Second-hand smoke (SHS) is a serious health hazard costing 890,000 lives a year globally. Women and children in many economically developing countries are worst affected as smoke-free laws are only partially implemented and homes remain a major source of SHS exposure. There is limited evidence on interventions designed to reduce SHS exposure in homes, especially in community settings. Following a successful pilot, a community-based approach to promote smoke-free homes in Bangladesh, a country with a strong commitment to smoke-free environments but with high levels of SHS exposure, will be evaluated. The study aims to assess the effectiveness and cost-effectiveness of a community-based intervention, Muslims for better Health (M4bH), with or without Indoor Air Quality (IAQ) feedback, in reducing non-smokers’ exposure to SHS in the home.khatibs) in mosques over 12 weeks (one message each week). The messages address key determinants of current smoking behaviours including lack of knowledge and misconceptions on specific harms associated with SHS exposure. IAQ feedback consists of personalised information on IAQ measured by a particulate matter (PM2.5) monitor within the home. Following adaptation of M4bH and IAQ feedback for the Bangladeshi context, a three-arm cluster randomised controlled trial will be conducted in Dhaka. Forty-five mosques and 1800 households, with at least one smoker and one non-smoker, will be recruited. Mosques will be randomised to: M4bH and IAQ feedback; M4bH alone; or usual services only. The primary outcome is 24-h mean household concentration of indoor fine particulate matter (PM2.5) at 12 months post randomisation. Secondary outcomes are 24-h mean household PM2.5 at 3 months post randomisation, frequency and severity of respiratory symptoms, health care service use and quality of life. A cost-effectiveness analysis and process evaluation will also be conducted.Based on behaviour-change theories, M4bH and IAQ feedback are designed to discourage people from smoking indoors. M4bH consists of a set of messages couched within mainstream Islamic discourse, delivered weekly by faith leaders  contains supplementary material, which is available to authorized users. Second-hand smoke (SHS) contains 4000 toxic chemicals and is a serious health hazard to non-smokers. Every year, an estimated 890,000 people die and 10·9 million disability-adjusted life years (DALYs) are lost due to SHS exposure, worldwide . A signiRecognising SHS as a public health threat, comprehensive smoke-free legislation is in place in 55 countries worldwide, including 35 low- and middle-income countries (LMICs), and covers almost 1.5 billion people (20% of the world’s population) . HoweverThere is limited high-quality evidence on the effectiveness of interventions that reduce SHS exposure in homes , especiakhatibs) and by providing Indoor Air Quality (IAQ) feedback. IAQ feedback has been used successfully with smoking parents of young children in studies in Scotland [This research focusses on community-based approaches to protect non-smoking adults and children from the harms of SHS in their homes. The proposal builds on the findings of a pilot trial conducted in England , which cScotland  and in EScotland .khatibs will be trained to encourage their congregations in mosques to change their smoking behaviours, with or without IAQ feedback, is effective and cost-effective in reducing SHS exposure in the home.This study will evaluate whether a community-based intervention called Muslims for better Health (M4bH) in which imams and The specific research objectives address effectiveness and cost-effectiveness questions (primary objective) as well as implementation questions (secondary objectives).To investigate the effectiveness and cost-effectiveness of a community-based intervention – M4bH – with or without IAQ feedback, in reducing (i) non-smokers’ exposure to SHS in the home, (ii) frequency and severity of respiratory symptoms, and (iii) health care service use; and in (iv) improving quality of life.b.To identify the mechanisms and contextual factors that are likely to influence the impact of M4bH and IAQ feedbackc.To estimate the likely costs and effects of scaling up M4bH with and without IAQ feedbackd.To develop a simple monitoring framework, that could be efficiently employed as the intervention(s) are scaled up, ande.To identify the likely obstacles to, and opportunities for, implementing and scaling up the intervention(s) and how best to work with communities and policy-makers to overcome the obstacles and maximise the opportunitiesWe will employ an effectiveness-implementation hybrid study design  that bleFigure Arm 1: M4bH intervention and IAQ feedbackArm 2: M4bH intervention aloneArm 3: Usual servicesA pragmatic, three-arm, open-label, cluster randomised controlled trial (cRCT) with concurrent economic evaluation will be conducted over 18 months in 45 mosques and their catchment communities in Mirpur area of Dhaka, Bangladesh. The three trial arms are as follows:khatibs in Dhaka, and five intervention development workshops with representatives of Ministry of Religious Affairs, Islamic Foundation (IF), Imam Training Academy, imams, khatibs, muftis and public health experts in Dhaka.The interventions have been co-produced with Muslim religious leaders and public health experts in Bangladesh, through an iterative process of adaptation of the SFH intervention developed as part of the MCLASS pilot trial MR/J000248/1)  to the B8/1  to tFor each cluster (mosque), the intervention period will last for 3 months after randomisation.khatibs in mosques over 12 weeks (one message for each week). The messages address key determinants of current smoking behaviours including: lack of knowledge on, and attitudes towards, smoking and SHS exposure by providing information on health consequences of smoking and SHS exposure including addressing any misconceptions; and perceptions about social norms by providing general information on others’ approval. The messages also target the following: prompting intentions; goal setting , self-efficacy, commitment, action planning, coping planning, and sources of social support. Each of the messages is supported by at least one verse (ayah) from the Qur’an, or Islamic faith-based decree including those on addiction, hygiene, health promotion, self-harm and inflicting harm to others, and sanctity of human life  from the Holy Qur’an and/or hadith and the order with which the messages are to be delivered over the 12 weeks, to support them in delivering the messages within mainstream Islamic discourse. Imams and khatibs in mosques randomised to arms 1 and 2 are also given copies of the M4bH intervention booklet to distribute to members of their congregation after Friday prayers or in study circles, as they feel appropriate.Imams and 2.5) concentration measurement time in their home, the mean PM2.5 concentration which will be compared to the World Health Organisation (WHO) guidance limit of 25 μg/m3 [2.5 concentrations within the home with a line representing the WHO guidance limit as a reference, and colour codes summarising levels of particulate matter during 1-h periods  , USA). The first page of the IAQ feedback leaflet will contain information on the total indoor fine particulate matter less than 2.5 μm in diameter , information about the adverse effects of SHS exposure, recommendations to reduce SHS exposure in the home, and a target that is achievable by implementing SFH rules within that home. This will be accessible to individuals with a diverse range of literacy and numeracy skills. Trial field investigators (FIs) will deliver and discuss the IAQ feedback with members of the households in person and answer any question raised, which may take approximately 10 min.The second page will have pictorial information about the level of 24-h mean PM3) were identified at baseline, plus a small number of households under this threshold if necessary. All followed up homes in all trial arms will receive details of their month-12 IAQ measurements and feedback after trial completion.Follow-up IAQ measurements will take place at 3 months and 12 months in 30 homes per mosque where high levels of SHS  and 1800 households (40 from each mosque catchment area) into the trial.Is based in the residential parts of Mirpur, DhakaHosts communal prayers (including Friday prayers)Is at least half a kilometre from another participating mosquekhatib who is a self-reported non-smokerHas an imam or Is enlisted with the Islamic Foundation (IF). These mosques will be under a government ministry and will be monitored by the governmentA mosque will be eligible if it:Is located in an area with restricted access such that it is difficult to get in and recruit householdsHas too small a catchment area to recruit at least 40 households fromkhatib who is willing to participate and deliver the M4bH interventionDoes not have an imam or A mosque will not be eligible if it:For this trial, a household is defined as a single housing unit shared by one or more individuals.Have at least one resident attending one of the participating mosquesHave at least one adult resident who smokes cigarettes or other forms of smoked tobacco  regularly (at least 25 out of 30 days/month)Have at least one non-smoking resident of any ageNot be planning to move home in the next 12 monthsFor a household to be eligible for the trial, it should:In circumstances where two or more families share a housing unit and stay together they will be enrolled as one household if all family heads agree to participate. The families will have to agree who would be the household lead for the trial.A resident is defined as an adult or child who has been staying in the home for at least the previous 3 months and plans to stay for at least one more