The hippocampal place cell system in rodents has provided a major paradigm for the scientific investigation of memory function and dysfunction. Place cells have been observed in area CA1 of the hippocampus of both freely moving animals, and of head-fixed animals navigating in virtual reality environments. However, spatial coding in virtual reality preparations has been observed to be impaired. Here we show that the use of a real-world environment system for head-fixed mice, consisting of an air-floating track with proximal cues, provides some advantages over virtual reality systems for the study of spatial memory. We imaged the hippocampus of head-fixed mice injected with the genetically encoded calcium indicator GCaMP6s while they navigated circularly constrained or open environments on the floating platform. We observed consistent place tuning in a substantial fraction of cells despite the absence of distal visual cues. Place fields remapped when animals entered a different environment. When animals re-entered the same environment, place fields typically remapped over a time period of multiple days, faster than in freely moving preparations, but comparable with virtual reality. Spatial information rates were within the range observed in freely moving mice. Manifold analysis indicated that spatial information could be extracted from a low-dimensional subspace of the neural population dynamics. This is the first demonstration of place cells in head-fixed mice navigating on an air-lifted real-world platform, validating its use for the study of brain circuits involved in memory and affected by neurodegenerative disorders. Place cells in the hippocampus encode spatial information during navigation, by firing selectively when the animal is in a certain part of its environment  platforms. In such systems, a mouse is typically head-fixed atop a polystyrene ball which is free to rotate. Optical sensors tracking ball movement are used to update a visual display  Act 1986 and according to Home Office and institutional guidelines. Subjects were C57BL/6 mice of age 1 to 9 months at the time of viral injection. Behavioral data were collected from 6 mice  for the circular track and from 5 mice  for the circular open field. Imaging data were collected from 3 mice  for the circular track and from 3 mice  for the open field. Animals were kept on a reverse 12-h light, 12-h dark cycle with lights on at 7 p.m.13 vg/ml, ~50 nL) was injected into the hippocampus  1.5 mm from the dural surface. The virus contains a green genetically encoded calcium indicator protein (GCaMP6s) and a red fluorescent protein (mRuby) for cell body localization. Two weeks post-injection, a hippocampal window was implanted as described by Dombeck et al.  and buprenorphine (0.07 mg/kg). A small (~0.5 mm) craniotomy was made and the virus AAV1.hSyn1.mRuby2.GSG.P2A.GCaMP6s.WPRSE.SV40  or in a circular open field (diameter: 32.5 cm), both floating on an air table . Infrared (IR) light illuminated the training area and an IR camera was used to monitor the animals. The floating tracks were made of carbon fiber (weight: 100 ± 2.8 g) and had 4-cm high walls lined with visual  and tactile cues . The phosphorescent tapes emitted light at 500 (blue) and 520 (green) nm and glowed for the duration of the imaging session  to either one of two inner regions , or vice versa. Daily water intake was limited to 1–3 mL and was individually adjusted for each mouse to maintain the target weight of 85% of the pre-restriction weight. At the start of each training session, two rewards were given to motivate the mice to lick for water. There was no limit to the number of rewards animals could have during sessions. If the animal did not reach the target volume for the day during training, the remaining volume was given at the end of the last training session for the day. Animals were trained for 11–14 sessions in the circular track and 16–18 sessions in the open field before imaging was started. Mice that did not have good GCaMP6s expression in the CA1 region were excluded from imaging experiments.We used a commercial two-photon resonant scanning microscope  equipped with a tiltable objective mount and a 16 × water-immersion objective . Ultrasound gel at 50% concentration was used as immersion liquid. GCaMP6s and mRuby were excited at 940 nm with a Ti:sapphire laser . The laser power underneath the objective was 60–166 mW. Images  were acquired at 30 Hz during imaging sessions which lasted up to an hour. In remapping experiments, mice were imaged for up to an hour in one track then moved to another track where they were imaged for up to another hour. SciScan software (Scientifica) was used for microscope control, and image acquisition was TTL-synchronized to position tracking and reward timing signals. Light from the phosphorescent tapes on the floating track walls used as visual cues was insignificant in the green and red imaging channels.To characterize stability of place fields, animals were imaged in the circular track in multiple sessions spanning up to 8 days and in the open arena up to 3 days. To locate the same set of cells in later sessions, we used the red channel image from previous sessions as reference.F/F, inherited from the original time series extracted from each ROI. Integrating the amplitude of such events over a fixed time window and dividing by the length of the window yields the neural activity rate (units ΔF/F.s−1).We used the MATLAB  implementation of the CaImAn software package  during the occupancy of a spatial bin and dividing it by the total time it was occupied. These rate maps were smoothed using a boxcar average over three bins and each map was normalized by its maximum value. Spatial information rate (in bits/event) for each cell was computed as described previously . We computed neural activity rate maps by accumulating the total amount of activity , which are expected to fall (approximately) on a log-normal distribution, thus a symmetric distribution of errors would be inappropriate even in the limit of large samples. In this case, therefore, we instead report the 90% confidence interval of the mean.In the Results that follow, we describe estimates of the means of distributions by the sample mean ± the standard error of the mean, unless otherwise indicated. One exception to this is in the reporting of mean activity rates , where M≪N is the number of manifold dimensions. To do this, we used Classical Multidimensional Scaling (MDS), after observing qualitatively and quantitatively (in terms of variance accounted for) better representation , we applied an Optimal Linear Estimator  to either one of two inner regions  and vice-versa and found this protocol to be effective in achieving the desired behavior. The mice adapted to controlling the circular track with their feet, tracing meandering trajectories that were sometimes near the wall and sometimes crossed the arena. This behavior resembles those of freely-moving  through the hippocampal window 3 weeks later showed robust expression of both the calcium indicator GCaMP6s and the static marker mRuby in a large population of CA1 neurons . We primF/F). We found 53–225 (median: 101) active neurons per FOV  imaged for 12–28 min  of the detected neurons in each imaging area showed location-specific activity characteristic of place cells . These cn = 721 cells) during locomotion around the circular track. This is consistent with previous observations from freely-moving mice . The distribution of place cell mean neural event rates during the session was skewed toward higher rates , the log-normal model could not be rejected for the distribution of place cell activities .Imaged hippocampal CA1 neurons produced an average of 0.65 ± 0.01 calcium transient events per second . Although place cells were significantly more reliable from trial to trial than non-place cells, our results suggest that CA1 activity, as measured from calcium fluorescence, is somewhat more variable from trial to trial than neocortical activity  for up to 24 min then transferred them to a second circular track with distinct visual cues and additional tactile cues (track B) where they were imaged for up to 28 more minutes. We found that upon switching to track B, the location-specific activity of many cells disappeared, indicating remapping . We analN days apart, To study the stability of place fields over days, we imaged mice in the circular track multiple times over 8 days. Place cells had consistent location-specific activity within a session but the location was not constant over days . For eac2 or 21 ± 1% of the environment , spatial goal-oriented behavior , and social-context discriminations (possible by having a second mouse in another fixed or floating mini-arena on the platform), and a variety of other tasks involving conjunctive representations with a spatial component.Compared to real-world free exploration, place coding in the floating track system does not persist for multiple days to the same degree, similar to VR systems. Another limitation of the floating track system compared to real-world free exploration is that it is constrained to relatively small environments—the longest track we used in this study was 1 m in length, although we have designed environments that work within the current setup that allow tracks as long as 2 m. Although not as long as the 18 m tracks of Brun et al. , this shThis study focused on CA1 hippocampal place cell representations during exploration. The floating track system presented here offers advantages over traditional VR systems and free exploration real-world systems for investigating neural encoding in a variety of brain systems, and may well prove a useful tool for dissecting neural pathways underpinning cognitive functions based upon spatial representations, such as spatial working memory tasks Lalonde,  and objeWe found that approximately 88% of the CA1 cells we detected were active (fired at least once) in any given session see . If a ceThe visual stimulus apparatus we used in this study consisted of phosphorescent tape formed into patterned structures on the walls of the floating track. This resulted in scotopic illumination conditions, which correspond to decreased spatial acuity  manifold learning approach, after observing that it performed better than Principal Components Analysis (PCA) in terms of variance accounted for, however we verified qualitatively that a similar picture was provided by both PCA and Isomap see , and expIn this paper we have validated a platform technology which, by combining multiphoton calcium imaging in head-fixed mice with floating track implementation of a behavioral task, allows robust and sensitive readout of memory encoding and retrieval performance from the activity of hippocampal neurons. As well as enabling advances in cognitive neuroscience, we expect this tool to be of great utility for the study of mouse models of neurodegenerative disorders, and a powerful tool for aiding the pre-clinical development of therapeutics for these diseases.The raw data supporting the conclusions of this article will be made available by the authors upon reasonable request.The animal study was reviewed and approved by Imperial College Animal Welfare Ethical Review Board and authorized under Home Office Project License 70/9095.MG and SS designed the experiments. MG worked out the technical details of the experiments. MG and JR performed the experiments with support from YL. MG processed the experimental data and performed the analysis with support from CD, GG, SP, and SS. MG and SS wrote the manuscript. MG and SS supervised the project. All authors discussed the results and commented on the manuscript.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


