To quantify the economic burden of bacterial antimicrobial resistance in Thailand and estimate potential savings from improving the rate of appropriate empiric treatment, where effective coverage is provided within the first days of infection.Cost-of-illness study.A cost-calculator, decision-tree model was developed using published data and records from 3 Thai hospitals for patients hospitalized with antimicrobial-resistant infections between 2015 and 2019. Direct and indirect costs of antimicrobial-resistant infections were assessed over a 5-year time horizon, with outcomes derived separately for cases having received appropriate empiric treatment versus inappropriate empiric treatment. In a real-world scenario, outcomes were estimated using actual rates of inappropriate empiric treatment, and in a hypothetical scenario, outcomes were estimated using decreased rates of inappropriate empiric treatment.Over 5 years, in-hospital antimicrobial-resistant infections produced costs of approximately Thai baht (THB) 66.4 billion (USD 2.1 billion) in the real-world scenario and THB 65.8 billion (USD 2.1 billion) in the hypothetical scenario . Most costs were attributable to income loss due to in-hospital mortality  and hospitalization .In-hospital antimicrobial-resistant infections produced a substantial economic toll in Thailand. This public health burden could be reduced with a strategy aimed at decreasing the rate of patients receiving inappropriate empiric treatment. Although AMR is a global issue, the burdens of AMR are greatest in low-resource settings and the prevalences of resistant bacteria, resource availability to treat antibiotic-resistant infections, and antimicrobial prescribing practices vary across countries and regions.Antimicrobial resistance (AMR) contributes to substantial morbidity, mortality, and economic costs The Thai National Strategic Plan on Antimicrobial Resistance  was finalized in 2016 as Thailand’s first coordinated national policy to combat AMR. NSPAR laid out strategies to prevent and control infections, reduce mean antimicrobial consumption, and improve appropriate AMR drug-prescribing practices, aiming to reduce AMR morbidity by 50% and AMR drug consumption in humans by 20% by 2021.In Thailand, AMR is considered an urgent health problem with widespread impact. Consequently, the full extent of the AMR burden in Thailand is unknown, and progress toward NSPAR goals cannot be reliably assessed. Despite implementation of some NSPAR initiatives, effective integrated AMR management in hospitals remains a challenge due to limited national leadership in implementing AMR initiatives, an insufficient number of hospital health professionals with knowledge of AMR, and delays in implementing AMR initiatives due to the COVID-19 pandemic.As of early September 2022, baseline AMR data for Thailand are still being developed in parallel with an integrated AMR surveillance system. In one study based on 2010 data, resistant pathogens were associated with 32% of in-hospital infections and accounted for 71% of in-hospital infection deaths, with AMR-related premature deaths accumulating costs estimated at Thai baht  100–107 billion (USD 3.2–3.5 billion). THB 100 billion (USD 3.2 million) was equivalent to 0.9% of Thailand’s 2010 gross domestic product (GDP).Although little is known about the economic impact of AMR in Thailand, available evidence suggests that burdens of AMR-related illness are substantial. for antimicrobial-resistant bacterial infections, where effective coverage is not provided within the first days of infection, could contribute significantly to the AMR burden by increasing mortality, lengthening hospital stays, and increasing direct costs relative to receiving appropriate  empiric treatment (AET). However, few studies have addressed the additional burden imposed by IAET for antimicrobial-resistant infections in Thailand. Two studies reported that educational interventions in Thai hospitals reduced inappropriate antimicrobial prescribing, but these studies combined all types of inappropriate microbial prescribing  and did not specifically address the impact of IAET for bacterial infections.Receiving inappropriate  empiric treatment (IAET) Evaluations of the cost burden of AMR and the specific effect of IAET in Thailand could fill the knowledge gap on the current situation and may help to inform future antibiotic stewardship in Thailand. We calculated the economic burden of AMR in Thailand and estimated the potential economic impact of decreasing the rate of IAET in resistant infections.Economic analyses conducted at a local level are necessary to provide a more realistic and contextualized cost picture of AMR because they can consider localized epidemiological priorities and health service norms. where more infections would be covered appropriately within several days of infection onset.A decision-tree model was developed to evaluate the costs of illness for in-hospital antimicrobial-resistant bacterial infections in Thailand. A real-world scenario estimating the actual burden of antimicrobial-resistant infections was compared with a hypothetical scenario estimating how this burden would be reduced with decreased rates of IAET, The model estimated the burden of in-hospital antimicrobial-resistant infections for the entire population of Thailand over a 5-year time horizon. Model inputs were derived from published studies, Thai national data, and 2015–2019 hospital data obtained directly from collaborators.The study adopted a societal perspective, considering both direct costs of healthcare resource use and indirect costs of productivity loss due to patient hospitalization and premature mortality. The societal perspective was chosen to understand the impact of AMR on the welfare of the whole population. AET was defined based on Infectious Diseases Society of America clinical practice guidelines for both the class and duration of antibiotic therapy. Patients were considered to have received AET if their antibiotic treatment met these guidelines and covered all index pathogens on the index date or ≤2 days later. Appropriate therapy received ≥3 days after the index date was considered IAET.In-hospital infections were defined as antimicrobial-resistant infections that were treated in the hospital; the study did not differentiate between hospital-acquired infections, healthcare-associated infections, and community-acquired infections.A decision tree was used to evaluate the economic burden of in-hospital antimicrobial-resistant infections. In the decision tree, cases of in-hospital antimicrobial-resistant infection were first split into 5 branches based on the type of infection, given that cost and clinical outcomes associated with each type of infection differ Fig. . The secThe following economic outcomes were estimated for each case: antibiotics cost, hospitalization cost, income loss due to sick days, and income loss due to premature mortality . The number of in-hospital deaths was also estimated for each scenario. New patients entered the model every year, and cost outcomes were accumulated for each scenario by infection type and model year. Given the short time horizon, discounting was not included in the model.Escherichia coli, carbapenem-resistant Klebsiella pneumoniae, carbapenem-resistant Pseudomonas aeruginosa, extended-spectrum β-lactamases (ESBL)-producing E. coli, and ESBL-producing K. pneumoniae. The pathogens of interest were selected based on expert clinical opinions regarding the most prevalent antibiotic-resistant organisms in Thailand.The model population included patients hospitalized in Thailand with an infection of interest associated with an antimicrobial-resistant pathogen of interest. Infections of interest were complicated urinary tract infections (cUTIs), complicated intra-abdominal infections (cIAIs), pneumonia, bloodstream infections, and surgical-site infections. Resistant pathogens of interest were carbapenem-resistant S1). The number of hospitalizations in subsequent years was increased by 4% per year based on the annual growth in hospitalizations from 2017 to 2018 according to Thai National Statistical Office data. The number of in-hospital infections for each year was calculated by multiplying the total number of hospitalizations for that year by the proportion of hospitalizations associated with infections (4.4%). This proportion was based on a published estimate from Thai hospital data and was assumed to remain constant throughout the time horizon.Total hospitalizations in model year 1 reflect the actual number of hospitalizations for all causes in Thailand in 2018 according to Thai National Statistical Office data (Supplementary Table S1). The number of infections caused by resistant pathogens was calculated according to data from the 2019 National Antimicrobial Resistance Surveillance Thailand report. Finally, the number of infections caused by resistant pathogens was stratified by infection type based on the infection distribution recorded in 2015–2019 Thai hospital data.The number of antimicrobial-resistant infections caused by each pathogen of interest Table  was calcPrivate data from 3 public Thai hospitals obtained between January 1, 2015, and December 31, 2019 (Supplementary Methods), were used to derive infection distributions, the real-world scenario proportions of patients receiving AET versus IAET, and in-hospital death rates stratified by receipt of AET versus IAET .Cost inputs for antibiotics costs, daily hospitalization costs, and hospital length of stay were based on hospital data collected from 2015–2019 and separated by infection type, receipt of AET or IAET, and whether the patient was discharged alive or died in hospital Table . Cost daDirect costs consisted of antibiotics costs and hospitalization costs. Antibiotics costs were input as costs per case, and hospitalization costs were calculated as costs per case by multiplying the relevant daily hospitalization cost by the relevant length of stay. and average monthly income based on the 2019 National Statistical Office Labor Survey Report. Productivity loss due to premature  death was calculated based on hospital data for average patient age and Thai national employment rates, average monthly income, and average retirement age based on the 2019 National Statistical Office Labor Survey Report.Indirect costs included in the model were productivity loss due to sick leave and productivity loss due to premature mortality; patients who died in hospital accumulated costs for both types of productivity loss Table . Product The hypothetical scenario estimated that a 1 percentage point decrease in the proportion of patients with antimicrobial-resistant infections receiving IAET would lead to cost savings of THB 604 million  relative to the real-world scenario.The model estimated that ∼406,000 in-hospital infections caused by resistant pathogens would occur in the real-world scenario over the 5-year time horizon and that these infections would generate total costs of approximately THB 66.4 billion (USD 2.1 billion)  and averted hospitalization costs .Most costs in the real-world scenario were associated with income lost due to premature death (80.9% of costs) and direct costs of hospitalization 15.5%) (Table .5% (TablS2). The relative decreases in costs for the hypothetical scenario relative to the real-world scenario were similar across infection types, ranging from 0.6% (pneumonia) to 2.0% (cUTI). Costs increased slightly across model years, while the proportion of costs saved in the hypothetical scenario remained the same (Supplementary Table S3).Pneumonia infections accounted for the greatest proportion of costs , followed by cUTIs (11.4%), bloodstream infections (11.1%), surgical site infections (3.2%), and cIAIs (2.9%) (Supplementary Table S4 and S5).The model estimated that AMR was associated with ∼115,000 in-hospital deaths in the real-world scenario and ∼114,000 in-hospital deaths in the hypothetical scenario, such that the hypothetical scenario averted 0.6% of in-hospital deaths  over a 5-year period. Decreasing the proportion of patients receiving IAET may help to alleviate this burden; a 1 percentage point decrease in IAET rate  was estimated to reduce the total costs of AMR by 0.9%  over 5 years.This decision-tree model showed that in-hospital antimicrobial-resistant infections impose a substantial economic burden in Thailand, with estimated costs of more than THB 66.4 billion , although comparison with the present model is limited as the prior model considered both outpatient and inpatient cases and different pathogens of interest. In addition, single-hospital studies based on data from 2008–2012 have estimated an overall societal burden of AMR in Thailand of USD 4.2 billion and ∼48,000 deaths (2011–2012 data). A larger-scale study of >1,000 Thai hospitals by Phumart et al estimated that in-hospital AMR infections in Thailand in 2010 resulted in >38,000 deaths, antibiotic costs of THB 2.5–6.1 billion (USD 81.8–196.0 million), and indirect costs of THB 40 billion (USD 1.2 billion) due to premature deaths. After extrapolating from the 1-year time horizon in Phumart et al to the 5-year time horizon in the present study, our model estimated proportionally fewer deaths, lower antibiotic costs, and lower income loss due to premature mortality than in Phumart et al. Differences in mortality and mortality-related income loss may be attributable to improved survival rates and improvement in the treatment landscape over the last decade. Differences in total costs