year in the home.It uses coal and/or biomass fuel for domestic useThe household head/lead is unwilling/unable to give written informed consentOne or more families in a shared housing unit do not want/do not agree to participateA household will not be eligible if:khatibs in the trial; withdrawal processes; risks, advantages and disadvantages of participating; how the results will be used; and confidentiality issues.FIs will collect Global Positioning System coordinates for mosques that are IF enlisted. Those that are more than half a kilometre away from another participating mosque will be approached via chairs of their respective committees and other relevant leaders (e.g. ward commissioners) to seek their expression of interest in participating in the MCLASS II trial. Existing links with the IF, including its local community officers, as well as links with mosque committee members will be used in making the initial contact. All interested mosques will be visited and their committee chairs and leaders met to inform them about the trial including explaining random allocation and its purpose. If interested in trial participation, mosques will be screened for eligibility and provided with an information sheet including: the aims and objectives of the trial; a description of the trial interventions and trial arms; details about the randomisation process; the role of the mosque and imams/khatib by FIs. They will be asked to sign a written agreement to participate for their mosque and themselves. This approach is considered to be reasonable given the organisational structures within mosques based on the MCLASS pilot trial findings [Agreement to participate in the trial will be sought from the mosque imam/findings .Implementing the M4bH intervention in the mosque, should that mosque be allocated to one of the M4bH groupsFacilitating the research team (recruitment officers) in the recruitment of participants in the respective mosque catchment areakhatibs to seek their consent to take part in interviews, andApproaching mosque imams and Recording of non-identifiable mosque data according to the trial protocolAgreement to participate will be sought for:Local researchers will approach Muslim community household heads, generally men, living in the catchment area and attending prayers in the participating mosques. Local researchers will be supported in the field by supervisors and caretakers of the IF who work in the area and are known to the community.khatibs or any other relevant persons from the mosque with a written script that they will use in announcements to inform members of their congregation about the studyWe will provide imams, khatibs will introduce an MCLASS II researcher who will introduce the trial to the audience in less than 5 minAt the end of Friday sermons (just before the Friday prayer), imams/The FIs, with the support of the imams, will also visit households in the catchment areas and provide households with information about the studyA number of strategies will be used to recruit participants:Each potential household will be allocated a unique screening number, which will be used as their unique trial identifier (ID) if enrolled in the trial. The participating household members will also receive trial IDs that will be unique for each participant but will include the household number so as to be able to identify participants from the same household. The following anonymised household screening information will be collected: mosque attended and eligibility for inclusion using the eligibility criteria described above.ConsentedDeclinedReason for declining, if givenThe following will be recorded for those eligible:For those not eligible for the trial, the reason for exclusion will be recorded.khatibs and not from individual participants. This approach is proportionate given the very low risk associated with participation and the likely potential benefits of the intervention. However, written informed consent will be sought from all adult participants in the household for all other research activities.Since the M4bH intervention is to be delivered in the mosque, agreement for delivering the intervention will be sought from the mosque imams and Households with at least one adult smoker and one child: In this case, consent will be sought from all the adult residents for completing the baseline (and follow-up when requested) questionnaire. Consent will also be sought from parents/guardians of any child aged below 18 years old, to collect baseline (and follow-up when requested) data on the childHouseholds with adult residents only including at least one smoking and one non-smoking adult: consent will be sought from all adult residents for completing the baseline (and follow-up when requested) questionnaireBased on the main eligibility criteria, i.e. households with at least one adult resident who smokes cigarettes or other form(s) of smoked tobacco  and at least another non-smoking resident of any age, eligible households will fall into two possible categories:Consent will also be sought from the household head/lead for the participation of the household, including IAQ measurement which will involve installing the Dylos DC 1700 for at least 24 h in their homes before and at 3 and 12 months after randomisation. Installation will involve plugging the meter into the electricity mains in the living area of the house (excluding the kitchen) most commonly used by family members.FIs will go through the respective information sheet with potential participants during their appointment and seek consent(s) as appropriate. Participants are not offered any personal incentive for taking part in the trial; however, each household will receive Taka 200 (GBP 2) at baseline and the 12-month follow-up time point, to compensate for the time they are giving, and to cover expenses associated with powering the air-quality monitors in their home for 24 h on up to three occasions.Informed consent will be obtained prior to registration of participants and before any trial-specific baseline assessments.For all individuals who have expressed an interest and are, therefore, approached for potential inclusion, the following non-identifiable data will be recorded in secure trial databases: mosque attended; date of birth; gender; smoking status; eligibility criteria; consent given/refused; and reasons for not consenting (if given). The time taken for screening will also be recorded.All consenting individuals (and households) will be registered in a secure trial database, using their name, date of birth, address, and unique trial ID . Only the chief investigator, trial coordinator, and researchers involved in collecting, quality checking and entering data will have access to these identifiable data at any stage of the trial. For the purpose of baseline and follow-up data collection and conducting analyses, only the unique trial ID will be used, thereby ensuring anonymity of data.Paper consent and agreement to participate forms will be kept in a locked filing cabinet at the ARK Foundation in Dhaka, separate from the rest of the trial data.Catchment areaType of mosque (ethnic and religious denomination)Average estimate of number of people who attend two or more daily prayersAverage estimated size of Friday congregationAverage estimated size of study circle (men)Qur’an classAverage estimated size of Average age (self-reported by teacher) of students/children taughtAverage estimated size of study circle (women)For each mosque participating in the trial the following information will be recorded at the start of the trial:khatibs on smoking and SHS exposure.A semi-structured questionnaire will be utilised to measure the pre-intervention training knowledge of the imams/Baseline data will be collected from households, and all consenting participants in the household, once informed consent has been provided and before randomisation of their mosque, using questionnaires.Presence of outside space; number of bedrooms; number of residents (adults and children); number of resident smokers (adults and children)Self-reported smoking restrictions, or lack thereof, in the homeNumber of shops that sell tobacco products in neighbourhoodPresence of household amenities Type of fuel used for cookingPresence of mould/moisture in homes and any damage causedPresence of cattle/pets/poultry in homeMosque attendanceThe following data will be collected about the household:2.5 concentration using the Dylos DC 1700 meter.IAQ of the household will also be measured at baseline as 24-h mean levels of PMSocio-demographic variables Self-reported smoking behaviour (≥ 11 years only)St George’s Respiratory Questionnaire (SGRQ) ≥ 11 years; frequency and severity of respiratory symptoms for under 11 year-oldsHealth service useHealth-related quality of life: Attitudes, social norms, intentions and action planning, self-efficacy, and coping planning with regards to smoking and SHS exposure (≥ 18 years only)The following data will be collected about all consenting participants in the household:Since the M4bH intervention is an ‘educational’ intervention delivered at a group level, this trial is a cRCT where mosques will be randomly allocated to one of the three trial arms.Mosque, household and participant recruitment, baseline data collection and randomisation of the 45 participating mosques will be conducted over a period of 6 months. Once recruitment has ceased and mosque and household baseline data collection are complete within a particular mosque, the mosque will be randomly allocated 1:1:1 to one of the three arms using minimisation to ensure balance across the groups on the average estimate size of the Friday prayer congregation (≤ 1500/> 1500) and geographical location (wards within the Mirpur area of Dhaka). Random allocation will be performed