As the development of core outcome sets (COS) increases, guidance for developing and reporting high-quality COS continues to evolve; however, a number of methodological uncertainties still remain. The objectives of this study were: (1) to explore the impact of including patient interviews in developing a COS, (2) to examine the impact of using a 5-point versus a 9-point rating scale during Delphi consensus methods on outcome selection and (3) to inform and contribute to COS development methodology by advancing the evidence base on COS development techniques.Semi-structured patient interviews and a nested randomised controlled parallel group trial as part of the Pelvic Girdle Pain Core Outcome Set project (PGP-COS). Patient interviews, as an adjunct to a systematic review of outcomes reported in previous studies, were undertaken to identify preliminary outcomes for including in a Delphi consensus survey. In the Delphi survey, participants were randomised (1:1) to a 5-point or 9-point rating scale for rating the importance of the list of preliminary outcomes.Four of the eight patient interview derived outcomes were included in the preliminary COS, however, none of these outcomes were included in the final PGP-COS. The 5-point rating scale resulted in twice as many outcomes reaching consensus after the 3-round Delphi survey compared to the 9-point scale. Consensus on all five outcomes included in the final PGP-COS was achieved by participants allocated the 5-point rating scale, whereas consensus on four of these was achieved by those using the 9-point scale.Using patient interviews to identify preliminary outcomes as an adjunct to conducting a systematic review of outcomes measured in the literature did not appear to influence outcome selection in developing the COS in this study. The use of different rating scales in a Delphi survey, however, did appear to impact on outcome selection. The 5-point scale demonstrated greater congruency than the 9-point scale with the outcomes included in the final PGP-COS. Future research to substantiate our findings and to explore the impact of other rating scales on outcome selection during COS development, however, is warranted.The online version contains supplementary material available at 10.1186/s12874-020-01197-3. Recently, there has been an increase in the development of core outcome sets (COS) to overcome the heterogeneity in outcome selection across clinical trials for a broad spectrum of health conditions . A COS iInvolving patients/health service users from an early stage is recommended in COS development; however, the most appropriate way to facilitate inclusion remains largely unknown , 7. PartAfter the preliminary list of outcomes has been identified, the Delphi technique is the most commonly used method for rating the importance of these outcomes for including in the COS . The DelTo determine if including patient interviews as an adjunct to systematic review for identifying the initial list of outcomes influences the final COS.To evaluate the use of a 9-point versus a 5-point rating scale in the Delphi phase of a COS development study on the number of “important” ratings received for each outcome in each round of the Delphi and on the final COS, as well as their impact on attrition rates and their ease and clarity of use.To inform and contribute to COS development methodology by advancing the evidence base on COS development techniques.The objectives of this study were:This study was embedded within the Pelvic Girdle Pain (PGP-COS) study and its protocol was published prospectively . EthicalAn International Steering Committee with members from five countries, including researchers, clinicians, and methodologists worked on the development of this COS. The day-to-day conduction of the study was performed by a project team of three people  working at the same institution  who designed and addressed key aspects of the study. The other members of the Committee were involved in conducting interviews, participated in meetings to discuss the progress and monitor the conduct of the study and provided consultation regarding critical decisions.n = 5), Sweden (n = 5) and Mexico (n = 5), were undertaken to seek patient’s views on their treatment needs and PGP outcomes that were important to them. Participants were recruited via physiotherapy and chiropractic clinics and provided written informed consent for taking part in the interviews. The phase of the study was descriptively qualitative .Interviews of 15 women with experience of PGP, either presently or previously, in three countries; Ireland  working group to assess the importance of evidence and is the scale used in DelphiManager, a web-based system designed by the COMET Initiative for facilitating and managing Delphi surveys , 26, 27.www.allocate.monster). When a participant accessed the link created by the tool, the background code selected one of the two versions of the questionnaires at random and redirected the participant to that version. The randomisation method was simple randomisation [All Delphi participants were randomly allocated to one of the two survey versions when they clicked the link to participate in round 1 of the Delphi study. Random survey allocation was achieved using a random redirect tool published online  and 7+ (9-point survey)]. In round 1, participants were also given the opportunity to suggest up to a maximum of three additional outcomes using a free-text response. The additional outcomes identified from both survey versions were combined so that all participants using either survey version had the opportunity rate these outcomes in round 2, in addition to rerating all outcomes included in round 1. After round 2, only outcomes that reached a priori consensus, that was ≥70% participants scoring the outcome as “important” [4+  or 7+ ] for 3/5 stakeholder groups, inclusive of the patient representative group, were included in the round 3 Delphi surveys. During the Delphi phase, each survey was treated independently. Following round 3, all outcomes that reached a priori consensus on either of the survey versions were included in a preliminary PGP-COS. These two preliminary PGP-COSs were then combined as one list of outcomes and presented at the face-to-face consensus meeting, where key stakeholders  voted on the outcomes for inclusion in the final PGP-COS. Additionally, at the end of round 3, questions on the ease of use, ease of understanding, and the clarity of the scale were posed to participants, with an opportunity to provide any additional comments about the survey using free-text.The embedded trial was based on an opportunistic sample of all 205 participants involved in the Delphi phase of the main PGP-COS development project. Therefore, no sample size calculations were performed as statistical analysis was intended to be exploratory and formative.To investigate objective 1, we analysed the following: the number of new outcomes identified in patient interviews, how the interview-derived outcomes were rated in each Delphi round, the number of interview-derived outcomes included in the final COS and the extent to which additional outcomes provided by patients in round 1 of the Delphi survey overlapped with the interview-derived outcomes. All descriptive statistical analyses were performed using Excel (Microsoft Excel 2016).We used descriptive statistics (counts and %) for demographic and survey response data. To investigate objective 2 we analysed the following: the proportion of outcomes reaching a priori consensus in each survey in each round, the proportion of outcomes included in the preliminary COS for each survey, differences between the scales whether the outcomes included in the final COS had reached consensus in each survey and attrition rate for each scale. Z-scores were calculated to test the differences in proportion of outcomes reaching a priori consensus and overall attrition between the surveys with alpha set to 0.05 using the formula below :\documeWhereby p1 = proportion from 5-point survey; p2 = proportion from 9-point survey; n1 = number of possible outcomes from 5-point survey; n2 = number of possible outcomes from 9-point survey; and p = pooled proportion. All statistical analyses were performed using Excel (Microsoft Excel 2016) . Scale “Figure n = 1); patient/researcher (n = 2); patient/researcher/clinician (n = 1); patient/clinician/service provider (n = 1)).The fifteen patient interviews identified 23 outcomes for inclusion in the round 1 Delphi questionnaire. Fifteen of these outcomes overlapped with outcomes identified from the systematic review, and eight were new outcomes . The patDuring round 1 of the Delphi study, patients suggested 16 additional outcomes using the free-text option, of which, 11 were considered actual outcomes by the PGP-COS study steering committee representatives  and four (36%) as new outcomes which were subsequently included in round 2 of the Delphi study. Six of the 11 outcomes (55%) overlapped with outcomes identified in the systematic review and 1 outcome (9%) overlapped with a patient interview-derived outcome. None of the additional outcomes suggested by patients were included in the final PGP-COS. Table Participant demographics for all three rounds of the Delphi study can be viewed in Table p = 0.01) and round 2  of the Delphi study. After round 1, consensus was reached on 41 outcomes (77%) on the 5-point survey compared with 29 outcomes (55%) on the 9-point survey. After round 2, consensus was reached on 37 of the 68 round 2 outcomes (54%) on the 5-point survey compared with 20 outcomes (29%) on the 9-point survey. After Delphi round 3, there was a significant difference in the proportion of outcomes included in the preliminary PGP-COS between the two survey versions . The resulting 5-point preliminary PGP-COS included 24 outcomes ; contrastingly the 9-point scale provided 11 outcomes (16%) for the preliminary PGP-COS. Ten of the 11 outcomes (91%) on the 9-point preliminary PGP-COS overlapped with outcomes from the 5-point