Article Text

Original research
Social isolation, depression, nutritional status and quality of life during COVID-19 among Chinese community-dwelling older adults: a cross-sectional study
  1. Xinxin Wang1,2,
  2. Chengrui Zhang2,
  3. Wei Luan1
  1. 1Nursing Department, Shuguang Hospital Affiliated to Shanghai University of TCM, Shanghai, China
  2. 2Shanghai Jiao Tong University School of Nursing, Shanghai, China
  1. Correspondence to Wei Luan; luanwei_shuguang{at}126.com

Abstract

Objective This survey investigated the relationship between social isolation, depression, nutritional status and quality of life among community-dwelling older adults during COVID-19.

Design This was a cross-sectional survey study.

Setting Communities in Pudong New Area, Shanghai, China that have contracted with Renji Hospital, affiliated with Shanghai Jiao Tong University School of Medicine.

Participants From May to July 2022, 406 community-dwelling older adults were selected by convenience sampling in Shanghai, China.

Primary and secondary outcome measures The Lubben Social Network Scale, Geriatric Depression Scale, 36-item Short Form Health Survey Scale and risk assessment of malnutrition were used in older adults. Mediation models were constructed to determine the mediating role of depression and nutritional status on social isolation and quality of life among older adults.

Results The prevalence of social isolation among older adults in the community was 44.3%. The total social isolation score in community-dwelling older adults was positively associated with the total malnutrition risk and quality of life scores, and negatively associated with depression (p<0.01). Logistic regression demonstrated that living alone, loss of families or friends during COVID-19 and depression were risk factors for social isolation among community-dwelling older adults (p<0.05). Social isolation could directly affect the quality of life (β=0.306). In addition, depression (β=0.334) and nutritional status (β=0.058) had a significant mediating effect on the relationship between social isolation and quality of life.

Conclusions Our findings showed that the prevalence of social isolation among older adults increased during COVID-19. Depression and nutritional status played parallel mediating roles on the effect of social isolation on quality of life. Community workers and healthcare providers should develop intervention plans to improve the status of social isolation in older adults, eliminating existing and ongoing adverse effects.

  • COVID-19
  • public health
  • quality of life

Data availability statement

Data are available on reasonable request. All data relevant to the study are included in the article or uploaded as online supplemental information. The data related to this study are available from the corresponding author on reasonable request and pending additional ethical approval.

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STRENGTHS AND LIMITATIONS OF THIS STUDY

  • This study was conducted in the middle to late stages of home quarantine, when symptoms of social isolation were more pronounced in older adults.

  • Nutritional status was used as a mediating variable rather than an outcome variable.

  • The sample excluded those who were visually impaired, hearing impaired or had low literacy levels, which led to limitations in the generalisability of the findings.

  • The cross-sectional observation design does not allow drawing causal inferences as risk factors.

Introduction

Shanghai is one of the first cities in China to be classified as an ageing society. Furthermore, the trend of advanced ageing is serious, and the number of older adults living alone and in purely elderly families is increasing annually.1 Therefore, actively coping with population ageing has become a long-term strategic task.2 The level of social participation of older adults is a crucial indicator of successful ageing and a vital sign for healthy ageing.3 Some studies have shown that social participation stimulates the sensory system and enhances physical activity levels, allowing older adults a higher quality of life.3–5

Social isolation is a state in which individuals lack a sense of social belonging, social participation, and connection and lack fulfilling and high-quality social relationships due to a reduction in the size of social networks and social contact.6 Findings have shown that social isolation can interact with a complex set of psychosocial factors that lead to adverse health outcomes, such as depression, poor nutrition, decreased physical activity, cognitive decline and increased rehospitalisation rates, in older adults.6–8 During the COVID-19 pandemic, social isolation owing to objective and subjective factors became an even more prominent public health problem for older adults.9 10 A study by Steptoe and Di Gessa found that older adults with lower levels of contact with family members were more likely to experience anxiety, depression and poor quality of life during the COVID-19 pandemic.11 Furthermore, as the negative consequences of social isolation in older adults from the COVID-19 pandemic continue to accrue, there may be lasting effects on the overall health of the ageing population.9 12 13

