Studies on the effect of urban environments on human risk to health and well-being tend to focus on either physiological or cognitive and emotional effects. For each of these effects, several indicators have been proposed. They are determined either by a physiological-emotional theory or by a cognitive theory of direct attention. However, the interrelationships between these indices have not been thoroughly investigated in environmental contexts. Recently, a neuro-visceral model that incorporates all three aspects has been proposed. The present article focuses on understanding the mechanism of coping with urban environments. More specifically, we analyze the interrelations among nine of the more commonly used indices that represent the physiological, emotional and cognitive aspects of coping with urban environments. The data were collected in the following four environments: home, park, city center and residential area. The participants were 72 healthy, middle-class mothers with either high school or postgraduate education. They wherein their fertile age (20–35) with average Body Mass Index (BMI) of 22.2 and S.D. of 0.8 (48 Arab Muslims and 24 Jewish). They were recruited in a snowball method. Path analysis and principal component analysis are used in order to identify the interrelations among the physiological, cognitive and emotional indices and the directions of these interrelations. According to the findings, the Autonomic Nervous System (ANS), as measured by Heart Rate Variability (HRV) and primarily the parasympathetic tone (High frequency-HF) is the pivotal mechanism that modulates emotional and cognitive responses to environmental nuisances. The ANS response precedes and may trigger the emotional and the cognitive responses, which are only partially interrelated. It appears that the autonomic balance measured by Standard Deviation of NN interval (SDNN) and HF, the cognitive index of restoration and the emotional indices of discomfort and relaxation are closely interrelated. These seemingly disparate operands work together to form a comprehensive underlying network that apparently causes stress and risk to health in urban environments while restoring health in green environments. The notion that urban environments are highly stressful and that green environments have restorative power is widely accepted in the literature ,4,5,6,7.Contemporary studies prove that long exposure to urban stresses is associated with cardiovascular diseases, obesity, immunological, gastroenterological, neurological and mental disorders ,16,17. FIn studies that investigate the stresses associated with urban environments and the recovery generated by green areas, three groups of stress indicators are used. One set of studies focuses on the physiological effects of urban nuisances  on the human ANS ,23, withA second set of indices examines the cognitive effects of urban and green environments on human stress and health risk. The most commonly used indices are the perceived restoration potential index and memory tests ,33,34,35The use of stress indices is determined by three different theoretical approaches to understand the effects of exposure to environmental factors in cities and green environments. First, the psychophysiological theory argues that exposure to restorative environments is associated with improved ANS balance and an associated decreased psychological stress . AccordiSecond, the perceived restoration theory argues that some environments require directed attention, resulting in fatigue and a reduction in cognitive capabilities, whereas others, such as green environments, evoke indirect attention, resulting in restoration ,43,44,45Advocates of both theories criticize the opposing theory for developing a one-sided framework that focuses on either the cognitive or emotional aspects, ignoring the complex relationships between emotional, cognitive, and behavioral response mechanisms considered in psychology . ScholarAttempts to improve our understanding of the interrelations among the responses of the three aspects to environmental nuisances have been reported. Learning from other contexts, it become clear that the three aspects are interconnected in their basic definitions. Scherer (1984) defines emotions as being constituted of several components including a cognitive and a physiological one . DespiteOne of the few studies on the environmental effects on stress and risk to health focused on the effects of leaving the house to selected urban environments on cognitive, emotional and physiological responses, identifying several relevant indices that tapped into the three suggested mechanisms . ReachinFormer theories of biophilia and stress recovery referred to these three domains but only with the introduction of the neurovisceral integration model over the last decade support the argument that emotion, cognition, and the ANS are intricately linked in response to environmental exposure . It alsoThe goal of the present study is to analyze the inter-relations among nine of the more frequently used indices of response to environmental challenges that represent the emotional, cognitive and physiological reaction to environmental challenges. Such conceptual mapping may support one of the three proposed theories: the physiological-emotional, the cognitive and the neuro-visceral integration model. It may highlight how cognitive and emotional aspects reinforce each other and to what extent the emotional and the cognitive aspects are affected by or effect the ANS physiological system. The study may point to indices that best represent human responses to environmental challenges in cities. The study may also contribute to a better understanding of the complexity of the effects of human environmental experience on human well-being. In this study, our focus is on the coping mechanism as reflected by the emotional, cognitive and physiological aspects and not on the effects of different environments on stress and risk to health. Therefore, we do not distinguish between ethnicities and environments but we analyze the whole data set without accounting for ethnicity as we did in former studies ,55.What are the inter-correlations among indices of the emotional, cognitive and physiological aspects of human coping with exposure to urban daily environments?What are the dominant directions of these interrelation?In the present pilot study, we ask the following questions:To what extent are the psycho-physiological, the attention restoration theory or the neuro-visceral integration theory empirically supported by the relationships between these respective domainsIn the method section we present the study population, the procedure of running the field test and the methods of analysis.The study is based on a relatively small sample. Therefore, we consider it as a pilot study. Data were collected from 72, middle class, healthy, young, non-smoking, and non-medicated married women aged 20 to 35 who took part in an ecological study . They weThe study area is in the lower Galilee, in northern Israel, and is centered on two neighboring towns, Afula and Nazareth. According to Koepen, both cities have a Mediterranean climate . The citThe underlying hypothesis of the original project was that each individual and possibly of a specific ethnic affiliation experiences differently urban environments with respective physiological and mental response. However, since our main interest in the present study was to elucidate the interrelations among different reactivity domains  in human beings, for the present analysis both ethnic groups and environments were collapsed into one cohort and undifferentiated environment to increase the statistical power.The fieldwork was divided into twelve sessions with 6 participants in each session (6 participants × 12 sessions). Each participant visited all four environments . All of the subjects in all of the sessions followed exactly the same route and siting sites. One of the researchers visited each participating woman at home one day before the experiment to explain the goal of the study to sign an agreement form, and to fill out a questionnaire on sociodemographic characteristics, supply the devices, and explain how to use them. The experiment began at home and progressed to the three outdoor environments. They visited the outdoor environments on the same day in a random order, with a 15 min break in an air-conditioned car set to 22 °C. The researcher followed the subjects along the route inspecting the devices and ascertaining adherence to the protocol.The sessions were held between June 2015 and February 2016. The devices were calibrated prior to the sessions per the producer’s instructions .The cognitive and emotional tests were given to each participant near the end of their 35 min stay at home and in each outdoor environment while they were sitting on the grass or on a bench. The outdoor experiments started at 12:00 p.m. Women were asked to avoid interacting with other participating women in all environments. All of themeasurements were taken at the participant’s home and in each environment.We employed a well-documented set of indices to assess the emotional, cognitive, and physiological responses to urban exposure . We relaSeveral physiological measurements are used in studies on stress and risk to health in urban environments. Among them skin conduction response, salivary cortisol, brain activity, gait pattern and Heart Rate Variability. HRV indices were chosen for this study since they are a well-documented method, accessible for use in trackable outdoor environments studies and cheap to implement. In addition, the results are in correlation with result extracted from the other methods. Several studies showed that HRV supply only a crude estimation of stress and risk to health suggesting supplementing HRV studies with one of the other mentioned indices. Bernston et al. (1991)  show, foFrom these recordings, a total of seven five-minute segments were sequentially analyzed. Both