may be due to additional costs included in Phumart et al that are beyond the scope of our model and due to inflation in Thailand.Comparison to prior research on the economic burden of AMR in Thailand is limited by a lack of robust data. A 2018 model by Shrestha et al Furthermore, the model provided the first estimation to our knowledge of the potential reduction in economic burden with reduced rates of IAET in Thailand.In the present study, we employed a cost-calculator model that was relatively simple, transparent, and allowed variables to be disaggregated for further analysis. The model benefitted from using clinical and economic inputs specific to Thailand, as well as considering several resistant pathogens of local interest. in the real-world scenario and THB 11.0 billion (USD 355.8 million) in the hypothetical scenario; savings in the hypothetical scenario would have decreased by THB 111 million (USD 3.6 million). Interpretation of our analysis should be considered in light of the challenges and potential solutions to decreasing the rates of IAET. Decreasing the rate of IAET may be difficult to implement amid the growing prevalence of AMR and lack of novel antibiotics for which resistance is not yet prevalent. Implementation of strategies laid out in NSPAR, including improved antimicrobial stewardship, may help reduce the overall prevalence of AMR infections and added costs associated with IAET.This study had several limitations. Some inputs, including hospitalizations and infection rates in model years 2–5, had to be assumed due to lack of sufficient data. Hospital records data were not adjusted for potential biases, including length of stay before infection, comorbidities, or disease severity. In addition, inputs for in-hospital death, rate of IAET, and direct costs were based on data from only 3 Thai hospitals. All included hospitals were public hospitals and were in the northern or central region of Thailand and thus may not be representative of in-hospital death or IAET rates across Thailand. For simplicity, this study did not apply discounting to any costs given the relatively short time horizon. However, productivity loss associated with premature death can be viewed as a future cost; thus, discounting might be considered appropriate. Had we applied 3% discounting to this cost, the cost associated with productivity loss due to premature death would have decreased by THB 11.2 billion (USD 359.4 million)In conclusion, this study provides evidence that in-hospital antimicrobial-resistant infections impose substantial economic impact in Thailand and that decreasing the proportion of cases treated with IAET can help alleviate this burden. Future studies are needed that adjust for potential biases in hospital records data and investigate the impact of IAET in more Thai hospitals.


Contact investigation is an important tool to identify unrecognized patients who are colonized with antibiotic-resistant bacteria. Many Dutch hospitals include already discharged contact patients by sending them a self-sampling request at home, incl. an information letter and sampling materials. Each hospital composes these information letters on their own initiative, however, whether discharged patients comprehend and comply with these requests remains unclear. Therefore, the aim was to provide insight into patients’ comprehension of and self-reported compliance with self-sampling requests post-discharge.This mixed-methods study was performed in eight Dutch hospitals. First, the Common European Framework of Reference (CEFR) language level of self-sampling request letters was established. Second, a questionnaire about patients’ comprehension of the letter, self-reported compliance, and reasons for compliance or non-compliance were sent to patients that received such a request in 2018/2019. Finally, a random selection of questionnaire respondents was interviewed between January and March 2020 to gain additional insights.P = 0.049).CEFR levels of 15 letters were established. Four letters were assigned level B1, four letters B1–B2, and seven letters B2. The majority of patients reported good comprehension of the letter they had received. Conversely, some respondents indicated that information about the bacterium (18.4%), the way in which results would be communicated (18.1%), and the self-sampling instructions (9.7%) were  unclear. Furthermore, self-reported compliance was high (88.8%). Reasons to comply were personal health (84.3%), the health of others (71.9%), and general patient safety (96.1%). Compliant patients appeared to have a need for confirmation, wanted to protect family and/or friends, and felt they were providing the hospital the ability to control the transmission of antibiotic-resistant bacteria. Although a limited number of non-compliant patients responded to the questionnaire, it seemed that more patients did not comply with self-sampling requests when they received a letter in a higher CEFR-level (B2) compared to a lower CEFR-level (< B2) (9.8% vs. 2.5%, This study showed an overall good comprehension of and high self-reported compliance with self-sampling requests post-discharge. Providing balanced information in self-sampling request letters has the potential to reduce patient’s ambiguity and concerns, and can cause increased compliance with self-sampling requests.The online version contains supplementary material available at 10.1186/s13756-023-01277-1. Staphylococcus aureus (MRSA) or highly-resistant microorganisms (HRMO), is contact investigation. In healthcare facilities, this procedure is started (either in endemic or epidemic settings) after the unexpected detection of an MRSA or HRMO in a clinical culture of a patient for whom isolation precautions were not applied [Antimicrobial resistance (AMR) poses a significant and growing threat to global public health. An important infection prevention and control (IPC) measure to prevent or reduce the transmission of antibiotic-resistant bacteria, such as methicillin-resistant  applied . Other p applied .To this day, international guidelines for a variety of antibiotic-resistant bacteria are inconclusive about whether and when screening cultures should be taken to identify carriers , 3. The There are different ways in which discharged patients can be screened, such as through home screenings by visiting healthcare workers or by screening in an outpatient clinic , 9, 10. When written health information is difficult to comprehend by persons with low/limited health literacy, this can contribute to lower levels of protective behaviours, such as health screening participation –15. SimiTherefore, the aim of this study was (1) to explore the language level and content of hospitals’ self-sampling request letters, (2) to provide insight into patients’ comprehension of these self-sampling request letters, (3) to investigate their self-reported compliance with self-sampling requests post-discharge, (4) and to provide insight into patients’ attitudes towards and reasons behind compliance or non-compliance.The South-western region of the Netherlands consists of eleven hospitals, all of which are affiliated with the Infection Prevention and Antimicrobial Resistance Care Network Southwest Netherlands (‘IP & ABR Zorgnetwerk Zuidwest-Nederland’). Ten out of eleven hospitals participated in this study. The non-participating hospital is a hospital specialized in ophthalmology in which contact investigations are unusual. The IP & AMR care network was established in 2015 as part of the Dutch AMR National Action Plan . The SouThe hospitals were asked to provide all their self-sampling request letters for MRSA and HRMO, which were sent to discharged contact patients in 2018/2019. Subsequently, each self-sampling request letter was assessed by one of three external communication experts whom established the Common European Framework of Reference (CEFR) language levels and analyzed the content of these self-sampling request letters following a predefined framework. The CEFR language levels are accepted as an international standard for grading language users’ proficiency, whereby six levels are grouped into three categories (from lower to higher level): basic (A1–A2), independent (B1–B2), and proficient users (C1–C2) [The research team developed the questionnaire during multiple discussion rounds. A validated questionnaire was not found in scientific literature, but the questionnaire was inspired by Currie et al.  and MercRespondents who were willing to provide additional clarification regarding their answers during an interview, could provide their consent by writing their contact details at the end of the questionnaire. Only respondents who gave their consent were eligible for a follow-up interview. A concise semi-structured interview was performed with a selection of those respondents to gain in-depth insights. The interview guide , CPE, multidrug-resistant Pseudomonas aeruginosa (MDR-PA), MRSA and vancomycin-resistant Enterococcus faecium (VRE), with preferably 20 patients per antibiotic-resistant bacterium. Patients were eligible to participate if they were aged 18 years or older. Questionnaires with the majority of answers (≥ 70%) related to comprehension and reasons for compliance or non-compliance missing were not eligible for inclusion. Between November 2019 and January 2020, selected patients received a package at their home address, which contained a patient information letter about the study, a paper-based questionnaire , and the original self-sampling request letter patients had received in the 2 years prior to serve as a memory aid.Hospitals were eligible to participate when self-sampling request letters were sent to discharged contact patients for MRSA and HRMO contact investigations in 2018/2019. The infection prevention teams of the ten hospitals were asked to make a random selection of 100 patients to whom they had sent a self-sampling request letter in 2018/2019. Furthermore, the sample of patients was to be chosen from contact investigations for the antibiotic-resistant bacteria for which most commonly a contact investigation among discharged patients was conducted, including multidrug-resistant The follow-up semi-structured interviews were conducted as unscheduled telephone calls instead of face-to-face interviews. This was the method of choice, because of practicality  and to make follow-up more accessible for the respondents . Respondents who were willing to participate in the follow-up, were stratified by their previous self-reported compliance or non-compliance. A computerized random selection program was used to select patients from these groups. Respondents were called unannounced and this process was continued until data saturation was reached. All semi-structured telephone interviews took place between January and March 2020 and lasted between 5 and 28 min. One infection prevention specialist and three researchers performed all interviews.P value of < 0.05 was considered statistically significant.CEFR language levels and questionnaire data were entered into IBM Statistical Package for the Social Sciences Solutions (SPSS) version 28 , which was used for all quantitative analyses. The actual CEFR-level of the self-sampling request letter was used for each respondent. However, when no self-sampling request letter was assessed for the specific antibiotic-resistant bacterium for which the respondent was requested to self-sample, we used the average CEFR-level of all provided letters by the specific hospital. Inconsistent, incomplete and missing answers from the questionnaires were excluded from the analyses. For descriptive purposes, a median with range, frequencies and percentages were calculated where appropriate. Self-sampling compliance was compared between patients receiving the letter in a higher CEFR-level (B2) to a lower CEFR-level (B1 and B1–B2) using Chi-Square test. A The semi-structured interviews were audio recorded and fully transcribed. Before the start of the interview, the interviewees gave their oral informed consent and were requested to give permission for audio recording. All privacy-related information was removed from the transcripts. Interview transcripts underwent thematic content analysis, using NVivo software, version 10 . To guarantee the reliability and validity of the analyses, two independent researchers (AV and DL) combined phrases to generate categories. All transcripts were analyzed in this manner until no new categories emerged from the data. Subsequently, axial coding was used to look for relationships between open codes and to link categories into overlying themes. Conflicting opinions on categories and themes were discussed and resolved by three researchers .Eight hospitals were found eligible for participation in sending out questionnaires. One hospital did not send any self-sampling request letters to discharged patients in 2018/2019. Another hospital did not perform any contact investigations for MRSA or HRMO, but delegated this task to local general practitioners (GPs).One of the eight hospitals did not provide self-sampling request letter(s) for analysis. The CEFR language level of fifteen self-sampling request letters from seven hospitals was established. Four letters (26.7%) were CEFR language level B1, four letters (26.7%) were language level B1-B2 and seven letters (46.7%) were language level B2 . Two questionnaires were excluded due to missing the majority of answers (≥ 70%), which was one of the inclusion criteria, and one questionnaire was excluded because the patient appeared