preliminary PGP-COS. All five outcomes of the final PGP-COS were included in the 5-point preliminary PGP-COS, whereas, four outcomes from the final PGP-COS were included in the 9-point preliminary PGP-COS.There was a significant difference in the proportion of outcomes reaching a priori consensus between the two scale versions in round 1 . Attrition rate for respondents using the 5-point scale was 25% between round 1 and round 2, 9% between round 2 and round 3 and 32% overall. Attrition rate for respondents using the 9-point scale was 32% between round 1 and round 2, 11% between round 2 and round 3 and 39%. Table There was no difference in overall attrition between the two surveys  of people said the 5-point scale was easy/very easy to use while this was 51% (33/64) for the 9-point scale. However, for the 9-point scale people had to scroll across to see the all options which was commented on by 10 (16%) people as not being practical. For the 5-point scale, four people (6%) suggested more options would be useful while one person would have preferred a 3-point scale. For the 9-point scale, eight people (13%) thought there were too many options. Feedback on the clarity of both scales revelated that 87% (61/70) of respondents who provided feedback (as this was a non-obligatory question) found the 5-point scale very clear/clear to understand whereas this was 73% (47/64) for the 9-point scale.This is the first embedded methodological study to examine the impact of patient interview-derived outcomes and to examine the comparison between a 5-point and 9-point rating scale on the development of a COS. Our embedded study found that none of the outcomes derived from the interviews only were included in the final PGP-COS. The rating scale used in the Delphi consensus process did influence the proportion of outcomes rated as “important” in the Delphi rounds and could potentially impact on a final COS whereby more or less outcomes would be available in the final COS consensus meeting.It is plausible that conducting patient interviews to identify the initial list of outcomes for inclusion in the Delphi phase of COS development may not be as important as one might have thought, and especially, if resources and time are limited. At the face-to-face consensus meeting, it was discussed that three of the interview-derived outcomes were not deemed “absolutely critical” to be measured and reported in all trials as unique outcomes, but could be captured by two outcome measures that were included in the final PGP-COS. For example, ‘sexual functioning’ was considered important but participants agreed that this was covered in the outcome ‘functioning/disability/activity limitations’ of the final PGP-COS. This is important to consider when identifying the most appropriate instrument to measure these outcomes. It is essential that the instrument used measures all aspects of the outcomes relevant to patients. While patient interview derived outcome data were not included in the final COS in this study, they could still be valuable in providing insight into why outcomes are important to inform the consensus meeting. Overall, this highlights the importance of patient participation throughout the Delphi study and in face-to-face consensus meetings in the development of COS on “what” to measure as well as in later stages such as the development of “how” to measure the COS and suggests that it may not be necessary to conduct patient interviews to identify the initial list of potential outcomes.Additionally, we identified that only one patient interview-derived outcome overlapped with the additional outcomes suggested by patients during round 1 of the Delphi study. However, due to our study design, the true extent of overlap between these outcomes remains unknown, as the initial list of potential outcomes was an amalgamation of the outcomes identified from the systematic review and interviews. We cannot ascertain if the other interview outcomes would have been suggested by patients if they were not included in the initial list that was presented during round 1 of the Delphi. Recommended Delphi methodology includes providing participants with the opportunity to suggest additional outcomes during the first survey round and asking participants to re-rate the initial list of outcomes in the subsequent round. A potential method for future studies to examine the full extent of overlap between these two outcomes would be to exclude any interview-derived outcomes from initial voting in the first round and introduce them in the second round of voting. This may offer more understanding on the extent of overlap between patient interview-derived outcomes and patient suggested additional outcomes in Delphi studies. It is plausible that patient interviews at the initial outcome identification stage are redundant if patients are included in the Delphi survey and in the consensus meeting.In our study, the 5-point scale resulted in twice as many outcomes in the preliminary PGP-COS after three rounds compared with the 9-point scale. This could potentially be a downside to using a 5-point scale considering that the intention in using the series of survey rounds is reductionist based on outcomes being viewed as ‘critically important’ to the COS. It could also indicate that use of this scale in this PGP-COS had less discriminatory power for the outcomes offered for rating than the 9-point scale. This result, interestingly, is in direct contrast to that of De Meyer and colleagues who found twice as many outcomes selected as “critical” on the 9-point scale compared to the 3-point scale . AlthougIn addition, our results highlight user experience concerns that research teams should consider when incorporating rating scales in COS protocols. For example, a number of our 9-point Delphi survey participants expressed that it was frustrating that they were required to scroll across to see all possible responses on the scale. This highlights the importance of user experience considerations when selecting a rating scale and also the platform in which a research group intends to use to disseminate their Delphi questionnaire, especially when a group intends to utilise e-Delphi surveys that are completed on mobile phones or computers with varying screen sizes. These considerations can be addressed with pilot testing of the surveys amongst end users before sending out the finalised e-Delphi surveys for data collection. It is important to note, however, that the comments regarding participant experience are only reflective of those who completed all three rounds of the Delphi. Additional insights could be derived from those who dropped out in earlier rounds; however, these were not collected. Future work should consider user experience during all rounds of a Delphi study. Our results also indicate the importance of designing a Delphi questionnaire that is user friendly with a goal of maintaining group involvement to completion if responses from this group is of importance. Although there was no difference in retention rates between rating scales, the patient-representative group had the highest rate of drop out on both surveys whilst researchers and clinician/researcher group representatives had the lowest attrition rates on both surveys. It is possible that researchers and clinician/researcher groups may have a stronger understanding of the implications of COS development and/or research methodology compared to other stakeholder groups, as explained by their high retention. These results combined with those from our patient interview analysis suggests that input from patient-group representatives should be considered in the initial questionnaire and provides the opportunity to include patients at an early stage in COS development. Overall, it is evident that COS development teams should also consider user experience when selecting an appropriate scale that best suits the target populations in COS methodology development.This study had several limitations. This study was embedded within one COS development project only. The inclusion of patient interview-derived outcomes in the initial list used in the first Delphi round did not allow us to evaluate the full extent of overlap between the interview-derived outcomes and the additional outcomes suggested by patients. Only two rating scales were used to study the influence of scale selection on outcomes made available to the final COS. We compared a 5-point and 9-point scale only, both of which included scale anchors for all response options, when several other scales and scale formats are used by COS developers. Additionally, participants were only asked about ease of use, understanding and clarity of scales at the end of round 3 of the Delphi. This decision was taken to avoid overwhelming participants with a lengthy survey at each round. As a result, we do not have data on these metrics from participants who dropped out in round 1 and round 2. Future work is warranted to explore the user experience across rounds with different scales. Furthermore, the proportion of outcomes rated as “important” in each round and the preliminary COS on both scales depends on the chosen definition of consensus. Therefore, the reproducibility of this study in other fields of COS development, focussing on other health related topics, different scales, larger samples and different consensus definitions is warranted. Finally, as previously mentioned, only one patient participated in our face-to-face consensus meeting due to circumstances out of our control (i.e. flight cancellations). We did, however, have members who identified with multiple groups (i.e. a researcher/patient and clinician/patient) so the patient voice was not unheard. We do not believe this influenced the final COS as all participants had the opportunity to discuss the outcomes and the facilitator actively encouraged comments on each outcome from all attendees. Equally all attendees voted independently at the consensus meeting thus ensuring stakeholder group input into all outcomes that made up the final COS.Overall, our results identified that outcomes derived from patient interviews did not directly impact the final COS in this study, but the scoring scale used to prioritise outcomes did, highlighting further methodological considerations and challenges when developing COS protocols. While this study shows that a 5-point scale may be recommended to use in Delphi consensus methods in terms of impact on outcome selection and ease of use and clarity, we acknowledge that this is one study only, and, as such, definitive conclusions cannot be drawn. Future research concerning the impact of patient-derived interviews outcomes on the final COS, overall patient involvement throughout the consensus methods and comparisons of other rating scales on the final outcome selection is needed.Additional file 1. PGP-COS Outcome Voting Each Round.