by a statistician at the University of York not involved in the recruitment of mosques or households, thus ensuring allocation concealment.The process of randomisation will minimise the chance of selection bias. There is a possibility that mosques/or households in the control arms could be exposed to the M4bH intervention. All mosques in the trial area will be identified, including a list of mosques enlisted with the IF. A geographical map of these potential mosques will be prepared and Geographic Information System maps used to ensure that the catchment areas of any two clusters do not overlap. A buffer zone of half a kilometre between mosques will be used to minimise the risk of contamination.khatibs delivering the intervention is not possible. Outcome data collection and data analysis are also not blinded.By the nature of the interventions used within this trial, blinding of the participants and the imams and In order to minimise loss to follow-up, the household head/lead will be requested to inform the trial team of any relevant changes in the household. FIs will also be in regular contact with the household head/lead over the phone. Households will be informed about follow-up prior to the date. Information materials in the appropriate language will be provided to ensure that participants understand clearly what the expectations would be if they decide and give consent to participate in the trial.Households and participants will be free to withdraw consent and leave the trial at any time without giving a reason. Written information on who to contact if they wish to withdraw will be provided to all participants. They will be able to withdraw by letting any member of the research team know if they wish to do so. If a household or participant withdraws consent to participate, no further data will be collected from them. However, data collected up to the point of withdrawal will be retained and used in the analysis, except where withdrawal of consent for the use of this data is explicit, in which case all data will be destroyed.2.5 concentration measurement at baseline, with an average PM2.5 of 66.5 μg/m3. The United States (US) Environmental Protection Agency (EPS) considers an Air Quality Index (AQI) of 101  or more to indicate unhealthy levels of air pollutants for sensitive groups such as children and the elderly (https://airnow.gov/index.cfm?action=aqibasics.aqi) [2.5 of 35 or more; among these households the mean  was 75.9 (44.2) μg/m3.Baseline air particulate data were considered for the households recruited from the first six MCLASS II study mosques to be randomised into the trial. Of the 240 recruited, 222 households had at least 22 h of PMics.aqi) . Eighty 2.5 of 35 μg/m3 or more. If there are more than 30 households in the mosque with average baseline PM2.5 of 35 μg/m3 or more, then 30 will be randomly selected for PM2.5 follow-up at 3 months, with randomly selected reserves for those households that are lost to follow-up at this time. If there are less than 30 households in the mosque with average baseline PM2.5 of 35 μg/m3 or more, then the shortfall will be made up of randomly selected households with PM2.5 under 35 μg/m3. The same households followed up at 3 months will be contacted for follow-up again at 12 months. We shall assume a 20% attrition rate at 12 months (the primary time point).We propose to recruit 45 clusters (mosques) and 40 households with at least one resident smoker per cluster, and shall follow up up to 30 households per mosque with average baseline PM3 to 62.5 μg/m3) assuming a SD of 45, between each intervention group and the control group, using a two-sided 5% significance level. In practice, we would expect to have greater than 90% power with this sample size by virtue of adjusting the analysis for baseline PM2.5 concentrations, which we would expect to be predictive of the follow-up measurement. We have not accounted for this potential pre-post correlation to ensure that the calculation is conservative, and to minimise the risk of the trial being underpowered. In the trial analysis, we may wish to compare the two intervention arms. The difference between these two arms is likely to be much smaller than one we could expect to observe between one of the intervention arms and the control arm. With these figures, we will retain 80% power to detect a smaller effect size of 0.2 between the two intervention groups, assuming a pre-post correlation of 0.6.The intracluster correlation coefficient (ICC) for salivary cotinine level was negligible in the MCLASS trial (< 0.01) , but we 2.5 levels are 35 μg/m3 or above (we envisage approximately 75% (n = 30) households per cluster) and (2) in a small random sample of households where 24-h mean PM2.5 levels are below 35 μg/m3, as described in the ‘Post-randomisation outcome data will be collected from households: (1) where 24-h mean PM2.5) at 12 months post randomisation.The primary outcome will be 24-h mean household indoor SHS concentration measured as fine particulate matter less than 2.5 μm diameter  a low-cost particulate counter validated for use in domestic settings [2.5 levels measured on the first day compared with levels measured over the following period of up to 6 days. This suggests that installation of these monitors for 24 h will provide a good representation of SHS levels within that home.PMsettings . Data frSetting up the Dylos: 24-h mean PM2.5 concentrations will be calculated for each household before randomisation, and at 3 and 12 months post randomisation. Within each household, the Dylos will be plugged into the electricity mains in the living area of the house (excluding the kitchen) most commonly used by family members, at least 1 m away from any doors, windows, or obvious potential sources of PM2.5. The Dylos will be switched on to start the logging process at the beginning of each data collection period and will be left to measure and log 1-min particle number concentrations for the duration. The monitors will be supplied with 6-h backup batteries in case of power cuts or brown-outs. Devices record IAQ every minute and enable estimation of SHS concentrations with 1-min resolution over the sampling period. The team is aware of the sensitivities of measuring IAQ in homes and has experience of doing so in various LMICs including Mexico, Pakistan, Indonesia, Chad, Bangladesh, and India [nd India . The teaAnalysing data from Dylos: data from the Dylos machine will be downloaded to a desktop portable computer at the ARK foundation in Dhaka using the AFRESH software version 4.2 at the end of each sampling day. Dylos particle number concentrations will be converted to equivalent PM2.5 mass concentrations and corrected for non-linearity of response. Entry and exit times for each venue will be matched to the sampling day record sheets and an average PM2.5 concentration will be calculated for each household. Feedback graphs  of the validated SGRQ  will be Quality of life: at 3, 6, and 12 months post randomisation the EQ-5D , 34 willHealth services use: a health service utilisation questionnaire previously used in the MCLASS pilot trial , and adaMediators of intervention effectiveness: mediators of intervention effectiveness will be quantitatively investigated focussing on those constructs that map onto the intervention logic model that are: attitude, social norms, intentions and action planning, self-efficacy and coping planning with regards to smoking and SHS exposure. These will be assessed at baseline, and at 3, 6, and 12 months’ follow-up using a pre-tested questionnaire, from adults (aged 18 years and over only)The following will be measured for each member of the household:Number of residentsBuilding environmentNeighbourhoodPresence of mould/moisture in homesUse of gas for cooking or gas/kerosene/oil heater.Mosque attendance and receipt/participation in M4bH programmesIn addition to socio-demographic variables, other confounders identified through the literature a priori will also be measured including:khatibs who will deliver the trial intervention:Pre- and post-intervention training knowledge on smoking and SHS exposure using a semi-structured questionnaireThe following will be measured for the imams and Data will be collected at baseline, and 3, 6, and 12 months post randomisation as indicated in Fig. A total of 16 FIs will screen, recruit households, and collect baseline and follow-up information from 1800 households in 18 months. The FIs will receive 3 days’ training on trial procedures including taking informed consent, administering and completing the questionnaires, delivering IAQ feedback, and ethical issues such as autonomy of households and individual participants on making decisions about participation, freedom to withdraw from the trial without giving any reason or consequence, privacy, confidentiality, anonymity. They will work in pairs during recruitment and baseline data collection for the first few mosques (approximately six mosques) and then work individually with each FI working with one mosque at a time.https://projectredcap.org/software/). Collected data will be stored on a central database server.Data will be collected using paper-based questionnaires designed specifically for the trial. Collected data will be quality checked and entered into the secure, password-protected database designed specifically for this trial on REDCap (Research Electronic Data Capture), a secure web application for building and managing online surveys and databases  and categorical data as counts and percentages. No formal statistical comparisons by trial arm will be undertaken on baseline data.The trial will be analysed and reported according to the Consolidated Standards of Reporting Trials (CONSORT) guidelines extension for cluster trials . A detaiScreening recruitment, and