Some studies have noted that the social isolation of older adults may develop or increase during the COVID-19 pandemic.14 15 In specific social contexts, increased negative life events, shrinking social network size or disruption of network integrity in older adults can increase the risk of social isolation, which may, in turn, affect older adults’ attitudes towards ageing.14 16 The existing related studies mainly focus on the factors influencing social isolation caused by COVID-19,7 17 18 the effects of social isolation on health status,19–21 interventions for social isolation,15 22–24 and the relationship between social isolation and loneliness.25–27 Additionally, studies have demonstrated that social isolation due to COVID-19 is associated with the occurrence of depression.17 21 25 28 Social isolation can significantly affect older adults’ quality of life.29–31 It is notable that most of the existing nutrition-related studies have focused on changes in the dietary habits of older adults during COVID-19.32–34 Furthermore, researchers usually employ the nutritional status of older adults as an outcome indicator.35 36 Researchers have paid less attention to the relationship between social isolation and nutritional status in older adults and the possible mediating role of nutritional status between social isolation and quality of life. Based on the above perceptions, our study innovatively used depression and nutritional status as parallel mediating variables to explore whether their mediating effects between social isolation and quality of life were established. The research objectives of this study are as follows:

  1. Investigate the incidence and influencing factors of social isolation on older adults in Shanghai, China, during the COVID-19 pandemic.

  2. Develop a parallel mediation model to determine the mediating role of depression and nutritional status in social isolation and quality of life among older adults.

Methods

Participants and data collection

This study was conducted to investigate the effects of various relevant factors on social isolation, while analysing the mediating role of depression and nutritional status in the relationship between social isolation and quality of life. Therefore, this study was conducted using a cross-sectional survey.37 From May to July 2022, we used convenience sampling to select older adults in Pudong New District, Shanghai. Renji Hospital, affiliated with Shanghai Jiao Tong University School of Medicine, and primary care institutions in Pudong New Area provide medical services to residents in the form of a medical consortium. Therefore, researchers can access the health records of the older residents in the Pudong New Area community through Renji Hospital and communicate with those eligible for enrolment based on the contact information shown in the records. At the midpoint of this study, the Shanghai government rescinded the citywide home order. On 30 March 2022, due to the problematic situation of COVID-19, Shanghai declared a region-wide stationery management. From 4 April, the number of single-day infections exceeded 10 000. From 30 April, the number of single-day infections dropped to less than 10 000. From 1 June, Shanghai returned to complete production and living order. However, to consolidate the results of pandemic prevention and control, regular management was still adopted for some neighbourhoods. According to the sample size calculation formula: n=Zα/22×p×(1−p)/δ, α is the significance level, generally taken as 0.05. Zα/2 is the coefficient corresponding to the confidence level under normal distribution; Z0.05/2 was taken as 1.96. P is the probability of the event occurring, and δ is the sampling error.38 The incidence of social isolation among older adults was calculated by referring to previous literature. As p=21.4%, δ was taken as 0.05, and the sample size was calculated as 277 cases, considering a 15% sample attrition rate, and the final sample size was determined as 320 cases. Inclusion criteria for this study were as follows: (1) respondent signed the informed consent form; (2) aged ≥65 years and lived in the community for more than 6 months; (3) home quarantine measures were implemented in the community within the last 2 months prior to commencing this study; (4) ability to read and comprehend and (5) willing to participate in this study. Exclusion criteria were as follows: (1) suffering from mental or cognitive impairment, major diseases or advanced to terminal diseases and (2) not interested in survey participation.

A web-based survey method was used in this study. A copy of the survey has been uploaded as a online supplemental file. The research team conducted uniform training for the investigators participating in the study. Researchers selected the study subjects in strict accordance with the inclusion and exclusion criteria. Prior to starting the study, all participants signed an informed consent agreement, which included a background description of the study, study content, and a privacy and confidentiality statement. After informed consent was obtained, the web-based questionnaire was sent via WeChat to the older adults or adult children living with the older adults. Older adults who could use smartphones independently answered on their own. Older adults who could not use smartphones independently were assisted by their adult children to answer the questionnaire. Older adults who needed the assistance of their children to answer the questions were informed and consented to the possibility that their children would know their answers. The survey process strictly adhered to the principles of anonymity and confidentiality. Researchers instructed adult children to refrain from interfering with older adults’ choices when assisting with their answers. Participants were not allowed to move to the next page with unanswered questions during the question-answering process. To prevent participants from repeatedly participating in the survey, one WeChat user could only submit the questionnaire once. The researchers rejected the questionnaires that took less than 2 min to fill in and checked the quality of the questionnaires that took too long. Finally, a valid sample size of 406 cases was obtained for this study.