As the coronavirus (COVID-19) epidemic passed initial infection peak in Washington State, phased re-opening lifted stay-at-home orders and restrictions leading to increased non-essential work, social activities and gathering, especially among younger persons.A longitudinal cohort analysis of Washington State Department of Health COVID-19 confirmed case age distribution 1) March-April 2020  and 2) March-August 2020  for proportional change over time using chi square tests for significance.From March 1st to April 19, 2020 COVID-19 age distribution shifted with a 10% decline in cases age 60 years and older and a 20% increase in age 0-19/20-39 years . Number of cases over the initial analysis period were 0–19 years n = 515, 20–39 years n = 4078, 40–59 years n = 4788, 60–79 years n = 3221, 80+ years n = 1332. After the peak , incidence declined in older age groups and increased among age 0–19 and 20–39 age groups from 20% to 40% of total cases by April 19 and 50% by May 3. During this time testing expanded with more testing among older age groups and less testing among younger age groups while case positivity shifted young. Percent positive cases age 0-19/20-39 years through August 2020 increased to a consistent average of 60% .An increased sustained proportion of COVID-19 incidence is present among children (age 0–19) and young adults (age 20–39) indicating an elevated role in disease spread during the epidemic creating a possible reservoir of disease with spillover risk to more vulnerable older persons and those with comorbid conditions. Media savvy age-appropriate messaging to enhance mitigation compliance among less vulnerable, more mobile and lower priority vaccination age groups will be a continued necessity and priority to reduce overall population incidence. The four counties with the highest number of cases by rank order were King (n = 5955), Snohomish (n = 2300), Pierce (n = 1300), and Yakima (n = 1119). As the epidemic progressed and the curve flattened, fewer older and more young persons tested positive for COVID-19 with the percentage of total cases among age 0–19 and 20–39 doubling from 20% to 40%  to 5% (4/19/2020). Age distribution of persons tested changed by a small margin over time with an increase in testing among persons age 60 and older (+6%) and a decrease in testing among 0–19 and 20–39 year olds (-3%) from the peak (3/22/2020) to the week of 4/19/2020, a statistically significant but small change   Table 2.From the first COVID-19 peak week, 3/22/2020, to week 4/19/2020, the COVID-19 hospitalization rate declined 49% for age 40 and older cases with a 54% decline in COVID-19 incidence. During the same time period, the hospitalization rate among age 20-39-year-olds declined 35% with a 36% decline in COVID-19 incidence [Over the longer time period of April to August 2020 the shift to younger age of COVID-19 confirmed positive cases continued to a sustained average of 60% of cases age less than 40 years June-August 2020  . The chiWe analyzed trend over time from March to April and from March to August 2020. The first time period analysis was done when we had accurate testing data available from March to April and was done to determine that COVID-19 infections were present not just in older age persons but also in children and younger adults who over time became the highest incidence age group and continued to be the highest incidence age groups as the epidemic progressed. As testing expanded after the beginning of the epidemic (January/February 2020) when only symptomatic hospitalized patients were tested to any symptomatic patient testing (March/April 2020), the existence of infection in younger age groups became evident.Following COVID-19 incidence by age over time as testing expanded it became evident all age groups were affected by COVID-19 infection and predominantly the most socially active, least likely to be symptomatic younger population. Although morbidity and mortality from COVID-19 infection is highest among the infected population age 60 years and older and those with underlying conditions, the percentage of 0–19 and 20-39-year-old cases in Washington State increased over time with no decline in COVID-19 incidence among 0-19-year-olds and 20-39-year-olds compared to older age groups when stay-at-home orders were enacted at the beginning of the epidemic. Our results show that a persistently high percentage of current infections in Washington State exist among the age 0–19 and 20–39 population who may also be at highest risk of contracting and spreading the virus but not at high risk of hospitalization or mortality. During the first time period from March to April 2020, COVID-19 related hospitalization rates declined without an equivalent rate of decline among confirmed COVID-19 cases.The shift from older to younger population COVID-19 infection illustrates the absence of a true decline in cases in the first time period and instead a shift to COVID-19 test positive incidence among children and young adults. This changed the need for mitigation messaging and extended the need for health care capacity as the currently infected portion of the population is younger, less likely to report symptoms, at less risk of a severe life-threatening disease requiring hospitalization but fully capable of spreading disease to older, vulnerable adults. It appears persons 60 and older, those with pre-existing conditions and nursing home residents in particular were a harbinger of the serious nature of the epidemic but emphasis on infection in older adults only created a false dichotomy of risk at the beginning between young and old age. In Washington State 53% of the population is age 0–39 years, 25% 0–19 years and 28% aged 20–39, with King County the most populous county in the state .From August 2 to September 5, 2020, weekly COVID-19 cases among persons aged 18–22 years increased 55% nationally with greatest increases in the Northeast 144%) and Midwest (123%). Increases in cases were not solely attributable to increased testing . In Germ4% and MiAs initial public warnings in Washington State and the nation targeted the population age 60 and older and those with underlying conditions, a misconception may have been at large that only persons age 60 and older were at risk for contracting COVID-19. Children were thought to be at low risk for COVID-19 and if infected to have a mild clinical course with few or no symptoms. However, a health alert was issued early in the epidemic by the CDC for Pediatric Multisystem Inflammatory System (MIS-C) linked to COVID-19 indicating serious morbidity risk exists although rare –23. DocuFindings from a survey by Canning et al, found persons under age 50 had twice the predicted number of close contacts as persons 50 years and older . In a reAlthough children and younger adults may not be at significant risk for serious COVID-19 related morbidity and mortality, evidence supports their ability to transmit disease and higher likelihood to have asymptomatic disease with silent transmission endangering others they come in contact with, especially older adults and those with comorbid conditions. In a CDC outbreak investigation, a single 13-year-old index case was traced to 11 subsequent cases . A largeWhile testing overall increased during the period evaluated, the percentage of younger people being tested decreased slightly compared to persons age 60 and older. Absent information to link family and household level data with parent’s occupation to identify the source of exposure in the 0-19-year-old age group, one could assume some infections are driven by contact with family members working outside of the home and social interaction as Washington State public schools have been closed since March 11, 2020 current to January 21, 2021. A study by the American Academy of Pediatrics found only 10 states reported testing by age further hampering the ability to evaluate level of COVID-19 disease incidence in the population by age . TestingThe Washington State stay-at-home policy is temporally associated with the first observed case decline after March 22, 2020 . HoweverWe document a shift of the majority of COVID-19 cases from older adults to children and younger adults over the eight-week time period after the state reached enough measurable cases to analyze on March 1, 2020. Further tracking of cases by age revealed a trend to majority COVID-19 infection among children and younger adults which continued upward to a sustained plateau regardless of fluctuations in statewide infection or testing rates. Our findings indicate justifiable concern regarding the health and safety of children and teenagers that led to the decision to delay reopening schools. The disproportionate spread of disease among younger adults influenced plans for phased reopening of Washington State counties which were paused at Phase II for most counties until reduced case incidence and disease control testing, tracking and tracing metrics were met  but wereWith the shift in COVID-19 incidence to a majority of current cases in the combined 0-19/20-39-year-old age group statewide, counties with a high percentage population age 0–19 and 20–39 years are more heavily affected. Continued tracking of age trends are warranted and will help target which activities can be opened safely and what extra safeguards and protocols will be required with monitoring for adherence.We cite observational studies indicating younger persons are less likely to adhere to recommended mitigation behaviors. This is precisely why we conducted our study with our primary recommendation to tailor mitigation and other protection guidelines to meet the needs of all age groups. As the mitigation and stay at home orders in Washington State were initially targeted at persons age 60 and older and those with comorbid conditions regardless of age, the use of print media and television advertising were not tailored to reach young adults or children and their risk of morbidity and mortality was not emphasized.While adherence to mitigation behaviors appear to have curbed disease spread for a time in older and more vulnerable adults, we have seen a dramatic increase in number of cases and morbidity and mortality from disease in the dark days of fall and winter in the United States. It is logical for mitigation behaviors to be more effective in those that adhere to them. The strategy to prevent morbidity and mortality is to address the issue of continued spread of the disease which in the case of COVID-19 requires focusing mitigation efforts on persons most likely to contract and spread the disease to contain the epidemic, in this case young adults and children as they appear to be the dominant reservoir of disease and not likely to be vaccinated in the current time period.In the case of future epidemics, it is hoped early recognition of disease in all ages and races in the population would trigger multi-lingual, age-appropriate mitigation messaging to the entire population instead of targeting a singular group most at risk for morbidity and mortality exclusive of groups most at risk of spreading disease. It would also be hoped that consistent messaging from public health and governmental agencies would be coordinated to provide the basis for cohesive and successful messaging and mitigation.Specific advisories tailored to children and adolescents age 0–19 years and 20-39-year-old adults to increase awareness of COVID-19 transmission and infection are advisable to reduce overall COVID-19 incidence levels and enhance movement towards levels that will allow a continuation of phased reopening of the state and counties. As of January 20, 2021, there are no vaccines approved for children 16 and under and adults age 20–39 are not on the CDC list for phased vaccination until after frontline essential workers, older age groups, persons with comorbid conditions and other essential workers are vaccinated . Adults Modelling studies with real data simultaneously incorporating race and county location with more precise age categories would be a tremendous asset to targeting specific public health mitigation and vaccination strategies. Vaccination first priority will be for medical workers and vulnerable persons by age or condition with children and young adults a lower priority creating a continued if not elevated need for specific mitigation for years to come as we await herd immunity with full population COVID-19 vaccination achievement. As in-person education can be a vector for COVID-19 transmission to households and beyond and testing of school age children is not on par with older age groups due to lower incidence of symptomatic disease, re-opening schools for in-person learning should be approached with caution and an eye to general population COVID-19 infection and vaccination rates.