time and frequency domain were included . Of the Low Frequency power (LF) which ranges between 0.04 and 0.15 Hz, is activated by both the parasympathetic and the sympathetic systems. LF may increase as a result of physical or psychological stress. These indices were empirically validated ,66. The www.kubios.uku accessed on 29 March 2022) was used.Time domain indices measure variation of the intervals between consecutive cardiac cycles, which is the standard deviation of consecutive heart bit picks of five minutes intervals. SDNN, the standard deviation of the average NN intervals, is the most general and frequently used time domain HRV index. For signal analysis, including artifact removal, the Kubios HRV software version 2  ,68. PartSelf-perceptions of a restorative experience were evaluated based on the ‘perceived restrictiveness scale’ (PRS). The scale is composed of 26 statements concerning participants’ experience of environments’ restorativeness. Each statement was rated on a 7-point Likert scale of agreement ranging from 0 (highly disagree) to 6 (highly agree) . The stamean)/Imean]. At the second stage, a path analysis was applied. Structural Equation Modeling (SEM) using an AMOS SPSS path model was run to calculate the coefficients among the indices. The model was run several times, changing the direction of the paths between each couple of indices and searching for the dominant directions of paths till the model with the best t was obtained. In the final model, the dominant paths between each two indices were chosen. Lastly, a component principal analysis was applied in order to identify clusters of variables that are interrelated. The two methods complement each other. While AMOS path analysis allows us to calculate one directional paths among single indices, component analysis allows for clustering several indices into higher level components as a basis for the verification of the aforementioned three competing theoretical frameworks.The analysis started by standardizing the nine physiological, emotional, and cognitive indices [(I − IThe nine indices differ in their distribution. Some have a wide distribution (relaxation and cheerfulness), indicating high sensitivity to environmental challenges, whereas others are less sensitive to the individual responses to environmental challenges (discomfort). With the exception of LnLF, the differences in the standardized indices of high and low levels of response to environmental challenges are significant in all measurements . FollowiThe paths among the three groups of indices were calculated in both directions between each couple of indices. In In testing associations between the emotional and the cognitive indices, as shown in In clustering the indices, two components appear to explain 83% of the variability in coping with environmental challenges . ComponeThe three research questions are all intertwined. Regarding the first question, it appears that the physiological, emotional, and cognitive aspects of dealing with environmental challenges in cities are all intertwined. With regards to to the second question, all of the paths between the physiological and the emotional or the cognitive indices are dominated by one direction, from the physiological to the cognitive or the emotional indices. According to the findings, higher levels of ANS activity, particularly the parasympathetic branch (HF), help to improve emotional regulation and cognitive functioning. In more detail, HF and SDNN have an impact on all of the emotional indices. At the same time, SDNN affect the two cognitive indices while HF affects only backward memory. This result contradicts the conclusions of Scherer (2010)  and MendThis is consistent with a plethora of studies on the effects of urban environments on human well-being, but it adds new insight to the interrelations among the emotional, cognitive and physiological responses. The majority of these studies concentrated on the effects of urban and green environments on one of the operative systems: physiological, emotional, or cognitive ,43,45. TThe autonomic indices themselves are only partially associated. Due to low internal variability, LF is not associated with any of the other indices. HF mainly affects relaxation and positive emotions, as well as all the other indices, except for restoration. The effect of SDNN on restoration and backward memory is dominant. SDNN withdrawal affects discomfort and, to a lesser extent, cheerfulness and positive emotions. The predominant fluctuations affecting ANS balance are attributed to the more sensitive parasympathetic response  in the face of both emotional and cognitive challenges. This finding has been discussed extensively in a previous review .With regards to the third question, our study does not contradict the psycho-physiological and the attention restoration theories. Both are linked to the physiological, i.e., autonomic indices. However, our findings suggest that the disclosive discrete approach, which relies on one of these theories to explain responses to environmental challenges, may be oversimplified. The findings of this study point to an interactive multidimensional mechanism, with the autonomic system playing a dominant role in modulating the other subserving mechanisms, as previously proposed ,57,58.The ANS, specifically as reflected by HRV, has previously been shown to play a dominant role in emotional and cognitive regulation, as well as positive mood regulation . FurtherIn our case, the paths between the physiological and the cognitive indices are stronger than the paths between the physiological and the emotional indices. The physiological and the cognitive indices are included in one component in the principal component analysis, and they take a stronger share in the explanation of variability in responses to environments 47%) compared to the share of the emotional component (36%), while the emotional indices are combined into a separate component. This ending emphasizes the critical role of cognitive processes in dealing with environmental challenges, as several scholars have argued ,47,48. A% compareAn additional comment is the fact that discomfort that was considered to represent an index of emotional response was found to belong to the cognitive component in the component analysis.The most obvious limitations of the study are two: First, the only physiological indices included in the study related to HRV. Other complementary methods such as skin conduction, salivary cortisol and brain tests were not included. This is due to lack of availability and the inflexibility of the use of some methods in outdoor tracking methods used in our study. Second, our sample includes young, middle class, healthy nonmedicated women. Wider studies employing a larger sample size that will be more representative of the population understudy are needed.The present study exposes an intricate relationship between the responses of human beings to their environment, particularly to a restorative experience. Emotional, cognitive and autonomic  underlying mechanisms are activated and affect each other. Furthermore, the theoretical equipotential bidirectional relationship between HRV and emotional regulation  is not c


Malaria is a constant reminder of the climate change impacts on health. Many studies have investigated the influence of climatic parameters on aspects of malaria transmission. Climate conditions can modulate malaria transmission through increased temperature, which reduces the duration of the parasite’s reproductive cycle inside the mosquito. The rainfall intensity and frequency modulate the mosquito population’s development intensity. In this study, the Liverpool Malaria Model (LMM) was used to simulate the spatiotemporal variation of malaria incidence in Senegal. The simulations were based on the WATCH Forcing Data applied to ERA-Interim data (WFDEI) used as a point of reference, and the biased-corrected CMIP6 model data, separating historical simulations and future projections for three Shared Socio-economic Pathways scenarios . Our results highlight a strong increase in temperatures, especially within eastern Senegal under the SSP245 but more notably for the SSP585 scenario. The ability of the LMM model to simulate the seasonality of malaria incidence was assessed for the historical simulations. The model revealed a period of high malaria transmission between September and November with a maximum reached in October, and malaria results for historical and future trends revealed how malaria transmission will change. Results indicate a decrease in malaria incidence in certain regions of the country for the far future and the extreme scenario. This study is important for the planning, prioritization, and implementation of malaria control activities in Senegal. Plasmodium spp. parasite, the Anopheles vector, and the human host. Beyond these essential factors, the risk of malaria transmission can be affected by environmental conditions or climatic conditions, as well as socio-economic factors. In particular, the transmission of malaria is very sensitive to climate and atmospheric conditions. When these are unusual, for example, during heavy rainfall, mosquito populations can multiply and trigger epidemics. We have previously studied the climate–malaria relationship [The threat of climate change is well known, and it has a huge impact on the emergence and re-emergence of certain vector-borne infectious diseases such as malaria. This disease continues to evolve in a changing world, and its geographical distributions are due to changes in vector transmission, which is influenced by rising temperature, moisture availability, and rainfall ,2. The ctionship  as well tionship . These stionship  by coupltionship  and the tionship . In thestionship ,11. SST This work is organized as follows. Senegal is located at the westernmost tip of West Africa and lies within 12–17° N latitude and 18–12° W longitude . The desRegarding climate change, like many Sahelian