to have dementia and answers were unreliable. Therefore, 234 questionnaires were eligible for analysis  found the information about the antibiotic-resistant bacterium partially or completely unclear. Particularly, the risks associated with carriage and the symptoms to be aware of in case of developing an infection caused by the antibiotic-resistant bacterium were unclear to patients. While self-sampling instructions were explicitly regarded as being clear by the majority, other respondents (9.7%) found these instructions partially or completely unclear. Also, respondents could not always distill from the information in the letter what the situation practically meant for them personally, e.g., whether scheduled appointments could take place or not until the self-sampling results would become available. Furthermore, about one fifth (18.1%) of respondents found it partially or completely unclear how they would receive either a positive and/or a negative result. In general, some patients (21.4%) would have liked more information and a quarter of the patients (24.2%) actually searched for more information, which was done mostly online.Another theme mentioned by respondents was the timing of the letter. In general, patients trusted their hospital in being accurate and as swift as possible in contacting them with the letter. However, not all patients were appreciative of the moment they received the self-sampling request, which was from their perspective quite late after discharge from the hospital.Interestingly, although the self-sampling request letter was mostly regarded as well understood, it seemed that the majority of respondents with the lowest levels of education, received the letter in the highest observed CEFR-level B2 . Patients’ comprehension of the self-sampling request letter by CEFR-level of the received self-sampling request letter and educational level is available in Additional file Self-reported compliance with self-sampling requests post-discharge was high .On the other hand, patients indicating that they did not comply with the self-sampling request 6.5%) pointed out a variety of reasons for doing so, e.g., not understanding the letter, thinking it was not necessary to participate, or not wanting to participate  and COVID-19 [A possible explanation for this observation can be derived from the Protection Motivation Theory . AccordiCOVID-19 , 24. WitIn the context of (increasing) inter-hospital patient transfers, during which patients may bring antibiotic-resistant bacteria with them, it is also important to develop uniformity of information provision, screening policies and IPC measures across a certain geographic area , 26. ForA strength of this study is the multicenter design. This provided us with the opportunity to approach a large number of patients from a variety of contact investigations for which different self-sampling request letters were used. Second, our sequential mixed-methods approach allowed us to explore and validate quantitative questionnaire findings with qualitative data from semi-structured interviews and provided us with in-depth information on patients’ considerations for compliance or non-compliance and on patients’ needs with regard to self-sampling request letters. We translated these findings into a toolbox with recommendations for drafting such letters Box .This study, however, is also subject to several limitations. First, questionnaire respondents seem to not reflect the regional population structure, i.e., a multicultural population and a relatively high number of  illiterate individuals, ranging from 13 to 16% across the South-western region . In 2020Second, this study may be subject to desirability bias. Both questionnaire respondents and interviewees may have been inclined to provide socially desirable answers. However, questions were formulated as neutral as possible and anonimity in analyses was ensured to minimize the occurrence of such bias.Third, self-sampling compliance rates for MRSA and HRMO contact investigations were not available for the participating hospitals in the South-western region of the Netherlands. Therefore, the self-reported compliance was used as a proxy variable instead.Lastly, findings and implications might not be generalizable to other countries as the way in which patients conceive the provided health information and their motives to participate in these contact investigations might be cultural dependent. Therefore, local and/or national differences should be taken into consideration when requesting patients to participate in contact investigations post-discharge.Future research validating our results by using a larger sample size of discharged contact patients, including more lower educated individuals, would be of added value. Also, research should be aimed at evaluating the effect(s) of providing more balanced information on discharged patients’ comprehension of and compliance with contact investigations for antibiotic-resistant bacteria on a regional, national and international level. Also, an assessment of the impact thereof on the prevalence of antibiotic-resistant bacteria as well as the cost-effectiveness of including discharged contact patients should be made.Contact investigation is an important IPC measure to prevent or reduce the transmission of MRSA and HRMO in healthcare facilities. In the Netherlands, it is common practice to also include discharged patients in contact investigations for these antibiotic-resistant bacteria. Our findings show that requesting these patients to self-sample at home by means of an information letter led to an overall good comprehension of the provided information and high self-reported compliance. An essential component within this procedure is to provide patients with balanced information, which has the potential to reduce patient’s ambiguity and concerns, and can, thereby, cause increased compliance.Additional file 1. Topic list for semi-structured interviews. The topic list used during semi-structured interviews with a selection of questionnaire respondents. Additional file 2. Additional Tables. Basic characteristics of questionnaire respondents by self-reported compliance. Patients’ comprehension of the self-sampling request letter, asked by four different questions in the questionnaire, by CEFR-level of the received letter and educational level.


Propofol is a widely used general anesthetic agent with a generally familiar and predictable adverse effect profile. Severe left ventricular dysfunction to an ejection fraction of < 35% is a rare adverse effect of propofol, with a scarcity of data in the literature. In this case, we report a 36-year-old female at 36 weeks gestation with a prior remote history of peripartum cardiomyopathy, who, while receiving propofol for general anesthesia during a C-section, developed severe left ventricular dysfunction with an ejection fraction of 20-25%, flash pulmonary edema, and cardiogenic shock. She required initiation of inotropic support and, following weaning of propofol, gradually recovered her ejection fraction over the next 24 hours to 40-45% and to 50-55% at follow-up two weeks after discharge. This case highlights a unique adverse effect of propofol with scarce pre-existing literature and no guidelines on appropriate management. It is essential for clinicians to be familiar with this uncommon complication, particularly as propofol use continues to rise worldwide. Propofol is a general anesthetic widely used for adults, including peripartum patients. While its multifaceted mechanism is believed to increase the GABA-mediated inhibitory tone within the central nervous system. While the adverse effects of hypotension and bradycardia are well-recognized in clinical practice, these are usually transient and rapidly improve without any need for intervention . Left veA 36-year-old G5P4004 female at 36 weeks gestation presented for pre-delivery optimization before a scheduled cesarean section. She had a history of peripartum cardiomyopathy in her last cesarean delivery 6 years prior, with a reduced ejection fraction (EF) of 25-30%, which had recovered to 50-55% after goal-directed medical therapy with lisinopril, that had since been discontinued for the past 5 years. She was asymptomatic in the intervening years, had an excellent functional status with a Duke Activity Status Index of 58.2, and was pre-emptively admitted for pre-delivery risk stratification, given her prior history of peripartum cardiomyopathy and advanced maternal age. On presentation, she denied shortness of breath, orthopnea, bendopnea, paroxysmal nocturnal dyspnea, palpitations, or chest pain. She was euvolemic on physical examination with no evidence of pulmonary edema, jugular venous distension, or lower extremity edema. Her baseline NT-pro-BNP was 192pg/ml [reference range 0 - 125pg/ml]. A transthoracic echocardiogram revealed an ejection fraction of 50-55%, with no abnormalities. She was deemed low-risk from a cardiac standpoint and was scheduled for a C-section with epidural anesthesia.While the patient was in her regular state of health before the C-section, immediately following the administration of epidural anesthesia with bupivacaine, she became acutely agitated and disruptive. At the time, delivery was considered urgent, and the decision was made to sedate and intubate the patient for an emergent C-section. She was given a bolus of 150 mg of propofol, followed by an infusion at 35 mcg/kg/min. However, 20 minutes following the initiation of propofol, the patient became hypotensive with an acute reduction in mean arterial pressure to 56 and hypoxic, requiring up-titration of FiO2 to 100% and initiation of an epinephrine infusion. An immediate bedside transthoracic echocardiogram (TTE) revealed an ejection fraction of 10-15% with global hypokinesis. She was transferred to the cardiac care unit after delivery for further management. Her exam revealed bilateral diffuse rales and an S3 gallop. A chest x-ray revealed diffuse pulmonary edema. A TTE demonstrated severe left ventricular systolic dysfunction with EF of 20-25% and global hypokinesis (Video The patient rapidly improved and was extubated to bilevel positive pressure ventilation shortly after. She continued a short course of bolus diuresis leading to a resolution of pulmonary edema and improvement in EF to 40-45% within 24 hours of admission to the cardiac care unit. She was discharged successfully on day 2 following admission on losartan 25 mg once daily and metoprolol succinate 50 mg once daily. On outpatient follow-up, two weeks after discharge, her EF on TTE was improved to 50-55%, and she was well-compensated, with no further complaints. The differential diagnoses of stress-induced cardiomyopathy and recurrence of peripartum cardiomyopathy were considered; however, the diagnosis of propofol-induced LV dysfunction was made based on the rapid acuity of LV systolic dysfunction with the initiation of intra-operative propofol, alongside the rapid improvement of LV systolic function with cessation of propofol.Our case represents a case of acute decompensated heart failure with reduced ejection fraction in a young female undergoing Cesarean section shortly after the initiation of propofol. The acute onset of LV systolic dysfunction and the rapid recovery of her ejection fraction after discontinuation supported a diagnosis of propofol-induced LV dysfunction. While recurrence of peripartum cardiomyopathy and stress-induced cardiomyopathy remained on the differential diagnoses, they were considered less likely due to a lack of recurrent postpartum cardiomyopathy in interim pregnancies and the onset of LV systolic dysfunction acutely following propofol infusion. Furthermore, while the meantime for recurrence of peripartum cardiomyopathy is within 2.2 years, our patient presents 6 years following her last occurrence of peripartum cardiomyopathy . Our casIt is prudent for clinicians to be aware of adverse outcomes from propofol use. Propofol can result in hypotension and bradycardia due to a sympatholytic effect at clinical doses, which typically is transient and requires vasoactive support in less than 0.2% of all cases . One stuIn higher doses, propofol has been shown to directly impair myocardial contractility . PropofoLV dysfunction from propofol is an extremely important consideration in peri-operative management. Given the ability of propofol to depress left ventricular systolic dysfunction, it is possible that it may result in clinical presentations of peripartum cardiomyopathy or as an agent that can exacerbate stress cardiomyopathy. It is reasonable, particularly those patients with a prior cardiac history of LV dysfunction, to use propofol judiciously to avoid this notable adverse outcome on patient care due to the paucity of data available on propofol's effect on myocardial contractility, further prospective studies and necessary to elaborate clinical recommendations from these findings.Left ventricular dysfunction from propofol use is an uncommon complication requiring increased awareness in peri-operative management. While propofol is a widely used anesthetic agent with a generally predictable adverse effect profile, we propose that in patients with pre-existing left ventricular dysfunction, propofol be used judiciously with a standardized management protocol should LV dysfunction arise. Due to the paucity of existing literature on propofol's effect on myocardial contractility, further randomized controlled trials are necessary to elucidate more data on this uncommon effect.