retention data for mosques and households will be summarised, and a CONSORT flow diagram produced. Reasons for non-eligibility/participation will be reported, where available. Follow-up and withdrawal rates at each time point will be presented by randomised group, with reasons for withdrawals given where available.All outcome data will be summarised descriptively by randomised group and time point.2.5 at 3 and 12 months post randomisation will be compared between the groups using a linear covariance-pattern mixed model incorporating the two post randomisation time points and controlling for pertinent baseline covariates at the household and cluster level  and the factors used in the minimisation (cluster-level)). Clustering at the mosque level will be accounted for using a random effect. The correlation of observations within households over time will be modelled by a covariance structure. The mean difference at 12 months will serve as the primary outcome, and the difference at 3 months as a secondary outcome. Residuals will be checked for normality and transformations for the PM2.5 data (e.g. log) will be considered. Parameter estimates and corresponding 95% confidence intervals will be presented.Twenty-four-hour mean household PMWe hypothesise that a combination of the M4bH intervention and IAQ feedback is more effective in improving IAQ than usual services. Therefore, the comparison between the M4bH intervention plus IAQ feedback (arm 1) and usual services (arm 3) will serve as the primary comparison, whilst the comparison between M4bH intervention alone (arm 2) and usual services (arm 3), and between M4bH intervention plus IAQ feedback (arm 1) and M4bH intervention alone (arm 2), will serve as secondary investigations.To account for non-compliance with the intervention, a complier average causal effect (CACE) analysis  will be 2.5 concentrations, with an additional random effect for household if feasible. Self-reported smoking behaviour and restrictions will be summarised descriptively per arm.Respiratory symptom questionnaire scores from months 3, 6, and 12 will be analysed in an analogous way as PMTo explore the potential for mediating mechanisms, the primary analysis model will be repeated, but this time including the mediator of interest as the outcome. The primary analysis will then be repeated including the mediator as a fixed effect. We will be looking for the intervention effect being reduced and the mediator effect being large. Further details including a full list of potential mediators to be explored will be pre-specified and detailed in the statistical analysis plan. These analyses will be exploratory and interpreted accordingly.An incremental cost-effectiveness analysis will be conducted to estimate the value for money afforded by the M4bH intervention with and without IAQ feedback over and above usual care.The costs of providing M4bH with and without IAQ feedback will be calculated. Costs will include the staff time required to deliver the M4bH intervention and the cost of materials used. Additional costs in the IAQ feedback arm include the costs of the Dylos DC1700 and the time taken to provide feedback to the household.The costs of training of individuals to deliver the intervention will be calculated. Training requires staff time of the trainer plus staff travel cost. Staff time is based on the salary of the trainer and allocated on a cost per minute basis plus costs of materials. Training costs are divided by the number of trained adults at each site. Training cost per adult benefits from economies of scale whereby cost per adult decreases as the number of adults trained increases at a site.Health care resource use data for each participating member of the household will be collected in all three trial arms at baseline and 3, 6, and 12 months’ follow-up. This data will allow the calculation of cost profiles for each individual based on local unit costs of care which are multiplied by quantities of resources consumed to calculate a per individual cost.EQ-5D , 34 willThe outcome for the cost-effectiveness analysis will be QALYs at 12 months and the cost-effectiveness analysis will be performed at this time point. The costs will include intervention cost and the cost of health resources during the 12-month period post randomisation. QALYs will be calculated during the same time period. An incremental cost-effectiveness analysis will be performed to estimate the incremental cost-effectiveness ratios (ICERs). Both costs and QALYs will be combined to calculate the incremental cost per QALY. ICERs will be calculated for M4bH and IAQ feedback over and above usual care, M4bH intervention only over and above usual care, and M4bH plus IAQ compared to M4bH alone.Sensitivity analysis is conducted by varying the cost components of the intervention to estimate the robustness of the cost-effectiveness ratios. Uncertainty around the decision to adopt the intervention will be assessed through a non-parametric bootstrap re-sampling technique. Bootstrapping has been proposed as an efficient approach for calculating the confidence limits for the ICER as its validity does not require any specific assumptions with regard to the underlying distribution. A cost-effectiveness acceptability curve (CEAC) will be plotted based on the outcomes of the 5000 bootstrap replications .No interim analyses will be conducted.The process evaluation will be carried out concurrently with the effectiveness and economic evaluations. The three key functions of a process evaluation for an effectiveness trial, identified by the MRC guidance for process evaluation  – mechan2.5 scores) will be interviewed to explore key issues that emerged in the questionnaire; for example, any messages worded within the mainstream Islamic discourse that were seen to be particularly influential or inappropriate. Based on the team’s previous experience, to avoid participant fatigue with data collection, these interviews will be administered a few days after the questionnaire.To capture the views and experiences of participants, all household head/lead participants in both intervention arms whose homes have been included in the follow-up sample will complete a short questionnaire at the end of the intervention, as part of month-3 follow-up, exploring which components of the M4bH intervention and IAQ feedback they engaged with, the acceptability of each component and any perceived benefits/non-benefits to themselves and their families including those which were unanticipated. A purposive sample of 15–20 participants, a mix of men and women from households who have/have not achieved SFH  the imams and A fidelity index, mapped onto the behaviour-change techniques  that undThe quantitative data from the short process evaluation questionnaires will be analysed using descriptive statistics.khatibs will be transcribed verbatim and translated into English and analysed using the Framework approach which is designed to address applied programme and policy-related questions [The qualitative data from the short process evaluation questionnaires will be analysed using content analysis . Interviuestions . NVivo 1uestions .The intervention fidelity data will be analysed descriptively.Based on a recent review  of the sA budget impact analysis will be conducted using secondary data to calculate the number of households who might benefit from the implementation of the M4bH intervention, both with and without feedback, and the associated cost of monitors. Based on estimates of the numbers of households, the budget impact of providing the M4bH intervention to all households who might benefit, both with and without a monitor, will be estimated. This component is important as it will demonstrate the potential cost which would be taken into account when assessing the affordability of intervention rollout.A simple monitoring framework which could be efficiently employed as the intervention gets disseminated widely will be developed. An expert panel will first examine the evaluative framework used in the effect and economic evaluation within MCLASS II and develop a consensus on a set of measure that could replace it during scale-up of M4bH.khatib interviews during process evaluation, will be presented to policy-makers, development partners, and respective faith-based and civil society organisations in a final ‘Way Forward’ workshop. The finalised intervention resources will also be presented. Participants will be facilitated to consider ways to overcome the identified blocks to implementation and to plan how to optimise opportunities for effective scale-up.The findings of the two activities above, as well as the imam and The MCLASS II study focusses on reducing SHS exposure in homes as a means of reducing the burden of lung diseases in LMICs. There is a need for measures to protect non-smokers, particularly women and children from SHS exposure within the home. This study addresses a major evidence gap in this area, which is partly responsible for no clear guidance on how to implement smoking restrictions and protect non-smokers from SHS exposure in homes.khatibs –to their congregations, with or without IAQ feedback. If found to be effective in changing smoking behaviour in homes, such an approach could shift the existing smoking norms, i.e. reducing visibility of smoking in social spaces and de-normalising it for children who might otherwise take up smoking. Previous studies in South Asia (one in Pakistan and another in Bangladesh) showed promise in community-based approaches in reducing visibility of smoking in social spaces and promoting SFH [The study evaluates an innovative intervention comprising messages to promote SFH embedded within the Islamic discourse and delivered by faith leaders – imams and ting SFH , 49. In With IAQ measurements