Patient and public involvement

Neither patients nor the public were involved in the design or conduct of our research.

Instruments

Social isolation

Social isolation was measured using a simplified version of the Lubben Social Network Scale-6. This scale is a streamlined version of the original social network scale developed by Lubben et al39 and is the most widely used tool for assessing social isolation in older adults. The scale reflects the social network status of individuals from two aspects: family network and friend network, and consists of six entries, each of which is scored on a scale of 0–5. A total score of less than 11 is considered social isolation,40 and a single item of less than 6 is considered family or friends isolation status. The internal consistency of this scale was high (α=0.862).

Depression

Depression was evaluated using the short version of the Geriatric Depression Scale-15.41 The scale reflects the participants’ daily feelings and moods during a half-month period. Responses were provided as ‘yes’ or ‘no’, with ‘yes’ being scored as 1 and ‘no’ as 0. Items 1, 5, 7, 11 and 13 are reverse scored. The higher the score, the more pronounced the depressive symptom, and a score of ≥8 indicates depressive symptoms. The internal consistency of this scale was high (α=0.817).

Nutritional status

The nutritional status was assessed using the Chinese health standard ‘Risk Assessment of Malnutrition in the Elderly’ (WS/T552-2017).42 The standard consists of two parts: initial screening and assessment. Initial screening includes six entries: body mass index, changes in body mass in the last 3 months, mobility, dental status, mental status and change in diet in the last 3 months. Assessment includes 14 entries including 4 aspects of disease status, basic condition, meal intake and physical data. The initial screening and assessment scores were combined, and the total score was adjusted according to age; when the age was ≥70 years, the total score was increased by 1 point. A total score of ≥24 was considered good nutrition, whereas 17.5–23.5 was considered at risk of malnutrition, and ≤17 was considered malnutrition. The internal consistency of this scale was satisfactory (α=0.720–0.728).43

Quality of life

Quality of life was measured by a 36-item Short Form Health Survey Scale.44 The scale is a general scale for measuring the quality of life, with 36 items and 8 dimensions (physical function, physical role, somatic pain, general health, vitality, social function, emotional function and mental health), with each dimension containing 2–10 items. The first four items can be combined as a Physical Component Scale (PCS), and the later four items can be combined as a Mental Component Scale (MCS). The total quality of life score is the sum of the actual scores of each dimension, and the higher the score, the better the health status of the older adults: <72 is considered poor, 72–117 moderate and >117 excellent. The internal consistency of this scale was high (α=0.796–0.886).

Basic information

The basic information survey included sex, age, education level, cohabitants, monthly household income, primary source of income, number of chronic diseases and self-assessed health status. Additionally, participants were asked whether, within the last 3 months, they had lost family members or friends, they had left their place of residence, and they had received in-home services.

Data analysis

All analyses were performed using IBM SPSS (V.25) statistical software. Sociodemographic information was expressed as the number of cases and percentages. The older adults were divided into social isolation and non-social isolation groups, as well as moderate and excellent quality of life groups. Differences in characteristics between groups were analysed by the χ2 test or Fisher’s exact probability method. To examine the relationship between social isolation scores and depression, nutritional status, and quality of life scores, we used Pearson correlation analysis (because these variables were tested to be bivariate normally distributed and linearly related). A binary logistic regression was performed to examine the effects of sociodemographic characteristics and depression and nutritional status on the presence of social isolation, using social isolation as the dependent variable (non-social isolation=0, social isolation=1). The errors between the variables were tested for independence, multicollinearity and the presence of strongly influential outliers. Regression models were developed using forward stepwise regression analysis. Model testing was performed by Hosmer-Lemeshow goodness of fit. To investigate the mediating role of depression and nutritional status in the correlation between social isolation and quality of life, we analysed the mediating effects using PROCESS V.3.3 of SPSS.45 We performed a multiple regression analysis with social isolation, depression, nutritional status and quality of life using model 4 in the Process programme. Parallel mediation models were developed to test the pathways of social isolation on quality of life. A bias-corrected nonparametric bootstrap method was used to test for indirect effects. To reduce the interference of covariates in the model fit, variables related to quality of life were controlled for in the construction of the model. The indirect effects were considered significant if the upper and lower limits of the 95th percentile CI did not contain zero. For all tests, the significance level was set at α=0.05 and all tests were two tailed.

