Association between social support and short-term cardiac readmission: a double-center study

doi:10.21203/rs.3.rs-3346020/v1

Download PDF

Research Article

Association between social support and short-term cardiac readmission: a double-center study

https://doi.org/10.21203/rs.3.rs-3346020/v1

This work is licensed under a CC BY 4.0 License

Version 1

posted

You are reading this latest preprint version

Introduction:

Coronary artery disease is among the most common causes of mortality worldwide. Prior studies in the field have mostly focused on biological risk factors, and the effects of psychosocial risk factors and their mechanisms of action are not yet well- known; studies in women-only patient populations are also scant. Therefore, in this study, we aimed to assess psychosocial risk factors in a patient population of women to better clarify the roles of these factors in coronary artery disease.

Methods

One hundred seventeen women with coronary artery disease were included in this cross-sectional study and were asked to complete the social support questionnaire and interviewed for their baseline characteristics (including age, level of education, occupational status, marital status, previous cardiac admission, history of hypertension, diabetes and smoking) between 2022 and 2023. Their total social support scores and their scores in each of the social support fields were calculated, and their correlations with other factors were analyzed statistically.

Results

The mean age of the participants was 61,37 ± 10 years. The mean social support score was 76.83 ± 13.31. There was a significant correlation between tangible support scores and being married (P = 0.012). We also found a significant association between readmission rates and affectionate support (P = 0.037), with scores being slightly higher in patients who were readmitted during follow-up. Associations between previous history of cardiac hospitalization, hypertension, diabetes, past or current smoking, age, level of education, occupational status and BMI were not significant.

Conclusions

Being married significantly increases perceived tangible social support. We also found slightly higher but significant affectionate social support scores in patients who were readmitted during follow-up. Given the differences in outcomes between cross-sectional and cohort studies, more cohort studies with longitudinal designs and extensive follow-up periods are needed to clarify the effects and mechanisms of action of social support in the incidence and prognosis of coronary artery disease.

Coronary artery disease

Psychosocial factors

Social support

Women

Coronary artery disease (CAD), with a mortality rate of 112.37 per 100,000, is responsible for most deaths due to noncommunicable disease globally. In 2020, it was estimated that 244.1 million people worldwide were living with ischemic heart disease (1, 2). Since 2001, almost 43% of deaths worldwide have been attributed to CAD. DALY values have also been increasing since 1990, reaching 182 million in 2019(3).

Although the role of biological risk factors such as hypertension has been known and extensively discussed, there is less certainty about psychosocial risk factors(4), including social support, which has been suggested to influence health issues in direct and indirect ways(5–8).

While most of the initial studies have been performed on men(4) or mixed male-female populations, considering the differences in disease emergence between men and women, gender disparities in the prevalence of certain psychosocial factors, and potentially distinct physiological mechanisms leading to CAD and ultimately an overall worse prognosis of CAD in women compared to men(7, 9–12), conducting women-only studies appears necessary to investigate CAD more precisely within this population.

In this study, we aimed to assess social support and its association with other biological and psychosocial factors and 3-month cardiac readmission rates in a patient population of 117 women with CAD in two centers.

Study design and participants:

This was a prospective observational study. Women with coronary artery disease in two university-based hospital catheterization labs in Tehran between 2022 and 2023 were enrolled. A final sample of 117 consecutive women with angiographically confirmed coronary artery disease was assessed. The exclusion criteria were age over 85 years, progressive cognitive diseases such as dementia and Alzheimer’s disease, history of cerebrovascular accidents, active cancers and other major cardiac diseases such as heart failure. Our study was in compliance with the Declaration of Helsinki and received approval from the local ethics research committees. Written informed consent was obtained from all patients.

Baseline characteristics:

Baseline characteristics and demographic data, including age, level of education, occupational status, marital status and previous hospital admission due to cardiac disease, were collected through self-report, and patients’ BMI (body mass index) was measured. Level of education was categorized into no education, elementary, junior-high school, high school and college degree or higher but was also analyzed continuously as years of education obtained. Marital status was categorized as currently married, separated/single and widowed. Occupational status was defined as housekeeper, currently working or retired. Patients’ medical and habitual histories were also obtained through self-report, and they were specifically asked about a previous history of hypertension, diabetes or use of antihypertensive and antidiabetic drugs and a history of cigarette smoking. Being diabetic or hypertensive was defined as a previous history of diabetes mellitus or hypertension or taking anti-hypertensive or anti-diabetic drugs.