for 1800 households at baseline, the study will provide a large dataset on the magnitude of SHS exposure within the home, as well as any key issues around IAQ measurement, in Bangladesh. IAQ feedback has a potential to bring about change in smoking behaviour in or around the home for some individuals , thus cakhatibs training curricula; and (3) producing effective dissemination materials, i.e. policy briefs, publications in high-impact journals, presentations and seminars at conferences, press releases, media reports, web-publishing, and social media feeds. It is anticipated that the results of this study will be published in 2020.The effectiveness-implementation hybrid design utilised in this study has a distinct advantage of allowing for the gathering of data on the delivery of an intervention during an effectiveness trial that inform its potential for implementation and scaling up in the ‘real world’. MCLASS II will be conducted with active engagement of policy-makers, a range of implementers and target communities. The intervention and its implementation will be tailored to the local context, which will be informed by qualitative research within target communities. A strong political will exists among policy-makers who are fully aware of the disease burden associated with SHS, the relevant gap in policy and its implementation, and the potential benefit of addressing SHS exposure in homes. Their engagement and interest in this and the other two MRC-funded studies on SFH in Bangladesh is evidence of the priority given to this area. Costs will be measured, economic modelling of scaling up SFH conducted, and an efficient monitoring framework developed. These are key success factors for implementation and scale-up . MoreoveAll these elements of the study have the potential to influence policy and practice on tobacco control, particularly SHS exposure, in Bangladesh


After describing the major results obtained with these omics technologies—including some with a high relevance for HIV-1 treatment—we discuss the next steps that the community needs to embrace in order to derive new actionable therapeutic or diagnostic targets. Only integrative approaches that combine all big data results and consider their complex interactions will allow us to capture the global picture of HIV molecular pathogenesis. This novel challenge will require large collaborative efforts and represents a huge open field for innovative bioinformatics approaches.In this review, we present the main large-scale experimental studies that have been performed in the HIV/AIDS field. These “omics” studies are based on several technologies including genotyping, RNA interference, and transcriptome or epigenome analysis. Due to the direct connection with disease evolution, there has been a large focus on genotyping cohorts of well-characterized patients through genome-wide association studies (GWASs), but there have also been several  HIV remains a major global public health issue, having claimed more than 35 million lives so far. In 2017, 940,000 people died from HIV-related causes globally . The actDue to the impact of the disease in the Western world, HIV research has been the subject of intense efforts for the past 35 years and has helped in promoting several new research technologies, in particular with high-throughput studies from the “omics” era.In this review, we will present large-scale studies based on various technologies that have been undertaken to tackle HIV molecular etiology and their main results. These large-scale studies encompass mainly genomics with genome-wide association studies (GWASs) based on genotyping chips and with exome sequencing, transcriptomic studies from SIV and HIV patient cells, and small interfering RNA (siRNA) studies in sensitive cell lines. We will also briefly describe the results obtained from proteomics and epigenomics screenings.3 or as the appearance of opportunistic infections or some cancers . Depending on the individual, AIDS, the most advanced stage of the infection course, can occur within a few months to several years after HIV infection, with an average of around 8 years in the Western world. This stage has been defined by the Center for Disease Control (CDC) either as a drop of CD4 T-cell count below 200/mm cancers . Quite e cancers . For insIf the CD4 cell count was the major marker to follow HIV-1 infection and immune deficiency in patients in the early 1980s, the progress of molecular biology techniques has made it possible to measure precisely HIV-1 viral load in the blood  by the late 1990s. Together with the CD4 cell count, this marker has become very useful to evaluate the status of an infected patient, either a low viral load suggesting a good control of HIV-1 infection or a high viral load suggesting a progressive infection at an early stage of infection or an uncontrolled infection at a late disease stage (AIDS). Most cohorts focus on viral load outcomes , but (slow and rapid) progression phenotypes have also been defined based on the CD4 counts .HLA (CCR5 gene (CCR5-Δ32)  and genes encoding HIV restriction factors  but were only partially replicated according to the phenotype and cohort tested. The functional interpretation for most of these variants is yet to be discovered, but the detailed account of each candidate gene is beyond the scope of this article and has been covered by previous enlightening reviews  is statistically associated with the presence or absence of a genetic variant, the corresponding gene or its product may be involved in the molecular mechanisms of viral infection/dissemination. Genetic association studies can thus provide new clues on the molecular mechanisms of infection and disease progression and, in the long run, identify new targets for the development of new therapeutic or diagnostic strategies. Initial studies have focused on candidate genes such as HLA , and a lCR5-Δ32) , but othpromoter  or in thCR2 gene  were alsCR2 gene . Other c reviews . Overall reviews .apriori. Since the 2007 publication, GWASs have taken the place of candidate gene studies in AIDS. More than 20 GWASs focusing on various phenotypes and cohorts have been published, and It was only in 2007 that the first large-scale genetic association studies or GWASs have been published with the seminal publication by Fellay et al. that focused mainly on viral load at set point as the main phenotype of interest . These lThe following major conclusions can be underlined from these GWA studies:HCP5 gene within the MHC region, which was described in nearly full linkage disequilibrium (LD) with the HLA-B*5701 allele  both for the viral load phenotype and also for the non-progression phenotype: rs2395029, an SNP in the 1 allele . This HL and TNF . Similar control . Beyond uropeans . This −3uropeans , which curopeans  and throuropeans .CCR5-Delta32 in resistance to infection but did not identify any additional common genetic variant , gathered up to 6,300 HIV-infected individuals and 7,200 HIV-uninfected controls who were genotyped on various Illumina or Affymetrix platforms. A first publication emerged from this consortium in 2013; it focused on HIV-1 acquisition and confirmed the major role of  variant . A seconnd HLA-B . In spitnd HLA-B .CXCR6 gene was associated with long-term non-progression in four independent cohorts  and hardly take into account the possible effect of rare variants or other classes of genetic polymorphisms such as indels or copy number variants that can also significantly impact disease outcome. To investigate the impact of such variants, we first focused on low-frequency SNPs (<5%) in our progression GWASs and identified the gene n factor . Later, n factor , which fnfection .invitro most known genes one by one in HIV permissive cell lines. These screenings identified 273  offers a unique chance to identify host genes required for HIV replication. In large-scale studies, authors used siRNAs  or shRNAfied 273 , 213  receptor association factor 4 (TRAF4), and cell cycle regulating genes such as CDKN1A (p21) and CCND2  co-infected subjects  or interacting gene variants. It could also be explained by the statistical constraints (such as stringent multiple testing corrections) that limit the use of genetic association data  and the overall low number of samples at stake (a few thousands) compared to other human diseases such as diabetes or obesity (hundreds of thousands)  . In lighousands)  or by thousands) . More geousands) . Anotherousands) . Other mousands) . Importaousands) . These nWith the massive research effort to fight AIDS, this has been a true field of experimentation and development for novel technologies. A first challenge is now the cross-usage of all this information gathered from so many large-scale studies, to transform this “gold mine” into diagnostic or therapy strategies to fight AIDS, and the same integration of omics big data should of course take place also for other human diseases. This systems biology challenge has not yet been met. A second challenge is to pursue the exploration of alternative technologies such as epigenomics or proteomics to derive more understanding of HIV-1 molecular pathogenesis. We hope that the AIDS field will remain a “cultural” leader for research progress in order to fully understand the molecular mechanisms at stake in HIV-1 infection and AIDS and allow the rationale development of diagnostic and therapeutic strategies to finally tackle the HIV-1 virus.SLC, SL, and J-FZ conceived this review, performed the bibliography search, and wrote it in a collective manner.The authors have had their work funded from several sources. This review was directly funded by their employer: Conservatoire National des Arts et Métiers et Ecole Centrale de Nantes.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.