The expansion of the Medicaid public health insurance programme hasvaried by state in the USA. Longer-term mortality and factors associatedwith variability in outcomes after Medicaid expansion are under-studied. Weaimed to investigate the association of state Medicaid expansion withall-cause mortality.This was a population-based, national, observational cohort studycapturing all reported deaths among adults aged 25–64 years via deathcertificate data in the Centers for Disease Control and Prevention’sWide-ranging Online Data for Epidemiologic Research (CDC WONDER) database inthe USA from Jan 1, 2010, to Dec 31, 2018. We obtained national demographicand mortality data for adults aged 25–64 years, and state-leveldemographics and 2010–18 mortality estimates for the overallpopulation by linking federally maintained registries . Stateswere categorised as Medicaid expansion or non-expansion states as classifiedby the Kaiser Family Foundation. Multivariable difference-in-differencesanalysis assessed the absolute difference in the annual, state-level,all-cause mortality per 100 000 adults after Medicaid expansion.Among 32 expansion states and 17 non-expansion states, Medicaidexpansion was associated with reductions in all-cause mortality(−11·8 deaths per 100 000 adults [95% CI −21·3to −2·2]). There was variability in changes in all-causemortality associated with Medicaid expansion by state (ranging from−63·8 deaths per 100 000 adults [95% CI −134·1to −42·9] in Delaware to 30·4 deaths per 100 000 adults[−39·8 to 51·4] in New Mexico). State-level proportionsof women (−17·8 deaths per 100 000 adults [95% CI−26·7 to −8·8] for each percentage pointincrease in women residents) and non-Hispanic Black residents(−1·4 deaths per 100 000 adults [−2·4 to−0·3] for each percentage point increase in non-Hispanic Blackresidents) were associated with greater adjusted reductions in all-causemortality among expansion states.After 4 years of implementation, Medicaid expansion remainsassociated with significant reductions in all-cause mortality, butreductions are variable by state characteristics. These results could informpolicy makers to provide broad-based equitable improvements in healthoutcomes. Medicaid expansion provided health insurance for an additional12 million people, accounting for the majority of the increase in insurance coverageresultant from the ACA.1 Somestates have chosen not to expand Medicaid and the future of Medicaid expansion isstill debated.2Medicaid is a public health insurance programme and the largest provider ofhealth insurance in the USA, covering more than 75 million people. Medicaid policiesare instituted at the state level and are intended to provide health coverage forvulnerable populations, including low-income individuals and people withdisabilities. The Affordable Care Act (ACA) was federal legislation taking effect in2014, which provided the option to states to expand Medicaid to individuals notpreviously eligible, increasing the income qualification to 138% of the federalpoverty level.5 includingall-cause mortality and within specific disease groups,7 fullnational outcomes studies have been limited to insurance eligibility, or 1-year or2-year mortality outcomes following Medicaid expansion, and none have providedin-depth analyses on potential state-level variability.11 Additionally, the lag-time of potential effects of Medicaidexpansion might not yet be apparent in these previous reports as the majority ofadult mortality can be linked to chronic medical conditions. For example, thelag-time to benefit of statins to reduce cardiac events is 2·5 years, and ofbreast and colorectal cancer screening to reduce neoplasia-related mortality are3·0 years and 4·8 years, respectively.13The most contemporary and complete appraisal of available data should be used toinform impending policy decisions.Objective and quantitative data regarding the granular ramifications ofMedicaid expansion can inform policy interventions involving public healthinsurance. Although previous studies have reported overall population-levelimprovement in health-related outcomes,Variability and underlying reasons for heterogeneous effects of Medicaidexpansion  onhealth outcomes are underexplored, but are relevant for policy makers in identifyingmedical conditions and geographical areas that would be most affected by policyinterventions to Medicaid coverage. Some states have expanded Medicaid, whereas somehave not—we sought to use this natural experiment to determine theassociation of state Medicaid expansion with all-cause mortality, and carry outexploratory analyses to elucidate more granular associations of Medicaid expansionwith cause-specific mortality to inform ongoing policy interventions, both withinthe USA and other countries considering changes to public health coverageoptions.This was a population-based, national, observational cohort studycapturing all reported deaths among adults aged 25–64 years via deathcertificate data in the Centers for Disease Control and Prevention’sWide-ranging Online Data for Epidemiologic Research (CDC WONDER) database in theUSA from Jan 1, 2010, to Dec 31, 2018  p 3. ThiState-level characteristics that we a-priori hypothesised could beconfounders or mediators of mortality were obtained from national registries. Wemodelled these characteristics as time-varying covariates by year, linked at theclosest year of available data. From the US Census Bureau, we obtained thestate-level proportion of residents aged 25–64 years who were female, andthe proportions who were non-Hispanic Black, Hispanic, non-Hispanic White, orAsian; the proportion of residents in each 10-year age group between 18 and 64years without health insurance; and for the overall population, median householdincome and the proportion living in poverty. From the US Bureau of LaborStatistics, we obtained the unemployment rate. From the Behavioral Risk FactorSurveillance System, we obtained the proportion of adults with diabetes,obesity, currently smoking, reporting any alcohol use in the past 30 days, bingedrinking in the past 30 days, self-reported good or better health, and routinemedical care in the past 5 years. From the Health Resources and ServicesAdministration, we obtained the number of primary care physicians per 100 000population, total hospital beds per 1000 population, and the proportion ofresidents living in rural areas. The full variable list with years of dataavailability is shown in the For the exposure variable, Medicaid expansion status, states werecategorised as expansion or non-expansion as classified by the Kaiser FamilyFoundation. The majority of expansion states (n=25) implemented Medicaidexpansion on Jan 1, 2014, including six states  with early (before2014 but after ACA passage in 2010) limited expansion before full Medicaidexpansion. Seven states  had late expansion . Early and lateexpansion states were categorised as expansion in the main analyses but excludedfrom sensitivity analyses. Four states implemented expansion in 2019–20, but were categorised as non-expansion,given the study period was 2010–18 . Massachusetts and Wisconsin had previousMedicaid expansion independent of ACA and were excluded from analyses. Expansiondates and categorisation are shown in the The primary outcome was the annual, state-level, all-cause mortality per100 000 adults. We extracted age-adjusted mortality rates by state of residenceand year of death from 2010 to 2018 in 10-year age groups  to specifically capture theMedicaid-eligible population. Mortality was standardised to the 2000 US standardpopulation using the direct method.3 We alsoevaluated cardiovascular-related, respiratory-related, neoplasia-related,prescription or opioid overdose-related, and infection-related mortality. Wedefined deaths attributable to prescription or opioid overdose as they aredefined in the ICD-10 manual of drug poisoning (similarly to the CDC14), which includes illicit drugpoisoning and prescription opioid poisoning  versus internal  causes of death were evaluatedby International Classification of Diseases version 10 (ICD-10) codes, as inother studies.oisoning  p 6. We The sample size was fixed and used the entirety of each state’spopulation, rather than a sample; therefore, estimates reflect the wholepopulation and sample size and power were not calculated as the study size hasfull national case ascertainment. CIs are provided to guide interpretation ofresults.t tests and χ² tests.Annual state-level, age-adjusted mortality was modelled as the dependentvariable using linear regression to calculate the mean age-adjusted mortalityamong expansion and non-expansion states. To address within-state correlation inrepeated measures by year, the models accounted for clustering by state and usedstandard errors based on Huber-White sandwich estimators, which are robust toheteroscedasticity and autocorrelation. Mortality was then adjusted for theproportion of the state population in each 10-year age group , the proportion who were female, theproportion who were non-Hispanic Black or Hispanic, the proportion living inpoverty, and the proportion who were unemployed using multivariable linearregression. Our a-priori hypothesis was that other covariates were correlatedwith or mediators of Medicaid expansion  and were notincluded in the primary multivariable model. To evaluate the association ofMedicaid expansion with mortality trends, the exposure (Medicaid expansionstatus) and era (pre-expansion vs post-expansion), along withan interaction term between the two , were added to the model. All-cause and cause-specific mortality werestimated in separate models. All models were weighted to the state populationaged 25–64 years.State-level characteristics were described by Medicaid expansion statusas mean (SD) and n (%), weighted by the state population aged 25–64years, and compared using 15 and applied a Holm step-down correctedsignificance level to address multiple comparisons.16 To test the hypothesis that state-levelheterogeneity in the effect of Medicaid expansion was associated with thedemographic makeup of states, the resulting state-specificdifference-indifferences for all cause-mortality were modelled as the outcomeusing linear regression and assessed for associations with 2014 statedemographics  adjusting for baseline mortality.State-specific difference-in-differences were estimated separately foreach expansion state compared with the sample of non-expansion states adjustedfor the main model covariates. As clustered inference issues might arise withonly one treated cluster, we calculated 95% CIs using the population-weightedlinear probability approach developed by Conley and TaberThe difference-in-differences assumption of parallel trends wasevaluated pre-Medicaid expansion (2010–13) for all-cause andcause-specific mortality and for each expansion state separately using aninteraction term between Medicaid expansion status and year as a continuouslinear variable  pp 7–9. In a prespecified exploratory analysis to inform generalisability of ourfindings, we sought to assess potential differential effects in the associationof changes in the uninsured population with mortality among expansion versusnon-expansion states. We modelled absolute change from 2010 to 2018 in all-causemortality (outcome) and state-level proportion of uninsured residents(explanatory variable) using linear regression adjusting