Social support questionnaire:

Patients completed a valid and reliable Farsi version of the MOS-SS social support questionnaire (13) developed by Sherbourne and Stewart (14). This questionnaire is designed to assess perceived social support in chronically-ill patients (15). The questionnaire assesses social support across five domains and asks the patients to mark “How often is each of the following kinds of support available to you if you need it?” for each of its 19 items, ranking from “none of the time” (1 score) to “all of the time” (5 scores). Social support domains are emotional/informational support (e.g., “Someone you can count on to listen to you when you need to talk”, “Someone to give you good advice about a crisis”), tangible support (e.g., “Someone to help you if you were confined to bed”), affectionate support (e.g., “Someone who shows you love and affection”) and positive social interaction (e.g., “Someone to have a good time with”)(14). A total score is obtained by adding scores for each item, and higher scores correspond to higher perceived social support. Total scores for this questionnaire range from 19–95 when all questions are answered. The maximum total scores in the emotional/informational support domain, tangible support domain, affectionate domain and positive social interaction domain were 40, 20, 15 and 15, respectively.

Follow-up:

Patients were followed for 3 months after the first visit, and readmissions due to cardiac causes were recorded. If patients did not have admission records in 3 months, follow-up was carried out through phone calls. Out of 117 initial patients, 2 were lost to follow-up, and 13 neither had admission records within 3 months nor answered phone calls. The association between total social support scores and support scores in each group with the rate of readmission was analyzed only for patients who did have available follow-up data (102 patients).

Statistical analysis:

The total social support score was assumed to be a continuous variable in the analysis. Support scores in each domain were also analyzed as continuous variables. The association between the social support score and other continuous variables was analyzed using Pearson’s correlation coefficient. The association between social support and categorical variables was analyzed using other tests as appropriate. Categorical variables are shown as frequencies and percentages, and continuous variables are shown as the means ± standard deviations. Associations between social support scores and continuous variables (BMI, age and years of education obtained) were analyzed using Pearson’s correlation test. Associations between social support scores and categorical variables the 2 groups were analyzed using independent t tests and Mann-Whitney U tests. Associations between social support scores and categorical variables with more than 3 groups were analyzed using one-way ANOVA and Krsukal-Wallis tests. All data were analyzed using SPSS version 22.

The baseline characteristics of the study population are presented in Table 1. The mean age of the participants was 61.27 ± 10 years, and approximately two-thirds of them were married. Eighty-six percent were housekeepers, and 55% had previous hospitalization due to cardiac problems. Almost 42% were diabetic, and 72% were hypertensive. Social support scores are reported as continuous variables with a mean ± SD of 76.83 ± 13.31 for total social support scores (Table 1). Correlation data and P values are shown in Tables 2-3.

Association between social support scores and categorical variables:

Mean total social support scores and mean scores in emotional/informative support, affectionate support and positive social interaction domains were not significantly different among single/separated, married or widowed women (P> 0.05). However, mean scores for tangible social support showed a significant difference (P=0.012, Table 2), and multiple comparisons based on post hoc tests for tangible social support revealed that married women had significantly higher scores than single/separated women (P=0.015). (Supplementary Table 1)

Analysis of occupational status did not show any significant difference in mean total social support score or support scores in any of the social support domains between currently working, housekeeper and retired women (P> 0.05-Table 2). In addition, analysis for previous hospitalization due to cardiac reasons, current smoking or previous history of smoking, hypertension and diabetes did not show significant differences for mean total social support score and support scores in any of the social support domains (P> 0.05).

After 3 months of follow-up, patients’ electronic medical records were checked for readmission due to cardiac causes, and if such data were not available, patients were followed up by phone calls, and readmission rates were recorded. Two patients were lost to follow-up, and 13 did not have further medical record data or answered phone calls. Out of 102 remaining patients, 16 had cardiac readmissions. Analysis of readmission rates showed a significant difference in affectionate social support scores between the two groups (P=0.037, Table 2). Scores were slightly but significantly higher in patients who were readmitted during follow-up (Supplementary Table 8).