Census data was used to calculate rates of within-country migration at the district level. We calculated differences in PM2.5 exposure between “current” (2016–2017) and “origin” (2012) districts for each migration patterns. Using an exposure-response relationship for PM2.5 extracted from a meta-analysis, and mortality rates from the Peruvian Ministry of Health, we quantified premature mortality attributable to each migration pattern. Changes in outdoor PM2.5 exposure were observed between 2012 and 2016 with highest levels of PM2.5 in the Department of Lima. A strong spatial autocorrelation of outdoor PM2.5 values  was observed. In Greater Lima, rural-to-urban and urban-to-urban migrants experienced 10-fold increases in outdoor PM2.5 exposure in comparison with non-migrants. Changes in outdoor PM2.5 exposure due to migration drove 137.1  premature deaths related to air pollution, with rural-urban producing the highest risk of mortality from exposure to higher levels of ambient air pollution. Our results demonstrate that the rural-urban and urban-urban migrant groups have higher rates of air pollution-related deaths.This study aims to quantify changes in outdoor (ambient) air pollution exposure from different migration patterns within Peru and quantify its effect on premature mortality. Data on ambient fine particulate matter (PM Nowadays, more than 244 million international migrants are estimated globally5 with considerable gaps in health care services. Furthermore, a significantly larger number of internal migrants − people moving within their country of birth − has been documented4, with a particular health burden despite being nationals. Migrants are commonly exposed to a range of challenges such as low quality of labor conditions, massive vehicular traffic, sedentarism , lack of social support, limited access to healthy food choices6, and stress8. Social vulnerability from economic instability, social isolation, poor access to healthcare services, increased exposure to infectious diseases, and traumatic events related to the migration itself have also been known to affect migrants’ health14.Migration is increasingly recognized as an important determinant for population health17, including how exposure to hazards or availability of ecosystem services can lead residents’ to migrate away from these harmful environments. Although there is abundant literature studying how environmental hazards can drive emigration, only one study conducted in the United States (U.S.) has investigated how migrants may also be at a differential risk for environmental exposures. Interestingly, this study found that international immigrants in the U.S. are generally less exposed to chemical toxins, while specific groups such as Mexican immigrants in high income areas have a disproportionate exposure18. To our knowledge, no study has considered exposure to outdoor (ambient) air pollution as an environmental health risk of concern for migrant populations.In addition to these stressors, migrants are exposed to new environments, which may have further impacts. Environmental changes and exposures have been studied as drivers of migration19. The burden of air pollutants is 6.4 million years of life lost20. Most health effects of air pollution have been documented in respiratory diseases, cardiovascular diseases, allergenic diseases, and diabetes, affecting individuals throughout the life course from developing fetuses to elderly populations21. Particulate matter under 2.5 micrometers (PM2.5) is known to be one of the more harmful pollutants due to its ability to penetrate deep into the lungs and enter the bloodstream22. With major increases in urbanization and migration to urban centers worldwide, which have higher levels of air pollution and other environmental exposures, the potential health implications of these changes and burden of migrant populations merits attention23. The study of health exposures among migrants also needs to further characterize the different profiles of within country migrants, as recently exemplified in the case of obesity24.Exposure to outdoor air pollution is one of the leading environmental causes of mortality globally25 with approximately 120,000 displaced families from the Andes to the Capital city of Lima26, nowadays most rural-to-urban migration has been largely driven by economic reasons, to seek access to better services. However, slow process of formal land titles and rising property prices in Lima have led some recent migrants to poor housing conditions in shantytowns with lack of water or electricity supply27. These harmful conditions expose migrants to environmental determinants that can lead to health disparities, such as increasing labor hours and unexpected health seeking costs that greatly affect low-income populations and reinforce the poverty cycle and negative health outcomes.As a country with a high rate of internal migration and high levels of particulate matter in some regions, Peru is a unique context to study this topic. After a period of the political violence 31. In addition, several studies have shown the impact of rural-to-urban migration in Peru on infectious and non-communicable diseases43 driven by changes in diet and physical activity patterns, increased poverty, and restricted access to health care. However, limited literature addresses outdoor air pollution effects in the migrant population, and to our knowledge, no study has quantified the health burden associated with changes in exposure to ambient air pollution due to migration between different locations.Previous studies have reported the air pollution status in Peru and highlighted the important geographical variability of air contaminants2.5 collide, and finally quantify the associated changes in premature mortality using a health impact assessment methodology.In this study, we aimed to quantify changes in outdoor air pollution exposure related to different internal migration patterns  in Peru, identify spatial clusters where high rates of migrants and high levels of PM2. Major ecological areas in the country were divided into the Coast, Andes, and Jungle , 196 provinces (level 2), and 1873 districts (level 3). Data encompasses information about 29,381,884 inhabitants in an area of 1,285,216 kmCensus data was provided by the National Institute of Statistics and Informatics (INEI in Spanish) via the REDATAM platform. Information from the 2017 Census was used to calculate within-country migration rates. In the census, all inhabitants were asked to report the district where they are currently living (2016–2017), henceforth referred to as “current district” and the district where they lived 5 years ago (2012), henceforth referred to as “origin district”. Children under 5 were excluded from this question. In addition, international migrants − people with a country of birth different than Peru − were excluded to analyze only within-country migration. After exclusion criteria, the information of 26,779,984 (91%) inhabitants was analyzed.For this study, non-migrants were defined as population that reported the same origin and current district and migrants were defined as population for which the origin and current district differed. Origin and current districts were labeled as rural or urban according to the INEI National registry classification and rural-to-rural, rural-to-urban, urban-to-rural, and urban-to-urban migration status were constructed. The distribution of districts relative to rural/urban category and city type is presented in Supplementary Table https://www.R-project.org).For each of the 1873 districts the number of non-migrants, immigrants (with details on origin districts) and out-migrants (with details on destination districts) were computed. The migration rate was calculated as the number of migrants (immigrants or out-migrants independently) in each district divided by the total population in the same district. Standardized migration rates were calculated by multiplying the migration rate by the proportion of population in each district in 2017 (district population/country population). REDATAM exported data were formatted and processed using R software v.3.6.2 (R Core Team (2019). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. URL 2.5, was used a measure of outdoor air pollution in this study given the strong evidence of its effects on adverse health outcomes45. Air quality data from the NASA - Socioeconomic Data and Applications Center (SEDAC) was used46. SEDAC provided an annual global surface of concentrations (micrograms per cubic meter - µg/m3) of mineral dust and sea-salt filtered PM2.547. Although PM2.5 predictions using ground measurements and chemical transport models in addition to satellite data have been reported recently for Peru that are likely more accurate48, this data was only available for Lima  preventing comparisons between rural and urban settings. Previous studies47 demonstrated that SEDAC-NASA data were highly consistent (R2 = 0.81) with out-of-sample cross-validated PM2.5 concentrations from ground stations in the Global Burden of Disease (GBD) network49.Particulate matter with a diameter less than 2.5 micrometers, PM2.5 gridded data sets were provided at a spatial resolution of 0.01 degrees46. Most proximate datasets to the study period were used for the analysis, 2012 and 2016. We harmonized migration and air quality data at the same spatial scale, by estimating the median PM2.5 (SEDAC gridded data) at each district. Spatial boundaries of the 1873 districts were obtained from the National Institute of Geography (IGN in Spanish). Data was provided in shapefile format with tolerances of 1,000 m and 1,000,000 m2.As a result, PM50. SEDAC dataset and IGN shapefiles were uploaded to GEE as assets. For data visualization purposes, districts were classified in 6 categories according the levels of PM2.5 in micrograms per cubic meter  relative to the WHO air quality guideline51.Data processing was conducted in Google Earth Engine (GEE)52. Each district was assigned the mortality rate for the department which it is nested.Mortality data  was collected at the departmental-level from the national center of epidemiology, prevention and control of disease of the Ministry of Health of Peru2.5 was extracted from a review conducted by Hoek et al. (2013) which conducted a meta-analysis on cardio-respiratory mortality from air pollution exposure from epidemiological studies around the globe53. The overall estimate for all-cause mortality of 1.06  per 10 μg/m3 increase in PM2.5 exposure was used.The exposure-response relationship for PM2.5 concentration and its association with migration status, spatial autocorrelation analyses and a spatial Bayesian regression were conducted. First, to determine the spatial autocorrelation of PM2.5, meaning that the values of PM2.5 in each district is influenced by the values of PM2.5 on their neighborhood districts, a Global Moran’s I and Local Getis-Ord Gi* statistics were calculated using a first-order queen contiguity-based weighted neighborhood. The statistic under the null hypothesis of spatial randomness was calculated by randomly permuting the observed values over the locations. The z-distribution was computed based on spatial random data sets (the permuted data sets). No statistical testing was applied over the Gi* statistic to prevent bias due to multiple and dependent tests55. Clusters were categorized based on the Gi* statistic sign in high- (hotspot) or low- (coldspot) concentration areas and percentile  of the z-distribution. Then the migration rates in each PM2.5 cluster were analyzed.To determine the spatial dependence of PM2.5 exposure’ defined as the difference in PM2.5 between 2012 and 2016 (dependent variable) accounting for the spatial structure of the districts in Peru, a Bayesian linear model was fitted using Integrated Nested Laplace Approximation 56 for all districts in Peru (1873). A spatial model was formulated by including a spatial structure using a convolution prior that combined area-specific overdispersion and a neighborhood dependency structure58. See S1 supplementary methods for specification of prior and hyper-prior distributions.To assess the relation between migration status (independent variable) and ‘change in PM2.5 caused by internal migration. HIAs have been used to quantify the mortality or morbidity associated with air pollution in various regions60 and are a standard tool to quantify the global burden of disease. These are based on exposure-response functions (ERF) for particulate matter obtained from epidemiological studies and can be extrapolated to a population to understand the burden and inform environmental policies61.We used a Health Impact Assessment (HIA) methodology to quantify mortality attributable to change in PM2.5 in the origin district.We first estimated changes in outdoor air pollution exposure due to migration by calculating the difference for each district to district migration (“current” and “origin”) in the year 2016. This represents the counterfactual scenario in which a migrant had not migrated and remained in the same district and therefore exposed to PM2.5 impacts, using the following equation [RR-1/RR]53. This AF was assumed to be the same for all districts in Peru and was multiplied by the PM2.5 concentration difference between the “current” and “origin” district and the all-cause mortality rate for each department in which the “current” district was nested to calculate the mortality rate attributable to change in PM2.5 exposure. The “current” department mortality rate was used for analysis to account for potential changes in baseline mortality between departments, to focus only on changes in outdoor air pollution exposure. For example, for those migrants that lived in Chachapoyas, Amazonas in 2012 and moved to Chorrillos, Lima in 2016, an ambient air pollution difference was considered at the district-level by considering the change in exposure between Chachapoyas and Chorrillos. This difference in ambient air pollution exposure was then multiplied by the mortality rate for the department of Lima which was considered homogenous and therefore assigned to the district of Chorrillos, and by the AF. This was considered the excess mortality for this migrant group, and differences were calculated for each migration pattern and weighted by the number of individuals that undertook this migration route to calculate average mortality rates from air pollution exposure attributable to migration. Finally, overall number of attributable deaths were calculated by taking into account the number of migrants that undertook each district-district route. 