for baselineage-adjusted mortality. We tested the interaction between insurance change andexpansion status to determine if the effect of a decrease in uninsured residentsdiffered by Medicaid expansion status.We carried out sensitivity analyses to assess the robustness of ourresults. We repeated the all-cause mortality analysis at the county level toassess for potential bias due to county-level variability within states.Counties with nine or fewer deaths per year are suppressed in the CDC WONDERdatabase for county-level analyses; therefore, the number of included countiesvaried by year from 2475 (79%) of 3143 counties  in 2010to 2513 (80%) in 2018 in this sensitivity analysis . To assess for confounding by staggered timingof Medicaid expansion, we did two analyses excluding states with limited earlyexpansion (n=6) and excluding states with late expansion (n=7). To assess forconfounding from concomitant expanded access to health insurance not throughMedicaid expansion, we did a sensitivity analysis adjusting for the proportionof the population without health insurance. We hypothesised that changes inincome could be a mediator of Medicaid expansion-related improved healthoutcomes over time, so income was excluded from the main model; however, we dida sensitivity analysis adjusting for median household income (inflation-adjustedto 2018 US dollars) under the assumption that income was a confounder, and not amediator.vs post-expansion).We repeated the state-specific all-cause mortality analysis using aninterrupted time-series approach to confirm the difference-in-differencesresults. Briefly, we compared each Medicaid expansion state to the control groupof non-expansion states using segmented ordinary least-squares regression withNewey-West standard errors to account for autocorrelation . Models remained weighted, accounted forclustering, and included covariates as in the main analysis. We report theeffect of Medicaid expansion on mortality as the interaction between Medicaidexpansion status and era  or Stata/MP (version16.1).The funder of the study had no role in study design, data collection,data analysis, data interpretation, or writing of the report.The study included 31 expansion states and the District of Columbia, and 17non-expansion states. At baseline in 2010, expansion states had smaller proportionsof people from minority racial and ethnic groups, smaller rural populations, andfewer uninsured adults than non-expansion states . BetweenBaseline age-adjusted all-cause mortality was 311·2 per 100 000adults in expansion states versus 365·2 per 100 000 adults in non-expansionstates. Fully adjusted all-cause mortality from baseline to the end of the studyperiod is shown in There was variability among states in the reduction in all-cause mortalityassociated with Medicaid expansion. In multivariable difference-in-differences,Delaware and Rhode Island had the greatest reductions in mortality, whereas NewMexico had increased all-cause mortality associated with Medicaid expansion . HoweverState demographic characteristics associated with reductions indifference-in-differences for all-cause mortality were the proportion of womenresidents, the proportion of non-Hispanic Black residents, and the change in theproportion of the population who were uninsured. From bivariate linear regressionadjusting for baseline age-adjusted mortality, there were 1·4 fewer deathsper 100 000 adults (95% CI 0·3–2·4) for each percentage pointincrease in non-Hispanic Black residents, and 17·8 fewer deaths per 100 000residents (8·8–26·7) for each percentage point increase inwomen residents. In a separate bivariate linear regression adjusting for baselineage-adjusted mortality, there were 5·4 fewer deaths per 100 000 adults(1·8–9·0) for each percentage point decrease in the stateproportion of uninsured residents from 2014 to 2018. The proportion of Hispanicresidents and the proportion of rural residents were not significantly associatedwith state-level difference-in-differences for all-cause mortality  p 11.In our exploratory analysis including non-expansion states, there were1·6 fewer deaths per 100 000 adults (95% CI 0·2–3·0) foreach percentage point decrease in state proportion of uninsured residents. There wasnot a statistically significant interaction of state expansion status with change instate proportion of uninsured residents (p=0·38), which suggests that theassociation of decrease in uninsured residents with reduction in all-cause mortalityis independent of Medicaid expansion status.The results of the exploratory analysis of fully adjusted mortality,including cardiovascular-related, respiratory-related, neoplasia-related,prescription or opioid overdose-related, and infection-related mortality forexpansion versus non-expansion states are shown in In the state-specific difference-in-differences analyses, states with areduction in all-cause deaths associated with Medicaid expansion  generally had a reduction incardiovascular-related, respiratory-related, neoplasia-related, andinfection-related deaths; states with an increase in all-cause deaths associatedwith Medicaid expansion  generally had anincrease in the same cause-specific deaths  pp 23–26Results of the sensitivity analysis done at the county level (rather thanstate level), and analyses excluding states that were early adopters of Medicaidexpansion (n=6) and excluding states that were late adopters of Medicaid expansion(n=7) are shown in the In this study, we provide a contemporary appraisal of the association ofMedicaid expansion with mortality in the USA. We show that Medicaid expansion hasbeen associated with significant reductions in all-cause mortality, but the effectshave been heterogeneous by state and cause-specific mortality. We show that thereduction in all-cause mortality from Medicaid expansion was associated with astate-level reduction in the proportion of the population who were uninsured, andthat this association was also present in the analysis including non-expansionstates. These findings support the hypothesis that the main mechanism by whichMedicaid expansion is likely to lead to improvements in mortality is through gainsin insurance coverage, as our study shows that the magnitude of change in theproportion of people who are uninsured is associated with the magnitude ofimprovement in overall mortality attributable to Medicaid expansion. This findingsupports consideration of generalisability of Medicaid expansion to other states andhighlights the potential public health benefits of expanding access to public healthinsurance.17 Additionally, previous studies show that even aftercontrolling for socioeconomic conditions, including employment, women and Blackindividuals have lower rates of health coverage and higher premiums for insuranceplans than men and individuals of other races.17 Also, Black individuals and women on average have moredependents in the household, and we hypothesise that these individuals might be moremotivated to obtain insurance when provided with the option. Thus, our resultssuggest that interventions to Medicaid policy might have especially pronouncedeffects on racial and sex differences in health outcomes, which need carefulconsideration by policy makers. Also, as non-expansion (vsexpansion) states had a higher proportion of Black individuals, and the associationbetween reduction in uninsured residents with decreases in all-cause mortality wasindependent of Medicaid expansion status, our analyses suggest that non-expansionstates might have the most to gain from adopting Medicaid expansion.We noted variability by state in the reduction in all-cause mortalityassociated with Medicaid expansion. We examined state-level characteristicsassociated with these reductions, identifying a greater proportion of women andnon-Hispanic Black residents to be associated with reductions in all-cause mortalityassociated with Medicaid expansion. These findings suggest that women andnon-Hispanic Black residents might have benefited most from Medicaid expansion.Women and non-Hispanic Black individuals have greater rates of poverty than men andindividuals from other ethnic back grounds.18 of national deaths in the USA, these results areimpressive, particularly given the relatively short time that Medicaid expansion hasbeen in place.We also assessed cause-specific mortality as a potential contributor tovariability in the effects of Medicaid expansion, which showed significantreductions associated with Medicaid expansion in internal causes,cardiovascular-related and respiratory-related mortality, but not for other causeswe examined. We hypothesise these findings to be secondary to greater access topreventive care, specialist referrals, and medications, among other servicesprovided by Medicaid. Given that cardiovascular-related and respiratory-relatedmortality accounts for more than a third19Additionally, a recent national study showed that Medicaid expansion was associatedwith a reduction in deaths from heroin and synthetic opioids, but an increase inmethadone-related overdose—this increase was hypothesised to be related tohigh rates of methadone used to treat pain and disproportionately associated withoverdose deaths among individuals using Medicaid.20 Whether Medicaid expansion con tributes toincreased opioid deaths through increased access to prescription pain medicationneeds further evaluation. Our results high light diseases that might requiretargeted policy interventions to ensure that not only insurance gaps are addressed,but treatment gaps as well, to provide more equitable and broad improvements inhealth outcomes.Prescription or opioid overdose-related deaths showed some discordantstate-specific trends compared with other causes of death examined. States with thegreatest reductions in all-cause mortality  generally had the greatest reductions in cardiovascular-related,respiratory-related, neoplasia-related, and infection-related deaths, but not inprescription or opioid overdose-related deaths. Indeed, our results showprescription or opioid overdose- related deaths more than doubled between 2010 and2018 among expansion states, more than the 61% relative increase among non-expansionstates, although this was not statistically significant. Treatment financing forspecific opioid use disorder services is not fully guaranteed by Medicaid and variesby state—previous studies show 80% of individuals with opioid use disorder donot receive active treatment—and there has been concern that Medicaidexpansion might be accompanied by declines in state-level spending on opioid usedisorder treatment.21 Medical debt is present among 18% of US adults, and ishigher among people with low incomes than those with high incomes, and among thosewithout health insurance, which might be alleviated by Medicaid expansion.22 A recent national study showedthat Medicaid expansion was associated with a 34% absolute difference in decline inmedical debt, compared with states without Medicaid expansion.22 Indeed, our main results were attenuated byadjustment for time-varying median income, which suggests that the associationbetween Medicaid expansion and mortality outcomes might be mediated by changes inincome. Income interventions for low-income populations, in the form of tax credits,have had positive effects on health outcomes, particularly among minority racial andethnic groups and women.21 Thesefindings, supplemented