Correlation analysis for continuous variables:

After performing correlation analysis for social support scores and age, an insignificant weakly positive correlation was found between age and total social support score and emotional/informative, tangible and affectionate support scores (r= 0.149, r=0.165, r=0.52 and r=0.164, respectively, Table 3). The correlation between age and positive social interaction score was also insignificant and weakly negative (r=-0.053).

Correlation analysis for years of education showed an insignificant but strongly negative correlation between tangible social support scores and years of education (r=-0.83). For total and emotional/informative social support scores, analysis revealed an insignificant and weakly negative correlation (r=-0.045 and r=-.0.086, respectively). For positive social interaction, an insignificant and weakly positive correlation was found (r= 0.113)

Correlation analysis for BMI showed an insignificant, weakly negative correlation between BMI and total social support scores (r=-0.023). Correlations between BMI and emotional/informational support and affectionate support were also insignificant and weakly negative (r=-0.039 and r=-0.064, respectively). The correlation between tangible social support and positive social interaction was also found to be insignificant and weakly positive (r= 0.003 and r=0.068, respectively).

The current double-center prospective observational study focused on evaluating associations between social support scores and biological factors (diabetes, hypertension, history of active or past smoking, previous hospitalization due to cardiac disease, BMI and 3-month readmission rates) and demographic factors (age, level of education, marital status, occupational status) in 117 women with coronary artery disease. Total social support scores and scores in each social support group were measured with the MOS-SS questionnaire, and their association with each of the aforementioned factors was evaluated. Approximately 15% of patients were readmitted within 3 months, and 1.7% were lost during follow-up. The association between tangible social support scores and marital status and the association between affectionate social support scores and 3-month readmission rates were statistically significant. This study was conducted on a women-only patient population, with the aim of comprehensively assessing social support.

The current study found a significant difference between affectionate social support scores between patients who were admitted within 3-months and those who were not (median (IQR) 15 (0.0), mean = 14.37 versus median (IQR) 15 (3.0), mean = 13.4). Similar studies investigating the effect of social support on cardiac readmissions have reported an association between low informational support and 6-month readmissions and found lower social support scores to be a predictor of 12-month readmissions in patients with CAD (16, 17). It has also been reported that women have higher 1-year readmission rates after coronary artery bypass grafting (CABG) (18) and are also more likely to be readmitted due to heart failure after the procedure (19, 20). On the other hand, it has been reported that women tend to wait longer before seeking medical help (21) and that a lack of social support is associated with a delay in seeking medical attention (22). We therefore assume that the difference between the two groups might be a result of these counteracting forces, which is to say that in the current study, women with lower affectionate social support scores waited longer before seeking help and were therefore less likely to be readmitted during short-term follow-up. There is a need for more studies focusing on factors influencing short- and long-term readmissions since there seems to be controversy in the present literature, but considering higher overall and due-to-heart failure readmission rates over longer periods of time, as mentioned, it could be deducted that investigating factors that influence readmission rates in this population might help lower healthcare costs.

We also found a significant association between marital status and tangible social support. Married women showed significantly higher tangible support scores than single/separated women. This was compatible with previous studies: Janevic et al reported higher total social support, emotional support, tangible, positive social interaction and affectionate support scores in married women, while Freeborne et al found marital status to be one of the confounding factors with the strongest association with social support (15, 23). Woloshin et al found adequacy of tangible support as a prognostic factor for CAD patients following MI and reported that a decline in physical activity in these patients was related to inadequate tangible support (24). The bulk of prior studies investigating the effects of social support on cardiac outcomes in patients with CAD are performed in mixed male-female patient populations and have investigated social support as a whole (25, 26) or just a particular subscale or component of it(24, 27). An issue we aimed to address was investigating social support using both a total score and individual scores for each of the four subscales. This could especially be useful in women, who reportedly had a lower contribution to cardiac rehabilitation programs (28) and an overall worse prognosis for CAD compared to men (7, 9–12), while social support has been reported to positively affect health-related behaviors, medical adherence, and self-efficacy and increase contribution to rehabilitation programs (29–32). It has been reported that social networks are facilitating factors that increase participation in rehabilitation programs, while inadequate family support serves as a barrier to such contribution (29), emphasizing that women are less likely to receive adequate family support. Therefore, when social support is investigated in subsets and in relation to specific aspects, a more precise understanding of these factors can be achieved.

