[Places of death of COVID-19 patients: an observational study based on evaluated death certificates from the city of Muenster, Germany (2021)]. / Sterbeorte von COVID-19-Patienten: eine Observationsstudie auf Grundlage ausgewerteter Todesbescheinigungen der Stadt Münster (2021).

INTRODUCTION: The places of death of COVID-19 patients have so far hardly been investigated in Germany. METHODS: In a places of death study in Westphalia (Germany), statistical evaluations were carried out in the city of Muenster on the basis of all death certificates from 2021. Persons who had died with or from a COVID-19 infection were identified by medical information on cause of death and analyzed with descriptive statistical methods using SPSS. RESULTS: A total of 4044 death certificates were evaluated, and 182 deceased COVID-19 patients were identified (4.5%). In 159 infected patients (3.9%), the viral infection was fatal, whereby the distribution of places of death was as follows: 88.1% in hospital (57.2% in the intensive care unit; 0.0% in the palliative care unit), 0.0% in hospice, 10.7% in nursing homes, 1.3% at home, and 0.0% in other places. All infected patients < 60 years and 75.4% of elderly patients ≥ 80 years died in hospital. Only two COVID-19 patients, both over 80 years old, died at home. COVID-19 deaths in nursing homes (17) affected mostly elderly female residents. Ten of these residents had received end-of-life care from a specialized outpatient palliative care team. DISCUSSION: The majority of COVID-19 patients died in hospital. This can be explained by the rapid course of the disease with a high symptom burden and the frequent young age of the patients. Inpatient nursing facilities played a certain role as a place of death in local outbreaks. COVID-19 patients rarely died at home. Infection control measures may be one reason why no patients died in hospices or palliative care units.

The Effect of Vaccination on Covid-19 Deaths: A Research According to Countries' Income Groups

This study aims to reveal the impacts of Covid-19 vaccination on Covid-19 based deaths in countries with different income levels. In this context, the study investigated data between 01.03.2021 and 08.08.2021 by Panel data analysis. In the research, firstly, countries were divided into three different categories according to income groups: low-income level, middle-income level and high-income level. Therefore, each country group was examined separately and three different econometric models were produced. According to the results of the research, a 1% increase in the population vaccinated will result in a 2.1% decrease in the number of deaths from Covid-19 in low-income countries, a 0.5% decrease in middle-income countries and a 13% decrease in high -income countries. According to the results of the research, it was concluded that vaccination will significantly reduce deaths from Covid-19. For this reason, it is recommended that people complete their vaccine doses as fast as possible.

Evaluating How the Social Restriction, the Government Response, the Health, and Economic Indices Affected the Prediction of the Number of Deaths Provoked by COVID-19 in Brazil Using Classical Statistical and Machine Learning Models

The COVID-19 death predictions are helpful for the formulation of public policies, allowing the use of more effective social isolation strategies with less economic and social impact. This article evaluates a wide range of forecasting methods to identify the best models for predicting cumulative and daily deaths caused by COVID-19 in Brazil, considering a forecast for a seven-day horizon. With the seven-day horizon, the predictions have more accuracy. The dataset is from Oxford Covid-19 Government Response Tracker. The jackknife resampling technique was implemented, thus providing an accurate estimate for evaluating the predictive capacity of the models. Each model was fitted with 266 jackknife samples considering 30-day training bases. The comparison between predictions was made using the average results, considering R-2, MAPE, RMSE, and MAE. Models from different classes were adopted: 1 ETS, 4 ARIMA, 18 regression models, and 7 machine learning algorithms. The cumulative death models produce better results than daily deaths, as the cumulative death models are less influenced by time series components: cycle and seasonality. The best results for predicting daily deaths were attained by the Ridge regression method. The best results for predicting cumulative deaths were obtained by the Cubist regression method.