Heterogeneity of echo-texture and lack of sharply delineated tissue boundaries in diagnostic ultrasound images make three-dimensional (3D) registration challenging, especially when the volumes to be registered are considerably different due to local changes. We implemented a novel computational method that optimally registers volumetric ultrasound image data containing significant and local anatomical differences. It is A Multi-stage, Multi-resolution, and Multi-volumes-of-interest Volume Registration Method. A single region registration is optimized first for a close initial alignment to avoid convergence to a locally optimal solution. Multiple sub-volumes of interest can then be selected as target alignment regions to achieve confident consistency across the volume. Finally, a multi-resolution rigid registration is performed on these sub-volumes associated with different weights in the cost function. We applied the method on 3D endovaginal ultrasound image data acquired from patients during biopsy procedure of the pelvic floor muscle. Systematic assessment of our proposed method through cross validation demonstrated its accuracy and robustness. The algorithm can also be applied on medical imaging data of other modalities for which the traditional rigid registration methods would fail. Medical image registration is the process of aligning two or more radiological images in two or three dimensions . It has A common application of 3D image registration is the longitudinal monitoring before and after an interventional procedure. Ultrasound imaging is attractive for longitudinal monitoring because it is inexpensive and noninvasive. 3D volumetric registration of ultrasound images is considered more challenging than other medical image modalities such as Computed Tomography and Magnetic Resonance Imaging (MRI)  because 3D volume registration methods can be generally classified into two types: rigid registration using linear transformation and non-rigid registration using nonlinear transformation to compensate for deformation of structures , 19. ForExisting 3D ultrasound registration methods are either feature-based –24, inteCompared to intensity-based methods, feature-based approaches are recognized to be more robust to intensity variation and less susceptible to local minima , howeverExisting registration approaches , 36–38 aOur proposed method is called M3VR, for Multi-stage, Multi-resolution, Multi-volume of interest Volumetric Registration. It is a two-stage, weighted multiple volume-of-interest (VOI) registration method. M3VR can be applied on volumetric data of any medical imaging modality that contain rigid non-deformable structures such as bone as well as deformable tissue such as muscle. In the first stage, a rigid registration is applied on a single VOI containing rigid structures to find initial alignment. In the second stage, multiple VOIs that are distributed across the whole volume and do not include the anatomically different regions are selected to add influence of soft tissue structures. Then a weighted multiple VOI registration is performed to calculate the optimal alignment of all these VOIs in order to refine the alignment obtained from the first stage.We evaluated our M3VR in a 3D Endovaginal Ultrasonography (3DEVUS) application for assessing and monitoring pelvic floor disorders, which affect nearly 24% of US women , 40. PelCompared to MRI, ultrasonography has the advantage of being fast, cost effective, and easily available. 3DEVUS provides a comprehensive overview of the pelvic floor anatomy by performing a 360° field-of-view scanning . QuantitIn this paper, we first describe the pelvic floor data used in this study and the M3VR method in Section 3. Validation metrics and procedure are also discussed. Section 4 presents our results on the application of M3VR on 3D endovaginal ultrasound data, parameter determination, as well as validation of the results. In Section 5, we discuss limitations of the proposed method and future directions of this work.As part of a previous study, we collected 3DEVUS data from subjects who had different degrees of levator ani muscle injury and underwent biopsy . 3DEVUS The proposed study involved secondary analysis of 3DEVUS data collected previously from patients who had different degrees of levator ani muscle injury and underwent biopsy. The previous study was approved by the Institutional Review Board at the University of Oklahoma in 2012 enroll 85 patients who were scheduled for surgery. Informed consent were obtained from all participants who met the eligibility criteria. Ultrasound volumes obtained from research and clinic patients were kept on secure servers. We also maintained a database of this population on a secure server. A data share agreement was made among the University of Oklahoma, Inova, and George Mason University for secondary analysis of the data. The data was anonymized by Dr. Shobeiri before being presented to the rest of the team so that the US volumes were analyzed blindly to other patient information.The long term goal is to apply quantitative ultrasound analysis to assess pelvic floor muscle injury, which correlates muscle characteristics obtained from statistical ultrasound texture analysis at the biopsy location to those obtained from histologic analysis from the needled biopsy sample. The biopsied tissue was not visible in the ultrasound volume obtained during biopsy because of the needle. However, it was completely visible and intact in the pre-biopsy data. In order to establish an accurate correlation, the needle location needs to be determined in the pre-biopsy data such that quantitative ultrasound analysis can be performed on the exact biopsied tissue. A precise spatial registration method that optimally aligns the ultrasound volumes with needle inside (D2 and D3) to the volume without needle (D1) is required to objectively estimate biopsied tissue location. Although these volumetric data were acquired from the same subject, optimal alignment is not a trivial problem. D1, D2 and D3 were taken at different time during the procedure with ultrasound probe repositioned for each dataset. There was unavoidable displacement of the probe by the clinician, which caused tissue deformation near the probe and variable transformation of the imaged volume. These volumetric data were also subject to significant tissue change due to insertion of the needle and development of post-biopsy hematoma.VF. D1 and D2 are defined as the moving ultrasound volumes VM, which are to be aligned with the fixed volume D3. In the first stage, an intensity-based multi-resolution rigid registration method was performed on the pubic bone sub-volume to provide an initial alignment of the moving ultrasound volumes (D1 and D2) to the fixed ultrasound volume (D3).The M3VR framework is shown in As shown in In the second stage, spatially weighted, intensity-based, multiple VOIs, and multiple resolution registration was performed to optimize the alignment between transformed D1 and D2. The sub-volume ultrasound data at the biopsy site can be obtained from the transformed needle location for further analysis. We performed extensive validation to ensure the robustness and accuracy of the proposed method. The whole registration pipeline was implemented in C++ and ITK .The M3VR method optimizes alignment by combining multiple volumes of interest that accommodate different anatomical structures. The main idea is to select multiple sub-volumes that do not contain changed anatomical structures. If these sub-volumes are dispersed over the volume, the bias of fitting is unlikely to occur. The pixel intensity values of different anatomical structures may have different ranges. This needs to be considered in the overall similarity measurement. For instance, bone appears brighter than muscle in ultrasound images and thus has higher intensity values. To adjust the contributions of different structures in the combined cost function to enforce either fairness or preference of alignment, weights are applied on the selected volumes of interest to reflect their significance. We will now describe the methodology in detail.Tθ was computed by optimizing the registration cost function In the first stage, we apply the intensity-based volumetric registration method to find the matching homologous regions across the fixed and moving ultrasound volumes demonstrated in ϕ in VF and the moving VOI VM. Multi-resolution registration is performed to enhance the registration robustness against ultrasound image texture variation [The object function ariation . Three iVF and VR represent voxel intensities at position  in fixed volume VF and registered volume VR respectively. M × N × T is the total number of voxels in the VOI. MSE is based on the assumption that the pixel intensity of the same homologous point remains constant. Smaller MSE indicates more precise registration.Mean Square Error (MSE) computes the averaged pixel-wise intensity difference:NCC) computes pixel-wise cross correlation normalized by the square root of the autocorrelation of the two volumetric images:NCC is between 0 and 1. NCC equal to 1 means that the two volumes have a linear relationship between intensities and thus indicates an optimal alignment. Misalignment leads to a smaller NCC value.Normalized Cross Correlation (MI) measures how much information that a random variable captures about another random variable. MI is calculated based on the entropy of each random variable. Mutual information between two random intensity variables of two volumetric images IF and IR is defined as:H(IF) and H(IR) are the entropies of random variables IF and IR respectively. H is the joint entropy of IF and IR. From our experiments, NCC gave the best registration results and was used on all datasets. A quantitative comparison of these three metrics is presented in Results.Mutual Information :w1, w2 and w3 are the weights applied on different VOIs which sum to one. These weights are introduced for two reasons: (1) ultrasound image pixel intensities of different anatomical structures such as bone and muscle are different, which influences the overall similarity measurement between two volumes. (2) the calculated similarity is also sensitive to VOI size, which is variable among selected VOIs. In order to manage impact of these factors, weight scalars are multiplied to the corresponding similarity measurements so that their contributions to the overall similarity can be adjusted. Values of