countries, Senegal faces many natural hazards such as floods, droughts, and heat waves. The latter are extreme weather events generally characterized by the persistence of high temperature for several days. With the progression of climate change, these heat waves are expected to increase in number and intensity and everywhere in the world .Regarding observation, we acquired malaria data from health districts, from MSAS  sentinel sites. The health data obtained so far contain the number of disease cases recorded on an annual scale and by region. The clinical data corresponding to the number of observed malaria cases were obtained from the PNLP . The PNLP records malaria cases in the framework field surveys in Senegal. The observed numbers of malaria cases used in this study were collected from various health districts in Senegal for the period 2011–2021. These malaria data were recorded for all health districts and hospitals to derive a monthly time series for 14 administrative regions . These sentinel sites provide a good representation of malaria transmission in the different climatic zones of Senegal.In this study, we used climate data from the WATCH Forcing Data applied to ERA-Interim data (WFDEI) as reference data . The mulRegarding the CMIP6, these simulations are available on a daily time scale for the period 1850–2014  and 2015–2100 (projections). The available models are shown in 2 (Watt per square meter), the RCP 4.5 scenario to +4.5 W/m2, and the same for the RCP6 and RCP8.5 scenarios. The latest scenarios, so-called SSPs, are presented according to the efforts that will need to be made in adaptation and mitigation if the world is moving towards such scenarios.To study possible future impacts, the Intergovernmental Panel on Climate Change (IPCC) in its sixth report  relies oThe LMM model is forced by daily rainfall and temperature data from the WFDEI, and the multi-model ensemble mean of the bias-corrected CMIP6 data to develop malaria incidence in different climatological periods, using historical experiments and SSP126, SSP245, and SSP585 emission scenarios separately. The datasets (observed and simulated) used were interpolated on the same grid to make them consistent. The LMM takes climate data at this interpolated resolution. The bias-corrected CMIP6 data used in this work were obtained from the LMDZ server . The data processing was performed using a MATLAB language that includes several functions for mapping NetCDF data and organizing time series to be easily used within the box plot function.The annual cycles of the rainfall, the temperature, and the malaria incidence are estimated from datasets by taking the average of monthly values for all years, and the interannual variability is calculated from datasets by taking the annual mean, that is, the average of all months for a given year. Climatologically, the annual cycle is commonly estimated from datasets by taking the average of all the months of the year. After removing the seasonal cycle first, standardized anomaly values were considered. Standardized anomalies are defined as the difference between annual values and the climatological average; this difference is then divided by the standard deviation of the seasonal values.Furthermore, the Taylor diagram described by Taylor et al.  is used The Liverpool Malaria Model (LMM) is a dynamic model of malaria driven by daily time series of rainfall and temperature. The different components of the malaria transmission model and the calibration of the parameters are described in more detail by Hoshen and Morse  then by In In between December and January, there is a period marked by a dry climate and very low temperatures linked to the polar invasions during the winter season.between February and May, it is very hot and dry with the first peak in temperature in May; this absolute peak in temperature precedes the start of the rainy season;the period from July to September (rainy season) is very rainy and wet with mild temperature due to cloud cover;the last period of this classification extends between October and November and is marked by high humidity and slightly high temperature. The second peak of the annual temperature cycle often occurs in October with a peak approaching 32 °C.For interannual variability c, signifIn The annual cycle of the simulated malaria incidence is shown in The Hovmöller diagram  of malarIn Climate projections for the Sahel, particularly in Senegal, include uncertainties for two reasons. On the one hand, because of the strong climate variability observed in the 20th century, it is more difficult to extract from the background noise a signal attributable to climate change; on the other hand, climate models give very divergent results for this region. This divergence is particularly noticeable in the case of rainfall, where even the sign of the change is different between the models. For this reason, we consider the multi-model ensemble mean of CMIP6 data from historical and the projections for rainfall and temperature. Given the wide divergence between models, it is usually advisable not to base assessments of future climate change on the results of a single model taken in isolation; so, in this study, we work on an average of a set of different models of CMIP6 (multi-model ensemble mean). The latitudinal gradient on precipitation in Senegal is well reproduced with CMIP6 data. There is a slight decrease in the precipitation signal in the projections. For the temperature, we note high temperature is more localized in the east of the country but also inside the country to a lesser extent, both for history and for projections. The SSP245 and SSP585 scenarios show a strong increase in temperatures for the future, especially toward eastern Senegal. Three statistical values including the mean squared error, the standard deviation, and the correlation coefficient indicate the performance of CMIP6 in reproducing rainfall and temperature observations (WFDEI). The integration of LMM with future climate scenarios reveals the effect of changes in rainfall and temperature on changes in malaria transmission. Based on the findings of this study, malaria is expected to increase in the southern part of the study region in the future. This agrees with earlier findings in previous studies over West Africa that showed epidemic fringe shifted southward for most malaria models. Otherwise, it is expected that by the 2080s, the climate will become unsuitable for malaria vectors in the northern part of the Sahel, with no more additional people at risk. Some studies  show an Such a decrease in malaria in the far future appears to be associated with climate change . A decreHowever, these results must be interpreted with caution as there are still uncertainties related to both the disease model and the CMIP6 projections for the future. Several sources of uncertainties come from the model structural uncertainty. In the far future, the model parameter uncertainty, particularly rainfall, can become relatively more important. This work aimed to ascertain how best to incorporate such a model effectively into a national decision-making process concerning health planning and interventions. Limitations related to uncertainties must be indicated to stakeholders and policymakers. This could be achieved by employing a multi-model experimental setup with additional modeling systems. We will continue in other upcoming projects to improve our understanding of the malaria incidence burdens in Senegal and elsewhere under the threat of climate change.


This paper analyzes recent developments in the British and European government bond markets with reference to the UK’s decision to leave the European Union. The two main goals of the study are, firstly, to examine whether the Brexit referendum result has affected the risk premium and, secondly, whether there are any changes in risk pricing following the referendum. The paper finds a significant impact of the Brexit referendum on the risk premium in selected economies. Furthermore, the results suggest that there is a considerable change in risk pricing after the announcement of the referendum result. Credit default risk and the risk aversion play a much important role in the post-referendum period than they did prior to the vote, particularly in the UK. On June 23, 2016, the British people voted in a referendum for the United Kingdom (UK) to leave the European Union (EU)—an event which became widely known as “Brexit.” This decision in favor of leave was, broadly speaking, unexpected by most observers. Hence, Brexit represents a unique shock that limits the extent to which previous analyses can be used to understand its effects. During 47 years of membership, a set of complex relationships between the UK and the economies of other EU member states developed. Thus, the departure of the UK from the EU would cause a significant loss for both sides.Against this background, the aim of this study is to analyze recent developments in the UK and the European government bond markets with reference to the UK’s decision to leave the EU and to discuss their implications for both policymakers and economists. Sovereign bond yields play a key role in the transmission process of the central bank’s monetary policy. Moreover, they are generally used as a benchmark to price key interest rates in financial markets, for asset allocation and asset pricing purposes (see ECB ). FurtheThis paper is related to the literature which focuses on the effects of the Brexit referendum result on financial markets. Using composite indicators of financial integration, Hoffmann et al.  find thaUsing an event study methodology, Ramiah et al.  confirm Analyzing the effects of the Brexit referendum on the exchange rate, Belke et al.  assessedFocusing on corporate bond markets in the UK as well as in the euro area (EA), Kadiric and Korus  find thaBelke et al.  elaboratOn account of this, the present study uses yield spreads as an indicator of a risk premium, calculated as