Re-visiting the COVID-19 analysis using the class of high ordered integer-valued time series models with harmonic features

The COVID-19 series is obviously one of the most volatile time series with lots of spikes and oscillations. The conventional integer-valued auto-regressive time series models (INAR) may be limited to account for such features in COVID-19 series such as severe over-dispersion, excess of zeros, periodicity, harmonic shapes and oscillations. This paper proposes alternative formulations of the classical INAR process by considering the class of high-ordered INAR models with harmonic innovation distributions. Interestingly, the paper further explores the bivariate extension of these high-ordered INARs. South Africa and Mauritius' COVID-19 series are re-scrutinized under the optic of these new INAR processes. Some simulation experiments are also executed to validate the new models and their estimation procedures. © 2022 The Author(s)

Probability of Contagion and Mortality by COVID-19 in Indigenous and Non-indigenous Patients Initiating the Pandemic in Mexico

The objective of this research is to identify contagion and mortality factors by COVID-19 among indigenous patients in Mexico, showing their greater fragility in contrast to non-indigenous patients at the beginning of the pandemic. Database of May 22, 2020, of the Undersecretariat of Epidemiology of the Ministry of Health of Mexico is used, with sociodemographic, territorial, diseases variables, among others, and binary logistic models of probability of contagion and mortality are elaborated. The results show a higher risk of contagion and mortality among indigenous patients, with similar determinants compared to non-indigenous patients, but with differences related to their current places of residence for the indigenous population, linked to intermediate cities and large cities, where they migrate from their places of origin to work mainly in the informality of street vendors and without social protection, on the streets of Mexican cities. © 2022 The authors.

Mourning and restoration

During the endless days of March and April 2020, New York City experienced more than 20,000 COVID-19 deaths and was considered the "epicenter" of a new global pandemic. Nursing homes witnessed the virus's contagion at staggering rates, with elderly and debilitated patients coming in by the dozens, gasping for breath, scared they would die and never see their loved ones again. Our hospital and our lives were quickly transformed. The author spent most of his clinical effort during those months running a new eight-bed hospice unit in our hospital. The author then presents the story of a hospice patient, a fifty-nine-year-old Black male-to-female transgender homeless woman. She had been diagnosed with an aggressive squamous cell carcinoma. She underwent chemotherapy, radiation therapy, and surgery, including a diverting colectomy, leaving her with a permanent ostomy. She had several other medical problems-chronic kidney disease, heart disease, diabetes, major depression, and chronic lymphedema. Taking care in her last days of life was agonizing. The possibility to have spent more time getting to know her. To explore her world and navigate the challenges of her health and condition together. This is the privilege of the doctor-patient relationship. (PsycInfo Database Record (c) 2023 APA, all rights reserved)

Multiple measures of structural racism as predictors of U.S. county-level COVID-19 cases and deaths

Minoritized racial groups in the U.S. have experienced disproportionately higher rates of COVID-19 cases and deaths. Studies have linked structural racism as a critical factor causing these disproportionate health burdens. We analyse the relationships between county-level COVID-19 cases and deaths and five measures of structural racism on Black Americans: Black–White residential segregation, differences in educational attainment, unemployment, incarceration rates, and health insurance coverage between Black and White Americans. When controlling for socioeconomic, demographic, health and behavioural factors significant relationships were found between all measures of structural racism with cases and/or deaths except Black–White differences in health insurance coverage. Black–White disparities in educational attainment and incarceration were the strongest predictors. The results varied greatly across regions of the U.S. We also found strong relationships between COVID-19 and mobility and the proportion of foreign-born non-citizens. This work supports the important need to confront structural racism on multiple fronts to address health disparities.

Covid-19 Pandemic: Global Scenario, Socialdeterminants and Its Socio-Economic Impact

Background: The COVID-19 pandemic has resulted in about 75.2M cases and 1.67M deaths worldwide, as on 18th December 2020 data live updates of World Health Organizations. In response to this pandemic, this study analyzes the global issue of rising and falling of COVID-19 cases and changing scenario of economies. Method(s): The data has been extracted from January 2020 to December 2020 from some of the reliable sources of the World like WHO Coronavirus disease (COVID-19) dashboard, Worldometer, and Centers for Disease Control and Prevention (CDC). It also represents the global scenario of the COVID-19 pandemic and its social determinants around the world. Result(s): There has been spatial heterogeneity in the number of cases and the number of deaths among regions worldwide. There is a great impact on the countries economy, both on the supply and demand side. It shows that several factors affect the determinants of health at various levels like income, healthcaresystem, education, etc also play a major role in it. Conclusion(s): The increasing cases worldwide have adversely affected the economy and have led to a scarcity of resources which further caused the collapse of the economy and trade.Copyright © 2020 Ubiquity Press. All rights reserved.