This study found no significant association between age and social support scores. Additionally, we did not detect a significant association between years of obtained education and social support score, which was similar to the findings of previous studies (8, 23, 24). There was no significant association between occupational status and social support in our study, although another cross-sectional study found a significant association between occupational status and tangible social support (23). This difference could be explained by the patient population size differences (519 compared to 117). In addition, no association was found between previous hospitalization due to cardiac disease, similar to Lett et al., who found no association between previous MI and social support scores(8). There were no significant associations between being diabetic, hypertensive or having a higher BMI and social support scores, similar to the study by Wang et al. (33), in which they did not find a significant association between current or past smoking and social support. Unlike Woloshin et al., who reported that current smoking was associated with lower tangible support, we only regarded smoking history as a whole and did not differentiate between past or current smoking.

Limitations:

There were a few issues in our study that need to be addressed. First, this was a cross-sectional study that observed short-term readmission rates after 3 months. It is important to note that the results might have differed if the study design was a cohort with long-term follow-up, which could affect generalizability. However, we attempted to compensate for the cross-sectional design by incorporating a short-term follow-up for cardiac readmission. Second, our study sample size was relatively small, which may impact the generalizability of our results. Third, the majority of patients' data were collected through self-reports and were not clinically confirmed, which decreased the data reliability. The reason behind this was that most of our patients did not have previous hospitalization, and their medical records were sourced from outside our center. Finally, we did not include measures for psychosocial factors that negatively affect readmission rates or might interact with social support (e.g., depression and anxiety). However, our study was conducted exclusively on a patient population of women, employing a multidimensional instrument for assessing social support, which allowed for a more precise and thorough investigation.

Having used a multidimensional scale for social support in an exclusively female patient population, our study showed that there was a significant association between tangible social support and marital status that affected social support and readmission rates. Our results imply the need for a more comprehensive and multidimensional investigation of social support as a psychosocial factor influencing the course of chronic diseases.

ETHICS

All patients provided written informed consent. The study protocol conformed to the declaration of Helsinki and was approved by Iran university ethics committee

NO. IR.IUMS.FMD.REC.1400.424.

CONSENT FOR PUBLICATION

All authors have read and agreed to publish the submitted manuscript.

AVAILABILITY OF DATA

All data generated or analyzed during this study are included in this published article and its supplementary information files.

COMPETEING INTERESTS

Authors have no conflict of interests to declare.

FUNDING SOURCES

None.

AUTHORS CONTRIBUTIONS

Conceptualization, M.S. and S.E.; methodology, S.E. and R.E.; investigation, M.S.; data gathering; M.S. and M.F.; data analysis and software, M.S.; writing, review and editing, M.S. and S.E.