Results

Descriptive results

The characteristics of the participants are shown in table 1. Among the 406 older adults surveyed, the mean age of the sample was 67.66 years (SD=2.66). Presence of social isolation was found in 180 (44.3%), family isolation in 163 (40.1%) and friend isolation in 161 (39.7%) participants.

Table 1

Sociodemographic features and clinical features of the studied sample

Association between sociodemographic factors and social isolation and quality of life

The results of the univariate analysis are shown in online supplemental table 1. Univariate analysis showed statistically significant differences in the effects of cohabitants, primary source of income, number of chronic diseases, conscious health status compared with a year ago, loss of family members or friends and absence from place of residence during home quarantine on social isolation of older adults in the community (p<0.05). In addition, in order to eliminate the effect of covariates in the mediated analysis, factors that have a statistically significant difference in the effect on quality of life need to be identified. The results showed that monthly income per household, primary source of income, self-assessed health status, conscious health status compared with a year ago and loss of family members or friends on the quality of life of older adults in the community were statistically significant (p<0.05).

Bivariate correlation among study variables

The correlations of all variables within this study are shown in table 2, and no multicollinearity was detected. Social isolation was associated with depression (r=−0.25, p<0.01), nutritional status (r=0.14, p<0.01) and quality of life (r=0.29, p<0.01).

Table 2

Correlations between depression, quality of life, risk of malnutrition and social isolation in community-dwelling older adults

Logistic regression model of social isolation of older adults

The assignments of the variables are shown in online supplemental table 2. A binary logistic regression analysis was performed with social isolation as the dependent variable. The results showed that living alone, depression, poor quality of life and loss of family and friends during home quarantine were risk factors for social isolation among older adults in the community (table 3). In the model significance test: χ2=5.189, p<0.05, the equation was statistically significant, and the Hosmer-Lemeshow goodness-of-fit test indicated a good fit of the regression model (χ2=3.508, p>0.05).

Table 3

Risk factors related to social isolation: multivariate logistic regression analysis (forward:LR, α=0.05)

Mediating effects of depression and nutritional status

Based on the correlation analysis and literature review conducted, this study constructed a parallel mediation model to determine the mediating role of depression and nutritional status in social isolation and quality of life among older adults. All analyses were conducted after controlling variables that were significantly associated with quality of life in the univariate analysis. Results showed that the direct effect of social isolation affecting the quality of life of older adults was significant (p<0.05), and the 95% CI for the indirect effect of social isolation affecting the quality of life through depression and nutritional status both did not contain 0, indicating that the mediating effects of depression and nutritional status were both established (table 4). The mediating effect of depression accounted for 47.82% of the total effect. 47.82% is the proportion of the mediated effect of depression between social isolation and quality of life (0.3340) to the total effect of social isolation on quality of life (0.6984), that is, 0.334/0.6984×100%=47.82%. The mediating effect of nutritional status accounted for 8.33% of the total effect. 8.33% is the proportion of the mediated effect of nutritional status between social isolation and quality of life (0.0582) to the total effect of social isolation on quality of life (0.6984), that is, 0.0582/0.6984×100%=8.33%. Depression and nutritional status on the effect of social isolation on quality of life in a parallel mediation model are shown in figure 1.

Table 4

Mediating effect of depression and nutritional status on the effect of social isolation on quality of life

Figure 1

Depression and nutritional status on the effect of social isolation on quality of life in a parallel mediation model. *p<0.05, **p<0.01, ***p<0.001.

Discussion

Influencing factors and the current situation of social isolation of the older population