Zero-Inflated Time Series Model for Covid-19 Deaths in Kelantan Malaysia

The development of zero-inflated time series models is well known to account for excessive number of zeros and overdispersion in discrete count time series data. By using Zero-inflated models, we analyzed the daily count of COVID-19 deaths occurrence in Kelantan with excess zeros. Considering factors such as COVID-19 deaths in neighboring state and lag of 1 to 7 days of COVID-19 death in Kelantan, the Zero-Inflated models (Zero-Inflated Poisson (ZIP) and the Zero-Inflated Negative Binomial (ZINB)) were employed to predict the COVID-19 deaths in Kelantan. The ZIP and ZINB were compared with the basic Poisson and Negative Binomial models to find the significant contributing factors from the model. The final results show that the best model was the ZINB model with lag of 1,2,5 and lag of 6 days of Kelantan COVID-19 death, lag of 1-day COVID-19 deaths in neighboring State of Terengganu and Perak significantly influenced the COVID-19 deaths occurrence in Kelantan. The model gives the smallest value of AIC and BIC compared to the basic Poisson and Negative Binomial model. This indicate that the Zero Inflated model predict the excess zeros in the COVID-19 deaths occurrence well compared to the basic count model. Hence, the fitted models for COVID-19 deaths served as a novel understanding on the disease transmission and dissemination in a particular area. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Availability of open data related to COVID-19 epidemic in Italy.

Background: Since the beginning of the COVID-19 outbreak in Italy, health authorities have released epidemiologic data about this disease. These data were the most important sources of information which were periodically updated and analyzed by researchers to predict the spread of the epidemic. However, comprehensive and timely data on the evolution of COVID-19 have not always been made available to researchers and physicians. Method: The aim of our work is to investigate quality, availability and format of epidemiologic data about COVID-19 in Italy in different territorial and temporal areas. We tried to access the online resources made available by each of the 19 Italian Regions and the two autonomous Provinces, and in more detail by the Local Health Authorities of one of them, the Emilia-Romagna Region. We analyzed the main sources and flows of data (namely new and cumulative cases of infection, total swabs, new and cumulative COVID-19 deaths, overall and divided by sex), describing their characteristics such as accessibility, format and completeness. We eventually reviewed the data published by the Italian Ministry of Health, the National Institute of Health (ISS) and the Civil Protection Department. The Tim Berners-Lee scale was used to evaluate the open data format. Results: The flow of COVID-19 epidemiologic data in Italy originated from the Local Health Authorities that transmitted the data - on a daily basis - to the regional authorities, which in turn transferred them to the national authorities. We found a rather high heterogeneity in both the content and the format of the released data, both at the local and the regional level. Few Regions were releasing data in open format. ISS was the only national source of data that provided the number of COVID-19 health outcomes divided by sex and age groups since Spring 2020. Conclusions: Despite multiple potential useful sources for COVID-19 epidemiology are present in Italy, very few open format data were available both at a macro geographical level (e.g. per Region) and at the provincial level. The access to open format epidemiologic data should be eased, to allow researchers to adequately assess future epidemics and therefore favor timely and effective public health interventions.

An exploration of structural determinants driving racial disparities in COVID-19 deaths in Michigan.

Aim: Racial disparities in COVID-19 death rates have largely been driven by structural racism in health, housing, and labor systems that place Black, Brown, and Indigenous populations at greater risk for COVID-19 exposure, transmission, and severe illness, compared to non-Hispanic White populations. Here we examine the association between taxable property values per capita, an indicator influenced by historical and contemporary housing policies that have disproportionately impacted people of color, and COVID-19 deaths. Methods: Taxable values serve as a proxy for fiscal health providing insight on the county's ability to address imminent needs, including COVID-19 responses. Therefore, higher taxable values indicate local governments that are better equipped to deliver these public services. We used county-level data from the American Community Survey, the Michigan Community Financial Dashboard, The Atlantic's COVID Tracking Project, and the Community Health Rankings and Roadmap for this cross-sectional study. Maps were created to examine the geographic distribution of cumulative death rates and taxable values per capita, and regression models were used to examine the association between the two while controlling for population density, age, education, race, income, obesity, diabetes, and smoking rates. Results: Seventy-five counties were included. The mean taxable value per capita was $43,764.50 and the mean cumulative death rate was 171.86. Findings from the regression analysis showed that counties with higher taxable values were associated with lower COVID-19 death rates (B = -2.45, P < 0.001). Conclusion: Our findings reveal a need to reevaluate current policies surrounding taxable property values in the state of Michigan, not solely for their inequitable impact on local governments' financial solvency and service quality, but also for their negative consequences for population health and racial health equity. Supplementary Information: The online version contains supplementary material available at 10.1007/s10389-022-01817-w.