the weights and sizes of the VOIs can be determined systematically through trial and error. The values leading to the best registration result are used.We developed a weighted multi-regional registration scheme to refine alignment of the whole volume in the second stage to compensate for the registration bias due to the single and local VOI considered. Multiple VOIs that include the pubic bone  to quantitatively evaluate the registration accuracy. Close proximity of the two sets of boundaries indicates effective registration. We use the Mean Distance Error (MDE) to evaluate the boundary proximity. After manual segmentation of the boundaries, 3D boundary points were calculated based on the spatial resolution of the volumetric data :pF is the point on SF that is closest to pM. E is the Euclidean distance between points pM and pF. The shortest distance map of a surface encodes the one-sided errors of all points on it. A shortest distance map associated with small values indicates the closeness of two surfaces whereas a shortest distance map with large values reflects greater misalignment. We used the shortest distance map  which qurface SM —green toe SM  after registration. Normalized cross correlation score increased from 0.77 ± 0.07 to 0.96 ± 0.01 (p <.0001) and mutual information score increases from 0.89 ± 0.37 to 2.27 ± 0.05 (p < 0.0001). All three scores show statistically significant alignment improvement after registration.The computational time of M3VR is mainly affected by sizes of the VOIs and quality of the initial registration. The closer the two volumes are after the initial registration, the less time it takes to converge to the minimum in the second stage. The overall registration time of M3VR on the night datasets included in our study took between 234.20 to 1243.73 seconds of CPU time on a Dell T7600 workstation . ConsideIn this study, we developed and validated M3VR, a volumetric image registration approach to optimally align multiple ultrasound data sets containing anatomical differences. The main contribution is that the method is able to optimally align volumetric datasets that are subject to significant variation at multiple locations. Our results on 3D endovaginal ultrasound data from women undergoing surgical biopsy demonstrated the feasibility and effectiveness of the proposed method. With minor modification on the similarity measurement, M3VR can be applied on registering 3D or 2D data of other medical imaging modalities, such as MRI and CT. 3D to 3D, 2D to 2D, 2D to 3D or 3D to 2D registrations can all be formulated in our framework. Furthermore, M3VR can perform inter-imaging modality registration and inter-subject registration.M3VR does not require any image intensity threshold to be specified but relies on the raw intensity values to find the optimal alignment. The only user inputs are size and location of each VOI. From the literature and our experiment, these parameters play an important role in the registration performance. Therefore, similar to many other registration approaches, parameter tuning is a necessary step to ensure the best results. What we found was that the optimal parameter values were fairly consistent across different datasets. Therefore, there is no need to fine-tune VOI parameters for every dataset but only for one or a few representatives. Furthermore, the process can be automated by systematically varying the parameter values within a reasonable range in the computer program while exporting the registration accuracy of each case for offline analysis. The program can be set to test all feasible parameter sets in one run. Then the optimal parameter values associated with the smallest error can be identified and used for all other datasets.We experimented with both feature-based and intensity-based registration. We decided to perform intensity-based registration. The main reason of using image intensities and not features was that it was difficult to robustly and automatically extract anatomical features of the inner boundary of the pubic bone and outer boundary of the levator ani muscle from all datasets. The advantage of intensity-based registration is that potential error or parameters involved in feature extraction are avoided. The limitation is that many intensity-based similarity metrics such as mean square error and the adopted normalized cross correlation, implicitly assume pixel-to-pixel intensity value correspondence. This may not hold even in the optimal alignment of medical image data . AlthougAs mentioned earlier, the pelvic floor volume being scanned was completely rigid mainly due to the removal and reposition of the ultrasound probe. However, our method did assume rigid relationships among the multiple VOIs, which unavoidably introduced error in similarity measurement. Future work may consider a hybrid approach that allows affine transformation between any two VOIs while assuming rigidity of each VOI. Spatial relationships of the VOIs can be used as priors to initialize and constrain their relative transformation to avoid overfitting if all VOIs are registered independently.Implemented in C++, M3VR took up to 21 minutes to register two high resolution volumetric datasets (800 × 800 × 650). Although it is not as fast as some of the existing volumetric registration algorithms that are able to complete in seconds or a few minutes , 32, 35,


Cognitive impairment is common in older people admitted to hospital, but the outcomes are generally poorly understood, and previous research has shown inconsistent associations with mortality depending on the type of cognitive impairment examined and duration of follow-up. This study examines mortality in older people with any cognitive impairment during acute hospital admission.Prospective cohort of 6,724 people aged ≥65 years with a structured cognitive assessment on acute admission were included in this study. Cognitive spectrum disorder (CSD) was defined as delirium alone, known dementia alone, delirium superimposed on known dementia, or unspecified cognitive impairment. Mortality associated with different types of CSD was examined using a non-proportional hazards model with 2-year follow-up.On admission, 35.4% of patients had CSD, of which 52.6% died within 2 years. After adjustment for demographics and comorbidity, delirium alone was associated with increased mortality in the 6 months post-admission  and again after 1 year . Patients with known dementia  had increased mortality only after 3 months from admission  compared with patients with unspecified cognitive impairment after 6 months . Similar but partially attenuated associations were seen after adjustment for functional ability.Mortality post-admission is high in older people with CSD. Immediate risk is highest in those with delirium, while dementia or unspecified cognitive impairment is associated with medium- to long-term risk. These findings suggest that individuals without dementia who develop delirium are more seriously ill (have required a larger acute insult in order to precipitate delirium) than those with pre-existing brain pathology (dementia). Further research to explain the mortality patterns observed is required in order to translate the findings into clinical care. The rising prevalence of cognitive impairment driven by rapid population aging is a growing public health concern and presents major challenges to all health services, including hospitals. Cognitive impairment has been reported to be present in 26%Cognitive impairment in hospital inpatients can be due to a number of overlapping conditions.8People with various individual CSDs have been shown to have a high mortality in many studies internationally.We can hypothesize that patients with any CSD are at increased risk of death and that associated physical illness severity (the precipitant of delirium) results in increased early risk of death. In addition, the presence of CSD in older people is strongly correlated with low functional ability, which in itself is associated with poor outcomes following hospital admission.The aim of this study was to use a large population-based data set of older people admitted to hospital to examine the mortality risks associated with different types of CSD up to 2 years after hospital admission adjusted for known confounders including patients’ functional ability.This is a prospective cohort study of people aged ≥65 years admitted as a medical emergency between January 1, 2012 and December 31, 2013, with 2-year follow-up, using linked national data.NHS Fife provides care to a varied urban and rural population of ~360,000. During the study, all emergency medical admissions were via a single acute medical unit (AMU) in a 640-bedded district general hospital. Trained specialist nurses assessed people aged 65+ years on admission using a locally developed Older Persons Routine Acute Assessment (OPRAA) based on the principles of comprehensive geriatric assessment.An incident cohort was defined as those aged 65+ years who had received an OPRAA assessment during the 2-year study period and had no previous acute medical admission in the prior 6 months. The incident cohort aimed to identify individuals at the beginning of a new interaction with acute hospital services and follow them through their subsequent care capturing all re-admissions and mortality. We used clinical judgment to define this, with a consensus that a patient with no admissions in the previous 6 months was starting a new interaction.Data for all eligible incident admissions were identified from the Scottish Morbidity Records 01 (SMR01) data, which is a validated NHS Scotland routine data set recording admission and discharge dates and destinations, and discharge diagnosis using ICD-10 codes. Discharge diagnosis (excluding dementia) from all previous admissions was used to calculate each participant’s Charlson Comorbidity Index (CCI) on admission.22The OPRAA data set was used to identify patients with an OPRAA assessment completed, which recorded CSD  prescribed to the patient 84 days prior to admission.23Box 1Definitions of cognitive spectrum disorderDelirium was defined as a clinical diagnosis of delirium made by the trained specialist nurse completing the OPRAA.Known dementia was defined as documentation during the OPRAA assessment of the presence of a preadmission diagnosis of dementia from self/informant report and/or hospital and primary care records; OR a prior ICD-10 code for dementia recorded during an acute hospital (SMR01) or