95% Confidence intervals were computed by calculating the attributable mortality based on variability of the PM2.5 ERF. All analyses were stratified by urbanicity of the origin and current district  and by department (using the migrants’ “current” department) to consider variation in risk spatially and by migration pattern.We then calculated an attributable fraction (AF) using the risk ratio of exposure-response relationship extracted from meta-analysis of PM2.5 were observed between 2012 and 2016 in the entire country . The highest average PM2.5 was located in Greater Lima with more than 4-folds higher in comparison with rural areas  Fig.  - left, 2.5 exposure were observed in migration in comparison to non-migrants which only experienced a change in PM2.5 exposure due to environmental and social changes within the same district over time  were migrants from districts with a lower PM2.5 concentration. In Greater Lima, rural-to-urban and urban-to-urban migrants experienced 10-fold increases in PM2.5 exposure in comparison to non-migrants  was observed in the study area  of PM2.5 exposure when comparing exposure in the current to the origin district . Finally, moderate changes were observed for rural-to-rural and urban-to-urban migrants  deaths related to outdoor air pollution exposure across Peru from 2012 to 2016. When considering only those that migrate from a rural-to-urban setting, there is an increase in 118  deaths related to outdoor air pollution. The largest migrant group, urban-to-urban, resulted in an increase in 62  ambient air pollution-related deaths, while the smallest migrant group, rural to rural, results in 0.5  additional deaths. There is a decrease in number of deaths for migrants going from an urban to rural setting, with 43  fewer deaths; this is one of the smaller groups, along with rural-rural migration. Mortality rates show similar results, with rural-urban revealing the greatest change in mortality rate and rural-rural migrants showing no change  exposure and burden related to migration status. Remarkable clustering patterns were observed in the spatial distribution of PM2.5 and migration rates. These distributions are spatially correlated in densely urban areas. This study highlights that rural-to-urban as well as urban-urban migrants experience a transition to environments with more deleterious ambient air pollution levels with a higher mortality burden, which adds to other previously reported socio-economic disadvantages. Additionally, although urban-to-urban migrants experience what may seem like a small change in exposure to particulate matter, this is associated with a considerable mortality burden, taking into account the important number of migrants that undertake this route. This demonstrates that even a slight increase in outdoor air pollution exposure associated with migration can have large impacts on population health, particularly when large groups are moving to environments with higher outdoor air pollution concentrations. The results of this study are important, because 1) it is the first study to quantify the effect of outdoor air pollution exposure on migrants health, and 2) it allows an understanding mobility and exposure patterns in Peru that can be integrated into urban planning policies to protect vulnerable populations.The substantial growth of internal human population mobility in low and middle income countries is changing population distributions across geographical areas, with subsequent changes in environmental exposures including air pollution, which fosters a need to study potential health implications in this setting. Migrants are a particularly vulnerable group in this context as they are moving to areas with higher or worse environmental risk profiles, often driven by economic, socio-political or geographical motives63 and the United States64 have argued that environmental inequalities may be shaped by social mechanisms such as residential segregation and racial income inequality. These same mechanisms were hypothesized in this study. In Greater Lima, the city that harbor most internal immigrants, the highest levels of PM2.5 are located in districts with the lowest average income. Albeit less marked than observed with PM2.5, districts with the highest migration rates are concurrently the most impoverished areas. Recent studies characterized the Peruvian internal migrants as primarily low-income population65, similar to other studies worldwide67. Taken together, the evidence points to the fact that the main mechanisms that drive migration and consequently higher PM2.5 exposure levels and burden are poverty and seeking of economic and educational opportunities. Many low-income rural populations are migrating to deleterious urban environments seeking better economic and educational opportunities but are exposed to much higher levels of PM2.5 and increased mortality risk. By identifying Lima as the department in Peru that far exceeds outdoor air pollution-related mortality burden for migrants, it can be considered a priority area for targeted ambient air pollution control measures, particularly for migrating populations.Previous studies in China2.5 in non-communicable diseases (NCD), and respiratory diseases68. In Peru, increased risk of asthma69 and influenza70 were described in urban cities and migrant population, mediated by high values of PM2.5. Conditions such as soil organic matter, industry, and traffic have been suggested as drivers to the increased outdoor air pollution level which is attributed as a major determinant to the quality of life in the megacity of Lima71. As portrayed in the spatial analysis, hotspots of ambient air pollution are located on the coast of the country. Furthermore, these areas are located near main ports and heavily industrialized areas. These findings urge for new environmental health strategies to control these detrimental conditions. This study was able to exploit the different migration patterns and its associated health burden driven by changes in ambient air pollution exposure. Political commitment to address these issues will have a positive effect in reducing environmental-related health outcomes and the social and environmental gap observed in the migrant population.Previous studies have described the effects of PM72. These conditions may deter the response capacity of the migrant population to affront environmental hazards for which recent migrants are exposed. However, it is important to acknowledge that migrants, and particularly those moving from rural to urban areas, may also experience increased economic opportunities and accessibility to healthcare. For example, financial reasons and other motivations related to improving people’s well-being is a strong driver of out-migration which may have a positive impact on the overall health of the migrant62. Although there are some nuances in the health effects of migration, it was beyond the scope of this study to consider these other factors, as the main purpose of this study was to quantify the outdoor air pollution-related health burden. Ultimately, it is well established that migrant populations in Peru are highly vulnerable to diseases due to environmental, social, and economic frailty37, and ambient air pollution exposure adds to this existing burden.This study is the first to describe a positive relation between internal migration and ambient air pollution exposure, and quantify the associated burden. Is important to mention that the social structure and social capital is also altered in the migrant population78. Migration from rural to urban areas for example may have a beneficial effect on exposure to indoor air pollution due to changes in cooking fuels. Although we did not have access to such data, previous studies in Peru have found that indoor biomass stove can increase exposure to polycyclic aromatic hydrocarbons and particulate matter79. Additionally, while these are two different sources of exposure, interventions aimed at reducing residential wood burning have also been shown to reduce outdoor particulate matter concentrations showing important health benefits80.This manuscript focuses specifically on changes in outdoor ambient air pollution. However, indoor pollution from different sources such as cookstoves, is also a major health concern in Peru and globally2.5 exposure related to other internal migrant groups or external migration. However, in comparison to refugee migration, internal migration can be more precisely estimated81 due to the fact that mechanisms that influence internal migration follow long-term trends. However, the technical challenges to harmonize temporal and spatial data of internal displacements often prevents accurate quantification of internal migrants67. Furthermore, limited evidence showed the impact of internal migration on exposure to deleterious environments. The spatial autocorrelation analysis in this study has shown the large spatial effects in the relation between migration and PM2.5. These spatial effects might be due to social, economic, environmental , or behavioral characteristics in the districts that receive the most migrants. This elucidates important evidence that geographically targeted interventions may have a great impact at the macro- and micro- scales.Additional work could expand the exploration of other drivers of health and health changes, particularly for migrant populations, including other environmental exposures, potential positive changes in accessibility to healthcare and services as well as changes in PM2.5 and migration being non-stationary processes within districts. Further studies are suggested that account for fine-scale estimation of the relationship between migration and outdoor air pollution exposure. Also, despite residency exposure being a standard measure for ambient air pollution, this does not account for total exposure to air pollutants. This study did not include the exposure during work or leisure activities due to the lack of this data in the census. Additionally, we assumed a exposure-response relationship of PM2.5 based on existing literature on long-term effects of particulate matter based on studies primarily conducted in North America and Europe, while the true relationship may be different in the Peruvian context. However, the risk would be likely to be an underestimation as the majority of studies in the meta-analysis were from North America or Europe while impacts have been shown to be higher in Peru and other underrepresented countries82. Lastly, the baseline mortality rate considered for calculating attributable mortality risk was available only at the departmental level, therefore we had to assume homogeneity in mortality rates for districts within each department.Several limitations should be acknowledged in this study. First, migration estimates were computed based on whether the participant lived in the same district five years before. This cutoff is based on the census questionnaire and may represent a threat to consistency, due to the wide range from 5 years before to recent migrants (~1 day). Second, there is no question to check if participants are currently living in a permanent or temporary household, thus we cannot ascertain if they had a chronic exposure to these levels of outdoor air pollution. In the same way, migrants may arrive at a different place than where they were interviewed, and thus they may carry over the environmental exposures of all previous places where they had been settled that may yield to exposure misclassification. Third, as detected in the high-resolution images (Fig. 2.5 suggest this relation might be shaped also by social and economic inequalities. Results suggest that internal migration in Peru drives an increase in mortality from air pollution exposure, with rural-urban migrants as one of the most vulnerable migrant groups, as well as the most prevalent. The findings of this study may represent the first steps to tailor strategies to improve the health of vulnerable populations such as internal migrants. Additionally, our results demonstrate the importance of considering air pollution as an environmental risk factors when studying migrant health in Peru and beyond.This study was able to demonstrate the effect of migration on change in exposure to air pollution and quantify the mortality burden from this change. The clustered co-occurrence of high values of migration and PMSupplementary Information.