Association of greenness with COVID-19 deaths in India: An ecological study at district level.

BACKGROUND: The world has witnessed a colossal death toll due to the novel coronavirus disease-2019 (COVID-19). A few environmental epidemiology studies have identified association of environmental factors (air pollution, greenness, temperature, etc.) with COVID-19 incidence and mortality, particularly in developed countries. India, being one of the most severely affected countries by the pandemic, still has a dearth of research exploring the linkages of environment and COVID-19 pandemic. OBJECTIVES: We evaluate whether district-level greenness exposure is associated with a reduced risk of COVID-19 deaths in India. METHODS: We used average normalized difference vegetation index (NDVI) from January to March 2019, derived by Oceansat-2 satellite, to represent district-level greenness exposure. COVID-19 death counts were obtained through May 1, 2021 (around the peak of the second wave) from an open portal: covid19india.org. We used hierarchical generalized negative binomial regressions to check the associations of greenness with COVID-19 death counts. Analyses were adjusted for air pollution (PM2.5), temperature, rainfall, population density, proportion of older adults (50 years and above), sex ratio over age 50, proportions of rural population, household overcrowding, materially deprived households, health facilities, and secondary school education. RESULTS: Our analyses found a significant association between greenness and reduced risk of COVID-19 deaths. Compared to the districts with the lowest NDVI (quintile 1), districts within quintiles 3, 4, and 5 have respectively, around 32% [MRR = 0.68 (95% CI: 0.51, 0.88)], 39% [MRR = 0.61 (95% CI: 0.46, 0.80)], and 47% [MRR = 0.53 (95% CI: 0.40, 0.71)] reduced risk of COVID-19 deaths. The association remains consistent for analyses restricted to districts with a rather good overall death registration (>80%). CONCLUSION: Though cause-of-death statistics are limited, we confirm that exposure to greenness was associated with reduced district-level COVID-19 deaths in India. However, material deprivation and air pollution modify this association.

SEIR models in the light of Critical Realism - A critique of exaggerated claims about the effectiveness of Covid 19 vaccinations.

In a recent modeling study Watson et al. (Lancet Infect Dis 2022;3099:1-10) claim that Covid-19 vaccinations have helped to prevent roughly 14-20 million deaths in 2021. This conclusion is based on an epidemiological susceptible-exposed-infectious-recovered (SEIR) model trained on partially simulated data and yielding a reproduction number distribution which was then applied to a counterfactual scenario in which the efficacy of vaccinations was removed. Drawing on the meta-theory of Critical Realism, we point out several caveats of this model and caution against believing in its predictions. We argue that the absence of vaccinations would have significantly changed the causal tendencies of the system being modelled, yielding a different reproduction number than obtained from training the model on actually observed data. Furthermore, the model omits many important causal factors. Therefore this model, similar to many previous SEIR models, has oversimplified the complex interplay between biomedical, social and cultural dimensions of health and should not be used to guide public health policy. In order to predict the future in epidemic situations more accurately, continuously optimized dynamic causal models which can include the not directly tangible, yet real causal mechanisms affecting public health appear to be a promising alternative to SEIR-type models.

Mourning and restoration

During the endless days of March and April 2020, New York City experienced more than 20,000 COVID-19 deaths and was considered the "epicenter" of a new global pandemic. Nursing homes witnessed the virus's contagion at staggering rates, with elderly and debilitated patients coming in by the dozens, gasping for breath, scared they would die and never see their loved ones again. Our hospital and our lives were quickly transformed. The author spent most of his clinical effort during those months running a new eight-bed hospice unit in our hospital. The author then presents the story of a hospice patient, a fifty-nine-year-old Black male-to-female transgender homeless woman. She had been diagnosed with an aggressive squamous cell carcinoma. She underwent chemotherapy, radiation therapy, and surgery, including a diverting colectomy, leaving her with a permanent ostomy. She had several other medical problems-chronic kidney disease, heart disease, diabetes, major depression, and chronic lymphedema. Taking care in her last days of life was agonizing. The possibility to have spent more time getting to know her. To explore her world and navigate the challenges of her health and condition together. This is the privilege of the doctor-patient relationship. (PsycInfo Database Record (c) 2023 APA, all rights reserved)

hiscovid for visualizing and identifying when policymakers made mistakes against COVID-19.