During the COVID-19 pandemic, the incidence of social isolation was 44.3%, which was higher than the results before the pandemic.46 47 Binary logistic regression analysis showed that cohabitants, loss of family members or friends during the pandemic, and depression were risk factors for social isolation among older adults in the community. The prevalence of social isolation was significantly higher among older adults living alone than those living with their spouses or children. These findings are consistent with previous studies.48 49 Compared with those not living alone, older adults living alone are often less likely to participate in social interactions due to physical, psychological and economic factors. Their social adjustment ability is usually poor.50 Reduced visitation further affects social participation, which in turn leads to shrinking social networks and reduced social contact for older adults.51 In our study, 15.8% of older adults had experienced losing family members or friends during the pandemic, and the risk of social isolation was 2.68 times higher in these older adults than in those who had not lost family members or friends (p<0.01). The results of this study were consistent with those of Selman et al.52 Compared with other age groups, older adults are at higher risk of developing prolonged grief disorder due to the inability to adjust to the death of a relative or friend,53 which predisposes them to more severe psychological and functional impairment. During home quarantine, older adults’ grief experiences of bereavement may be exacerbated by home quarantine and the lack of coping methods, thereby increasing the risk of social isolation.53 54 Moreover, our results showed that the prevalence of social isolation among older adults in a depressed state was 58.0%, which was higher than that of non-depressed older adults (40.3%), and depression was a risk factor for social isolation among older adults (OR=2.28, p<0.01); these results were consistent with the findings of related studies.55 Depression in older adults is usually associated with deteriorating physical health, reduced social support and the presence of chronic diseases.17 It often manifests as a reluctance to interact with people, reduced contact with the outside world, and gradually closing oneself off.56 During home quarantine, older adults have fewer interactions with family and friends, and their previous activities and hobbies cannot be carried out normally, resulting in reduced social participation. Their depressive symptoms may develop or intensify, which may result in social isolation.7

Correlations of social isolation with depression, risk of malnutrition and quality of life in older adults

In our study, the total quality of life score and PCS scores of the community older adults were at an average level, and the MCS scores were lower than other related studies in China.57 58 Around 24.4% of the community-dwelling older adults had depressive symptoms, slightly higher than the findings of Dziedzic et al (19.15%).17 Approximately 71.7% of the older adults were at risk of malnutrition, slightly higher than the results of the study by Lina et al (61.5%).59 A possible reason for this analysis is the difference in selection criteria between the above-mentioned study and the present one regarding the study population. The older adults in the previous study were either partially or not in home quarantine. In contrast, the subjects in this study had been in home quarantine for more than 2 months. Older adults with good quality of life and low risk of malnutrition had a lower risk of social isolation. Older adults with more severe depressive symptoms had a higher risk of social isolation. These findings are consistent with those of previous studies.7 60 Strengthening social participation is a core component of addressing ageing population challenges.3 Social participation helps increase the frequency of physical activity and improves the cognitive level of older adults, which can effectively reduce the prevalence of chronic diseases and geriatric syndrome.3 Furthermore, social participation is effective in reducing depression and anxiety and increasing overall life satisfaction in older adults. However, during home quarantine, older adults have less social interaction with family and friends, less physical activity and more sedentary behaviour, which may result in decreased voluntary mobility, increased risk of falls, fractures and malnutrition.7

Mediating effects of depression and nutritional status in the relationship between social isolation and quality of life of older adults

Our study found that depression and nutritional status play a partially mediating role between social isolation and quality of life. In the conceptual model of health-related quality of life proposed by Ferrans et al,61 it was suggested that the individual’s biological functioning, symptoms, functional status (including physical, psychological, social and role functioning) and general health perceptions are all key factors influencing overall quality of life under the combined influence of personal and environmental characteristics.61 According to this conceptual framework, social isolation and depression can be classified as psychological functioning and nutritional status can be classified as physical functioning. During home quarantine, older adults’ daily social activities are suspended and social participation, personal and community social cohesion, and perceived social support are reduced, which may lead to social isolation.3 On this basis, increased fear and anxiety among older adults due to the pandemic may trigger depression and lead to a decline in their psychological functioning. Furthermore, medical care and food availability are limited in the context of home quarantine. In this particular state, the reduced interpersonal interaction of older adults may make them more prone to irregular diet and increase the risk of malnutrition, which may, in turn, affect physical functioning. Poor physical and mental status can affect older adults’ evaluation of their health, ultimately resulting in a decrease in happiness or satisfaction with life and quality of life.