Association AH. 2022 Stat Update factsheet GIobal Burden of Disease. American Heart Association.
collaborators NCDC. NCD Countdown 2030: worldwide trends in noncommunicable disease mortality and progress towards Sustainable Development Goal target 3.4. Lancet. 2018;392(10152):1072-88.
Roth GA, Mensah GA, Johnson CO, Addolorato G, Ammirati E, Baddour LM, et al. Global Burden of Cardiovascular Diseases and Risk Factors, 1990-2019: Update From the GBD 2019 Study. Journal of the American College of Cardiology. 2020;76(25):2982-3021.
Brezinka V, Kittel F. Psychosocial factors of coronary heart disease in women: a review. Social science & medicine (1982). 1996;42(10):1351-65.
Barth J, Schneider S, von Kanel R. Lack of social support in the etiology and the prognosis of coronary heart disease: a systematic review and meta-analysis. Psychosomatic medicine. 2010;72(3):229-38.
Cohen S, Wills TA. Stress, social support, and the buffering hypothesis. Psychological bulletin. 1985;98(2):310-57.
George LK, Blazer DG, Hughes DC, Fowler N. Social support and the outcome of major depression. The British journal of psychiatry : the journal of mental science. 1989;154:478-85.
Lett HS, Blumenthal JA, Babyak MA, Sherwood A, Strauman T, Robins C, et al. Depression as a risk factor for coronary artery disease: evidence, mechanisms, and treatment. Psychosomatic medicine. 2004;66(3):305-15.
Barrett-Connor EL, Cohn BA, Wingard DL, Edelstein SL. Why is diabetes mellitus a stronger risk factor for fatal ischemic heart disease in women than in men? The Rancho Bernardo Study. Jama. 1991;265(5):627-31.
Low CA, Thurston RC, Matthews KA. Psychosocial factors in the development of heart disease in women: current research and future directions. Psychosomatic medicine. 2010;72(9):842-54.
McSweeney JC, Cody M, O'Sullivan P, Elberson K, Moser DK, Garvin BJ. Women's early warning symptoms of acute myocardial infarction. Circulation. 2003;108(21):2619-23.
Vaccarino V, Parsons L, Peterson ED, Rogers WJ, Kiefe CI, Canto J. Sex differences in mortality after acute myocardial infarction: changes from 1994 to 2006. Archives of internal medicine. 2009;169(19):1767-74.
Khazaee-Pool M, Bahrami M, Luque JS, Pashaei T, Taymoori P, Roshani D. Validation of the Farsi version of the medical outcomes study-social support survey for mammography. BMC public health. 2018;18(1):1280.
Sherbourne CD, Stewart AL. The MOS social support survey. Social science & medicine (1982). 1991;32(6):705-14.
Freeborne N, Simmens SJ, Manson JE, Howard BV, Cené CW, Allison MA, et al. Perceived social support and the risk of cardiovascular disease and all-cause mortality in the Women's Health Initiative Observational Study. Menopause (New York, NY). 2019;26(7):698-707.
Green YS, Hajduk AM, Song X, Krumholz HM, Sinha SK, Chaudhry SI. Usefulness of Social Support in Older Adults After Hospitalization for Acute Myocardial Infarction (from the SILVER-AMI Study). The American journal of cardiology. 2020;125(3):313-9.
Polsook R, Aungsuroch Y. A cross-sectional study of factors predicting readmission in Thais with coronary artery disease. Journal of research in nursing : JRN. 2021;26(4):293-304.
Guru V, Fremes SE, Austin PC, Blackstone EH, Tu JV. Gender Differences in Outcomes After Hospital Discharge From Coronary Artery Bypass Grafting. Circulation. 2006;113(4):507-16.
Kwok CS, Potts J, Gulati M, Alasnag M, Rashid M, Shoaib A, et al. Effect of Gender on Unplanned Readmissions After Percutaneous Coronary Intervention (from the Nationwide Readmissions Database). The American journal of cardiology. 2018;121(7):810-7.
Lam L, Ahn HJ, Okajima K, Schoenman K, Seto TB, Shohet RV, et al. Gender Differences in the Rate of 30-Day Readmissions after Percutaneous Coronary Intervention for Acute Coronary Syndrome. Women's health issues : official publication of the Jacobs Institute of Women's Health. 2019;29(1):17-22.
Majidi M, Eslami V, Ghorbani P, Foroughi M. Are women more susceptible to ischemic heart disease compared to men? A literature overview. Journal of geriatric cardiology : JGC. 2021;18(4):289-96.
Reisinger MW, Moss M, Clark BJ. Is lack of social support associated with a delay in seeking medical care? A cross-sectional study of Minnesota and Tennessee residents using data from the Behavioral Risk Factor Surveillance System. BMJ Open. 2018;8(7):e018139.
Janevic MR, Janz NK, Dodge JA, Wang Y, Lin X, Clark NM. Longitudinal effects of social support on the health and functioning of older women with heart disease. International journal of aging & human development. 2004;59(2):153-75.
Woloshin S, Schwartz LM, Tosteson AN, Chang CH, Wright B, Plohman J, et al. Perceived adequacy of tangible social support and health outcomes in patients with coronary artery disease. Journal of general internal medicine. 1997;12(10):613-8.
Bucholz EM, Strait KM, Dreyer RP, Geda M, Spatz ES, Bueno H, et al. Effect of low perceived social support on health outcomes in young patients with acute myocardial infarction: results from the VIRGO (Variation in Recovery: Role of Gender on Outcomes of Young AMI Patients) study. Journal of the American Heart Association. 2014;3(5):e001252.
Lei S, Ding RJ, Wang L, Xia K, Zhang LJ, Yao DK, et al. [Social support status and related influential factors of patients with acute coronary syndrome]. Zhonghua xin xue guan bing za zhi. 2017;45(5):399-403.
Bucholz EM, Rathore SS, Gosch K, Schoenfeld A, Jones PG, Buchanan DM, et al. Effect of living alone on patient outcomes after hospitalization for acute myocardial infarction. The American journal of cardiology. 2011;108(7):943-8.
Beckie TM, Fletcher GF, Beckstead JW, Schocken DD, Evans ME. Adverse baseline physiological and psychosocial profiles of women enrolled in a cardiac rehabilitation clinical trial. Journal of cardiopulmonary rehabilitation and prevention. 2008;28(1):52-60.
Clark AM, King-Shier KM, Spaling MA, Duncan AS, Stone JA, Jaglal SB, et al. Factors influencing participation in cardiac rehabilitation programmes after referral and initial attendance: qualitative systematic review and meta-synthesis. Clinical rehabilitation. 2013;27(10):948-59.
Doherty WJ, Schrott HG, Metcalf L, Iasiello-Vailas L. Effect of spouse support and health beliefs on medication adherence. The Journal of family practice. 1983;17(5):837-41.
Jönsson D, Rosengren A, Dotevall A, Lappas G, Wilhelmsen L. Job control, job demands and social support at work in relation to cardiovascular risk factors in MONICA 1995, Göteborg. Journal of cardiovascular risk. 1999;6(6):379-85.
Väänänen A, Kouvonen A, Kivimäki M, Pentti J, Vahtera J. Social support, network heterogeneity, and smoking behavior in women: the 10-town study. American journal of health promotion : AJHP. 2008;22(4):246-55.
Wang HX, Mittleman MA, Orth-Gomer K. Influence of social support on progression of coronary artery disease in women. Social science & medicine (1982). 2005;60(3):599-607.