by our results showing the most substantial effects ofMedicaid expansion among states with a higher proportion of Black individuals,highlight the potential pleiotropic effects of expanded access to health-carecoverage to affect health and financial wellbeing for vulnerable populations.Medicaid expansion, which increased access to health insurance forindividuals with low income, might potentially be a policy intervention to bridgedisparities across the wealth spectrum. In the USA, the wealthiest 1% of women live10 years longer than the poorest 1% of women, and the wealthiest 1% of men live 14years longer than the poorest 1% of men.This study has limitations. First, the data are observational andaggregated, so residual confounding and individual-level characteristics affectingestimates are possible. For example, Medicaid expansion (or choosing not to expand)could be associated with other health-related state-level changes, and couldinfluence our estimates regarding the effects of Medicaid expansion for specificdemographics and states. However, we provide several analytical approaches toaddress these limitations, including difference-in-differences to adjust for seculartrends, county-level sensitivity analyses to adjust for more granular county-levelvariability, and adjustment for time-varying covariates, which is distinct fromprevious Medicaid expansion studies that largely adjusted for fixed baselinecovariates. Second, different timing of Medicaid expansion among states and lag-timeof exposure effect, especially for chronic diseases, might introduce bias. However,these limitations would likely bias results towards the null, and results fromsensitivity analyses excluding states with different timing of Medicaid expansiondid not substantially differ from the primary estimates. Third, the data areaggregated, so the covariates assessed  are at thestate level and might not reflect individual-level associations. Finally, the CDCWONDER data are based on death certificates, focusing on a single underlying causeof death by ICD-10 code, so misclassification of cause-specific mortality ispossible and analysis of cause-specific mortality should be considered anexploratory outcome. Data are expected to be 100% complete for all-cause mortality;however; bias from death certificate data in specifying underlying cause of death ispossible.In conclusion, our study provides contemporaneous results regarding theassociation of Medicaid expansion with mortality across the USA, and novel insightinto the variability of this association. Policy makers might find such resultshelpful to inform potential ramifications of impending policy decisions, and forfuture policies aiming to provide broad-based equitable improvements in healthoutcomes.1


The study included 101 patients admitted to a sub-intensive care unit from November 2021 to April 2022. The discriminative capacity of MNA-sf, HGS, and body composition parameters  was assessed computing the area under the receiver operating characteristic curves (AUC). Analyses were stratified by age groups (<70/70+ years). The MNA-sf alone or in combination with HGS or BIA was not able to reliably predict our outcome. In younger participants, HGS showed a sensitivity of 0.87 and a specificity of 0.54 (AUC: 0.77). In older participants, phase angle (AUC: 0.72) was the best predictor and MNA-sf in combination with HGS had an AUC of 0.66. In our sample, MNA- sf alone, or in combination with HGS and BIA was not useful to predict our outcome in patients with COVID-19 pneumonia. Phase angle and HGS may be useful tools to predict worse outcomes in older and younger patients, respectively.Hospitalized patients with respiratory failure due to SARS-CoV-2 pneumonia are at increased risk of malnutrition and related mortality. The predictive value of the Mini-Nutritional Assessment short form (MNA-sf Malnutrition is highly prevalent in hospitalized older adults and may affect length of hospital stay and mortality ,2. ScreeThis consideration may be even more evident in patients receiving high-flow oxygen and non-invasive ventilatory support, in whom oral nutrition is rather difficult and the infection’s course tends to be longer and more severe . MoreoveSeveral studies have previously compared different nutritional screening tools to find one that could better identify an increased risk for worse outcome and mortality in people with COVID-19. In a systematic review on people over 65 years of age with COVID-19, the Mini Nutritional Assessment short form (MNA-sf) was demonstrated to have the best predictive validity for poor appetite and weight loss over 2.6 kg as the Nutritional Risk Screening 2002 (NRS-2002) for the length of hospital stay (LOS) . In anotIn addition to this, the European Society of Clinical Nutrition and Metabolism (ESPEN) definition of malnutrition does not fully consider the loss of muscle function seen in patients with lung disease, that should be preferably monitored for pulmonary cachexia ,15. In cFinally, Gómez-Uranga et al.  showed aThe objective of the study was to assess the ability of HGS and BIA-derived body composition parameters in addition to MNA-sf in predicting in-hospital mortality or endotracheal intubation, in patients affected by a severe acute respiratory failure and admitted to a sub-intensive COVID-19 unit (SICU). This observational retrospective study included 101 patients with SARS-CoV-2 infection who were consecutively admitted to the SICU of Campus Bio-Medico Hospital in Rome from November 2021 to April 2022. Viral infection was confirmed by a positive result of real-time reverse transcriptase polymerase chain reaction (RT-PCR) assay of nasal and pharyngeal swab specimens. The study protocol was followed in accordance with the tenets of the Helsinki Declaration and was approved by the Campus Bio-Medico University Ethical Committee in April 2020 (protocol code 22/20 OSS). A written informed consent form was signed for inclusion prior to the start of the study from all subjects. Clinical data, including age, sex, smoking habit, associated comorbidities (Charlson comorbidity index), and type of O2 therapy and pharmacological therapy started, were collected from the medical charts. In addition, CT severity score, a CT scan-based index, ranging from 0 (best) to 20 (worst), was used by our radiologists to visually quantify lobar regions affected by COVID-19 pneumonia. This scale was validated in a previous SARS-CoV-2-related study as well .2) and routine biochemistry exams were collected as well.The MNA-sf was performed as an initial screening test, as the score has a high negative predictive value for malnutrition . This qu2 and <5.5 kg/m2, for males and females, respectively, according to the EWGSOP criteria [The European Working Group on Sarcopenia in Older People (EWGSOP) stated that sarcopenia is defined as low skeletal muscle mass and reduced muscle function . Furthercriteria . These ccriteria ,31. It wcriteria .Outcome was defined as death or as a transfer to the intensive care unit (ICU) for endotracheal intubation during hospitalization in SICU. Since the baseline risk of and risk factors for adverse outcome differ between younger and older patients , analysen = 60%) were males  years; 61  needed non-invasive ventilation (NIV) or continuous positive airway pressure (CPAP); the remaining 54% (n = 55) received oxygen supplementation through high-flow nasal cannula (HFNC). No significant differences in the oxygen support used during acute respiratory failure was found among the two groups.In this sample, 43% (n = 35%) were transferred in the ICU for endotracheal intubation or died during our hospitalization. As expected, a twofold percentage of patients older than 70 years had an adverse event compared to younger ones.Respiratory failure was moderate–severe in both study samples with an average PaO2/FiO2 ratio of 122.7 (SD: 53.9). Similarly, the severity score of parenchymal infection involvement evaluated by chest computed tomography scan (CT) was on average 12.4 out of 20 (SD: 4.4), with no relevant variation according to patients’ age group. Overall, 36 patients (2 (SD: 7.3), while the total protein serum concentration was 6 g/dL (SD: 0.7). The MNA-sf average score was 10.3 (SD: 2.2) with 40% and 60% of patients being categorized as having normal nutritional status and being at risk for malnutrition, respectively. The risk of malnutrition was higher in older participants  compared to younger adults  and reduced HGS was found in 13 older patients and in 2 younger ones. Body composition showed normal SMI in all participants, while 51 older participants and 36 younger ones had a reduced PhA, respectively. As shown in Overall, MNA-sf had a fair sensitivity (0.70) and a poor specificity (0.45) for outcome discrimination, with an AUC of 0.6. The MNA-sf predictive ability did not significantly change with the addition of HGS (AUC: 0.66) and BIA-derived parameters such as SMI (AUC: 0.59) and PhA (AUC: 0.60).As shown in In addition, the combination of MNA-sf and HGS did not have a better predictive ability compared to the HGS alone , while PhA demonstrated a greater sensitivity and specificity with an AUC of 0.72. Similar results were found with the combination of MNA-sf and PhA (AUC: 0.73). The SMI also did not reveal a significant discriminative ability . FinallyIn our population, the MNA-sf, with or without the addition of grip strength or indicators of body composition, was not able to adequately discriminate the risk of mortality or ETI during COVID-19 pneumonia. The parameters that could better predict the outcome were the PhA and the HGS in older and younger participants, respectively.Our results are in line with a previous Italian study conducted in an internal medicine ward, that showed that PhA was effective in predicting the occurrence of worse clinical outcomes compared to other BIA-derived parameters that did not add a further predictive value  . In thisIn addition, after stratification for age, our results indicate that HGS has a satisfying predictive power in patients younger than 70 years and a modest discriminative ability in older patients when combined with MNA-sf. These data agree with a previous study on COVID-19 showing that a pre-existing condition of reduced HGS may increase the vulnerability to COVID-19 and aggravate disease severity leading to ICU admission, the need for mechanical ventilation, and mortality . Indeed,Although we found a denoting divergence among the “age-related” groups, the assessment of both muscular function and body composition represents a strength of this study. Indeed, the results of this prognostic analysis underline the need for using both muscle mass and muscle strength evaluation in the diagnosis of sarcopenia as EWGSOP recommendations suggest. This is explained by the fact that the relationship between muscle mass and function is not linear and the assessment of only one variable may be of limited clinical value. As an example, it could have underestimated the diagnosis of “pre-sarcopenia”, characterized by low muscle mass without impact on muscle strength or physical performance. Similarly, in our study sample, it would have omitted those younger patients presenting a poor physical performance status as measured by HGS with non-pathological PhA or SMI values. Recognizing different stages of sarcopenia and predicting a poor prognosis at