psychiatric admission (SMR04); OR prior community prescribing of a drug for dementia .Delirium superimposed on dementia was defined as the presence of delirium in a patient with known dementia.Unspecified cognitive impairment was defined as an Abbreviated Mental Test (AMT) score <8 in people with no delirium and no known dementia.8Categorization of functional statusPersistently low-ADL Changed-ADL group (pre-ADL score ≥5 and current-ADL score <5)Persistently high-ADL (both pre- and current-ADL scores ≥5).Functional status was assessed during OPRAA using the activities of daily living (ADL) assessment of six basic activities: eating, bathing, dressing, toileting, transferring , and continence adding up to a maximum score of 6.Abbreviation: OPRAA, Older Persons Routine Acute Assessment.All patients were followed up from the date of their index admission to the earliest of their date of death or 2 years follow-up. Death was ascertained from the Community Health Index data set (CHI – the NHS Scotland population register), which was also used to define participant age, sex, and postcode-defined socioeconomic status (measured using quintiles of the Scottish Index of Multiple Deprivation) on admission.26The CHI number (the NHS Scotland unique patient identifier) was used to deterministically link SMR01 to CHI, OPRAA, SMR04, and community dispensed prescribing.Data on delirium diagnosis was missing in 3.7% of cases in the incident cohort. Based on OPRAA alone, 9.8% of cases were recorded as having known dementia and 20.3% of cases had missing data for dementia. After adding information on dementia from SMR01, SMR04, and prescribing data sets, the percentage of people with known dementia was 15.3%, with the rest of cases being treated as absence of dementia. 20.9% of cases had a missing AMT score within OPRAA, of which 15.5% had neither delirium nor dementia; these were classified as not having any CSD. 27% of ADL scores within OPRAA had missing values. Multiple imputation based on PROC MI in SAS was used to impute the missing ADL categories based on five imputations and assuming data were missing at random.Summary statistics based on numbers and the corresponding proportions for categorical variables and mean with standard deviations for continuous variables were used to describe prevalence of the different CSDs in older people admitted to AMU and how this varied with their demographics. Characteristics of older people in the CSD groups were examined in terms of CCI , number of drugs 84 days prior to admission , and ADL function .Data analysis was carried out using SAShttps://www.dundee.ac.uk/hic), with analysis of anonymized data carried out in an ISO27001 and Scottish Government accredited secure safe haven. HIC Standard Operating Procedures have been reviewed and approved by the NHS East of Scotland Research Ethics Service, and consent for this study was obtained from the NHS Fife Cal-dicott Guardian.38Data provision and initial management including linkage was carried by the University of Dundee Health Informatics Centre  had an OPRAA. The mean age for patients in the incident OPRAA cohort was 79.2 years, 56.3% were women, and 7.4% were admitted from a care home. About 20.5% of patients lived in the most deprived fifth of areas, whereas 14.6% lived in the most affluent fifth.CSD was present in 35.4% of the incident OPRAA admissions. Delirium alone was present in 15.8%, known dementia alone in 7.8%, delirium superimposed on dementia in 7.6%, and unspecified cognitive impairment in 4.2% of admissions.Table S1). We considered all these differences large enough to potentially influence any observed association between CSDs and mortality. There were no major differences by socioeconomic deprivation.People with CSD were older than those without (Table S1).In general, the presence of any CSD was strongly associated with low functional ability with 81.0% of patients with CSD having a persistently low-ADL or changed-ADL, compared to 41.8% of patients without a CSD patterns of ADL varied by CSD, with over 50% of patients with known dementia having a persistently low-ADL, whereas almost 50% of patients admitted with delirium alone had a changed-ADL at admission . In the adjusted model, associations with the numbers of drugs dispensed were weaker and less consistent, and social deprivation was found not significant and removed from the model.Patients’ sex, age, care home residence, and CCI were significantly associated with mortality with non-constant HRs providing a better fit over the 2-year follow-up . These associations weakened in the period 1 month to 2 years . Reflecting the strong correlation between the presence of CSD and functional decline .Patients with persistently low-ADL and changed-ADL were at higher risk of death in the first month following admission compared to those with persistently high-ADL  and the complete cases data (Table S5). The HR estimates obtained from the model fitted to the imputed data are comparable to the HR estimates obtained from the complete cases data with the exception of HR estimates for patients with delirium after 1 year from admission. When applied to the complete cases only, the non-proportional survival model adjusted for ADL indicated that patients admitted with delirium are not at an increased risk of death after 6 months from admission until the 2-year end of follow-up time , whereas based on the imputed data set, this group of patients are still at risk between the 1 year and 2 years follow-up time , a result which is consistent with the results of the unadjusted model, or the model adjusted for demographics and comorbidity only , the situation being completely reversed after 3 months in these two groups of patients . In turn, mortality risk associated with delirium superimposed on dementia was not different from dementia alone for the whole follow-up time . Moreover, patients admitted with an unspecified form of cognitive impairment had a reduced risk of death compared to patients with known dementia alone in the period between 3 and 6 months from admission .HR estimates comparing mortality among the different CSD groups are presented in In this study, over a third of people aged ≥65 years with an incident admission to the AMU had a CSD, of which more than half died in the 2 years after admission. People with CSD at the time of admission had a higher mortality than people without CSD, which is not explained by differences in age, sex, comorbidity, care home residence, or functional status. The study found that the risk of death was not constant over the 2-year follow-up time but varied with the underlying CSD condition; compared to people without CSD, those with delirium have an increased risk of death in the short term following admission, while people admitted with delirium superimposed on dementia, dementia alone, or unspecified cognitive impairment have increased medium- to longer-term risk.These findings are consistent with Inouye’s concept that delirium occurs in the face of both precipitating and predisposing factors.Table S3). The patients in our study had a long post-admission follow-up time; however, the use of the non-proportional hazards model allowed appropriate estimation of short- and long-term risk of mortality associated with the different type of CSD, information that is critical to health carers to appropriately manage this group of inpatients.An important strength of the study is the examination of all types of CSD in a large, unselected population of people aged ≥65 years admitted to hospital as a medical emergency who received a structured cognitive assessment on admission, and with complete 2-year follow-up of mortality after admission using linked data from routine clinical practice. The study included 6,724 incident patients with a new episode of acute hospital care, which is more than the total patients in all studies included in the most recent systematic reviews of dementiaThe main limitation is that all admissions were based within the same health care system, which may limit generalizability although this is true of most previous studies. In addition, by design OPRAA assessment was not offered to patients with brief admissions or patients who were critically ill, meaning that only 72% of incident admissions in the study period were included. Again though, this is similar to most consented research cohorts which often exclude patients who are admitted for <24 hours and which have similar proportions of all admissions included.Other limitations of the OPRAA cohort16.20,33 have reported significant associations between delirium and mortality at 1 year, whereas others have identified significant associations with short-term mortalityA number of previous studies have examined mortality in hospitalized people with CSD. Some studies on deliriumFunctional decline has been identified as an important outcome in people with CSD,One-third of medical inpatients have a CSD at admission and form a highly vulnerable population. Half will die in the 2 years after admission. Notably, after adjustment, having delirium alone is associated with higher early mortality during the 6 months after admission, whereas dementia  had no early increase in mortality but did have higher mortality after 3 months and unspecified cognitive impairment only after 6 months. The findings are suggestive that those with unspecified cognitive impairment may have early or undiagnosed dementia requiring follow-up after discharge to clarify diagnosis and optimize care. Additionally, this study does not support previous suggestions that delirium superimposed on dementia is associated with particularly poor prognosis, after adjusting for age and other covariates. This would be consistent with the role that established dementia plays as a predisposing factor for delirium onset, meaning that delirium in patients with dementia occurs with less severe physical disease precipitating factors and therefore that dementia rather than delirium dominates associations with mortality. In turn, in the absence of dementia, individuals require a larger acute insult in order to precipitate delirium, being more acutely unwell at the time of their delirium onset. Further research to explain the mortality patterns observed is required.https://www.dundee.ac.uk/hic/).The data controller of the data analyzed is NHS Fife. Patientlevel data are available subject to standard information governance requirements for use of anonymized, unconsented NHS data (