the difference between the respective government bond yield and a “risk-free” rate; in this case, the OIS rate is used. The risk premium is expected to embody the risk conditions exposure of the UK and selected EA countries. The frequency of data is daily, covering the period from October 1, 2014, to March 29, 2019. The choice of the risk premium determinants is mainly based on the theoretical background and on the existing literature in this field. This analysis addresses several questions: firstly, did the UK’s decision to leave the EU (Brexit) have an immediate direct effect on sovereign risk in the UK and other EA countries? Secondly, has Brexit triggered some changes in the pricing of sovereign risk due to expected challenges in the future economic development in the UK and selected EA countries?This paper offers several contributions to the existing literature on the determinants of sovereign bond yield spreads. Firstly, this is to my knowledge the first study that focuses on the potential effects of the Brexit referendum result on risk premiums in the UK and European government bond markets. Secondly, it extends the existing literature on the effects of Brexit on financial markets. Thirdly, analyzing risk premiums in the UK and the EA government bond markets simultaneously allows for direct comparison of Brexit effects in those markets. Fourth, estimating the period before and the period after the announcement of the Brexit referendum result enables an analysis of potential changes in the investors’ risk assessment. Finally, this paper employs a newly developed regional risk aversion variable in order to capture the willingness of investors to bear county-specific risks.There are three key findings of this paper. Firstly, the announcement of the Brexit referendum result led to an immediate increase of the risk premium in the UK and some other selected European government bond markets. Secondly, the results suggest that there is a considerable change in the importance of the determinants of sovereign bond spreads due to the change in the risk pricing triggered by the Brexit referendum result. This holds particularly for the UK, where the credit default risk and risk aversion play a much more important role in the post-referendum period  than they did before. Thirdly, the empirical results indicate that using regional rather than international risk aversion might be more appropriate in order to capture investors’ risk assessment, especially when analyzing euro area countries.The reminder of the paper is organized as follows. Section There is an extensive, financially rewarding and outstanding body of empirical literature that deals with the determinants of sovereign bond yield spreads in the euro area. The establishment of the European Monetary Union (EMU) has eliminated the exchange rate risk as a source of market segmentation and a key obstacle to financial integration between participating member states. Some other aspects and sources of risk, such as expected inflation and central bank credibility , were aThe predominant commonality of the previous studies lies in their use of three main explanatory determinants which should reflect investors demand for higher return—in a relation to that of a benchmark—as a compensation for the bearing of a higher risk. Two of them, namely the credit and the liquidity risk, are country-specific risks, whereas risk aversion represents an investors’ related risk . Therefore, risk aversion is coupled with expectations about the future state of an economy. In periods of increased market turmoil and high uncertainty, the risk aversion is higher; consequently, investors are less willing to bear an additional risk—since their primary source of income is already at risk—and are rebalancing their portfolio toward less risky and more liquid assets . The peWolswijk ). Thus, There is a unanimous consensus in the literature that international risk aversion plays a crucial role in explaining the yield spreads in sovereign bond markets. The common finding in the literature is that euro area sovereign yield spreads strongly comove, particularly in the period before the financial and sovereign debt crises in Europe. This phenomenon is well observed and econometrically supported. Principal component analysis shows that the first principal component generally can explain more than 90% of the variation in sovereign bond yield series . In theThus, market participants can put pressure on governments by pricing different risks of default. Investors demand higher credit premiums for bonds of governments that follow unsound fiscal policy, forcing market discipline on them (see Manganelli and Wolswijk ). FinancIn the previous studies, a wide range of fiscal and macroeconomic variables are used to proxy credit risk.Focusing on the first years of the EMU, Codogno et al.  find thaHaugh et al. , BarriosPozzi and Wolswijk , Aßmann From a general economic sense of risk and from a theoretical point of view, it is to be expected that for a less liquid asset, investors would demand a higher return as a financial compensation for bearing the risk of having to sell the asset at a lower price in relation to a respective benchmark. Nevertheless, the multifarious aspects make the concept of market liquidity hard to grasp. Following the definition provided by Gravelle  and moreliquidity risk on yield spreads in sovereign bond market can be fairly challenging. In times of market distress, investors demand for safer and liquid assets rises. Hence, flight-to-quality and flight-to-liquidity could occur simultaneously and be positively correlated causing the differentiation between those two effects to be quite difficult . Barrior et al.  pointed The results of previous empirical studies regarding the effects of liquidity risk are rather heterogeneous. Due to the many-sided issues of the liquidity concept, various different measures of liquidity risk were used in previous contributions.Codogno et al. , Geyer eIn contrast, Gomez-Puig , 2008, JFavero et al.  provide There are only a few studies that analyze sovereign bond yield spreads in advanced economies and therefore also in the UK. The previous literature usually studied sovereign risk in emerging markets or has dealt with euro area counties, particularly since the onset of the financial and, later on, the sovereign debt crisis. Moreover, the majority of those analyses are conducted in a panel setup . The frequency of data is daily, covering the period from October 1, 2014, to March 29, 2019.4Starting the empirical analysis from October 2014 facilitates avoiding consequential effects of the sovereign debt crisis in Europe. March 29, 2019, is chosen, since the UK was previously supposed to leave the EU by the end of that day, before additional extensions were arranged. Assuming that the market pricing behavior has changed with the UK’s decision to leave the EU, the sample is additionally sub-divided into the period before the Brexit referendum and the period after the Brexit referendum. The dependent variable in this empirical approach is the daily 10-year sovereign bond yield spread relative to the respective 10-year OIS rate. Before performing a rigorous econometric analysis explaining the yield spreads, it is worth presenting graphically the government bond spreads and determining variables for the particular period.Figure In order to approximate the credit default risk of the selected country, the corresponding 10-year credit default swap (CDS) premium on government bonds is used. Bond market size discounted by the yield of the corresponding bond is used as a proxy for the liquidity risk . To conThis section introduces a simple estimation model to analyze sovereign bond yield spreads in the UK and selected euro area countries and whether the Brexit referendum result affected the respective risk premiums and whether there are any changes in risk pricing after the announcement of the referendum result.j. The risk premium in country j is defined as the difference between a 10-year government benchmark bond yield and a respective 10-year OIS rate. When studying a sovereign bond market, a first crucial issue is the definition of the yield spread and a second is the identification of the determining variables.The choice of yield spread determinants is mainly based on the theoretical background and on the existing literature in this field. Estimation is conducted via OLS using daily data, over the period from 1 October, 2014, to 29 March, 2019.Given the questions raised by this analysis, using the OIS rate as a benchmark offers some advantages. Firstly, it allows analyzing the risk premium in the German government bond market itself. Since the German Bund is generally used as a benchmark, not much is known about the associated risk premium and its determinants. Secondly, analyzing the effects of the Brexit referendum result on the risk premium in the UK is one of the key aspects of this study. Using the OIS rate and not the German Bund as a benchmark has the advantage of avoiding exchange rate aspects between the British pound and euro that arises through the comparison of bonds denominated in different currencies , the ouIn order to account for credit risk, the corresponding 10-year credit default swap premium on government bond . BernotThis work follows on from the previous consideration by developing a pure corporate bond spread for the EA and the UK, respectively. The novel regional risk aversion . To sumAs shown by Attinasi et al.  and GerlThe next section investigates whether the announcement of the Brexit referendum result had an impact on risk premiums in the UK and the EA countries, respectively. The benchmark specification also includes the liquidity premium, the CDS premium, the regional risk aversion measurement, and the lagged dependent variable. The estimation results are reported in Table The first lag of the dependent variable is highly statistically significant for all countries indicating a strong persistence