distinct ages may help in selecting treatments and setting appropriate recovery goals, even during an acute illness, as in COVID-19 disease.Our study has some limitations. First, due to the challenging conditions of the COVID-19 pandemic, the number of patients included in the analysis was rather small and it was a single-center study. Second, performing the MNA-sf questionnaire and HGS required patient collaboration and did not permit to include in the sample study all those patients affected by severe cognitive disorders or those who were sedated to permit NIMV compliance  that were not able to collaborate. Another limitation of the study was that we did not conduct a dynamic control of patients’ caloric intake during the hospitalization, and we did not verify any effect of nutritional interventions. Furthermore, it was not possible to differentiate whether poor nutrition was due to a chronic condition or was induced by the acute disease. Finally, statistical analysis was not adjusted for any intra-hospital complications’ development, such as infections or cardiovascular events and/or causes of death, since this information was not present in our dataset.Despite all these limitations, integrating BIA and HGS as a nutritional assessment at admission revealed to be economical and easily accessible, and could permit to focus on undernourished patients with COVID-19, other than disease severity. In the context of systemic inflammation induced by SARS-CoV-2, patients with malnutrition could enter a vicious cycle that may predispose them to an increased risk of complications and adverse events. Indeed, COVID-19 disease, in terms of acute infections in general, not only includes respiratory symptoms but also all those features leading to an insufficient nutritional intake such as loss of appetite, gastrointestinal disturbances, and chronic fatigue. This aspect could further worsen patients’ nutritional status ,6,7,8. TFurthermore, while the exact pathogenesis of malnutrition in pulmonary diseases remains unclear, it seems likely that factors contributing to energy imbalance include changes in metabolism and caloric intake, disuse atrophy, tissue hypoxia, inflammation, and medications . This syIn conclusion, patients hospitalized for COVID-19 pneumonia, malnutrition, sarcopenia, and frailty showed to be interrelated entities, especially in patients with greater baseline functional impairment prior to admission . Thus, rThis study lends support to the use of BIA in very old patients, and HGS primarily in younger patients, who are receiving non-invasive or high-flow oxygen support during COVID-19 infection or for any clinical illness, as prognostic indices to possibly evaluate disease severity and adverse events. New studies need to be conducted to monitor patients’ clinical evolution and caloric intake during hospitalization in other non-COVID-19 units and verify any effect of nutritional interventions that may also influence the outcome. A proper identification of malnourished patients, who are admitted for respiratory failure and who require respiratory support as NIMV or HFNC, may allow clinicians to include nutritional support in addition to standard medical therapy to begin during hospitalization. In these patients, active nutritional supplementation could improve respiratory muscle function and exercise performance during respiratory rehabilitation programs, and possibly ameliorate the respiratory mechanics, compliance to the diverse respiratory supports, and promote their weaning.


Primary biliary cholangitis (PBC) has been long associated with impairment of various aspects of health-related quality of life (HRQoL) with substantial differences among populations. This study evaluated for the first-time the HRQoL in Greek PBC patients in conjunction with clinical and laboratory parameters of patients. We analyzed prospectively collected data regarding the HRQoL by using the PBC-40 and SF-36 questionnaires in 374 Greek PBC patients and 131 age- and sex-matched non-PBC controls. P < 0.001 for all comparisons). Emotional dysfunction, social impairment, and fatigue  were amongst those with the highest, while cognitive dysfunction (32%) with the least impact on quality of life. Fatigue was associated with female sex (P = 0.02), longer disease duration (P = 0.01), presence of cirrhosis (P = 0.02) and positivity for PBC-specific ANA (P < 0.05), while social dysfunction with increased age (P < 0.001), longer disease duration (P < 0.001) and presence of cirrhosis (P = 0.004). Living in urban areas was linked to impaired social function (P = 0.04), cognition (P = 0.02), fatigue (P = 0.04) and increased total PBC-40 score (P = 0.01). The PBC-40 questionnaire is a reliable tool for HRQoL assessment in Greek PBC patients . Implementation of PBC-40 and SF-36 demonstrated significant impairment of HRQoL in Greek PBC patients compared to controls (Implementation of PBC-40 and SF-36 revealed impaired HRQoL in Greek PBC patients with fatigue, social and emotional dysfunction exerting the highest impact. However, total, and individual PBC-40 scores were lower than that reported in studies from Northern/Central Europe and Canada. Deranged HRQoL was associated with severity of liver disease and presence of PBC-specific ANA. Primary biliary cholangitis (PBC) is an autoimmune-mediated liver disease which mainly affects middle-aged women. Although the disease might progress slowly without symptoms, it can affect patients’ life in numerous ways including progression to end-stage liver disease and complications related to liver failure.,3 Fatigue, itching, arthralgias, depression, sleep disturbances, social and emotional dysfunctioning may affect patients’ daily activity and wellbeing independently of the disease stage.During the last decades there is an increasing appreciation that health-related quality of life (HRQoL) of PBC patients can be frequently impaired by various factors associated with the disease.,5 The need for a disease specific measure of HRQoL was satisfied by the PBC-40 questionnaire which was originally validated in UK PBC patients. It is a psychometric measure which consists of six domains of symptoms covering all factors associated with the disease that may affect HRQoL of PBC patients.Assessment of HRQoL in PBC patients had previously been performed by using existing generic psychometric tools for the evaluation of HRQoL in other chronic diseases, though their specificity in evaluating PBC cases has been questioned.,7 Variations in the burden of HRQoL impairment have been reported across different populations pointing towards cultural inferences in PBC wellbeing. In line with this, our aim was to explore for the first-time aspects of HRQoL in PBC patients from Greece, originating from two distinct areas of the country.Application of PBC-40, along with its shorter versions PBC-27 and PBC-10 as well other measures for evaluation of several aspects of HRQoL, has highlighted impairment of HRQoL in PBC patients.We have prospectively enrolled 374 patients  with well-defined PBC attending the outpatient clinic of two University Departments in Greece between 2017 and 2020. In detail, 300 patients were seen at the General University Hospital of Larissa (follow-up: 2000-2020) and 74 patients at the University Hospital of Heraklion, Crete, Greece (follow-up: 1991-2020). 131 sex- and age-matched healthy individuals from both centers were included as controls .According to internationally accepted criteria, PBC diagnosis was established when at least two of the following criteria were present: a) positivity for AMA or anti-nuclear antibodies (ANA) specific for PBC, b) increased cholestatic enzymes for > 6 months and c) histological features of PBC.Patients having a PBC diagnosis of < 12 months, those suffering from other concurrent chronic liver diseases and patients post liver transplantation were excluded from the study. PBC patients with other co-morbidities that could have significantly influenced their quality of life, such as, diabetes mellitus with complications, heart failure ≥ NYHA II, renal failure requiring hemodialysis, dementia, psychosis, and malignancies were also excluded from the study. At last, patients of non-Greek origin or not comprehensive in Greek language were not included in the study.Data related to patients’ demographics, lifestyle, residency, medical history, clinical and laboratory characteristics at the time of diagnosis as well as at the time of evaluation, stage of the disease, follow-up, duration, and response to treatment with ursodeoxycholic acid (UDCA) were collected. Otherwise, the diagnosis of cirrhosis was based on ultrasonography , and/or endoscopic findings of cirrhosis , and/or clinical findings of decompensation as we have reported. Data from liver elastography using Fibroscan® 502  equipped with the standard M probe, used as a non-invasive evaluation of fibrosis near questionnaire completion, were also included where available. The Mayo Risk Score was used as a measure of disease severity.In patients having undergone a liver biopsy at initial evaluation, classification was undertaken according to Ludwig’s classification.,12 AMA were also detected by enhanced performance M2 ELISA (M2 EP (MIT3) ELISA, QUANTA Lite (R), INOVA Diagnostics Inc. San Diego, CA, USA) and/or Western blot using in-house mitochondrial subfraction of rat livers. PBC-specific ANA against nuclear pore membrane glycoprotein 210 (anti-gp210) and nuclear sp100 protein (anti-sp100) were detected by commercial ELISAs according to the manufacturers’ instructions (INOVA Diagnostics).AMA and PBC specific ANA positivity were also recorded. AMA were initially evaluated by indirect immunofluorescence on 5 μm fresh frozen sections of in-house rodent multiorgan tissue substrates, as described.Evaluation of HRQoL in our study population was performed using a Greek version of the PBC-40, and the generic SF-36 questionnaires. Each patient received and completed both questionnaires by himself/herself at the same time under the supervision of an experienced hepatologist.PBC-40: It consists of 40 items divided into the six domains of fatigue, itch, cognition, social function, emotional function and (other) symptoms. Each item might score from 1  to 5. Higher scores denote greater impact on HRQoL. The PBC-40 was translated to Greek and backward to English by two independent translators to ensure reliability and avoid discrepancies with the original English version.,16 It consists of 36 items which are divided into the following 8 sections: physical functioning, role limitations due to physical health, role limitations due to emotional problems, vitality, mental health, social functioning, bodily pain, general health. Scale scores range between 0 and 100, with 100 representing the highest level of functioning possible.SF-36 questionnaire: The SF-36 questionnaire is a generic, patient-reported measure of health status commonly used for the assessment of HRQoL in chronic diseases. It had already been translated and validated in Greek population.The study was conducted in accordance with the protocol and the principles of the Declaration of Helsinki. The protocol was approved by the Ethics Committee of Thessaly University, Medical School. Each patient who participated in the study had been properly informed and gave his/her oral consent. Written consent was not