There are two types of policy outcome analysis tools: snapshot tool and time-series tool. hiscovid is a time-series policy outcome scoring tool of COVID-19 policies by country where the daily cumulative population mortality is used for scoring the outcomes of COVID-19 country policies to visualize and identify when policymakers made mistakes. hiscovid allows policymakers to observe the progress and transition of scores over time to learn lessons from the past decision-making mistakes for correcting the current policies to reduce unnecessary deaths. The lower the score, the better the policy. hiscovid attracted 1480 users worldwide.

Non-COVID-19 deaths in times of pandemic.

BACKGROUND AND OBJECTIVE: To investigate the effect of the COVID-19 pandemic on non-COVID-19 deaths in Mexico. METHODS: This study analyzes monthly administrative data on 15 different causes of death in Mexico from 2017 to 2020. The effects of the COVID-19 pandemic on non-COVID-19 deaths are conducted using a difference-in-differences methodology and an event study. RESULTS: The evidence shows mixed results. There is an increase in six causes of death: diabetes (36.8%), hypertension (25.8%), heart attacks (40.9%), bronchitis- asthma (24.2%), anemia (28.6%) and prostate cancer (21.4%). There is a decrease in two causes of death: traffic accidents (8.8%) and HIV (13.8%). There are null effects for seven causes of death: breast cancer, cerebrovascular disease, malnutrition, alcohol-related liver disease, renal insufficiency, homicides and suicides. CONCLUSIONS: The COVID-19 pandemic affected non-COVID-19 deaths caused by diseases that require intensive healthcare services. Conversely, this pandemic reduced social interactions, which contributed to a decrease on deaths such as traffic accidents.

[Description of excess of overall and cause-specific mortality in the Agency for Health Protection of the Metropolitan Area of Milan (Lombardy Region, Northern Italy) in 2020]. / Descrizione degli eccessi di mortalità per tutte le cause e specifici per causa nell'ATS di Milano nel 2020.

BACKGROUND: as a result of the SARS-CoV-2 pandemic, a generalised mortality excess was recorded in 2020. However, the mortality for COVID-19 cannot fully explain the observed excesses. The analysis of cause-specific mortality could contribute to estimate the direct and indirect effects of the SARS-CoV-2 outbreak and to the monitoring mortality trends. OBJECTIVES: to describe the impact of the SARS-CoV-2 epidemic in overall and cause-specific mortality in population residing in the Agency for Health Protection (ATS) of Milan. Descriptive analysis of cause-specific mortality within thirty days of SARS-COV-2 infection. DESIGN: descriptive analysis of overall and cause-specific mortality in the ATS of Milan area in 2020 and comparison with a reference period (2015-2019). SETTING AND PARTICIPANTS: overall deaths in ATS of Milan in 2020 were collected, using the Local Registry of Causes of Death, and were classified according to the ICD-10 codes. MAIN OUTCOME MEASURES: total and weekly overall and cause-specific mortality, by age. RESULTS:  in 2020, 44,757 deaths for all causes were observed in people residing in the ATS of Milan with percentage change of 35%. The leading cause of death in 2020 were cardiovascular disease and neoplasm; COVID-19 infection was the third cause. An excess of mortality was observed for most of all causes of deaths. Starting from 40-49-year age group, an increase of mortality was observed; the largest increase was observed in the group 70+ years. The largest increases were observed for endocrine, respiratory, and hypertensive diseases. On the contrary, for neoplasm, infectious (not COVID-19) diseases, traffic-related mortality, and cerebrovascular disease and ictus, a decrease of mortality was observed. The greater mortality increase was observed during the first pandemic wave. The leading cause of death after positive swab was COVID-19 infection, with little variation with age class. Other frequent causes of death were respiratory diseases, cardiovascular diseases, and neoplasm. CONCLUSIONS: the study showed a generalised increase for most causes of death; observed mortality trends may indicate delay in access to health care system, in diagnosis and treatment.