in the risk premium data in sovereign bond markets. The proxy for market liquidity is likewise highly significant for all countries. The results show that this variable has, as expected, a negative impact on yield spreads, which is consistent with the literature . Hence,In this work so far, the possible time variation in investors’ risk sensitivity and the pricing of risk due to the Brexit referendum result is not taken into account. Bloom et al. , 2019 fiThe estimation results confirm the time-varying behavior of the risk premium determinants indicating a change in risk pricing after the announcement of the Brexit referendum result. The proxy variable for liquidity became highly statistically significant for all countries in the post-referendum period. This finding confirms that in episodes of increased financial market turmoil, investors are more averse to illiquidity shocks and they respond by switching to more liquid assets . BeyondIndeed, the most pronounced change in the behavior of the risk premium determinants can be observed for the UK. Results of estimating the risk premium for the time before and after the referendum show that the proxy variable for credit risk as well as for risk aversion turned significant. In the post-referendum period, both variables became statistically significant with a positive impact on the risk premium in the UK. These results are similar to the results of Belke et al. . Analyzi15The focus of this section is to examine the robustness of the findings regarding risk aversion as previously reported. As a reminder, this work uses a newly developed regional risk aversion to capture and measure investors’ risk sensitivity and changes in the pricing of risk. The variable is calculated as a yield difference between BBB and AAA corporate bond index in the respective market. Alternative measures of risk aversion are also used in some other studies, for example in Geyer et al. , Haugh eThe most commonly used variable to account for international risk aversion is a spread between US corporate and government bonds. As shown in Fig. R-squared is observable. The largest increase in adjusted R-squared can be found for Ireland (11 percentage points) and the second largest for Germany (4 percentage points) and then for Portugal, Spain, and Austria. An increase in adjusted R-squared might indicate not only statistical but also economic importance of the regional risk aversion in explaining changes of risk premium in the selected European countries , a rise in adjusted ee Brown ).There is an ongoing debate about the potential effects of the UK’s decision to leave the EU. This paper contributes to the literature on the impact of the Brexit referendum on financial markets. It examines whether the announcement of the referendum result has affected the risk premiums in the UK and selected EA countries and whether there are any changes in investors’ risk assessment triggered by the Brexit referendum result. The model includes daily yield spreads data covering the period from October 1, 2014, to March 29, 2019. An important feature of this study is the lead use of the newly developed regional risk aversion variable, which is shown to be an appropriate measure of investors’ willingness to bear a country-specific risk.The results show that the Brexit referendum had a significant impact on yield spreads leading to higher sovereign risk premiums in the UK and most other selected EA countries. Rising risk premiums may cause government bond yields to increase and force governments to exhibit more fiscal discipline and therefore make it more difficult for policymakers to milden the effects of Brexit and its aftermath. Additionally, the sample is split into the period before and the period after the Brexit referendum, to consider the potential change in the importance of the determinants of risk premiums. The estimation results show that, particularly in periods of stressed macroeconomic and financial conditions, the credit risk factor plays a much more important role by determining changes in risk premiums. This finding implicates that a more aggressive fiscal approach could have further positive effects on credit risk leading again to higher risk premiums. Moreover, the highly statistically significant positive effect of the regional risk aversion variable on the risk premium in the UK after the Brexit referendum indicates that the UK government bond might have lost its reputation as a safe asset. Further weakening of the safe haven status could have extensive consequences for the UK’s financial market and the economy.Although the underlying study includes nine of the largest EA economies, subsequent research might consider broadening the sample of countries in order to address concerns regarding high heterogeneity amongst the countries of the EA. Furthermore, in analyzing 10-year government bond markets, the study focuses on the effects of Brexit in the longer term. Thus, one might consider incorporating additional maturities, for example 2-year and 5-year maturities, that could help to better understand the dispersion of Brexit effects over time.In 2019, leader of the Conservative Party Boris Johnson won a resounding electoral victory, giving him a clear mandate with regard to negations on the future of the UK-EU relationship. On January 31, 2020, when the Withdrawal Agreement entered into force, the UK officially withdrew from the European Union. This marked the start of a transition period that ended on December 31 same year, and with the entering into force on January 1, 2021, of the EU-UK Trade and Cooperation Agreement, the UK is now a third country.Still, this is far from the end of the Brexit process. Despite the broad agreement achieved, there remain clear differences, as the months which followed have shown. Particularly in the area of financial services, no clear agreement could be achieved. Upon leaving the EU, UK financial services lost their passporting rights to service EU clients from the UK and faced the prospect of relying on “equivalence” decisions in order to access the single market. However, the British government no longer assumes that equivalence deals from the EU for financial services will be forthcoming .Therefore, it would be of particular interest for future research to extend the sample in order to include the announcement of the Withdrawal Agreement and the Trade and Cooperation Agreement between the UK and the EU in the analysis. However, one should not overlook the effects of COVID-19 pandemic on international government bond markets. To account for the pandemic effects, incorporating an alternative or additional Brexit variable might be considered.


We combine periodical and freely available remote sensing information describing temperature and precipitation with curated biological information from several groups of animals and plants in the Brazilian Atlantic rainforest to design an indirect remote sensing framework that monitors potential loss and gain of biodiversity in near-real time. Using data from biological collections and information from repeated field inventories, we demonstrate that this framework has the potential to accurately predict trends of biodiversity change for both taxonomic and phylogenetic diversity. The framework identifies areas of potential diversity loss more accurately than areas of species gain, and performs best when applied to broadly distributed groups of animals and plants.Monitoring biodiversity change is key to effective conservation policy. While it is difficult to establish  Real-time monitoring of biodiversity is a fundamental goal of biological conservation. If implemented at large geographical scales, it can strongly impact policy and action. Yet the difficulty of establishing real- or near-real time diversity monitoring on the ground poses a significant challenge for large-scale conservation planning and management . The incYet satellite imagery can only view so much—and often cannot provide species level identifications or information below the canopy level. Most proposals to measure essential biodiversity variables from remote sensing include only direct measurements such as canopy tree observations and, so far, do not integrate with the genetic dimension of diversity . Even aeTo overcome these challenges, it has been proposed that organisms such as understory plants and animals may be monitored indirectly, by tracking the environmental conditions that are statistically associated with their presence . Severalvia remote sensing may allow for the dynamic monitoring of changes of biodiversity components that cannot be directly observed. However, a platform that applies these concepts and allows governments and scientists to envision how ongoing environmental changes may potentially change the distribution of diversity, in near-real-time, is missing. Importantly, it remains to be uncovered how reliably this approach can generate predictions across different geographical areas, and for biological groups of varying levels of diversity, ecological constraints, and geographical range.Because many of the environmental variables relevant to models of diversity patterns under a static environment can be derived from remote sensing data, it has been proposed that near-real time indirect remote sensing may provide a lens to describe and predict the potential displacement of biodiversity in response to continuous environmental shifts . In otheHere, we build and test such a platform for near-real time prediction of diversity change. By applying it to environmental and biodiversity data from the Brazilian Atlantic Forest hotspot, we critically evaluate its limitations and the conditions under which it may prove most useful. We focus on the Atlantic Forest to build on our previous studies , which dThis new model for biodiversity monitoring through indirect remote sensing is built and implemented in a reproducible machine-learning framework that integrates Google Earth Engine , the R sBecause machine