with more severe CXR. FRS offers more predictive utility and fits best to our cohort. These findings may have implications for clinical practice and for the identification of high-risk groups to be targeted for the vaccine precedence.