Marijuana and alcohol each play a significant role in fatal crash initiation. We decomposed the total effect of marijuana use in the presence or absence of alcohol on fatal crash initiation into direct and indirect effects.Pair-matched data on 5856 culpable drivers (initiators) and 5856 nonculpable drivers (noninitiators) involved in the same fatal 2-vehicle crashes recorded in the Fatality Analysis Reporting System between 2011 and 2016 were analyzed using the conditional logistic regression model and the unified mediation and interaction analysis framework.P < 0.001), alcohol  and both substances . Adjusted odds ratios of fatal 2-vehicle crash initiation revealed a positive interaction on the additive scale between marijuana and alcohol. Of the total effect of marijuana use on fatal 2-vehicle crash initiation, 68.8% was attributable to the direct effect  and 31.2% to the indirect effect .Crash initiators were more likely than noninitiators to test positive for marijuana (16.1% vs. 9.2%, Our results indicate that the increased odds of fatal 2-vehicle crash initiation associated with marijuana use is due mainly to the direct effect. Driving under the influence of drugs has increased over the past two decades and poses a serious threat to traffic safety in the United States  Use of marijuana can slow reaction time, impair judgement and concentration, and decrease psychomotor skills , which is maintained by the National Center for Statistics and Analysis of the National Highway Traffic Safety Board. Since 1975, the FARS has served as the census of fatal motor vehicle crashes occurring on public roads in the United States. Crashes eligible to be recorded in the FARS are those that have resulted in at least one personal fatality  within 30 days of the crash  Trained FARS analysts use standardized operational manuals and uniform coding practices to code more than 140 de-identified data elements into as many as 20 data files each year National Center for Statistics and Analysis (NCSA) In this study, driver-related factors or unsafe driver actions such as lane weaving or speeding (codes 17–60), obtained from police reports and other supporting documents, were used to assign crash responsibility  A pair-matched study design was used to assess the individual and joint effects of marijuana and alcohol on the risk of fatal 2-vehicle crash initiation. In this pair-matched study, crash initiators were drivers who were responsible for initiating the fatal 2-vehicle crashes while noninitiators were drivers who were involved in the same 2-vehicle crashes but were not responsible for these crashes. From January 2011 to December 2016, the FARS recorded a total 187,870 fatal crashes involving 280,041 drivers. Excluded from the analysis were 112,643 crashes involving a single vehicle or more than 2 vehicles, 17,753 crashes involving heavy vehicles or commercial vehicles , 49,402 crashes with missing toxicological testing results, 304 crashes in Maryland, Montana, New Mexico, and North Carolina with toxicological testing results recorded unreliably in the FARS, 1554 2-vehicle crashes in which both drivers were culpable of crash initiation, and 358 2-vehicle crashes where toxicology tests were based on urine samples  Toxicological drug tests were conducted on blood or urine specimens using liquid/gas chromatography and radioimmunoassay techniques for screening, and liquid/gas chromatography and mass spectrometry for confirmation  and 95% confidence intervals (CIs) of crash initiation associated with marijuana use, alcohol use, and other driver characteristics. To assess separate and joint effects of marijuana and alcohol, drivers testing negative for marijuana and alcohol were assigned as the reference group. The interaction of marijuana and alcohol was assessed on the multiplicative and additive scales. Additive interaction was assessed using 3 statistics: the relative excess risk due to interaction (RERI), attributable proportion due to interaction (AP), and the synergy index (S). The corresponding 95% CIs were computed using a method suggested by Zou , more likely to be male , and more likely to be involved in a crash in the previous 3 years . Drivers included in the analysis did not significantly differ from the excluded drivers with regards to survival status and other driving histories in the previous 3 years, such as license suspension, driving-while-intoxicated conviction, and speeding conviction.Compared with drivers excluded from the study due to missing or incomplete drug testing results, those included in the analysis were slightly younger , followed by failure to yield right of way 18.9%) (Table 8.9% (TabP = 0.0001) among crash initiators, and from 7.0 to 13.4% (P < 0.0001) among noninitiators. Overall, crash initiators were more likely than noninitiators to test positive for marijuana , alcohol , and both substances . Initiators were more likely than noninitiators to be under 35 years of age , to have died in the crash , and to have had a crash , a driving-while-intoxicated conviction , a speeding conviction , or license suspension  within the previous 3 years . However, there was a significant interaction on the additive scale as assessed by all 3 statistics: RERI = 1.29 , AP = 0.19 , and S = 1.29 . Of the total effect of marijuana on fatal crash initiation risk, 68.8% was attributed to direct effect  and 31.2% to indirect effect [7.8% to mediated interaction through alcohol and 23.4% to pure indirect effect (Table There was no significant interaction on the multiplicative scale as shown by the interaction term of marijuana and alcohol (β = − 0.1103, Results of this study indicate that marijuana use and alcohol use are each associated with a significantly increased risk of fatal 2-vehicle crash initiation and that concurrent use of marijuana and alcohol confers a significant interaction effect on the risk of fatal crash initiation on the additive scale. Furthermore, over two-thirds of the total effect of marijuana use on fatal crash initiation are attributed to the direct effect, including 52% due to controlled direct effect and 17% due to reference interaction. Our findings shed more light on the causal role of marijuana use in crash initiation. Specifically, the decomposition analysis indicates that the increased risk of crash initiation associated with marijuana use is operationalized through dual pathways, with 69% being attributed to the direct effect and 31% to the indirect effect. These results are generally consistent with findings from experimental studies (Crancer Jr et al. There is a paucity of research assessing the mediation effect of marijuana by alcohol on crash risk and crash initiation. Our study reveals that about 23% of the total effect of marijuana use on fatal 2-vehicle initiation is due to mediation through alcohol, which is consistent with the additive effect model of risk factors where one exposure contributes to another exposure that cumulatively increase the risk of the outcome (Bean et al. Assessing interactions between alcohol and other drugs on driving safety may help identify subgroups of drivers to maximize public health impact in resource allocation and risk reduction (Blot and Day According to VanderWeele , the uniOur study shows that the prevalence of marijuana detected in drivers involved in fatal 2-vehicle crashes has increased steadily in the past decade. This is likely due in a large part to the increased permissibility and availability as more states have enacted laws to legalize marijuana for medical and recreational use. Marijuana use is associated with impairment of psychomotor skills necessary to operate a motor vehicle safely such as reaction time which may lead to failure to yield right of way (Chihuri et al. This study had several limitations. First, testing positive for marijuana indicates marijuana use but does not necessarily imply marijuana-induced impairment at the time of crash. Given that marijuana metabolites stay longer in the urine compared to blood (AIT Laboratories Results of this study indicate that marijuana use and alcohol use are each associated with a significantly increased odds of fatal 2-vehicle crash initiation. When used together, marijuana and alcohol confer a positive additive interaction effect on the odds of fatal 2-vehicle crash initiation. The decomposition analysis shows that over two-thirds of the total effect of marijuana on crash initiation are due to the direct effect whereas the remaining is due to the indirect effect through alcohol. Given the increasing prevalence of marijuana use and concurrent use of marijuana and alcohol in the driver population, multifaceted intervention programs are needed to target driving under the influence of specific substances and driving under the influence of polysubstances.