learning techniques are known to need a substantial amount of data , and givTo illustrate our modeling framework and evaluate its sensitivity to the geographical range of the group being modeled, we first applied it to nine organismal groups that differ in their spatial distribution. To this end, for each group, we combined its species richness map with temporally-explicit environmental data (temperature and precipitation), derived from remote sensing sources, to generate a model that describes how they are associated. We then performed the same procedure with the group’s phylogenetic diversity map, to model this second biodiversity metric as well. We evaluated model fit for all groups and for both diversity metrics, exploring how they relate to the total range size of the group and the median individual species range size. Models with the best fit were then projected over time, yearly, based on that year’s environmental conditions. Those yearly projections allowed us to map trends in diversity change across time , for each group. They also allowed us to map areas of potential mismatch between species richness and phylogenetic diversity—which often indicate important ecological processes—and identify whether they may be changing over time. Finally, we applied our framework to an available dataset of observations of amphibians and epiphytes over time, to test the accuracy of the model predictions.Bertolonia , others have broad individual species distributions and broad group-wide spatial coverage (like the Thraupinae), while others have discordant patterns . Before proceeding to modeling, we removed all highly redundant (correlated) variables (VIF > 10) using the vifstep function of the usdm R package  data and temperature data from NASA’s Moderate Resolution Imaging Spectroradiometer MODIS;  instrume package . To cali package . This re package .To predict the spatial distribution of species richness and phylogenetic diversity as a function of environmental change in near-real time, we independently modeled species richness and phylogenetic diversity with data describing temperature and precipitation. Those models with the best fit were then projected over time, yearly, based on each year’s environmental conditions.get.block in the package ENMeval, partitioning the data into bins across latitude and longitude lines  to be transferred to a subsequent time period based on the same predictor variables . This resulted in the use of data collected up to the year of 2015 for the amphibians, and up to the year of 2018 for the epiphytes. We then used the available inventory data from all but the last four years of observations to generate maps of species richness , measured as the number of species present in each pixel in each year. Following the same modeling procedure applied previously, we combined those species richness maps with bioclimatic variables derived from remote sensing sources as described above, and for the same period, to train a predictive model. The model was then projected onto the four following (most recent) years, for which observational data are available for ground-truthing. As such, we obtained yearly model projections of amphibian species richness for the years of 2012, 2013, 2014, and 2015, and yearly model projections of epiphyte species richness for the years of 2015, 2016, 2017, and 2018. As done previously, we used these yearly model projections to create maps of inferred trends in species richness across time for each of the target groups, computing the slope of a linear regression for each group. We then compared these modelled trends with the observations recorded in the datasets, evaluating both directionality and magnitude of change. To evaluate if results were better than random predictions, we created 10,000 random predictions of gain and loss across the forest, and, for each one, computed the number of rightly predicted pixels. We used those results to create a null distribution of random predictions and compared the observed number of correct predictions with the null distribution. A script used to run the entire framework (in R and python) is available in the GitHub repository for this manuscript.For the purposes of ground-truthing the model, we limited our analyses to the years for which the datasets (i) documented the year of the observations, (ii) provided coordinates for the observation localities and (iii) provided a relatively broad sampling coverage of the region : they included two plant groups  and three animal groups , but they have variable individual species ranges  was most important variable (explaining 18% of the variation). Finally, for the Boana frogs, precipitation of the driest month (bio 14) was most important  and, again, the most important predictor variables were all related to precipitation. Precipitation of the warmest quarter (bio 18) was the main predictor of phylogenetic diversity in the Miconieae and the Boana frogs (25% and 22% respectively), whereas precipitation of the wettest month was the main driver for the other three groups  and loss (blue) of predicted diversity across the forest; & allies . Models & allies . Maps of& allies  and S2.Fridericia & allies, the projections mostly suggest that phylogenetic diversity is expected to accumulate relative to species richness except for sites in the northwest (similar to Miconieae) and a large region in interior São Paulo  differ from the two plant groups in the predicted pace of the changes; they appear less drastic over time , the modelled trends of potential change in species richness are broadly congruent with those measured with empirical data, particularly regarding species loss  in the epiphyte study, and 76% of the pixels  in the amphibian group (When the model framework is applied to inventory data that include multiple records per year (enabled by the ATLANTIC-amphibian and ATLANTIC-epiphytes datasets), this framework predicts the directionality of biodiversity change well. Although the predictive power of the amphibian and epiphyte models is lower than those observed for the groups analyzed in the first part of the study . Models for the two groups with the smallest total area, the Bertolonia and the Bromelioidae , had comparatively good performance for at least one of the two models . Although the small sample size precludes statistical evaluations (N = 3 groups), it is possible that these results reflect the impact of the ecology of these species, particularly their dependence on the environment, on model performance. Because the ecology and life history traits of treefrogs and bromeliads render them strongly dependent on the abiotic environment, we hypothesize that the models of these groups fit better than expected given their geographical ranges and those of their species. As such, we expect that this model framework will work best for groups of species that are tightly associated with specific environmental conditions , or broadly distributed in a biome of interest.Model outcomes of some groups, including the highly performing models for the 2 ranging from 0.08–0.55, i.e., over the span of a few years), when species gain might be more difficult to document via inventory work, and when species gain may lag behind environmental change . In those areas, the model is predicting the loss of distantly related species, or the gain of closely related ones. This might be pointing at areas where environmental conditions are becoming more extreme and so species persisting, or colonizing, will be more like each other than expected by chance. While the present study is unable to validate these hypotheses, they are testable through field observations and experimental work.Likewise, the models may be used to identify potential ecological processes at play—and test them. While patterns of predicted diversity change seem similar between species richness and phylogenetic diversity for all groups, the predicted residuals for each year show that their mismatch varies in time. Positive residuals are more widespread, showing a general pattern of more phylogenetic diversity than expected given the number of species. The latter can mean that the species to be gained locally are distantly related, or that species that are closely related are being lost. The gain of distant species or loss of redundant ones can be driven by species interactions driving similar species to compete . As suchThe modeling framework proposed here allows for the flagging of areas that are at risk of losing diversity in the face of climatic changes. The incorporation of two dimensions of diversity—taxonomic and phylogenetic—enables the identification of areas that might be at risk of losing evolutionary diversity due to environmental change. However, the predictions of the model should be taken carefully when predicting diversity gain, as those seem to be overly optimistic. Because the strength of the predicted change is overestimated by the model, we propose that practitioners be careful when interpreting magnitude; it may be best to focus only on the expected direction of change. This would allow, for example, for a better design of new protected areas and for targeted monitoring of areas either predicted to lose diversity  or to disproportionately lose evolutionary diversity.Key to this indirect remote sensing effort is the periodicity of the remote sensing information to be correlated with—and used as proxy for—biological diversity. Remote sensing data are available at different periodicities, ranging from days to years, depending on the agencies that collect them, their instruments, and costs. Accessing free data has become easier with the advent of Google Earth Engine , a cloude.g., phylogenetic endemism) are not easily predicted by climate descriptors . Diversity patterns can vary in different geographical areas, making model transfer problematic, especially in regions with area limitations, such as islands.10.7717/peerj.13534/supp-1Supplemental Information 1Click here for additional data file.