Time-series COVID-19 policymaker analysis of the UAE, Taiwan, New Zealand, Japan and Hungary.

There are two types of policy analysis: socioeconomic analysis and public policy outcome analysis. The socioeconomic analysis is used for understanding the relationship between COVID-19 incident and mortality and building effective governance. There are two types of policy outcome analysis: general policy analysis and time series policy analysis. This paper is a policy outcome analysis of COVID-19, not a policy analysis. This paper examines COVID-19 policy outcome analysis of five countries such as the UAE, Taiwan, New Zealand, Japan and Hungary. Two policy outcome analysis tools are used in this paper such as scorecovid to generate a snapshot list of sorted scores and time-series hiscovid to identify when policymakers made mistakes for correcting mistakes in the near future policy update. Scores in both tools are based on the population mortality rate: dividing the number of COVID-19 deaths by the population in millions. The lower the score, the better the policy. The higher the score, the more deaths that make people unhappy. COVID-19 death is the most unfortunate event in life and is caused by policy. The introduced time-series policy analysis tool, hiscovid discovered ten facts of five countries. Discovered ten facts will be detailed in this paper. Visualization of policy outcomes over time will play an important role in mitigating the COVID-19 pandemic.

Dynamics and Co-movements Between the COVID-19 Outbreak and the Stock Market in Latin American Countries: An Evaluation Based on the Wavelet-Partial Wavelet Coherence Model.

The COVID-19 outbreak and the global uncertainty it causes produce an apparent panic in stock markets. Efforts to explain the economic spillover effects of COVID-19 can guide authorities to design a control policy against the financial impacts of pandemics. The paper examines the effects of the COVID-19 cases on the stock markets in the emerging Latin American countries of Argentina, Brazil, Chile, Colombia, Mexico, and Peru. The paper employs a continuous partial wavelet methodology to observe lead-lag relations between the daily variables of new COVID-19 cases and the stock market index for each Latin American country. Brazilian new COVID-19 cases led the Bovespa (BVSP) index to decline during the whole period, except February and June 2020, at one month-two month-frequency band. The wavelet and phase difference analyses indicate that, except for Brazil, COVID-19 cases did not affect the stock market indexes adversely during the whole sample period but did affect the stock exchange markets negatively during some sub-sample periods of the entire sample of each country. Dynamics of Latin American stock exchange markets in the short and long run can be explained by some other parameters of real and financial sectors and COVID-19 cases.

The First Three Months of COVID-19: Epidemiological Evidence for Two SARS-CoV-2 Strains Spreading and Implications for Prevention Strategies.

About a month after the COVID-19 epidemic peaked in Mainland China and severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) migrated to Europe and then the USA, the epidemiological data began to provide important insights into the risks associated with the disease and the effectiveness of intervention strategies such as travel restrictions and lockdowns ("social distancing"). Respiratory diseases, including the 2003 severe acute respiratory syndrome (SARS) epidemic, remain only about two months in any given population, although peak incidence and lethality can vary. The epidemiological data suggested that at least two strains of SARS-CoV-2 had evolved during the first months of the epidemic while the virus migrated from Mainland China to Europe. South Korea (SK), Iran, Italy (IT), and Italy's neighbors were then hit by the more dangerous "SKII" variant. While the first epidemic in continental Asia was about to end and in Europe about to level off, the more recent epidemic in the younger US population was still increasing, albeit not exponentially anymore. The same models that help us to understand the epidemic also help us to choose prevention strategies. The containment of high-risk people, such as the elderly with comorbidities, and reducing disease severity, by either vaccination, reduction of comorbidities (seen as risk factors already in Italy), or early treatment of complications, are the best strategies against a respiratory virus disease (RVD). Lockdowns can be effective during the month following the peak incidence of infections when the exponential increase of cases ends (the window of opportunity). From the standard susceptible-infectious-resistant (SIR) model used, containing low-risk people too early, instead, merely prolongs the time the virus needs to circulate until the incidence is high enough to reach "herd immunity." Containing low-risk people too late is also not helpful, unless to prevent a rebound if containment started too early.