required because the analysis used anonymous data that were obtained after each patient agreed to participate by codification of the subjects. Bivariate analysis was performed by measuring correlations using Pearson’s correlation coefficient test (r). The ANOVA test was used to compare means of variables. Two-sided P values less than 0.05 were considered statistically significant.Analysis was performed using the software SPSS statistics 27.0 . Data distribution was assessed by Kolmogorov- Smirnov. Categorical variables were presented as proportions. Internal consistency of PBC-40 and SF-36 was measured by Cronbach’s coefficient α. A Cronbach’s alpha score of more than 0.7 was considered satisfactory.P < 0.001). Amongst those having a liver biopsy, patients from Crete had more frequently advanced disease stage (P < 0.001). Moreover, patients from Crete had overall more severe disease in terms of fibrosis stage compared to patients from Larissa, as evaluated by liver elastography.Demographic and clinical characteristics of 374 patients at the time of HRQoL assessment are presented in The internal consistency of the entire scale of PBC-40 and five of six domains was satisfactory as assessed by Cronbach’s coefficient α (α > 0.70). Estimates have been made for all patients as well as the two subgroups. The results were similar for all groups .The internal consistency of the entire scale of SF-36 questionnaire and its eight sections was good (Cronbach’ a score > 0.8) .P < 0.001 for comparisons of all individual components of PBC-40). Average total PBC-40 score was 77.8 ± 25.4. The scores of each PBC-40 domain for patients and controls are presented in PBC-40 demonstrated impaired HRQoL in all Greek PBC patients compared to controls (P < 0.001). During the time of questionnaire completion, less than 20% of PBC patients reported having any of PBC-related symptoms .Among 327 PBC patients, 49% (161/327) agreed their health to be rather excellent/good, while 51% (166/327) answered they disagree. Correspondingly, 92% (119/129) of healthy controls consider having excellent/good life and 8% (10/129) poor  . In detaP < 0.001 for all comparisons, Analysis of SF-36 revealed PBC patients to have significant impairment in all domains compared to healthy controls (P < 0.05) were seen between all domains of PBC-40 and SF-36 for the whole patients’ group , symptoms  and total PBC-40 score concerns . Regarding SF-36 domains, female patients showed impaired mental health  and bodily pain , social , emotional domains  and total PBC-40 score [68.5 (37-128) vs. 78.5 .There were significant differences across all three educational levels  regarding domains itch, social, emotional and total PBC-40 score. Compared to patients with higher education, those with primary education has significantly higher scores for itch [3 (0-9) n = 292) had significantly higher scores in all PBC-40 domains, including the total PBC-40 score compared to PBC patients with no clinically significant fatigue  and lower bilirubin levels (P = 0.04).Patients with significant fatigue , which is in line with previous evaluation of this questionnaire in the Greek general population.,8,20,21 However, of interest, Greek PBC patients had significantly lower total and individual PBC-40 scores compared to other PBC populations mainly from North or Central Europe and Canada.,21,22 Of note, our non-PBC controls had also lower scores compared to the control populations included in other studies, that lends further support to the assumption that HRQoL is influenced by an interplay between cultural, social, financial and geographical factors.,8Next, implementation of both tools proved HRQoL to be impaired in Greek PBC patients compared to controls. Concerning the PBC-40 score, our data are consistent with those from several studies showcasing impaired HRQoL in PBC patients around the world.,24 We anticipate PBC patients in our study to be representative of the general PBC population in Greece, as both centers act also as primary and secondary healthcare facilities based on the structure of the existing healthcare system in our country. This is of great importance as previous work from the UK has emphasized that evaluation of clinic-based PBC patients visiting tertiary centers might lead to overestimation of symptoms such as fatigue when comparing them to community based PBC population.Moreover, our population is considered homogeneous, as most of the PBC patients were Caucasians of Greek origin.,24 This is of outmost importance as differences in socioeconomic, cultural, even geographical characteristics can have an impact on HRQoL in PBC patients and thus may lead to potential bias in cases of studies of multi-ethnic origin. Along this line, a study encompassing patients from the UK, Spain, Italy, and Japan have shown significant differences in fatigue, cognitive and emotional domains amongst the four countries, suggesting that differences in latitude, genetics or even cultural characteristics could play a decisive role in HRQoL in PBC patients. However, in our study we believe that the reported differences between the two PBC cohorts were mainly attributed to the more advanced disease of the Cretan cohort at the time of questionnaire completion compared to the Larissa group. The latter was also confirmed histologically, as liver biopsy was performed more frequently in patients from Crete because histology has been a prerequisite for establishing definite PBC diagnosis during the previous decades.Our study assessed HRQoL in PBC patients from Greece by prospectively recruiting PBC patients from two well-established liver centers in Greece. Data from both centers have previously shown PBC prevalence to be among the highest in Europe. Accordingly, we have demonstrated that PBC patients with significant fatigue have higher scores of all other PBC-40 domains, verifying previous data which suggested that there is interrelationship between individual components of PBC-40.,27Our study manifested significant differences between perceived HRQoL and presence of symptoms reported by PBC patients. While up to 50% of PBC patients regarded their well-being to be good or excellent, implementation of PBC-40 showed significant impairment in most of our cohort (>80%). Emotional dysfunction, social impairment and fatigue were amongst those with the highest impact on quality of life. Fatigue has been identified as the most frequent and disabling symptom in PBC patients, with a study demonstrating stable characteristics over time.,29 Differences in fatigue scores between genders were attributed to autonomic dysfunction in females in a study from the UK. We did not evaluate PBC patients for symptoms of autonomic dysfunction, as our goal was mainly to evaluate the feasibility of PBC-40 in a well-defined cohort of Greek PBC patients. Fatigue was worse in patients with cirrhosis in our study, which failed to agree with the majority of studies displaying fatigue and HRQoL in general to be independent of disease severity.,3,20 In line with this, presence of PBC-specific ANA, namely anti-sp100 and anti-gp210, known to characterize patients with unfavorable disease outcome, including more severe histological stage, were associated with worse fatigue and cognitive function in our population.,14 However, contrary to our findings, fatigue was not associated with cirrhosis in the Polish PBC population.Female PBC patients had worse fatigue scores, and this is line with previous reports from the UK and Japan. It is of interest that cognitive function was the least impaired domain in our population. Previous reports have emphasized significant differences in cognitive impairment between different PBC populations.,31 Whether cognitive impairment might relate to individual predisposing factors prevalent in specific areas or whether such patients constitute a separate group with distinct features needs to be further evaluated.Worse social domain scores were linked to increased age, longer duration of follow-up and cirrhosis, all indicative of severe, long-standing disease. Our data do not comply with those from other studies showcasing presentation of PBC at younger age to be associated with worse HRQoL with social functioning exerting the greatest contribution to this impairment.,22 This might probably relate to the fact that most of our patients were under treatment with UDCA that is shown to partially ameliorate the intensity of itching. Living in urban compared to rural areas has been one of the features associated with impairment in several aspects of HRQoL in our population, including fatigue, cognition, social and the total PBC-40 score. The impact of residency on HRQoL has been linked to various factors, like access to health care facilities and information, differences in income and educational status as well as ethnicity, sex, and disease burden. Our data are in line with a study assessing HRQoL in the general population of Thessaly (place of residency of most of our patient population), where rural residency was associated with better general health in individuals of Greek origin. However, whether living in the Greek countryside offers advantages needs to be evaluated in largescale studies. Even though data on patients’ income were not available, unemployment and lower educational attainment were factors significantly affecting domains such as social, emotional and the total PBC-40 score in our PBC patients. Our data are in line with the study by Parikh-Patel and colleagues showing lower level of education to be significantly associated with lower levels of functional capabilities in PBC patients from the US. Moreover, several studies illustrate low socioeconomic status, as attested by several interrelated factors such as lower income, lower educational level and impaired social support, to affect HRQoL in various autoimmune diseases.Akin to previous studies, itch had the lowest score among PBC-40 domains.Our study has some limitations. Unfortunately, data on other covariates which can potentially affect quality of life, such as income and social support were not included as per protocol. In addition, data on education and employment were available only in Larissa group which, however, was the largest group of patients in the study. Still, we report for the first-time results based on prospectively collected questionnaires in a large number of well-characterized Greek PBC patients from two geographical regions of the country with long-term follow-up.In conclusion, we have demonstrated that PBC-40 is a reliable tool for HRQoL assessment in Greek PBC patients. Both PBC-40 and SF-36 revealed significant impairment of HRQoL in a representative sample of PBC patients from two geographical areas of the country, compared to healthy controls. However, the total and individual PBC-40 scores were lower both in patients and controls compared to scores in PBC and control populations from Europe and Canada, while cognitive function was the least impaired domain in our population suggesting that HRQoL might be influenced by many local factors. Emotional dysfunction, social impairment and fatigue displayed the highest impact on quality of life in our patients while female gender, cirrhosis, and presence of PBC-specific ANA played a decisive role in impairing individual components of HQRoL.Supplementary materialClick here for additional data file.