We found that greater anxiety and depression were significantly associated with worse quality of life. Additionally, quality of life, according to Ferti-QoL, was significantly worse in women, participants with a high level of education, those with greater depressive symptoms, and those with greater state scores. Findings of this study highlight the need for implementing interventions of supportive care methods, counseling, stress reduction methods, and improving the fertility-related quality of life of infertile couples.Fertility-related stress can negatively impact infertile couples’ quality of life (QoL). Most previous studies have concentrated on the effects of stress and infertility on individual persons, especially women, though infertility affects the QoL of both spouses. Our research aimed to investigate the roles of infertility and stress in couples’ quality of life as a single unit. The research sample consisted of 202 spouses, i.e., 101 couples, with a mean age of 39.5 years (SD = 4.9 years) undergoing fertility treatment at Athens Naval Hospital-Assisted Reproduction Unit. Data collection was completed via self-administered questionnaires: the FertiQoL International Questionnaire for measuring the quality of life in infertility and The Demographic Information and Medical History Questionnaire. Data collection was conducted between January and November 2022. Quantitative variables are expressed as mean values (standard deviation) and as median interquartile range, and qualitative variables are expressed as absolute and relative frequencies. Pearson’s (r) and Spearman’s (rho) correlations coefficients were used to explore the association of two continuous variables. Multiple linear regression analysis was used with dependence on the Ferti-QoL’s subscales. The regression equation included terms for participants’ demographics and information from their medical history. Adjusted regression coefficients (β) with standard errors (SE) were computed from the results of the linear regression analyses. All reported p values are two-tailed. Statistical significance was set to  Infertility is a worldwide health issue that affects millions of people of reproductive age. According to available data, infertility affects 48 million couples and 186 million individuals worldwide .Infertility is a male or/and female reproductive system disease characterized by the inability to achieve pregnancy after 12 months or more of regular unprotected sexual intercourse. Primary infertility is the inability to achieve conception, and secondary infertility is a couple’s inability to conceive after a successful conception in the past.Infertility has been ranked as one of the most stressful situations a person can face, being comparable to divorce or the death of a family member, or even physical illnesses such as cancer. It is estimated that one in six couples in Western societies faces infertility to some degree in their lives, and the World Health Organization calls for the recognition of infertility as a global public health problem .Infertility may occur due to male or female factors, a combination of male and female factors, or unexplained .Depending on the problem faced by each couple, the treatment that best suits their needs will be preferred. It can range from simple monitoring or induction of ovulation, intrauterine insemination, in vitro fertilization in a natural cycle, and micro insemination (intra-cytoplasmic sperm injection—ICSI) to selection of a sperm or egg donor, embryo transfer of a frozen fertilized egg, and even surrogacy .However, environmental and lifestyle factors such as smoking, excessive alcohol consumption, obesity, and exposure to environmental pollutants have been linked to lower fertility rates .Undoubtedly, clinical efforts and technology have improved therapeutic results in the treatment of infertility. However, the nature of the disease and the painstaking effort to treat it leads to the assumption that the treatment of infertility is equally important to the quality of life of infertile patients.It is argued that for many people, infertility is a major life crisis and can cause depression, anxiety, social isolation, and sexual dysfunction .Psychosocial studies among couples suffering from infertility demonstrate a high frequency of negative reactions and low satisfaction with life because of infertility and its treatment .The overall well-being of the couple, the success of the treatment, the willingness to continue treatment, and the assessment of the satisfaction or lack of satisfaction that people can expect due to the success or failure of the treatment are all affected by the psychological burden experienced by the couple. Therefore, the need to measure and consider the quality of life in infertility is essential, and the use of reliable measurements could lead to better management of patients .Quality of life (QoL) according to World Health Organization, is defined as a wide-ranging concept that is influenced in a complex way by physical health, mental state, personal beliefs, the level of independence of the individual, and his relationship with the environment and the conditions  that it offers .Thus, all the problems related to the couple’s physical and mental health adversely affect their quality of life.In addition, there is a new definition in the literature for the fertility quality of life (FertiQoL), which specifically indicates the impacts of fertility problems on various life dimensions . InfertiTo separate the quality of life, in its general sense, from the quality of life that concerns the requirements of medicine and clinical applications, the term health-related quality of life (HRQL) is used .Therefore, the question that arises is whether psychological stress and its consequences adversely affect fertility. The research evidence regarding the statistically significant relationship between stress and infertility is contradictory . HoweverThere are many ways and research tools to measure the psychological burden and quality of life associated with infertility, the in vitro fertilization process, and several treatment options . Tools tInterventions to reduce and alleviate the various clinical symptoms, regardless of whether or not they contribute to an increase in pregnancy rates, are extremely beneficial to the emotional balance and harmonious relationship of the couple .Data indicate that 10–15% of the global population of reproductive age experience infertility , yet theSome studies have shown that infertility leads to intense stress and a decrease in affection between infertile couples . Many peAccording to research carried out on 1000 infertile couples, the mental pressures caused by infertility in both men and women are always accompanied by reduced quality of life in all aspects .A clinical study showed that infertility will lead to a lower quality of life index for infertile individuals through the development of mental and social stress, reduced life satisfaction, and increased marital problems . Boivin Therefore, research has identified various individual factors, such as gender ,21, age Research has also highlighted that stressful life events should be considered as factors that broadly influence parameters of physical and psychological health ,27 and pSeveral studies have shown that the presence of stressful events involving the family  is often reported in the biographical background of infertile couples ,31, reveMany studies have been conducted on the analysis of the quality of life in infertile individuals, but most have focused on one of the spouses, especially women, and few have considered the quality of life in both spouses as a single unit. Analyzing the relationships between infertile individuals and their quality of life will help us examine couples’ differences in how they deal with the issue of infertility. Studies have shown that higher correspondence between the quality of life outcomes has been associated with less stress and a better ability to manage stressful situations .To better understand the causes of this phenomenon, it is primarily imperative to examine closely the spouses who are undergoing fertility problems.The purpose of this study was to analyze the relationships between infertility stress and the quality of life in a convenience sample of infertile couples and to investigate the differences between men and women in how they deal with the issue of infertility.Some research hypotheses emerged concerning the purpose of the research and the literature review. We hypothesized that infertile couples undergoing infertility treatment would experience high levels of infertility-related stress and that infertility stress would be negatively related to infertile couples’ quality of life.The COVID-19 pandemic has claimed millions of lives globally and has impacted all features of healthcare worldwide, including the delivery of care to patients with fertility-related issues. Recent studies state that infertile persons frequently expressed added levels of anxiety and depression compared to the general population during the COVID-19 era ,39,40,41Sub-objectives were the determination of the factors that influence the quality of life of infertile couples; and the social, family, and demographic relationships that are affected by infertility and have an impact on the quality of life.The psychometric tools used in this research to collect the data were self-administered questionnaires: The Demographic Information and Medical History Questionnaire—a COMPI Questionnaire ; and the FertiQoL International Questionnaire for measuring the quality of life. The collection of the questionnaires took place during the period of January–November 2022.The study sample consists of 101 infertile couples (101 women and 101 men) who were undergoing assisted reproduction techniques at the Athens Naval Hospital-Assisted Reproduction Unit (ANH-ARU) during the first eleven months of 2022 (1 January 2022–30 November 2022).Men and women who live in the wider area of Attica and in various cities in Greece come to the ANH-ARU.Self-administered questionnaires were delivered in a paper-and-pen format to eligible participants. Participation in the survey was voluntary. A short paragraph was included at the beginning of the questionnaires to inform participants of the study’s objectives and their responses’ confidentiality. All participants gave informed consent. Data were collected anonymously, and participants had the right to access their answers and withdraw from the research whenever they wished to. The research protocol was approved by the Athens Naval Hospital Research Ethics Committee (ANH-RPA-325/30-12-2021). Contact details of the researcher, such as telephone and email, were also provided.Due to the fact that filling out the questionnaires was time-consuming, it was deemed appropriate for the health recipients to have the option of either filling in the questionnaires immediately or filling in the questionnaires outside of the ANH–ARU. The completed questionnaires were returned to the ANH–ARU during the next appointment in person.It was considered appropriate for couples to fill in the medical history at the ANH–ARU to avoid incorrect answers. Furthermore, the researcher gave a