Currently, more than 30% of the population in the gulf demonstrate a body mass index (BMI) exceeding 30. This burden of obesity has proven to take a toll on the population; therefore, we created the first Kuwait National Bariatric Surgery Database to report on bariatric surgeries performed in Kuwait.Data was collected from the six public hospitals in Kuwait. This data was then submitted to a merged National Registry. Data web portal were used to upload, merge, and analyze the data.2 for males and 43.3 kg/m2 for females. 16.4% of males and 12.3% of females presented with type 2 diabetes, while the most prevalent obesity related disease was a poor functional status in both males and females . Most procedures performed in Kuwait are sleeve gastrectomy. The most encountered in-hospital complication after primary bariatric surgery was bleeding (1.5%), with Roux-en-Y gastric bypass (RYGB) having the highest recorded rate of post-operative complications (3.6% bleeding). The overall rate of operative complications was 2.6%, which was most prevalent post-RYGB (10.3%) and lowest post-sleeve gastrectomy (2.5%).The average age for participants was 32.6 years. The average preoperative BMI was 45.9 kg/mThe importance of tracking and documenting the journey and change in the rates of obesity and effectiveness of bariatric procedures in individual countries with significantly high obesity rates is imperative to be able to create a plan of action to tackle this worldwide epidemic. This report will be able to provide the population with an accurate accounting that demonstrates further the safety of bariatric/metabolic surgery. As of 2006, the number of overweight and obese people had overtaken the number of people with malnutrition in the world , 2. In EWhen looking at numbers around the world, in 2014, the International Federation for the Surgery of Obesity and Metabolic Surgery of Obesity and Metabolic Disorders (IFSO) was able to show that the number of bariatric and metabolic surgeries performed in the Asia–Pacific Chapter was about a 1/3 of those of the other three chapters . HoweverTherefore, we thought it was imperative to create the first Kuwait National Bariatric Surgery Database Report to report on bariatric surgeries performed in Government hospitals in Kuwait, baseline obesity-related diseases, operation types, operative outcomes and status after bariatric surgery. It will be the first of its kind in the State of Kuwait, as well as the Gulf and Middle-East, and has the potential to help the bariatric community establish essential benchmark knowledge and outcomes about the patients we are operating upon, their age and gender distributions, body mass index (BMI) and the burden of comorbidities as well as track national trends in surgery over time.For the report, permission was obtained from the ministry of health to collect data from the six public hospitals in Kuwait on bariatric results and outcomes. Invitations were sent to bariatric surgeons working in these hospitals, of which 63 contributed to the data collection. This data was then submitted to a merged National Registry. A Direct Data Entry system, and an Upload-My-Data web portal were used to upload, merge, and analyze the data. Data was collected on 3302 cases, of which 2704 were primary procedures Table . Data co(i)Impaired glycaemia or impaired glucose tolerance.(ii)Insulin treatment.(iii)OAD & insulin treatment.(iv)Oral hypoglycaemics.Type 2 diabetes:(i)YesBack pain or leg pain:(i)Depression on medication.Depression:(i)Can climb 1 flight of stairs without resting.(ii)Can climb half a flight of stairs without resting.(iii)Walking.(iv)Requires wheelchair or is housebound.Impaired functional status:(i)Daily medication (H2 receptor antagonists (H2RA) or proton pump inhibitors (PPI))(ii)Intermittent medication.(iii)Intermittent symptoms; no medication.Gastro-Esophageal Reflux Disease (GERD):(i)Treated hypertension.(ii)Untreated hypertension.Hypertension:(i)Dyslipidaemia.Dyslipidaemia:(i)Fatty liver.(ii)Mild steatosis.(iii)Severe steatosis.Liver disease:(i)Yes.Sleep apnea:(i)History or risk factor for DVT or PE.(ii)Venous edema with ulceration.(iii)Vena cava filter.(iv)Obesity|hypoventilation syndrome.Increased risk of deep vein thrombosis (DVT) or pulmonary embolism (PE) contains any one or more of:We aimed to set a standard baseline for defining obesity related diseases as to be able to compare between individual patients. Positive responses (data denoting patients who have the condition) were:Male gender.Age ≥ 45 years at the time of surgery.2.BMI > 50 kg mHypertension.Risk factors for deep vein thrombosis/pulmonary embolism.The Obesity Surgery Mortality Risk Score (OSMRS) stratifies patients undergoing bariatric surgery into three categories depending on how many of the following risk factors they possess:Group A: score 0–1 (low risk)Group B: score 2–3 (moderate risk)Group C: score 4–5 (high risk)The patient is ascribed one point for each of the above risk factors and a cumulative score determined, giving a total score in the range zero to five; this score is normally grouped into one of three categories:Patients with higher OSMRS, are thought to be at a greater risk of post-operative complications and mortality. The score is only calculated when all of the required data are available in the operation record.Descriptive statistics were used for the analysis of the data. The contributors were reassured that no statistical comparison would be attempted between different units. Furthermore, as data from different hospitals may only provide variable representation of the population, no comparative analysis was performed between hospitals.On the whole, unless otherwise stated, the tables and charts in this report record the number of procedures. The numbers in each table are bolded so that entries with complete data for all of the components under consideration are shown in regular black text. If one or more of the database questions under analysis is blank, the data are reported as unspecified in bold text. The totals for both rows and columns are highlighted as italic text. Some tables record percentage values; in such cases this is made clear by the use of an appropriate title within the table and a % symbol after the numeric value.Rows and columns within tables have been ordered so that they are either in ascending order or with negative response options first  followed by positive response options .All entries with missing data are excluded from the analysis used to generate the graph. In the charts prepared for this report, most of the bars plotted around rates  represent 95% confidence intervals. The width of the confidence interval provides some idea of how certain we can be about the calculated rate of an event or occurrence. If the intervals around two rates do not overlap, then we can say, with the specified level of confidence, that these rates are different; however, if the bars do overlap, we cannot make such an assertion.Bars around averaged values  are classical standard error bars or 95% confidence intervals; they give some idea of the spread of the data around the calculated average. In some analyses that employ these error bars there may be insufficient data to legitimately calculate the standard error around the average for each sub-group under analysis; rather than entirely exclude these low-volume sub- groups from the chart their arithmetic average would be plotted without error bars. Such averages without error bars are valid in the sense that they truly represent the data submitted; however, they should not to be taken as definitive and therefore it is recommended that such values are viewed with extra caution.2 for male patients and 43.3 kg/m2 for female patients ; Hypertension; Dyslipidemia; Liver disease; Sleep apnea; Increased risk of deep vein thrombosis (DVT) or pulmonary embolus (PE) Table , Fig. 2.As shown in the Table Table The vast majority of procedures performed in Kuwait are sleeve gastrectomy, which reflects current global trends; single anastomosis gastric bypass (OAGB/MGB) is the second most common procedure. When looking at revisional surgeries, a single anastomosis gastric bypass has mainly been performed as a subsequent procedure, most commonly after a sleeve gastrectomy Fig. , Table 5The majority of these procedures are carried out laparoscopically, constituting 94% of the procedures. Open procedures are very rare (0.1%) and this approach is generally only employed in a small minority of revisional procedures Table . This isGallbladder and hernia repair were the most common additional procedures performed at the time of bariatric procedures Table .Table 7AThe most encountered in-hospital complication after primary bariatric surgery was seen to be bleeding (1.5%). When it came to looking at post-operative complications, Roux-en-Y gastric bypass has the highest recorded rate of post-operative complications, with 3.6% of patients presenting with bleeding.On average most patients were discharged home within 2–3 days after their bariatric procedure.Patients tend to go home sooner after a gastric band procedure (over 50% discharged by post-operative day 1) than after undergoing other procedures Fig. .Fig. 4PrThe overall rate of operative complications was seen to be 2.6%. The highest rate of operative complications was after Roux en Y gastric bypass  and the lowest after sleeve gastrectomy . Table .Table 8CFurthermore, this report demonstrates that bariatric procedures in Kuwait are safe for patients with a 0.0% reported in-hospital operative mortality rate. The major cardiac complication encountered post-operatively was Dysrhythmia, encountered in 15.5% of patients, with the majority 9.5%) seen in post Roux-en-Y patients. The only other major complication encountered was vomiting/poor intake post-operatively, seen in 18.9% of patients. This can be seen in Table .5% seen Achieving a milestone not to be celebrated, Kuwait finds itself as having one of the highest prevalence of obesity in the world . In addiIn the general population in Kuwait there are approximately as many obese men as there are obese women. Despite this, as previously demonstrated, about three-quarters of the surgical patient population are female. A recent analysis (2018) from the International Federation for the Surgery of Obesity and Metabolic Disorders (IFSO) Global Registry  shows thThe reported rates of obesity-related disease vary from region to region, and from country to country. The data we collected puts the reported rates in the Middle East in a wider context, and allows us to compare the rates we see in Kuwait to other regions. Generally, rates of most associated obesity-related conditions that we investigated are low in the Middle East compared to other regions; in the Kuwaiti population some rates are relatively low compared to the average for the Middle East as a whole  . FurtherWhen it came to looking at the bariatric surgeries performed, data from the IFSO Global Registry demonstrate that sleeve gastrectomy is the most commonly performed bariatric surgery operation in many countries. Where sleeve gastrectomy is less common, patients tend to have a Roux-en-Y gastric bypass operation instead. The data from Kuwait fall in the portion of the ordered distribution where rates of sleeve gastrectomy are very high. Australia, however, reported that 100% of the surgeries performed were LSG, while in Lithuania, the majority of surgeries performed were a Roux-en-Y operation, and OAGB/MGB dominated in Kazakhstan [Comparing post-operative length-of-stay from the IFSO Global Registry and the current data from the Kuwait National Bariatric Surgery Registry was able to demonstrate that after sleeve gastrectomy, half of patients in the IFSO Registry are discharged by around 1 day after the operation, whereas half of bariatric surgery patients in Kuwait are discharged by about 2 days after surgery. However, within 5 days of treatment, over 90% of patients have been discharged The differences in the two patterns of post-operative stay for the patients who have had an OAGB/MGB are quite small, especially when one considers the fact that the number of Kuwaiti patients treated using this technique is quite low at the moment, which means that the observed distribution will almost certainly change as more data are accumulated for this group.Limitations of the paper include lack of generalizability due to incomplete case ascertainment, and inability to assess incomplete or erroneous data submission, as well as incomplete follow-up data. In addition to the limitations of the Yes/No questions, there is unknown selection bias for those patients with recorded follow-up. Long-term survival is currently not known and there is a desire to collect long-term follow-up data for all patients operated upon in Kuwait. In addition some patients are transferred from other hospitals for management of complications of surgery carried out elsewhere. The outcomes of these patients are not currently collected nor are they reported here in this Report, but it would be desirable to track the outcomes of these patients in the future. Nevertheless, it is very gratifying to see that there were no reported in-hospital deaths for any patients undergoing bariatric surgery at Government hospitals during the period of study of the Registry.The burden of obesity-related disease in the operated populations is high but varies greatly between regions. The importance of tracking and documenting the journey and change in the rates of obesity and effectiveness of bariatric procedures in individual countries with significantly high obesity rates is imperative to be able to create a plan of action to tackle this worldwide epidemic. With Kuwait being one of the countries with the highest rate of obesity and bariatric surgeries, the challenges faced in the creation of this report are negligible compared to the benefits it will provide for the future of its population. Furthermore, this report will be able to provide the population an accurate accounting that would be able to demonstrate further the safety of bariatric/metabolic surgery on a generalizable basis.