Recommendations

First, screening of the nutritional status of older adults should be improved. Community medical workers should pay more attention to those who have developed malnutrition or are at risk of malnutrition. Older adults should be provided with appropriate diets based on the results of their nutritional assessment, and nutrition-related knowledge should be strengthened to guide them to establish active nutritional behaviours. Older adults should be instructed to reduce sedentary behaviour, promote home fitness exercises and evaluate fitness outcomes.62 63 Additionally, community-based mental health assessments for older adults should be conducted, especially to strengthen psychological and social support for bereaved older adults. A standardised psychological intervention programme should be established for older adults in the community, guiding them to adopt methods such as confessions and positive thinking therapy to overcome pessimism and restore a positive state of life.64 Finally, older adults’ ability to use digital technology should be improved to ensure their social participation. The adoption of the Internet and artificial intelligence technologies can effectively increase their sense of social belonging and reduce loneliness.65 In digital technology competency education, we should consider the learning capabilities of the older population, mobilise their enthusiasm for learning, and focus on teaching practical skills. Simultaneously, we should develop various teaching methods, such as the family collaboration, peer support and intergenerational education models, to help older adults improve their confidence in using digital technology, while reducing the digital divide.

Limitations

In our study (due to pandemic restrictions), the survey was administered using an online questionnaire, and although the researchers assisted in the completion of the questionnaire, it still resulted in a certain amount of bias. Owing to the nature of the close relationship between parent and child, although the researchers instructed their children not to interfere with the older adults’ choices during the study, it is possible that children had access to the older adults’ answers. This was likely to result in a certain amount of bias presenting itself in the results. Therefore, a face-to-face approach to data collection should be adopted in future studies to mitigate this bias. Our survey was conducted using convenience sampling and the study site was limited to Shanghai, China, with a large proportion of young seniors (61–70 years). Moreover, the older adults all had good reading and comprehension skills; however, the sample excluded those who were visually impaired, hearing impaired or had low literacy levels. This is a limitation in the sample selection and limits the generalisability of the findings. In future studies, a more scientific sampling method should be adopted and the sample selection should be broader so that the sample is more representative. The current study uses a cross-sectional observational design, which can be used to assess the relationship between a risk factor and health outcome, but does not allow drawing causal inferences as risk factors; thus, it is difficult to reveal the long-term effects of the COVID-19 pandemic on the social isolation of the older population. Therefore, a multicentre longitudinal study can be conducted at a later stage to address this.

Conclusion

Home quarantine is an effective method of reducing transmission in the event of widespread COVID-19 outbreaks in large, densely populated cities; however, it may result in social isolation of older adults. The findings suggest that social isolation is prevalent among older adults in the community, and it can affect the quality of life directly or indirectly through depression or nutritional status. It should be highlighted that helping older adults to maintain social support networks, establish new external social support networks through the Internet, and increase their social participation are essential during the COVID-19 pandemic. Simultaneously, attention should be given to assessing older adults’ depression and nutritional status and providing early interventions for those at risk, thereby assisting older adults in the community to address social isolation and its continuing adverse effects that can result from home quarantine.

Data availability statement

Data are available on reasonable request. All data relevant to the study are included in the article or uploaded as online supplemental information. The data related to this study are available from the corresponding author on reasonable request and pending additional ethical approval.

Ethics statements

Patient consent for publication

Ethics approval

This study involves human participants and was approved by Renji Hospital, Shanghai Jiao Tong University School of Medicine No: RA-2021-465. Participants gave informed consent to participate in the study before taking part.

References

Supplementary materials

Footnotes

  • Contributors XW and WL were responsible for literature review, constructing the research protocol, and forming the questionnaire. XW and CZ conducted data collation and analysis. XW wrote the first draft of the manuscript. WL contributed to manuscript revision and approved the submitted version. WL acted as the guarantor for this study.

  • Funding This work was supported by the Shanghai Education Science Research Project (C2023136); Shanghai Hospital Association Hospital Management Research Fund (X2020083); Specialised Clinical Research in Health Industry of Shanghai Municipal Health Commission (202150032); Shanghai Jiao Tong University Liberal Arts Young Talent Cultivation Program (2022QN00X).

  • Competing interests None declared.

  • Patient and public involvement Patients and/or the public were not involved in the design, or conduct, or reporting, or dissemination plans of this research.

  • Provenance and peer review Not commissioned; externally peer reviewed.

  • Supplemental material This content has been supplied by the author(s). It has not been vetted by BMJ Publishing Group Limited (BMJ) and may not have been peer-reviewed. Any opinions or recommendations discussed are solely those of the author(s) and are not endorsed by BMJ. BMJ disclaims all liability and responsibility arising from any reliance placed on the content. Where the content includes any translated material, BMJ does not warrant the accuracy and reliability of the translations (including but not limited to local regulations, clinical guidelines, terminology, drug names and drug dosages), and is not responsible for any error and/or omissions arising from translation and adaptation or otherwise.