series of instructions regarding the filling in of the questionnaires, such as urging the couples to answer with spontaneity and honesty and to not fill in the questionnaires in collaboration with their respective partners.A request for approval was made to the scientific council of the Athens Naval Hospital in order to approve the study protocol and methodology and the compliance with the General Data Protection Regulation (GDPR). The questionnaires were accompanied by an information brochure in which it was stated in detail that the choice to participate in the research was entirely optional and would have no effect on their fertility treatment.All personal data and the answers given remained accessible only to the main researcher. Responses were strictly confidential and were destroyed after the survey was completed. Strict confidentiality, and of course, the anonymity of the participants, were maintained. Furthermore, they had the right to stop their participation in the research at any time they wanted and were informed about the aims and purposes of the research.p < 0.05, and analyses were conducted using SPSS statistical software (version 22.0).Quantitative variables are expressed as mean values (standard deviation) and as median (interquartile range); qualitative variables are expressed as absolute and relative frequencies. Pearson’s (r) and Spearman’s (rho) correlations coefficients were used to exploring the association of two continuous variables. Multiple linear regression analysis was used with dependence on the FERTI-QoL subscales. The regression equation included terms for the participant’s demographics and information from his medical history. Adjusted regression coefficients (β) with standard errors (SE) were computed from the results of the linear regression analyses. All reported p values are two-tailed. Statistical significance was set to The sample consisted of 202 spouses, i.e., 101 couples, with a mean age of 39.5 years (SD = 4.9 years). Their characteristics are presented in The mean overall FERTI-QoL score was 70.2% (SD = 13.8); see When multiple regression analysis was conducted, the lowest score was found for the “emotional” subscale, indicating worse emotional quality of life for women, participants with higher levels of education, those who suffered from s chronic somatic disease, those who had experienced a miscarriage, and those who had been under treatment for infertility . AdditioLower scores for the “Environment” subscale, indicating worse quality in that specific sector, were had by participants with higher levels of education or greater monthly income and those with greater state scores . MoreoveOverall, quality of life, according to FERTI-QoL, was significantly worse in women, participants with higher levels of education, those with greater depressive symptoms, and those with greater state scores .According to the research results, as they were studied and analyzed, the research hypotheses can be verified. The study showed that stress and infertility are related to the quality of life and that the higher the levels of stress and anxiety, the lower the level of quality of life. Women experience stress at higher levels, and this can be seen in the fact that they show intense symptoms of anxiety and depression compared to men, negatively affecting their quality of life.Conflicts in the couple increase but are easier to deal with when the couple has been together for several years.It is worth noting that in this research, the higher the educational level, the worse the couple’s quality of life. Higher professional expectations impact one’s reproductive behavior due to the intense goals beyond those related to the family. A higher level of education impacts their attitudes and preferences as individuals. Perhaps the most important of these changes is related to the fact that a higher level of education is associated with greater and more competitive participation of individuals in the labor market.Regarding sociability and the appearance of the problem in the rest of the social environment, the results show that social interactions are affected by fertility problems, such as social inclusion, expectations, stigma, and support. Similar results in recent studies ,43,44 stIt is important to mention that women with a high level of education and those who had experienced a miscarriage showed the impacts of fertility problems on physical health, cognition, and behavior. Bearing a child is often their life goal, and women focus all their efforts on this, as they consider themselves biologically unsuccessful without children.Greece is facing a dynamic, protracted, and complex demographic problem. Of particular concern is the long-term course of very low fertility, which is intertwined with increasing rates of infertility. According to data from the European Statistical Service (Eurostat), the above is also confirmed by the fertility index of the countries of the European Union (March 2019), which ranks Greece in last place .Through the results of this research, an attempt was made to better understand the psychology of infertility and how infertility affects the couple’s quality of life.The evaluation of the conclusions can be useful to health professionals working in hospitals, obstetric clinics, and specialized artificial insemination centers. The results are particularly useful regarding the approach for psychological support to achieve the intended upgrade in the quality of life of infertile couples.The present results could be used by midwives and health professionals involved in counseling infertile couples to help them externalize their feelings and better understand the experience of impaired fecundity. Based on the latest data mentioned, it is necessary to establish organizations and social actions with a supporting and advisory role for couples facing involuntary childlessness. The findings of the study highlight the need to implement interventions both for the early identification of infertile couples who present low levels of quality of life and to reduce the effects of infertility stress on these people.The working environment of the armed forces presents some peculiarities compared to other work environments (public or private sector). Combat stress or operational stress is defined as the internal process that prepares the combatant’s psychosomatic response to the changing and life-threatening conditions in which he performs his mission. It can undermine his judgment, performance, and effectiveness .Our study sample came from the medically assisted reproduction unit of Athens Naval Hospital. In this particular unit, those serving in the Hellenic Armed Forces and Security Forces , and their family members, are entitled to free healthcare. Officers serving in the Armed Forces and Security Forces face the demands of operational life and are exposed to a stressful military work environment .In 2018, the Service Women’s Action Network (SWAN) conducted an online survey focused on reproductive health services in the military. Of the 799 total surveys of active-duty service women who answered questions about infertility, 37% said that they had trouble getting pregnant when actively trying to do so . The resRegarding the limitations of this particular study, the core one is the relatively small sample (200 people) used to conduct the research, from which the results and conclusions were derived, due to the suspension of medically assisted reproduction units during the period (March–April 2022) of the emergence and spread of the SARS-CoV-2 virus, following the recommendations of E.S.H.R.E, A.S.R.M., and W.H.O.Additionally, the sample came only from the medically assisted reproduction unit of Athens Naval Hospital, so the sample could not be representative, and the results cannot be generalized to the entire Greek population. However, it appears that the infertile sample consisted of women and men with varied demographic and medical characteristics.The sample of the current research involved only participants who had decided to seek assisted reproductive treatment. Thus, the investigation of the relationship between quality of life and infertility stress was examined using only a sample of infertile couples who were about to undergo or had previously undergone fertility treatment.During the completion of the questionnaires, no specialized laboratory tests were carried out to achieve an accurate measurement of stress levels. Participants’ reports and statements of stress levels as indicators of quality of life are likely to have been subjective.What is less clear is whether stress could be a cause of infertility. Its potential impact is difficult to explore because stress is subjective and difficult to measure, and when starting infertility treatments, most couples are optimistic about their outcomes. Thus, it is not easy to conduct long-term, reliable studies investigating fertility before and after the onset of stress. However, the existing, albeit small, studies suggest that stress is probably an aggravating factor that negatively affects the quality of life.Finally, an important factor while conducting this research (January–November 2022) is the continuity of SARS-CoV-2’s spread, along with the efforts to control the infection rates and the progress of the pandemic. Research till now ,38,39,41Despite these limitations, the study had several strengths. The sample size was sufficient to achieve all research objectives, study the effects of stress and infertility on couples’ quality of life, and test all research hypotheses amidst the COVID-19 pandemic.An advantage of the present study is the fact that although many studies have been conducted on the analysis of quality of life in infertile individuals, most have focused on one of the two spouses, especially women. This study also investigated the role of infertility and stress in couples’ quality of life as a dyad.The questionnaires and scales used are valid, international, weighted to Greek data, have satisfactory psychometric properties, and have been reused in similar studies.Sophisticated statistical tools were used to make the analyses and correlations.It is not clear whether an elevated level of distress arises in all infertile couples. The level of stress and changes in QoL may be related to non-medical conditions, and factors predicting QoL may vary in different infertile populations and genders. Therefore, the identification of aspects related to better or worse health-related QoL is crucial for suggesting and testing scientific interventions for infertile populations. Based on the conclusions drawn, proposals can be formulated for the benefits of a short-term stress management program that will contribute to improving the quality of life of those facing infertility.Therefore, suggestions for clinical practice generally concern the development of methods and interventions to reduce psychological stress in infertile individuals undergoing fertility treatment, considering the particularities that require changing their behavior beyond the usual routine.The present results could also be utilized to provide information, support, and treatment of the stress experienced by female army officers who seek treatment options to get pregnant.