Table 1 – Baseline characteristics of the study population (N=117)

Characteristic

n (%)

Age

61.27 ± 10

Years of education obtained

4.7 ± 5.37

Marital status

Single/Separated

Married

Widowed

9 (7.7%)

75 (64.1%)

33 (28.2%)

Occupational status

Housekeeper

Working

Retired

100 (85.5%)

7 (6%)

10 (8.5%)

Previous hospitalization due to cardiac reasons

Yes

64 (54.7%)

53 (45.3%)

Smoker or history of smoking

Yes

12 (10.3%)

105 (89.7%)

Diabetes

Diabetic

Not diabetic

49 (41.9%)

68 (58.1%)

Hypertension

Hypertensive

Not hypertensive

84 (71.8%)

33 (28.2%)

BMI

29.84 ±5.16

Perceived social support by MOS-SS

Total social support score

Emotional/Informational support

Tangible support

Affectionate support

Positive social interaction

76.83 ± 13.31

29.32 ± 8.24

18.3 ± 2.79

13.56 ± 2.24

12.17 ± 2.57

-Data are shown as the mean ± SD or frequencies (%).

Table 2 –P values for association between social support scores and categorical baseline biological and demographic factors

	Total social support score (P)	Emotional/ informational support (P)	Tangible support (P)	Affectionate support (P)	Positive social interaction (P)
Marital status	0.145	0.167	0.012*	0.326	0.943
Occupational status	0.232	0.316	0.051	0.705	0.568
Previous hospitalization	0.814	0.650	0.267	0.567	0.494
Smoking	0.245	0.175	0.276	0.579	0.388
Hypertension	0.477	0.912	0.142	0.185	0.423
Diabetes	0.422	0.435	0.199	0.306	0.817
Readmission	0.794	0.518	0.943	0.037*	0.568

-Significant values are marked by *

Table 3 –P values and Pearson’s correlation coefficients for social support scores and quantitative demographic and biological factors

	Total social support score	Emotional/ informational support	Tangible support	Affectionate support	Positive social interaction
Age	0.149 (0.109)	0.165 (0.076)	0.052 (0.577)	0.164 (0.076)	-0.053 (0.570)
Years of education	-0.045 (0.640)	-0.086 (0.370)	-0.83 (0.389)	0.44 (0.643)	0.113 (0.238)
BMI	-0.023 (0.803)	-0.039 (0.673)	0.003 (0.975)	-0.064 (0.492)	0.068 (0.469)

-Data are shown as correlation coefficients (P)

No competing interests reported.

Download PDF

Version 1

posted

You are reading this latest preprint version

Association between social support and short-term cardiac readmission: a double-center study

Status:

Version 1

Abstract

Introduction:

Methods

Results

Conclusions

INTRODUCTION

METHODS

Statistical analysis:

RESULTS

DISCUSSION

CONCLUSIONS

Declarations

References

Tables

Additional Declarations

Supplementary Files

Status:

Version 1